STA dan Krigging Project (Part 1)
Velya
9/15/2021
Metode Space Time Analysis
yaitu pemodelan deret waktu secara bersama dari lokasi-lokasi berdekatan dimana antar lokasi saling berpengaruh dan masing lokasi-lokasi punya deret waktu.
# Package yang digunakan
library(readxl)
library(starma)
library(tseries)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(psych)
library(gstar)## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphalibrary(ggplot2)Informasi Data
Untuk penelitian ini, digunakan data penderita DBD di Provinsi DKI Jakarta dan jumlah penduduk untuk setiap kecamatan di Provinsi DKI Jakarta. Data penderita DBD secara mingguan sepanjang tahun 2013 yang kami peroleh dari Dinas Kesehatan. Sedangkan data jumlah penduduk untuk setiap kecamatan di Provinsi DKI Jakarta dari link berikut: https://data.jakarta.go.id/dataset/jumlahpendudukberdasarkanusiaperkelurahandkijakarta/resource/7a6be211-4e8b-487c-a67a-bc796e793eb0
# Input data_train
data <- read_excel("D:/SEMESTER 7/Kapseltat/Tubes Space Time/dataa.xlsx")
data <- data[1:52,]
koor <- read_excel("D:/SEMESTER 7/Kapseltat/Tubes Space Time/koord.xlsx")# Plot Data Koordinat untuk setiap kecamatan di Provinsi DKI Jakarta
ggplot(koor, aes(x = X, y = Y, color = 'red')) + geom_point()
# Banyaknya Observasi per lokasi
T = 52
# Banyaknya lokasi
N = 41
# Membentuk data menjadi data time series dalam bentuk matrix
data_train <- data.matrix((ts(data))) Stastistika Deskriptif
# Statistik Deskriptif
describe(data_train)## vars n mean sd median trimmed mad min max range skew
## GAMBIR 1 52 3.08 1.38 3.0 3.02 1.48 1 7 6 0.30
## SAWAH BESAR 2 52 4.06 1.91 4.0 3.98 2.22 1 9 8 0.27
## KEMAYORAN 3 52 7.54 3.43 8.0 7.40 2.97 2 17 15 0.33
## SENEN 4 52 3.88 1.73 4.0 3.83 1.48 1 8 7 0.20
## CEMPAKA PUTIH 5 52 2.96 1.34 3.0 2.90 1.48 1 6 5 0.21
## MENTENG 6 52 2.69 1.21 3.0 2.64 1.48 1 6 5 0.20
## TANAH ABANG 7 52 5.44 2.42 6.0 5.31 2.97 2 12 10 0.44
## JOHAR BARU 8 52 4.25 1.87 4.5 4.19 2.22 1 9 8 0.17
## PENJARINGAN 9 52 12.42 5.07 12.0 12.21 5.93 4 24 20 0.33
## TANJUNG PRIOK 10 52 16.67 6.84 17.0 16.33 8.90 6 32 26 0.35
## KOJA 11 52 13.77 5.57 14.0 13.50 7.41 5 26 21 0.34
## CILINCING 12 52 16.98 6.95 17.0 16.69 8.90 6 32 26 0.31
## KELAPA GADING 13 52 11.79 4.77 12.0 11.57 5.93 4 22 18 0.32
## CENGKARENG 14 52 13.17 4.60 13.5 12.90 5.19 6 23 17 0.40
## GROGOL PETAMBURAN 15 52 5.63 1.98 6.0 5.52 2.97 2 10 8 0.40
## TAMAN SARI 16 52 3.13 1.17 3.0 3.05 1.48 1 6 5 0.53
## TAMBORA 17 52 6.77 2.37 7.0 6.62 2.97 3 12 9 0.43
## KEBON JERUK 18 52 8.17 2.80 8.5 8.07 3.71 3 14 11 0.23
## KALI DERES 19 52 10.27 3.57 10.5 10.05 4.45 4 18 14 0.37
## PALMERAH 20 52 5.50 1.95 6.0 5.38 1.48 2 10 8 0.42
## KEMBANGAN 21 52 6.60 2.21 7.0 6.48 2.97 3 11 8 0.34
## TEBET 22 52 9.25 3.29 9.0 9.00 2.97 4 17 13 0.62
## SETIA BUDI 23 52 4.50 1.67 4.0 4.40 1.48 2 8 6 0.53
## MAMPANG PRAPATAN 24 52 5.94 2.05 6.0 5.81 2.22 3 11 8 0.60
## PASAR MINGGU 25 52 11.77 4.20 11.5 11.50 3.71 5 22 17 0.54
## KEBAYORAN LAMA 26 52 11.79 4.24 11.5 11.50 3.71 5 22 17 0.57
## CILANDAK 27 52 8.04 2.82 8.0 7.83 2.97 4 15 11 0.61
## KEBAYORAN BARU 28 52 5.94 2.05 6.0 5.81 2.22 3 11 8 0.60
## PANCORAN 29 52 6.23 2.25 6.0 6.05 1.48 3 12 9 0.69
## JAGAKARSA 30 52 11.77 4.20 11.5 11.50 3.71 5 22 17 0.54
## PESANGGRAHAN 31 52 8.98 3.12 9.0 8.76 2.97 4 17 13 0.56
## MATRAMAN 32 52 7.73 3.08 8.0 7.74 4.45 3 13 10 -0.02
## PULO GADUNG 33 52 12.37 4.92 12.0 12.40 7.41 4 21 17 -0.03
## JATINEGARA 34 52 13.06 5.23 13.0 13.10 7.41 4 23 19 0.00
## KRAMAT JATI 35 52 11.92 4.78 12.0 11.95 7.41 4 21 17 -0.04
## PASAR REBO 36 52 8.48 3.44 8.0 8.45 5.19 3 15 12 0.01
## CAKUNG 37 52 20.37 8.12 20.0 20.43 11.86 7 35 28 -0.03
## DUREN SAWIT 38 52 16.48 6.59 16.0 16.55 10.38 5 28 23 -0.05
## MAKASAR 39 52 8.40 3.34 8.0 8.43 4.45 3 15 12 -0.03
## CIRACAS 40 52 11.46 4.58 11.0 11.50 7.41 4 20 16 -0.03
## CIPAYUNG 41 52 10.04 4.05 10.0 10.07 5.93 3 17 14 -0.06
## kurtosis se
## GAMBIR -0.18 0.19
## SAWAH BESAR -0.55 0.27
## KEMAYORAN -0.31 0.48
## SENEN -0.48 0.24
## CEMPAKA PUTIH -0.66 0.19
## MENTENG -0.34 0.17
## TANAH ABANG -0.31 0.34
## JOHAR BARU -0.52 0.26
## PENJARINGAN -0.90 0.70
## TANJUNG PRIOK -0.93 0.95
## KOJA -0.95 0.77
## CILINCING -0.97 0.96
## KELAPA GADING -0.97 0.66
## CENGKARENG -0.68 0.64
## GROGOL PETAMBURAN -0.50 0.27
## TAMAN SARI -0.28 0.16
## TAMBORA -0.61 0.33
## KEBON JERUK -0.75 0.39
## KALI DERES -0.65 0.49
## PALMERAH -0.33 0.27
## KEMBANGAN -0.86 0.31
## TEBET -0.30 0.46
## SETIA BUDI -0.49 0.23
## MAMPANG PRAPATAN -0.40 0.28
## PASAR MINGGU -0.37 0.58
## KEBAYORAN LAMA -0.32 0.59
## CILANDAK -0.32 0.39
## KEBAYORAN BARU -0.40 0.28
## PANCORAN -0.09 0.31
## JAGAKARSA -0.37 0.58
## PESANGGRAHAN -0.36 0.43
## MATRAMAN -1.41 0.43
## PULO GADUNG -1.34 0.68
## JATINEGARA -1.34 0.73
## KRAMAT JATI -1.32 0.66
## PASAR REBO -1.29 0.48
## CAKUNG -1.37 1.13
## DUREN SAWIT -1.37 0.91
## MAKASAR -1.27 0.46
## CIRACAS -1.35 0.64
## CIPAYUNG -1.38 0.56Rata-rata penderita DBD setiap kecamatan di Provinsi DKI Jakarta sepanjang tahun 2013 dengan minimum 2,69 orang yang berada di Kecamatan Menteng dan maksimum 20,36 orang yang berada di Kecamatan Cakung. Banyak penderita terkecil hanya seorang dan terbesar sebanyak 35 orang di Kecamatan Cakung di tahun 2016. Penderita DBD setiap kecamatan di Provinsi DKI Jakarta tahun 2013 juga sangat beragam ditandai dengan besarnya standar deviasi. Kemencengan (skewness) positif yang artinya data menumpuk di nilai yang lebih kecil dari rataan. Kemudian keruncingan (kurtosis) < 3 yaitu distribusi lebih landai dari distribusi normal. Kurtosis negatif menandakan distribusi yang relatif datar.
# Plot Data
plot(data_train[,1], type = "l", lty = 1, col = 1, ylim = c(0,50),
ylab = "Jumlah Penderita DBD", xlab = "Minggu")
for (i in 2:N) {
lines(data_train[,i], type = "l", col = i, lty = i)
}
Berdasarkan plot deret waktu tersebut data penderita DBD setiap kecamatan di Provinsi DKI Jakarta sepanjang tahun 2013 secara mingguan sangatlah beragam. Terlihat bahwa sekilas data tidak stasioner, yaitu terdapat variansi yang cukup besar pada beberapa kecamatan.
Cek Stasioneritas
digunakan uji ADF (Augmented Dickey-Fuller) untuk mengecek apakah data yang digunakan stasioner atau tidak.
# Uji Kestasioneran
for (i in 1:N){
print(adf.test(data_train[,i]))
}##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.9148, Lag order = 3, p-value = 0.2064
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -3.2923, Lag order = 3, p-value = 0.08233
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -3.2567, Lag order = 3, p-value = 0.0879
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -3.0374, Lag order = 3, p-value = 0.1571
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.7616, Lag order = 3, p-value = 0.2681
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -3.0232, Lag order = 3, p-value = 0.1628
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -3.1643, Lag order = 3, p-value = 0.1061
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -3.1273, Lag order = 3, p-value = 0.121
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.3822, Lag order = 3, p-value = 0.4207
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.3417, Lag order = 3, p-value = 0.437
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.3451, Lag order = 3, p-value = 0.4356
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.3576, Lag order = 3, p-value = 0.4306
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.3572, Lag order = 3, p-value = 0.4307
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.8896, Lag order = 3, p-value = 0.2166
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -3.1004, Lag order = 3, p-value = 0.1318
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.8295, Lag order = 3, p-value = 0.2408
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.8421, Lag order = 3, p-value = 0.2357
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.9915, Lag order = 3, p-value = 0.1756
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.9925, Lag order = 3, p-value = 0.1752
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.6999, Lag order = 3, p-value = 0.2929
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -2.9752, Lag order = 3, p-value = 0.1821
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.3795, Lag order = 3, p-value = 0.824
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.3214, Lag order = 3, p-value = 0.8474
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.2302, Lag order = 3, p-value = 0.8841
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.4319, Lag order = 3, p-value = 0.8029
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.4236, Lag order = 3, p-value = 0.8063
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.4253, Lag order = 3, p-value = 0.8056
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.2302, Lag order = 3, p-value = 0.8841
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.336, Lag order = 3, p-value = 0.8415
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.4319, Lag order = 3, p-value = 0.8029
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.4255, Lag order = 3, p-value = 0.8055
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.9204, Lag order = 3, p-value = 0.6064
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.8312, Lag order = 3, p-value = 0.6423
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.881, Lag order = 3, p-value = 0.6223
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.8101, Lag order = 3, p-value = 0.6508
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.821, Lag order = 3, p-value = 0.6464
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.8431, Lag order = 3, p-value = 0.6375
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.8124, Lag order = 3, p-value = 0.6499
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.7708, Lag order = 3, p-value = 0.6666
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.8344, Lag order = 3, p-value = 0.641
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: data_train[, i]
## Dickey-Fuller = -1.8463, Lag order = 3, p-value = 0.6362
## alternative hypothesis: stationaryBerdasarkan uji ADF (Augmented Dickey-Fuller), setiap kecamatan di Provinsi DKI Jakarta sepanjang tahun 2013 diperoleh p-value > 𝛂, dengan 𝛂 = 0.05. Sehingga disimpulkan bahwa data tidak stasioner. Maka, selanjutnya data didiferensi dan di cek kembali kestasioneritasnya.
# Differencing data
diff_data_train = matrix(0, T, N)
for (i in 2:T){
diff_data_train[i,] = data_train[i,] - data_train[(i-1),]
}
# Menghilangkan baris 0 di t=1
diff_data_train_used = matrix(0, (T-1), N)
for (i in 1:(T-1)){
diff_data_train_used[i,] = diff_data_train[i+1,]
}
# Uji Kestasioneran
for (i in 1:N){
print(adf.test(diff_data_train_used[,i]))
}## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.4506, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.0713, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.8106, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.0148, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.3406, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.7954, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.1336, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.1591, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.1849, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.1707, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.0922, Lag order = 3, p-value = 0.01248
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.2551, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.2862, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.9777, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -6.256, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.4196, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.7682, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.9271, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.8244, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -5.4323, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -6.1126, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.5821, Lag order = 3, p-value = 0.04316
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.1186, Lag order = 3, p-value = 0.01135
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.6963, Lag order = 3, p-value = 0.03364
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.6956, Lag order = 3, p-value = 0.0337
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.6476, Lag order = 3, p-value = 0.0377
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.8114, Lag order = 3, p-value = 0.02451
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.6963, Lag order = 3, p-value = 0.03364
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.5442, Lag order = 3, p-value = 0.04632
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.6956, Lag order = 3, p-value = 0.0337
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.776, Lag order = 3, p-value = 0.027
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.3052, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.206, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.2523, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(diff_data_train_used[, i]): p-value smaller than printed p-
## value##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.2868, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.0586, Lag order = 3, p-value = 0.01392
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.9388, Lag order = 3, p-value = 0.01905
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.0155, Lag order = 3, p-value = 0.01577
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -3.69, Lag order = 3, p-value = 0.03417
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.1063, Lag order = 3, p-value = 0.01187
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: diff_data_train_used[, i]
## Dickey-Fuller = -4.0745, Lag order = 3, p-value = 0.01324
## alternative hypothesis: stationaryBerdasarkan hasil uji ADF (Augmented Dickey-Fuller), dengan 𝛂 = 0.05 diperoleh p-value = 0,01 < 𝛂 sehingga disimpulkan bahwa data yang telah didiferensi sekali telah stasioner.
Matriks Bobot Seragam
#Matriks Bobot Seragam
w0 = diag(N)
w1 = diag(0, N)
for (i in 1:41) {
for (j in 1:41) {
if (i != j) {
w1[i,j] = 1/40
}
}
}
W = list(w0,w1)Identifikasi Model
Selanjutnya dilakukan estimasi model dengan melihat plot ACF (Autocorrelation Function) dan PACF (Partial Autocorrelation Function).
#Membentuk matriks Y
Y <- matrix(0, nrow = (T-1), ncol = N)
for (i in 1:N){
Y[,i] <- diff_data_train_used[,i]
}
#Identifikasi Orde
stacf(Y, W)
stpacf(Y, W)
Model yang mungkin adalah GSTAR(1;1) dan GSTAR(2;1,1)
GSTAR(1;1)
Estimasi parameter setiap kecamatan di Provinsi DKI Jakarta
fitmodel1 <- gstar(data,w1,p=1,d=1,est="OLS")
summary(fitmodel1)##
## Coefficients:
## Estimate Std.Err t value Pr(>|t|)
## psi10(GAMBIR) -0.226321 309.790612 -0.001 0.999
## psi10(SAWAH BESAR) -0.352196 150.092489 -0.002 0.998
## psi10(KEMAYORAN) -0.199820 52.728138 -0.004 0.997
## psi10(SENEN) -0.344609 184.145500 -0.002 0.999
## psi10(CEMPAKA PUTIH) -0.365417 247.669493 -0.001 0.999
## psi10(MENTENG) -0.396588 282.772713 -0.001 0.999
## psi10(TANAH ABANG) -0.234222 91.240499 -0.003 0.998
## psi10(JOHAR BARU) -0.294878 148.821461 -0.002 0.998
## psi10(PENJARINGAN) -0.480456 45.460592 -0.011 0.992
## psi10(TANJUNG PRIOK) -0.409340 26.561042 -0.015 0.988
## psi10(KOJA) -0.439171 40.031222 -0.011 0.991
## psi10(CILINCING) -0.432912 23.666290 -0.018 0.985
## psi10(KELAPA GADING) -0.451912 52.693671 -0.009 0.993
## psi10(CENGKARENG) -0.130284 49.699171 -0.003 0.998
## psi10(GROGOL PETAMBURAN) -0.306420 190.391246 -0.002 0.999
## psi10(TAMAN SARI) -0.175733 554.210176 0.000 1.000
## psi10(TAMBORA) -0.248614 188.557059 -0.001 0.999
## psi10(KEBON JERUK) -0.134141 144.185294 -0.001 0.999
## psi10(KALI DERES) -0.109651 83.601632 -0.001 0.999
## psi10(PALMERAH) -0.179812 254.451659 -0.001 0.999
## psi10(KEMBANGAN) -0.208979 180.140676 -0.001 0.999
## psi10(TEBET) 0.042699 182.425535 0.000 1.000
## psi10(SETIA BUDI) -0.274574 431.677128 -0.001 0.999
## psi10(MAMPANG PRAPATAN) -0.171046 362.978347 0.000 1.000
## psi10(PASAR MINGGU) -0.285971 88.400742 -0.003 0.997
## psi10(KEBAYORAN LAMA) -0.298337 90.855379 -0.003 0.997
## psi10(CILANDAK) -0.068602 211.083926 0.000 1.000
## psi10(KEBAYORAN BARU) -0.171046 362.978347 0.000 1.000
## psi10(PANCORAN) -0.162443 314.547605 -0.001 1.000
## psi10(JAGAKARSA) -0.285971 88.400742 -0.003 0.997
## psi10(PESANGGRAHAN) -0.156904 163.281948 -0.001 0.999
## psi10(MATRAMAN) -0.475807 187.292992 -0.003 0.998
## psi10(PULO GADUNG) -0.451456 59.626015 -0.008 0.994
## psi10(JATINEGARA) -0.510933 54.122021 -0.009 0.992
## psi10(KRAMAT JATI) -0.572298 69.860027 -0.008 0.993
## psi10(PASAR REBO) -0.451872 131.820830 -0.003 0.997
## psi10(CAKUNG) -0.463637 25.122559 -0.018 0.985
## psi10(DUREN SAWIT) -0.517441 38.055590 -0.014 0.989
## psi10(MAKASAR) -0.558417 138.448438 -0.004 0.997
## psi10(CIRACAS) -0.431709 77.439728 -0.006 0.996
## psi10(CIPAYUNG) -0.429268 106.484784 -0.004 0.997
## psi11(GAMBIR) -0.098348 103.817400 -0.001 0.999
## psi11(SAWAH BESAR) 0.002989 95.975289 0.000 1.000
## psi11(KEMAYORAN) -0.227369 105.514256 -0.002 0.998
## psi11(SENEN) -0.056450 95.723732 -0.001 1.000
## psi11(CEMPAKA PUTIH) -0.020734 85.837487 0.000 1.000
## psi11(MENTENG) 0.021271 81.109862 0.000 1.000
## psi11(TANAH ABANG) -0.159803 103.790155 -0.002 0.999
## psi11(JOHAR BARU) 0.018792 88.976326 0.000 1.000
## psi11(PENJARINGAN) 0.155350 179.893781 0.001 0.999
## psi11(TANJUNG PRIOK) 0.008723 204.273448 0.000 1.000
## psi11(KOJA) 0.075085 202.202670 0.000 1.000
## psi11(CILINCING) 0.091811 189.020673 0.000 1.000
## psi11(KELAPA GADING) 0.044532 201.498579 0.000 1.000
## psi11(CENGKARENG) -0.275462 178.361062 -0.002 0.999
## psi11(GROGOL PETAMBURAN) -0.093145 147.362998 -0.001 0.999
## psi11(TAMAN SARI) -0.087018 175.876489 0.000 1.000
## psi11(TAMBORA) -0.054709 169.345364 0.000 1.000
## psi11(KEBON JERUK) -0.145220 176.016580 -0.001 0.999
## psi11(KALI DERES) -0.293835 175.855438 -0.002 0.999
## psi11(PALMERAH) -0.095736 152.322558 -0.001 0.999
## psi11(KEMBANGAN) -0.084665 154.203858 -0.001 1.000
## psi11(TEBET) -0.270682 264.434312 -0.001 0.999
## psi11(SETIA BUDI) 0.024387 186.248357 0.000 1.000
## psi11(MAMPANG PRAPATAN) 0.033320 213.164903 0.000 1.000
## psi11(PASAR MINGGU) 0.128925 244.901783 0.001 1.000
## psi11(KEBAYORAN LAMA) 0.123127 259.754549 0.000 1.000
## psi11(CILANDAK) -0.136250 248.595611 -0.001 1.000
## psi11(KEBAYORAN BARU) 0.033320 213.164903 0.000 1.000
## psi11(PANCORAN) 0.014373 212.468203 0.000 1.000
## psi11(JAGAKARSA) 0.128925 244.901783 0.001 1.000
## psi11(PESANGGRAHAN) -0.025830 242.147048 0.000 1.000
## psi11(MATRAMAN) 0.359308 217.671263 0.002 0.999
## psi11(PULO GADUNG) 0.576648 169.890323 0.003 0.997
## psi11(JATINEGARA) 0.691158 190.275391 0.004 0.997
## psi11(KRAMAT JATI) 0.722881 207.953631 0.003 0.997
## psi11(PASAR REBO) 0.395852 192.135847 0.002 0.998
## psi11(CAKUNG) 1.010824 203.681428 0.005 0.996
## psi11(DUREN SAWIT) 0.902423 202.037699 0.004 0.996
## psi11(MAKASAR) 0.471744 184.420051 0.003 0.998
## psi11(CIRACAS) 0.482411 196.060904 0.002 0.998
## psi11(CIPAYUNG) 0.496811 193.800056 0.003 0.998
##
##
## AIC: 9540Terdapat dua paramater dalman model GSTAR(1;1) yaitu psi10 dan psi11 untuk setiap setiap kecamatan di Provinsi DKI Jakarta. Diperoleh pula nilai AIC untuk model GSTAR(1;1) adalah sebesar 9540.
phi = fitmodel1$B
phi10_1 = phi[1:41,]
phi11_1 = phi[42:82,]
phi_10_1 = diag(phi10_1,41)
phi_11_1 = diag(phi11_1,41)
D = phi_10_1 + (phi_11_1%*%w1)##Generate Data GSTAR(1;1)
model_Y1=t(Y)
model1_1=matrix(0,nrow=N,ncol=T)
for(i in 2:T){
model1_1[,i]=D%*%(model_Y1[,(i-1)])
}T = 52
#Returns data_train to initial data_train (diff)
modeldata_train1 = matrix(0,nrow=N,ncol=T)
data_train1_1 = t(data_train)
I = diag(41)
for (i in (3:N)) {
for (j in (3:T)){
modeldata_train1[,j] = (D+I)%*%data_train1_1[,j-1]-D%*%data_train1_1[,j-2]
}
}
modeldata_train1 = t(modeldata_train1)
## menghilangkan baris 0 di t=1
modeldata1 = matrix(0,(T-2),N)
for (i in 1:(T-2)){
modeldata1[i,]=modeldata_train1[i+2,]
}
data_train1 = data_train[1:50,]Uji Diagnostik
Model dikatakan cocok jika rataan nol dan variansi residual konstan, residual berdistribusi normal, dan residual saling bebas.
Residual1 = matrix(0,nrow=T-2,ncol=N)
par(mfrow=c(2,2))
for (i in 1:N){
Residual1[,i]=modeldata1[,i]-data_train1[,i]
plot(Residual1[,i],type='l',main = paste("Residual Kecamatan", i))
abline(h=0)
acf(Residual1[,i],main = paste("ACF Residual Kecamatan", i))
qqnorm(Residual1[,i],main = paste("Normalitas Residual Kecamatan", i))
qqline(Residual1[,i])
hist(Residual1[,i],main = paste("Histogram Residual Kecamatan", i))
}
# Uji Kenormalan Residual
for (i in 1:N) {
print(shapiro.test(Residual1[,i]))
}##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.97243, p-value = 0.2898
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.9401, p-value = 0.0136
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.97823, p-value = 0.4801
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95657, p-value = 0.06389
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.93618, p-value = 0.009543
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.91678, p-value = 0.001807
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96005, p-value = 0.08933
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.93445, p-value = 0.008175
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95976, p-value = 0.08688
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96569, p-value = 0.1537
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96271, p-value = 0.1154
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96888, p-value = 0.2083
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96606, p-value = 0.1594
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.94968, p-value = 0.03309
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.9595, p-value = 0.08467
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.90293, p-value = 0.0006013
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.91545, p-value = 0.001622
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95534, p-value = 0.05677
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95586, p-value = 0.05966
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95347, p-value = 0.04743
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.94658, p-value = 0.02473
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.98137, p-value = 0.6112
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.93043, p-value = 0.005739
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95879, p-value = 0.07909
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96424, p-value = 0.1338
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96139, p-value = 0.1016
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.98525, p-value = 0.7823
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95879, p-value = 0.07909
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.94643, p-value = 0.02439
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96424, p-value = 0.1338
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95539, p-value = 0.05703
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.9625, p-value = 0.1131
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.9733, p-value = 0.3136
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96912, p-value = 0.2131
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.9536, p-value = 0.04805
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96736, p-value = 0.1804
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96333, p-value = 0.1225
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96174, p-value = 0.1051
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.96627, p-value = 0.1626
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.97128, p-value = 0.2607
##
##
## Shapiro-Wilk normality test
##
## data: Residual1[, i]
## W = 0.95703, p-value = 0.06674#Uji Independensi Residual
for (i in 1:N) {
print(Box.test(Residual1[,i],lag = 20, type = "Ljung-Box"))
}##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 25.747, df = 20, p-value = 0.1743
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 23.318, df = 20, p-value = 0.2735
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 26.647, df = 20, p-value = 0.1455
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 20.051, df = 20, p-value = 0.4547
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 29.314, df = 20, p-value = 0.08175
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 21.379, df = 20, p-value = 0.3751
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 33.664, df = 20, p-value = 0.02849
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 24.192, df = 20, p-value = 0.2341
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 40.859, df = 20, p-value = 0.003884
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 42.223, df = 20, p-value = 0.002586
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 40.873, df = 20, p-value = 0.003868
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 38.53, df = 20, p-value = 0.007623
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 45.031, df = 20, p-value = 0.001093
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 15.106, df = 20, p-value = 0.7703
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 20.217, df = 20, p-value = 0.4445
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 17.005, df = 20, p-value = 0.6526
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 13.931, df = 20, p-value = 0.834
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 15.686, df = 20, p-value = 0.7359
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 19.1, df = 20, p-value = 0.5153
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 15.878, df = 20, p-value = 0.7242
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 16.445, df = 20, p-value = 0.6887
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 18.481, df = 20, p-value = 0.5558
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 37.831, df = 20, p-value = 0.009286
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 31.374, df = 20, p-value = 0.05044
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 22.365, df = 20, p-value = 0.321
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 23.018, df = 20, p-value = 0.2879
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 19.386, df = 20, p-value = 0.4969
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 31.374, df = 20, p-value = 0.05044
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 18.897, df = 20, p-value = 0.5285
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 22.365, df = 20, p-value = 0.321
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 20.056, df = 20, p-value = 0.4544
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 25.525, df = 20, p-value = 0.1821
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 26.413, df = 20, p-value = 0.1526
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 27.232, df = 20, p-value = 0.1289
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 30.678, df = 20, p-value = 0.05959
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 33.249, df = 20, p-value = 0.03167
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 28.417, df = 20, p-value = 0.09989
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 29.51, df = 20, p-value = 0.07818
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 32.461, df = 20, p-value = 0.03863
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 26.2, df = 20, p-value = 0.1593
##
##
## Box-Ljung test
##
## data: Residual1[, i]
## X-squared = 28.708, df = 20, p-value = 0.09367#Menghitung MSE
MSE1 = matrix(0,1,N)
for(i in 1:N){
MSE1[,i] = mean(Residual1[,i]*Residual1[,i])
}
MSE1## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.5020394 0.8183722 3.187395 0.6155828 0.412107 0.3326597 1.685706
## [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
## [1,] 0.9294936 3.81934 7.520311 4.933853 7.780821 3.509586 6.104108 0.9074726
## [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23]
## [1,] 0.4916701 1.351122 2.162755 3.489659 0.9819807 1.362207 3.174613 0.7395112
## [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31]
## [1,] 1.313937 4.85182 4.799774 2.400799 1.313937 1.46478 4.85182 2.973117
## [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39]
## [1,] 2.288505 5.71564 6.564393 5.496648 2.941494 15.86808 10.17545 2.504134
## [,40] [,41]
## [1,] 5.005412 4.189847Berdasarkan plot residual data setiap kecamatan di Provinsi DKI Jakarta, secara umum terlihat residual tersebar di sekitar nol tanpa tren. Berdasarkan QQ Plot, terlihat pada awal dan akhir data nilainya cukup jauh dari garis. Sedangkan pada histogram sekilas teradapat beberapa data residual yang tidak berdistribusi normal dan berdistribusi normal. Untuk menguji kenormalan digunakan Uji Shapiro-Wilk diperoleh terdapat 14 kecamatan yang memiliki p-value < 0.5. Artinya untuk uji kenormalan Shapiro-Wilk 𝐻0 ditolak, sehingga tidak berdistribusi normal. Selanjutnya, pada grafik residual terdapat beberapa ACF yang tidak terdapat lag yang melebihi batas signifikansi sehingga dapat diasumsikan tidak ada korelasi antar residual. Untuk pembuktian lebih lanjut, gunakan Uji Ljung-Box diperoleh terdapat 9 kecamatan yang memiliki p-value < 0.05, sehingga 𝐻0 ditolak. Artinya, terdapat korelasi antar residual. Karena data tidak memenuhi 3 asumsi tersebut mengakibatkan beberapa kecamatan tidak terpenuhinya asumsi residual dalam pemodelan.
Forecasting
Selanjutnya diperiksa prediksi berdasarkan model terpilih.
prediksi1 <- as.matrix(predict(fitmodel1))
prediksi1## GAMBIR SAWAH BESAR KEMAYORAN SENEN CEMPAKA PUTIH MENTENG
## 3 2.8549363 4.004409 6.664631 3.9167367 2.9694178 3.0313741
## 4 1.8400642 1.293516 3.565381 1.3502973 1.6485782 1.1919346
## 5 4.2410731 5.704018 9.622198 5.6962736 4.3685271 3.3933974
## 6 2.3186985 3.950288 7.489318 2.8363397 2.2209601 2.6533978
## 7 3.2632015 3.998804 8.090947 4.3657773 3.3731922 2.9914918
## 8 3.9336149 5.354288 9.040662 3.9618965 2.9860048 3.0143576
## 9 2.7958075 3.647132 6.851337 4.0112899 3.0041467 2.9957459
## 10 4.4869438 5.344274 10.996484 5.1510028 4.0554626 2.9431013
## 11 3.2425986 5.663947 8.184116 4.3505631 3.5226211 4.1153927
## 12 3.1253940 3.643918 7.289895 4.0719733 3.0264354 2.5757603
## 13 4.4279348 5.698338 10.054199 5.4603305 4.4079211 3.3529834
## 14 6.9802104 8.641526 17.083517 7.7739356 5.6781236 5.9558636
## 15 4.8145438 6.606537 12.258909 6.6754581 5.1618691 3.9197793
## 16 4.0714224 5.356904 9.035851 4.9125030 2.9678628 3.4300891
## 17 4.5018159 7.055168 12.289870 6.0606397 5.1060997 4.7825407
## 18 5.1180179 6.644142 12.884570 7.0677396 4.6599817 4.5773556
## 19 4.7318811 6.649075 11.509412 5.2882027 4.6257711 3.6124520
## 20 3.9655781 6.001046 11.114557 5.3234401 3.9927432 4.4045645
## 21 5.1279726 7.355185 11.972452 6.2881590 5.3446834 4.0207388
## 22 2.8769448 3.644665 7.038917 3.7146634 2.6563533 2.9781977
## 23 3.7181821 5.048591 9.595945 4.8416315 3.7868150 3.3386257
## 24 4.8819821 6.003587 11.533021 5.9322604 4.9751196 4.0255247
## 25 3.5350647 4.295982 8.183665 4.3037264 3.2665741 2.6066026
## 26 3.1328901 4.355036 8.372093 4.2909815 3.3457201 3.4167950
## 27 4.3935129 5.347114 10.586347 5.4405731 4.4006643 2.9633083
## 28 3.6187806 5.652512 9.247938 4.5664831 3.6019274 4.0340329
## 29 3.9901652 4.648028 8.977263 4.9943550 2.9979266 2.6050073
## 30 3.8401841 5.357128 8.824662 4.9082693 4.3312065 3.4316844
## 31 4.1893204 4.994245 10.437685 5.1086656 3.6750136 2.9590542
## 32 2.9704955 4.000897 7.737653 3.9830651 2.9937799 3.0063812
## 33 4.3541736 6.348310 11.689536 6.4179933 4.3923708 3.9718165
## 34 1.3999558 2.306893 4.547906 2.0976851 1.5557950 1.6842401
## 35 3.6345860 5.051132 8.402682 5.1368468 3.7691914 3.7538255
## 36 2.9015318 2.643918 6.095759 2.7287759 1.6615367 1.5757603
## 37 2.9606607 4.353466 8.103189 4.3206176 3.3566053 3.4056280
## 38 3.1794856 3.642274 7.026675 3.7598232 2.6729402 2.5640615
## 39 2.2753751 2.991630 5.830768 3.1580591 2.0580543 1.9404425
## 40 2.3222104 2.349281 4.615641 2.3996471 2.3856324 1.3758492
## 41 1.9803303 2.352868 4.148663 2.3319075 1.9958533 2.4013739
## 42 1.0565502 1.998281 4.130737 2.0324586 1.0119219 0.5906497
## 43 1.2656602 1.998729 4.096632 2.0239911 1.3737105 1.3880798
## 44 0.9950826 1.000149 2.988632 0.9971775 0.9989633 1.0010635
## 45 2.1401463 2.348011 4.518137 2.4236382 1.0295455 0.9696895
## 46 1.5941936 1.653260 3.385232 1.5523707 1.9616427 2.0393505
## 47 1.0319632 1.999028 3.073895 2.0183461 0.6418397 0.5959673
## 48 1.3369627 1.996562 3.455610 2.0649171 1.3887424 1.3726586
## 49 0.9704955 1.000897 1.931789 0.9830651 0.9937799 1.0063812
## 50 0.9778716 1.352943 2.142978 1.3304963 0.9953349 1.0047859
## 51 0.8770647 1.003737 1.715789 0.9294379 0.9740829 1.0265882
## 52 2.0024587 2.999925 6.005684 3.0014112 2.0005183 1.9994682
## TANAH ABANG JOHAR BARU PENJARINGAN TANJUNG PRIOK KOJA CILINCING
## 3 4.764291 4.027718 2.291781 3.965075 3.346557 3.959386
## 4 2.643418 2.397090 7.836383 10.042121 8.715388 10.116358
## 5 7.488420 5.292059 7.004133 10.179357 8.104763 10.113519
## 6 4.699560 4.453935 11.400276 15.792042 13.296231 15.912431
## 7 6.294148 4.992483 10.000499 14.539161 11.764724 14.352033
## 8 5.892133 5.012684 14.104861 18.005888 15.050683 19.061973
## 9 5.031961 3.996242 7.000249 9.588697 7.543935 9.111224
## 10 7.657699 5.245080 16.490480 22.253132 18.886485 22.671268
## 11 5.672616 5.806597 7.936733 9.361298 7.878951 9.581213
## 12 5.203749 3.680693 11.739289 15.446228 13.109508 15.494183
## 13 6.792045 5.551703 6.677648 9.981900 7.844198 9.809491
## 14 12.101364 8.665660 19.130669 25.210574 21.167343 26.115113
## 15 8.783668 6.916796 14.483962 20.311762 16.099741 19.951608
## 16 5.982533 5.324475 16.756453 22.194404 18.675334 22.707101
## 17 9.239027 6.875707 18.883238 25.223877 20.840800 25.255125
## 18 8.731309 6.682102 17.329240 24.579974 19.468849 24.454619
## 19 8.697862 6.713109 22.069908 29.594367 24.033788 29.606108
## 20 7.944069 6.006577 22.117012 29.955262 24.262087 29.856099
## 21 9.074419 7.313669 22.182786 30.189388 25.191111 31.219102
## 22 4.933571 3.685391 18.325106 24.400617 19.923038 23.905893
## 23 7.360366 4.834366 20.994488 28.842919 23.439805 29.229175
## 24 8.578010 7.022550 17.702080 23.191351 19.649054 24.239760
## 25 6.281353 4.117246 16.511527 22.820643 18.454188 23.451274
## 26 6.312636 5.608077 18.112379 22.827185 18.951550 23.955340
## 27 7.505887 4.967584 16.247681 22.984953 18.870478 23.841625
## 28 6.514089 6.030067 14.248560 17.604399 14.679088 18.711691
## 29 5.984020 4.706532 17.468556 23.048663 18.771702 23.185217
## 30 5.970547 5.325884 11.283764 14.785500 12.239917 15.278779
## 31 7.307620 4.963826 16.638312 22.030999 17.619654 21.999298
## 32 5.721832 5.005637 9.593584 12.364384 10.699381 13.286715
## 33 8.441966 5.975101 19.731522 26.445791 21.664705 26.054385
## 34 2.928300 2.776530 12.203833 15.937783 13.758815 17.434315
## 35 6.224533 4.850339 16.157855 21.621659 18.180483 22.131180
## 36 3.973522 3.680693 11.801929 16.988878 13.904266 16.882940
## 37 6.166306 5.007517 14.515161 18.232164 15.353179 19.777553
## 38 4.831186 3.670358 8.232146 10.984080 8.862969 11.397237
## 39 4.677675 3.242731 7.502380 10.432925 8.995003 10.354171
## 40 3.390030 2.276556 11.297671 14.219952 11.807011 15.649017
## 41 3.198266 3.299106 5.093709 7.953953 6.250824 7.407120
## 42 3.091887 1.693847 9.363694 11.223659 10.279971 12.688037
## 43 3.067916 2.287361 9.449636 13.996293 10.527040 13.525773
## 44 1.992010 2.000940 7.039087 8.362203 7.121658 8.698969
## 45 3.457946 1.973222 12.747306 18.625803 15.216148 18.609997
## 46 2.474138 3.034764 6.350037 8.149230 6.492618 8.123402
## 47 2.051936 1.698545 7.886872 10.454514 9.180839 10.581404
## 48 2.414000 2.273737 7.790028 10.218643 7.795749 10.200038
## 49 1.952059 1.005637 6.562265 8.593059 8.022526 8.592336
## 50 1.964044 1.299576 10.034954 14.001963 10.575846 14.020658
## 51 1.800247 1.023490 6.741167 8.963113 7.770712 8.938729
## 52 4.003995 2.999530 8.964797 11.228457 9.880220 12.303326
## KELAPA GADING CENGKARENG GROGOL PETAMBURAN TAMAN SARI TAMBORA KEBON JERUK
## 3 2.253583 8.976708 3.254428 1.6980906 4.424810 5.394269
## 4 7.783156 12.425845 5.364636 3.2301195 6.282808 8.224904
## 5 7.086156 17.801411 7.712209 3.8416703 8.762328 10.764393
## 6 11.199713 10.605395 3.866505 2.6197744 5.128325 7.263571
## 7 10.170084 15.850568 7.949533 4.2083650 8.763623 10.449619
## 8 13.030059 11.443872 4.328944 2.7677050 5.468578 7.640956
## 9 6.538068 10.425284 4.626813 2.1909614 5.505435 6.290065
## 10 16.599030 20.983655 9.553254 5.4063310 10.393594 12.649485
## 11 6.070409 17.271647 5.974230 4.0072537 8.466968 10.429118
## 12 11.208349 18.351214 8.422852 4.1109480 10.069755 11.185156
## 13 6.907595 14.818378 6.193276 3.3541202 7.113522 9.431843
## 14 17.813646 24.065172 10.801459 6.3541202 12.855262 14.562353
## 15 13.702426 20.450137 8.296951 5.0246573 10.477910 12.588672
## 16 15.616000 22.573034 9.855625 4.8651221 11.915200 13.774908
## 17 17.881558 18.151504 8.051230 4.2214177 9.030090 11.079871
## 18 16.493535 15.947540 6.719958 4.2779795 8.807391 9.565795
## 19 21.020040 17.246234 7.566268 3.9608419 9.222627 10.195671
## 20 20.203484 19.656794 7.663307 4.9695437 9.733605 11.818663
## 21 21.590393 16.361233 6.605092 3.7415996 8.699412 10.597390
## 22 16.954354 16.652540 7.399565 4.2627513 8.303324 10.279361
## 23 20.142664 14.880917 6.256148 3.2392996 7.397698 9.529867
## 24 16.600413 13.916241 6.192318 3.0691362 7.181595 8.956246
## 25 16.460819 13.951794 5.983700 2.9847718 6.990426 8.974586
## 26 16.494218 13.745198 5.913841 2.9195083 6.949394 8.865671
## 27 15.923182 15.475171 6.160675 4.1501061 8.094374 9.250505
## 28 13.071252 13.435864 5.850968 2.6872133 6.665218 8.767647
## 29 16.363530 14.095851 5.990686 3.1648560 7.241776 8.985478
## 30 10.166314 13.552375 5.848640 2.8585957 6.911097 8.764017
## 31 15.726378 11.530264 5.179304 3.1675097 6.105316 7.279549
## 32 8.654283 13.287553 6.276148 2.9738946 7.230834 8.217454
## 33 18.753097 20.118192 7.819325 5.1152989 9.825244 12.061907
## 34 10.999157 13.082578 5.035537 3.3200404 7.048997 7.922491
## 35 15.274465 14.140364 6.785159 3.3388921 7.598441 8.667450
## 36 11.943221 14.598008 6.118760 3.2845058 7.317001 9.315666
## 37 12.923864 11.766418 4.962742 2.9651928 5.978116 6.811402
## 38 7.918728 9.749512 4.474081 2.1588079 5.347091 6.526048
## 39 7.687412 12.141485 5.564898 3.4172083 6.400433 7.537128
## 40 9.861519 10.028667 3.789053 1.9134602 4.807463 6.014710
## 41 5.649830 9.068305 4.285463 2.1561542 5.236305 6.101466
## 42 7.880445 8.281788 4.053558 2.0500354 4.031458 5.083502
## 43 9.981074 8.240469 4.039587 2.0369827 4.270498 5.192229
## 44 5.643150 5.986227 1.995343 0.9956491 2.997265 2.992739
## 45 13.295618 8.639327 4.740915 2.2975585 4.325208 5.467960
## 46 5.817257 7.243601 2.219498 1.6654588 3.651541 4.470322
## 47 7.797629 8.089525 3.334364 2.0282809 4.017781 5.047197
## 48 6.854839 7.316781 3.107117 2.1000707 4.062916 4.167004
## 49 7.013360 6.040759 2.972057 0.9738946 2.983587 4.086944
## 50 9.556994 8.061418 3.979042 2.1539788 4.234937 4.967325
## 51 6.696589 7.408878 2.579477 1.7176696 3.684367 4.687964
## 52 7.904938 8.006887 3.306420 2.0021755 4.001368 5.003631
## KALI DERES PALMERAH KEMBANGAN TEBET SETIA BUDI MAMPANG PRAPATAN
## 3 6.259679 3.503951 4.461393 8.650209 3.760787 6.049147
## 4 10.395603 5.239648 6.261895 8.126478 4.259332 4.978342
## 5 13.744506 6.839335 8.807954 8.997904 4.995732 6.166048
## 6 8.892923 4.417788 5.178332 8.406595 4.782735 5.907255
## 7 12.526754 6.393133 8.654454 9.108273 3.990245 5.986672
## 8 9.597052 4.757959 5.529125 8.817289 4.291645 6.022491
## 9 8.263377 4.196566 5.430659 6.054136 2.995123 3.993336
## 10 16.990617 8.433514 10.433343 10.575678 5.209949 6.254629
## 11 12.587507 6.770954 8.506377 5.729411 3.581932 4.837000
## 12 14.476944 8.122064 9.107948 5.196723 3.244090 4.129397
## 13 11.712012 6.198653 7.382543 4.462734 2.224581 3.102741
## 14 18.916622 10.553491 11.589406 9.512200 4.224581 6.102741
## 15 15.748579 8.790101 9.730172 7.833013 3.577054 5.658457
## 16 17.544556 9.851609 10.868769 8.580442 5.037800 6.051645
## 17 14.161609 7.052655 9.046566 7.148875 2.711404 3.981674
## 18 12.761822 6.647141 8.515324 9.225887 5.521103 6.303775
## 19 13.072385 7.134338 8.168763 9.977125 4.460607 5.671235
## 20 14.794853 7.789073 9.970367 10.954727 5.008535 7.011662
## 21 12.508901 6.729238 8.503726 8.785550 4.023777 5.860607
## 22 12.505791 7.275549 8.293645 7.178518 3.250187 4.137727
## 23 11.910350 6.263275 7.439693 9.645445 5.208120 6.252130
## 24 10.749704 6.062535 7.105264 9.774113 4.478897 6.696225
## 25 10.948579 5.983246 6.985184 12.002096 6.004268 7.833952
## 26 10.728203 5.911444 6.921685 9.848550 4.747374 5.858941
## 27 12.506865 6.165145 7.146048 12.367996 6.233116 8.286282
## 28 10.427560 5.846822 6.864535 13.665840 6.763835 8.709553
## 29 11.072922 5.990426 6.991533 16.071863 7.727255 9.831453
## 30 10.522519 5.844428 6.862419 13.659073 6.764445 8.882265
## 31 8.565631 5.184293 5.162981 14.422132 7.228239 9.107739
## 32 10.218765 6.148698 7.388326 9.918795 5.007316 7.009996
## 33 15.184721 7.949432 9.698456 16.160791 8.518055 10.299610
## 34 10.364558 5.278970 6.055567 16.261435 7.267729 10.611810
## 35 10.865201 5.536737 6.781452 16.563762 7.953665 9.764813
## 36 11.579249 6.299483 7.314811 15.295654 6.968907 9.129397
## 37 8.780161 4.784286 5.759271 12.842261 6.284938 8.185207
## 38 7.740858 4.352138 5.361377 9.395064 4.230678 5.111071
## 39 10.027347 5.445481 6.443926 7.560048 3.482084 4.422343
## 40 7.089224 3.918317 4.877803 8.171751 4.250797 5.138560
## 41 7.145843 4.158272 5.189930 6.995329 2.729694 4.834785
## 42 7.168955 4.055048 4.048683 6.106177 3.261161 3.980841
## 43 6.124880 3.040688 4.242846 5.065574 1.989636 3.157719
## 44 4.087613 2.172632 2.995767 3.937000 2.276403 3.173545
## 45 7.623324 4.313843 4.327511 6.336257 2.965249 3.952519
## 46 5.149491 2.468050 3.636506 4.598169 2.769932 3.889762
## 47 6.197801 3.208533 4.027516 5.038506 1.992074 2.989171
## 48 6.337910 3.110097 4.097365 5.311285 2.247139 3.133562
## 49 4.911850 2.971279 2.974600 6.918795 3.007316 4.009996
## 50 6.036192 2.978459 4.187813 8.038028 3.730303 4.835618
## 51 5.530402 2.880329 3.687306 9.711113 4.755300 5.869770
## 52 6.007346 3.002393 4.002117 7.105699 2.724207 3.827288
## PASAR MINGGU KEBAYORAN LAMA CILANDAK KEBAYORAN BARU PANCORAN JAGAKARSA
## 3 10.611775 10.578782 6.668641 6.049147 5.858398 10.611775
## 4 10.494588 10.522797 7.218953 4.978342 5.153460 10.494588
## 5 12.266633 12.279868 8.023844 6.166048 6.160287 12.266633
## 6 11.727808 11.689597 7.611211 5.907255 5.871333 11.727808
## 7 10.948430 10.950749 8.054500 5.986672 5.994251 10.948430
## 8 12.376219 12.384526 7.908031 6.022491 6.009702 12.376219
## 9 6.685020 6.673960 5.027250 3.993336 3.997125 6.685020
## 10 14.101098 14.177710 9.560054 6.254629 7.287157 14.101098
## 11 8.964251 8.859474 5.000063 4.837000 4.589566 8.964251
## 12 7.703204 7.747258 5.369305 4.129397 4.307279 7.703204
## 13 5.600064 5.648756 4.347914 3.102741 3.132978 5.600064
## 14 12.310870 12.347341 8.347914 6.102741 6.132978 12.310870
## 15 9.649271 9.533434 7.027313 5.658457 5.586692 9.649271
## 16 12.489028 12.492262 7.788813 6.051645 6.022278 12.489028
## 17 8.639897 8.630865 6.074937 3.981674 4.992095 8.639897
## 18 12.712874 12.756492 8.293891 6.303775 6.145555 12.712874
## 19 12.190433 12.151162 8.808297 5.671235 6.843665 12.190433
## 20 13.755929 13.741680 9.887117 7.011662 6.842228 13.755929
## 21 10.836507 10.818634 7.801961 5.860607 6.014014 10.836507
## 22 9.735435 9.778040 6.270047 4.137727 5.148070 9.735435
## 23 12.223845 12.264231 8.505078 6.252130 6.123277 12.223845
## 24 12.287126 12.243508 8.706109 6.696225 6.854445 12.287126
## 25 14.733367 14.720132 9.910961 7.833952 7.839713 14.733367
## 26 12.540867 12.511063 8.808774 5.858941 6.850493 12.540867
## 27 15.355993 15.390436 10.300226 8.286282 8.138009 15.355993
## 28 17.627891 17.594173 11.716805 8.709553 8.860194 17.627891
## 29 19.145309 19.108068 13.855984 9.831453 10.838635 19.145309
## 30 16.631114 16.597251 11.648203 8.882265 8.697751 16.631114
## 31 18.619403 18.667225 12.392672 9.107739 9.297937 18.619403
## 32 12.749483 12.735523 8.959125 7.009996 7.004312 12.749483
## 33 21.275147 22.343931 14.376117 10.299610 11.306561 21.275147
## 34 20.468777 20.059641 14.152868 10.611810 11.403767 20.468777
## 35 19.176654 19.464644 13.193679 9.764813 10.809889 19.176654
## 36 19.414010 19.445843 13.304109 9.129397 10.144477 19.414010
## 37 17.340764 17.350666 10.945500 8.185207 9.168552 17.340764
## 38 11.343101 11.378123 7.379047 5.111071 6.136572 11.343101
## 39 9.374177 9.463734 6.642281 4.422343 4.448163 9.374177
## 40 10.738659 10.781118 7.201445 5.138560 5.311232 10.738659
## 41 8.447396 8.421796 5.907555 4.834785 4.840072 8.447396
## 42 7.215063 7.230617 5.143539 3.980841 3.991736 7.215063
## 43 6.523596 6.550501 4.123102 3.157719 3.156694 6.523596
## 44 5.295641 5.307571 4.058383 3.173545 3.163521 5.295641
## 45 8.105476 8.125959 5.194156 3.952519 3.979518 8.105476
## 46 6.370928 6.323540 4.682743 3.889762 3.863788 6.370928
## 47 6.247294 6.261399 4.044281 2.989171 2.995329 6.247294
## 48 6.140931 6.159819 4.221883 3.133562 3.146274 6.140931
## 49 9.038677 9.036938 5.959125 4.009996 5.004312 9.038677
## 50 9.161425 9.123459 6.838953 4.835618 4.677629 9.161425
## 51 12.871962 12.852494 8.764492 5.869770 7.017966 12.871962
## 52 7.129193 7.092677 5.873015 3.827288 3.674036 7.129193
## PESANGGRAHAN MATRAMAN PULO GADUNG JATINEGARA KRAMAT JATI PASAR REBO
## 3 7.649386 7.529979 11.850556 11.491247 11.066250 7.583882
## 4 7.329305 5.281661 9.159306 9.550747 9.530127 6.742696
## 5 9.160778 8.421910 12.364959 13.407259 11.873496 7.930726
## 6 8.626139 6.883778 11.437795 11.056866 11.536102 7.478380
## 7 8.010332 8.341066 13.235213 14.779960 12.301218 9.303428
## 8 9.138823 7.757744 11.457493 11.881897 11.897574 7.343663
## 9 5.848908 5.928138 10.350543 10.389980 8.855424 7.382598
## 10 10.537868 12.008429 17.389210 18.735787 17.837404 12.402865
## 11 6.234843 10.040511 17.401338 17.995629 15.608296 11.376885
## 12 5.501706 8.026671 12.264773 13.118773 12.668697 8.957057
## 13 4.366112 5.708804 10.132817 10.206905 8.861503 6.563913
## 14 9.209854 7.708804 11.666945 13.206905 12.451874 8.563913
## 15 8.083751 11.907806 20.081243 21.026666 20.005943 14.144789
## 16 8.959964 10.617771 17.632697 18.373814 15.987872 12.304727
## 17 7.014206 10.317591 16.751098 17.563440 16.012045 11.320513
## 18 9.343511 11.053620 18.239767 19.227033 17.313284 12.448515
## 19 9.675861 10.161689 15.259492 16.311021 15.325297 11.178133
## 20 10.834702 11.610547 18.599445 19.826540 18.433749 12.600317
## 21 8.818558 9.865536 14.630487 15.617456 14.524068 10.924187
## 22 7.338991 8.116498 12.340680 13.347986 12.439788 8.056020
## 23 9.383547 8.981481 14.811834 15.683950 13.783188 10.296713
## 24 9.656489 7.946380 12.294361 13.244755 12.686716 8.551485
## 25 11.839222 11.547668 19.032659 20.177376 17.716875 12.531043
## 26 9.663592 6.877992 11.204037 11.998263 10.307183 7.980857
## 27 12.357072 10.834561 17.334644 18.448810 17.704882 11.702461
## 28 12.646157 10.605314 16.525020 17.521218 14.795128 11.709826
## 29 14.841159 12.551142 20.591792 22.540904 21.072288 14.577817
## 30 12.645511 10.614297 17.073564 17.482074 15.222829 11.257954
## 31 13.362238 11.277910 18.287569 19.782367 17.969936 12.623291
## 32 9.992251 11.107792 18.172995 19.207347 17.216864 12.118756
## 33 15.502998 12.523917 19.235941 20.084216 18.042182 13.475496
## 34 16.119912 11.404775 18.273935 19.587257 18.584280 12.590597
## 35 14.736561 9.286893 14.301986 14.271434 12.807267 9.171419
## 36 14.345448 11.511461 18.662391 20.231620 18.439808 12.880595
## 37 13.145926 6.174145 10.367169 9.635405 8.927671 6.773035
## 38 8.203397 6.283420 10.742852 11.436117 10.222965 7.124645
## 39 6.853612 4.963516 7.783002 7.649392 7.747044 4.815152
## 40 8.338345 3.640692 5.889224 6.837053 5.277119 4.065917
## 41 6.682318 4.556651 7.047075 7.666443 7.325317 4.540939
## 42 6.171110 3.793398 5.668427 6.602584 5.584343 3.772385
## 43 5.167235 3.847294 5.754924 6.706258 5.692775 3.831763
## 44 4.154966 3.017965 4.028832 4.034558 4.036144 3.019793
## 45 6.193065 2.972775 5.110021 5.599734 5.150635 2.897680
## 46 4.639699 4.664720 7.600927 7.750431 6.746960 5.732326
## 47 5.164652 2.398436 3.880844 3.718951 3.174693 1.947811
## 48 5.029704 4.071585 6.268599 6.261591 5.349428 4.468307
## 49 6.992251 2.623003 4.707122 5.679136 4.626494 3.656987
## 50 7.681673 4.565634 7.595618 7.155511 7.162648 5.089067
## 51 9.811455 3.964346 5.789066 6.335736 5.722861 4.033046
## 52 5.688130 4.991017 8.451456 9.510933 8.572298 6.451872
## CAKUNG DUREN SAWIT MAKASAR CIRACAS CIPAYUNG
## 3 19.002058 14.791072 7.695822 10.267786 9.732796
## 4 14.854057 12.413425 6.693367 8.686433 7.235385
## 5 20.800921 16.382078 7.917445 11.359348 10.354746
## 6 18.445076 15.523249 7.550180 10.258186 9.296549
## 7 21.573486 17.179033 9.381514 12.250806 10.242964
## 8 19.215583 15.529132 7.178005 10.438088 9.893659
## 9 16.286743 13.359517 7.475862 9.347288 7.900638
## 10 29.251676 23.206024 11.308297 16.597091 14.554408
## 11 28.639013 22.955624 11.708154 16.066920 14.603379
## 12 20.711199 16.849410 8.398527 11.384926 10.366566
## 13 15.813801 13.367484 6.731765 9.217847 7.735871
## 14 20.813801 16.367484 8.731765 11.774077 9.735871
## 15 32.837017 27.095155 13.324438 18.189286 16.712459
## 16 28.188793 22.538739 11.450358 16.085118 14.119926
## 17 28.466231 22.423663 11.170330 16.290904 14.285066
## 18 28.764826 22.997096 12.004119 15.864877 13.845516
## 19 26.943778 21.946092 11.212285 14.660855 13.665253
## 20 29.820511 24.395852 12.735321 17.056383 14.615572
## 21 26.007738 20.799859 10.889739 13.582811 13.042702
## 22 19.941721 16.155020 8.086674 11.393069 9.932457
## 23 24.153680 20.218346 10.413338 14.004680 11.958836
## 24 21.257358 16.922900 8.425670 11.247583 10.712796
## 25 30.154710 25.237928 12.652766 16.971961 14.528630
## 26 18.512659 14.594730 7.725730 10.671151 9.134485
## 27 29.167590 24.223334 11.896875 15.942921 13.909755
## 28 26.683872 20.743867 10.614368 15.440548 13.469832
## 29 34.612175 28.090242 15.047174 19.604471 16.607993
## 30 27.220235 22.226426 10.485739 15.008838 13.482252
## 31 30.987610 24.422843 12.802527 17.846439 15.368705
## 32 29.814340 24.270727 12.141523 16.700953 14.707355
## 33 31.660658 25.804289 13.374940 17.360806 15.341726
## 34 30.399679 24.831763 12.663997 17.401451 15.458612
## 35 22.523973 18.445404 9.244109 12.970959 10.939437
## 36 30.644645 25.549420 13.109160 17.716236 14.691631
## 37 15.981976 13.120958 6.478064 9.417885 8.873659
## 38 17.044323 13.673093 7.419912 9.782219 7.860074
## 39 12.614231 10.173225 4.819540 6.980560 5.933995
## 40 10.478084 8.637579 4.098467 5.961359 4.944878
## 41 11.179980 8.720487 4.664560 6.540252 5.541050
## 42 9.907684 8.021109 3.728747 5.722614 4.714334
## 43 9.570400 7.616470 3.799509 5.794975 4.788855
## 44 7.050541 5.045121 3.023587 4.024121 3.024841
## 45 8.026299 7.334053 2.897976 5.200104 4.175422
## 46 12.381117 9.589479 5.872726 6.448690 5.477411
## 47 6.204759 4.626709 1.706261 3.399447 3.396848
## 48 9.304275 8.582219 4.597917 5.332767 4.870356
## 49 8.814340 6.190723 3.571312 4.700953 3.707355
## 50 11.227435 9.743047 5.106142 6.108542 5.553471
## 51 10.285715 8.048025 4.019468 5.159244 5.179325
## 52 14.463637 11.517441 6.558417 8.431709 6.987580plot(fitmodel1, n_predict = 30)
GSTAR(2;1,1)
Estimasi parameter setiap kecamatan di Provinsi DKI Jakarta
fitmodel2 <- gstar(data, w1, p = 2, d = 1, est = "OLS")
summary(fitmodel2)##
## Coefficients:
## Estimate Std.Err t value Pr(>|t|)
## psi10(GAMBIR) -3.803e-01 3.261e+02 -0.001 0.999
## psi10(SAWAH BESAR) -4.794e-01 1.656e+02 -0.003 0.998
## psi10(KEMAYORAN) -3.510e-01 5.496e+01 -0.006 0.995
## psi10(SENEN) -4.665e-01 2.073e+02 -0.002 0.998
## psi10(CEMPAKA PUTIH) -5.301e-01 2.801e+02 -0.002 0.998
## psi10(MENTENG) -5.511e-01 3.269e+02 -0.002 0.999
## psi10(TANAH ABANG) -3.878e-01 9.869e+01 -0.004 0.997
## psi10(JOHAR BARU) -3.922e-01 1.615e+02 -0.002 0.998
## psi10(PENJARINGAN) -3.707e-01 5.443e+01 -0.007 0.995
## psi10(TANJUNG PRIOK) -3.230e-01 3.036e+01 -0.011 0.992
## psi10(KOJA) -3.348e-01 4.702e+01 -0.007 0.994
## psi10(CILINCING) -3.533e-01 2.686e+01 -0.013 0.990
## psi10(KELAPA GADING) -3.135e-01 6.477e+01 -0.005 0.996
## psi10(CENGKARENG) -2.843e-01 5.458e+01 -0.005 0.996
## psi10(GROGOL PETAMBURAN) -4.886e-01 2.506e+02 -0.002 0.998
## psi10(TAMAN SARI) -3.565e-01 5.902e+02 -0.001 1.000
## psi10(TAMBORA) -3.520e-01 2.042e+02 -0.002 0.999
## psi10(KEBON JERUK) -2.804e-01 1.545e+02 -0.002 0.999
## psi10(KALI DERES) -2.726e-01 9.241e+01 -0.003 0.998
## psi10(PALMERAH) -2.941e-01 2.802e+02 -0.001 0.999
## psi10(KEMBANGAN) -4.134e-01 2.026e+02 -0.002 0.998
## psi10(TEBET) 2.508e-02 1.700e+02 0.000 1.000
## psi10(SETIA BUDI) -2.223e-01 4.266e+02 -0.001 1.000
## psi10(MAMPANG PRAPATAN) -1.100e-01 3.538e+02 0.000 1.000
## psi10(PASAR MINGGU) -2.377e-01 8.325e+01 -0.003 0.998
## psi10(KEBAYORAN LAMA) -2.507e-01 8.577e+01 -0.003 0.998
## psi10(CILANDAK) -3.434e-02 1.976e+02 0.000 1.000
## psi10(KEBAYORAN BARU) -1.100e-01 3.538e+02 0.000 1.000
## psi10(PANCORAN) -1.273e-01 2.944e+02 0.000 1.000
## psi10(JAGAKARSA) -2.377e-01 8.325e+01 -0.003 0.998
## psi10(PESANGGRAHAN) -1.106e-01 1.525e+02 -0.001 0.999
## psi10(MATRAMAN) -5.251e-01 2.092e+02 -0.003 0.998
## psi10(PULO GADUNG) -5.388e-01 6.873e+01 -0.008 0.994
## psi10(JATINEGARA) -6.171e-01 6.011e+01 -0.010 0.992
## psi10(KRAMAT JATI) -6.744e-01 8.376e+01 -0.008 0.994
## psi10(PASAR REBO) -5.723e-01 1.569e+02 -0.004 0.997
## psi10(CAKUNG) -5.135e-01 2.808e+01 -0.018 0.985
## psi10(DUREN SAWIT) -5.989e-01 4.377e+01 -0.014 0.989
## psi10(MAKASAR) -6.942e-01 1.734e+02 -0.004 0.997
## psi10(CIRACAS) -5.010e-01 8.618e+01 -0.006 0.995
## psi10(CIPAYUNG) -4.433e-01 1.211e+02 -0.004 0.997
## psi20(GAMBIR) -2.527e-01 3.139e+02 -0.001 0.999
## psi20(SAWAH BESAR) -2.950e-01 1.657e+02 -0.002 0.999
## psi20(KEMAYORAN) -2.907e-01 5.415e+01 -0.005 0.996
## psi20(SENEN) -2.136e-01 2.039e+02 -0.001 0.999
## psi20(CEMPAKA PUTIH) -3.207e-01 2.832e+02 -0.001 0.999
## psi20(MENTENG) -3.705e-01 3.364e+02 -0.001 0.999
## psi20(TANAH ABANG) -2.256e-01 9.782e+01 -0.002 0.998
## psi20(JOHAR BARU) -2.899e-01 1.680e+02 -0.002 0.999
## psi20(PENJARINGAN) 2.974e-01 5.287e+01 0.006 0.996
## psi20(TANJUNG PRIOK) 3.559e-01 2.909e+01 0.012 0.990
## psi20(KOJA) 3.503e-01 4.498e+01 0.008 0.994
## psi20(CILINCING) 3.124e-01 2.612e+01 0.012 0.990
## psi20(KELAPA GADING) 4.357e-01 6.065e+01 0.007 0.994
## psi20(CENGKARENG) -4.115e-01 5.338e+01 -0.008 0.994
## psi20(GROGOL PETAMBURAN) -3.062e-01 2.437e+02 -0.001 0.999
## psi20(TAMAN SARI) -4.293e-01 5.745e+02 -0.001 0.999
## psi20(TAMBORA) -2.593e-01 1.998e+02 -0.001 0.999
## psi20(KEBON JERUK) -4.355e-01 1.497e+02 -0.003 0.998
## psi20(KALI DERES) -3.881e-01 9.243e+01 -0.004 0.997
## psi20(PALMERAH) -2.822e-01 2.731e+02 -0.001 0.999
## psi20(KEMBANGAN) -4.723e-01 2.031e+02 -0.002 0.998
## psi20(TEBET) 1.800e-01 1.821e+02 0.001 0.999
## psi20(SETIA BUDI) 1.809e-01 4.436e+02 0.000 1.000
## psi20(MAMPANG PRAPATAN) 6.296e-02 3.483e+02 0.000 1.000
## psi20(PASAR MINGGU) 2.423e-01 8.985e+01 0.003 0.998
## psi20(KEBAYORAN LAMA) 2.838e-01 9.295e+01 0.003 0.998
## psi20(CILANDAK) 1.511e-01 2.135e+02 0.001 0.999
## psi20(KEBAYORAN BARU) 6.296e-02 3.483e+02 0.000 1.000
## psi20(PANCORAN) 1.561e-01 3.277e+02 0.000 1.000
## psi20(JAGAKARSA) 2.423e-01 8.985e+01 0.003 0.998
## psi20(PESANGGRAHAN) 1.612e-01 1.609e+02 0.001 0.999
## psi20(MATRAMAN) -2.415e-01 1.952e+02 -0.001 0.999
## psi20(PULO GADUNG) -2.448e-01 6.337e+01 -0.004 0.997
## psi20(JATINEGARA) -3.255e-01 5.773e+01 -0.006 0.996
## psi20(KRAMAT JATI) -2.454e-01 7.850e+01 -0.003 0.998
## psi20(PASAR REBO) -3.289e-01 1.451e+02 -0.002 0.998
## psi20(CAKUNG) -2.290e-01 2.603e+01 -0.009 0.993
## psi20(DUREN SAWIT) -2.454e-01 4.098e+01 -0.006 0.995
## psi20(MAKASAR) -2.822e-01 1.620e+02 -0.002 0.999
## psi20(CIRACAS) -2.623e-01 8.174e+01 -0.003 0.997
## psi20(CIPAYUNG) -1.661e-01 1.069e+02 -0.002 0.999
## psi11(GAMBIR) -1.071e-01 1.038e+02 -0.001 0.999
## psi11(SAWAH BESAR) -2.235e-02 1.002e+02 0.000 1.000
## psi11(KEMAYORAN) -2.392e-01 1.084e+02 -0.002 0.998
## psi11(SENEN) -7.790e-02 9.909e+01 -0.001 0.999
## psi11(CEMPAKA PUTIH) -2.879e-02 9.088e+01 0.000 1.000
## psi11(MENTENG) 1.943e-02 8.788e+01 0.000 1.000
## psi11(TANAH ABANG) -1.742e-01 1.098e+02 -0.002 0.999
## psi11(JOHAR BARU) -7.040e-03 9.911e+01 0.000 1.000
## psi11(PENJARINGAN) -1.836e-02 1.816e+02 0.000 1.000
## psi11(TANJUNG PRIOK) -1.955e-01 2.011e+02 -0.001 0.999
## psi11(KOJA) -1.265e-01 2.040e+02 -0.001 1.000
## psi11(CILINCING) -1.046e-01 1.852e+02 -0.001 1.000
## psi11(KELAPA GADING) -1.745e-01 2.055e+02 -0.001 0.999
## psi11(CENGKARENG) -3.395e-02 1.921e+02 0.000 1.000
## psi11(GROGOL PETAMBURAN) 2.719e-02 1.749e+02 0.000 1.000
## psi11(TAMAN SARI) -1.474e-02 1.797e+02 0.000 1.000
## psi11(TAMBORA) 9.850e-03 1.749e+02 0.000 1.000
## psi11(KEBON JERUK) -1.390e-02 1.835e+02 0.000 1.000
## psi11(KALI DERES) -8.044e-02 1.934e+02 0.000 1.000
## psi11(PALMERAH) -3.449e-02 1.617e+02 0.000 1.000
## psi11(KEMBANGAN) 6.960e-02 1.709e+02 0.000 1.000
## psi11(TEBET) -2.965e-01 2.519e+02 -0.001 0.999
## psi11(SETIA BUDI) -1.581e-02 1.850e+02 0.000 1.000
## psi11(MAMPANG PRAPATAN) -4.358e-02 2.200e+02 0.000 1.000
## psi11(PASAR MINGGU) 8.823e-03 2.360e+02 0.000 1.000
## psi11(KEBAYORAN LAMA) 4.531e-04 2.491e+02 0.000 1.000
## psi11(CILANDAK) -2.104e-01 2.363e+02 -0.001 0.999
## psi11(KEBAYORAN BARU) -4.358e-02 2.200e+02 0.000 1.000
## psi11(PANCORAN) -3.414e-02 2.081e+02 0.000 1.000
## psi11(JAGAKARSA) 8.823e-03 2.360e+02 0.000 1.000
## psi11(PESANGGRAHAN) -1.211e-01 2.319e+02 -0.001 1.000
## psi11(MATRAMAN) 3.672e-01 2.428e+02 0.002 0.999
## psi11(PULO GADUNG) 6.609e-01 1.931e+02 0.003 0.997
## psi11(JATINEGARA) 7.863e-01 2.070e+02 0.004 0.997
## psi11(KRAMAT JATI) 8.295e-01 2.397e+02 0.003 0.997
## psi11(PASAR REBO) 4.931e-01 2.272e+02 0.002 0.998
## psi11(CAKUNG) 1.037e+00 2.281e+02 0.005 0.996
## psi11(DUREN SAWIT) 9.977e-01 2.276e+02 0.004 0.997
## psi11(MAKASAR) 5.809e-01 2.192e+02 0.003 0.998
## psi11(CIRACAS) 5.330e-01 2.180e+02 0.002 0.998
## psi11(CIPAYUNG) 4.726e-01 2.255e+02 0.002 0.998
## psi21(GAMBIR) -1.670e-01 1.019e+02 -0.002 0.999
## psi21(SAWAH BESAR) -1.973e-01 9.600e+01 -0.002 0.998
## psi21(KEMAYORAN) -3.964e-01 1.049e+02 -0.004 0.997
## psi21(SENEN) -1.679e-01 9.436e+01 -0.002 0.999
## psi21(CEMPAKA PUTIH) -1.791e-01 8.563e+01 -0.002 0.998
## psi21(MENTENG) -1.052e-01 8.227e+01 -0.001 0.999
## psi21(TANAH ABANG) -3.605e-01 1.041e+02 -0.003 0.997
## psi21(JOHAR BARU) -2.419e-01 9.239e+01 -0.003 0.998
## psi21(PENJARINGAN) -7.023e-01 1.775e+02 -0.004 0.997
## psi21(TANJUNG PRIOK) -1.164e+00 1.962e+02 -0.006 0.995
## psi21(KOJA) -9.089e-01 1.965e+02 -0.005 0.996
## psi21(CILINCING) -1.106e+00 1.822e+02 -0.006 0.995
## psi21(KELAPA GADING) -9.419e-01 1.954e+02 -0.005 0.996
## psi21(CENGKARENG) 2.590e-01 1.854e+02 0.001 0.999
## psi21(GROGOL PETAMBURAN) 7.512e-02 1.555e+02 0.000 1.000
## psi21(TAMAN SARI) 5.472e-02 1.746e+02 0.000 1.000
## psi21(TAMBORA) 6.895e-02 1.702e+02 0.000 1.000
## psi21(KEBON JERUK) 1.957e-01 1.793e+02 0.001 0.999
## psi21(KALI DERES) 1.863e-01 1.843e+02 0.001 0.999
## psi21(PALMERAH) 2.635e-02 1.557e+02 0.000 1.000
## psi21(KEMBANGAN) 1.470e-01 1.624e+02 0.001 0.999
## psi21(TEBET) -3.761e-01 2.650e+02 -0.001 0.999
## psi21(SETIA BUDI) -1.705e-01 1.892e+02 -0.001 0.999
## psi21(MAMPANG PRAPATAN) -2.139e-01 2.078e+02 -0.001 0.999
## psi21(PASAR MINGGU) -6.638e-01 2.462e+02 -0.003 0.998
## psi21(KEBAYORAN LAMA) -7.360e-01 2.618e+02 -0.003 0.998
## psi21(CILANDAK) -3.654e-01 2.532e+02 -0.001 0.999
## psi21(KEBAYORAN BARU) -2.139e-01 2.078e+02 -0.001 0.999
## psi21(PANCORAN) -2.451e-01 2.184e+02 -0.001 0.999
## psi21(JAGAKARSA) -6.638e-01 2.462e+02 -0.003 0.998
## psi21(PESANGGRAHAN) -4.405e-01 2.395e+02 -0.002 0.999
## psi21(MATRAMAN) 1.628e-01 2.370e+02 0.001 0.999
## psi21(PULO GADUNG) 3.291e-01 1.942e+02 0.002 0.999
## psi21(JATINEGARA) 4.509e-01 2.109e+02 0.002 0.998
## psi21(KRAMAT JATI) 3.030e-01 2.439e+02 0.001 0.999
## psi21(PASAR REBO) 2.649e-01 2.277e+02 0.001 0.999
## psi21(CAKUNG) 3.891e-01 2.259e+02 0.002 0.999
## psi21(DUREN SAWIT) 4.003e-01 2.313e+02 0.002 0.999
## psi21(MAKASAR) 2.560e-01 2.274e+02 0.001 0.999
## psi21(CIRACAS) 2.381e-01 2.201e+02 0.001 0.999
## psi21(CIPAYUNG) 1.245e-01 2.121e+02 0.001 1.000
##
##
## AIC: 9396Terdapat empat paramater dalman model GSTAR(2;1,1) yaitu psi10. psi20, psi11, dan psi21 untuk setiap setiap kecamatan di Provinsi DKI Jakarta. Diperoleh pula nilai AIC untuk model GSTAR(2;1,1) adalah sebesar 9396.
phi2 = fitmodel2$B
phi10_2 = phi2[1:41,]
phi20_2 = phi2[42:82,]
phi11_2 = phi2[83:123,]
phi21_2 = phi2[124:164,]
phi_10_2 = diag(phi10_2,41)
phi_20_2 = diag(phi20_2,41)
phi_11_2 = diag(phi11_2,41)
phi_21_2 = diag(phi21_2,41)T = 52
##Generate data_train GSTAR(2;1,1)
model_Y2=t(Y)
D1=phi_10_2+(phi_11_2%*%w1)
D2=phi_20_2+(phi_21_2%*%w1)
model1_2=matrix(0,nrow=N,ncol=T)
for(i in 3:T){
model1_2[,i]=D1%*%(model_Y2[,(i-2)])+D2%*%(model_Y2[,(i-1)])
}
#Returns data_train to initial data_train (diff)
modeldata_train2 = matrix(0,nrow=N,ncol=T)
data_train1_2 = t(data_train)
I = diag(41)
for (i in (3:N)) {
for (j in (3:T)){
modeldata_train2[,j] = (D1+I)%*%data_train1_2[,j-1] -(D1)%*%data_train1_2[,j-2]+(D2+I)%*%data_train1_2[,j] -(D2)%*%data_train1_2[,j-1]
}
}
modeldata_train2 = t(modeldata_train2)
# Menghilangkan baris 0 di t=1
modeldata2 = matrix(0,(T-2),N)
for (i in 1:(T-2)){
modeldata2[i,] = modeldata_train2[i+2,]
}
data_train2 = data_train[1:50,]Uji Diagnostik
Model dikatakan cocok jika rataan nol dan variansi residual konstan, residual berdistribusi normal, dan residual saling bebas.
#Residuals
Residual2 = matrix(0,nrow = T-2,ncol = N)
par(mfrow = c(2,2))
for (i in 1:N){
Residual2[,i] = modeldata2[,i]-data_train2[,i]
plot(Residual2[,i], type = 'l', main = paste("Residual Kecamatan", i))
abline(h = 0)
acf(Residual2[,i],main = paste("ACF Residual Kecamatan", i))
qqnorm(Residual2[,i],main = paste("Normalitas Residual Kecamatan", i))
qqline(Residual2[,i])
hist(Residual2[,i],main = paste("Histogram Residual Kecamatan", i))
}
# Uji Kenormalan Residual
for (i in 1:N) {
print(shapiro.test(Residual2[,i]))
}##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.97925, p-value = 0.5208
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.97997, p-value = 0.5507
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.9767, p-value = 0.4225
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.98464, p-value = 0.7561
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.98206, p-value = 0.6413
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.9661, p-value = 0.1599
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.96726, p-value = 0.1786
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.97631, p-value = 0.409
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.96653, p-value = 0.1667
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.9692, p-value = 0.2147
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.96501, p-value = 0.1441
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.96996, p-value = 0.2306
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.9666, p-value = 0.1677
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.93578, p-value = 0.009202
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95606, p-value = 0.06084
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.934, p-value = 0.007852
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.94167, p-value = 0.0157
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95888, p-value = 0.07979
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.94945, p-value = 0.03239
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95519, p-value = 0.05596
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.94137, p-value = 0.01527
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.94544, p-value = 0.02224
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.96461, p-value = 0.1386
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95823, p-value = 0.07493
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95695, p-value = 0.06626
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95536, p-value = 0.05684
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.94577, p-value = 0.02293
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95823, p-value = 0.07493
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95262, p-value = 0.04376
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95695, p-value = 0.06626
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.96075, p-value = 0.09557
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95239, p-value = 0.04281
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95298, p-value = 0.04528
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.9575, p-value = 0.06983
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95978, p-value = 0.08703
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95852, p-value = 0.0771
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95707, p-value = 0.06701
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95761, p-value = 0.07061
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95542, p-value = 0.05718
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95635, p-value = 0.06256
##
##
## Shapiro-Wilk normality test
##
## data: Residual2[, i]
## W = 0.95867, p-value = 0.07822#Uji Independensi Residual
for (i in 1:N) {
print(Box.test(Residual2[,i],lag = 20, type = "Ljung-Box"))
}##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 108.22, df = 20, p-value = 4.13e-14
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 116.84, df = 20, p-value = 1.11e-15
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 103.68, df = 20, p-value = 2.751e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 122.94, df = 20, p-value < 2.2e-16
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 129.67, df = 20, p-value < 2.2e-16
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 121.34, df = 20, p-value < 2.2e-16
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 102.95, df = 20, p-value = 3.724e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 94.917, df = 20, p-value = 1.012e-11
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 104.24, df = 20, p-value = 2.178e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 94.273, df = 20, p-value = 1.315e-11
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 94.647, df = 20, p-value = 1.129e-11
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 95.215, df = 20, p-value = 8.958e-12
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 92.869, df = 20, p-value = 2.325e-11
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 78.304, df = 20, p-value = 7.604e-09
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 90.468, df = 20, p-value = 6.14e-11
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 52.482, df = 20, p-value = 9.681e-05
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 85.773, df = 20, p-value = 4.025e-10
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 87.691, df = 20, p-value = 1.873e-10
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 82.637, df = 20, p-value = 1.394e-09
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 81.472, df = 20, p-value = 2.205e-09
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 84.922, df = 20, p-value = 5.646e-10
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 113.77, df = 20, p-value = 3.997e-15
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 116.9, df = 20, p-value = 1.11e-15
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 109.69, df = 20, p-value = 2.243e-14
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 102.31, df = 20, p-value = 4.847e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 105.87, df = 20, p-value = 1.104e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 107.03, df = 20, p-value = 6.817e-14
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 109.69, df = 20, p-value = 2.243e-14
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 99.191, df = 20, p-value = 1.758e-12
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 102.31, df = 20, p-value = 4.847e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 92.623, df = 20, p-value = 2.57e-11
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 98.422, df = 20, p-value = 2.411e-12
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 101.33, df = 20, p-value = 7.265e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 105.97, df = 20, p-value = 1.062e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 102.73, df = 20, p-value = 4.083e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 97.925, df = 20, p-value = 2.956e-12
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 101.74, df = 20, p-value = 6.147e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 105.73, df = 20, p-value = 1.171e-13
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 108.43, df = 20, p-value = 3.797e-14
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 98.754, df = 20, p-value = 2.103e-12
##
##
## Box-Ljung test
##
## data: Residual2[, i]
## X-squared = 95.242, df = 20, p-value = 8.858e-12#Menghitung MSE
MSE2 = matrix(0,1,N)
for(i in 1:N){
MSE2[,i] = mean(Residual2[,i]*Residual2[,i])
}
MSE2## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 11.89304 21.14966 72.2952 18.83012 10.93716 9.147546 37.22817 22.95196
## [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## [1,] 191.8117 346.7117 235.2134 359.3501 172.3838 207.3766 37.2993 11.96141
## [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
## [1,] 54.18294 79.52453 125.3098 35.96767 51.06249 102.8162 24.54072 42.02307
## [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
## [1,] 165.9274 166.5445 77.69615 42.02307 46.84428 165.9274 96.65721 73.49866
## [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40]
## [1,] 187.4202 208.971 174.6568 88.84463 509.659 333.3952 86.22222 161.1837
## [,41]
## [1,] 125.1799Berdasarkan plot residual data setiap kecamatan di Provinsi DKI Jakarta, secara umum terlihat residual tidak tersebar di sekitar nol. Berdasarkan QQ Plot, terlihat pada awal dan akhir data nilainya cukup jauh dari garis. Sedangkan pada histogram sekilas teradapat beberapa data residual yang tidak berdistribusi normal dan berdistribusi normal. Untuk menguji kenormalan digunakan Uji Shapiro-Wilk diperoleh terdapat 10 kecamatan yang memiliki p-value < 0.5. Artinya untuk uji kenormalan Shapiro-Wilk 𝐻0 ditolak, sehingga tidak berdistribusi normal. Selanjutnya, pada grafik residual terdapat beberapa ACF yang tidak terdapat lag yang melebihi batas signifikansi sehingga dapat diasumsikan tidak ada korelasi antar residual. Untuk pembuktian lebih lanjut, gunakan Uji Ljung-Box diperoleh seluruh kecamatan yang memiliki p-value < 0.05, sehingga 𝐻0 ditolak. Artinya, terdapat korelasi antar residual. Karena data tidak memenuhi 3 asumsi tersebut mengakibatkan beberapa kecamatan tidak terpenuhinya asumsi residual dalam pemodelan.
Forecasting
Selanjutnya diperiksa prediksi berdasarkan model terpilih.
prediksi <- as.matrix(predict(fitmodel2))
prediksi## GAMBIR SAWAH BESAR KEMAYORAN SENEN CEMPAKA PUTIH MENTENG
## 4 1.4457355 0.7658878 2.535813 0.8739621 1.2250765 0.7290834
## 5 4.2564039 5.5096282 9.492150 5.6330523 4.3346425 2.8807635
## 6 2.2680487 4.1251168 7.618597 2.8695052 2.2203795 2.8809089
## 7 2.5269071 2.6701593 5.750298 3.4688948 2.4829909 2.3744335
## 8 4.2429646 5.5426544 9.342083 4.2239464 3.3684683 3.0552057
## 9 2.5311082 3.6825668 6.716066 3.9022793 2.8848435 2.9250873
## 10 4.4491015 5.2879873 11.128403 5.2419479 4.1128501 2.9690669
## 11 3.7363423 6.2177034 9.289176 4.5618628 3.7935595 4.3868887
## 12 1.9822446 2.1893807 4.312177 2.9793348 1.7486608 1.8528427
## 13 4.8128125 5.9654761 11.036785 5.8401950 4.8175597 3.2774959
## 14 7.4396698 9.5570573 18.471282 8.2547739 6.2182830 6.5459074
## 15 4.4053877 6.0871388 11.251039 6.3382547 4.8461703 3.6652472
## 16 2.5936124 3.2530979 5.001709 3.3740697 1.3868947 1.9282024
## 17 4.8038109 7.4331072 13.148716 6.1763073 5.3264307 5.2971815
## 18 5.7173796 7.5267491 14.499821 7.8139566 5.5524106 5.2187882
## 19 4.7745567 6.4577480 11.116457 5.2372790 4.3563356 3.2155870
## 20 3.6388726 5.6133180 10.521313 4.9429735 3.5930614 4.1431040
## 21 5.2147079 7.3879958 12.253806 6.5392326 5.4386464 4.3500028
## 22 2.8178902 3.6418246 6.794504 3.6610315 2.6416916 2.8774894
## 23 3.9725025 5.4100774 10.508367 5.1059712 4.0053787 3.6059599
## 24 5.8276096 7.3859370 13.861977 6.7868894 6.0905664 4.6805705
## 25 3.0256604 3.8016981 6.821679 3.8558555 2.7211663 2.3255909
## 26 2.7523077 3.8434613 7.621189 3.9443415 2.8389334 3.1832136
## 27 4.6564494 5.6249986 11.233351 5.6530424 4.7296382 3.2370349
## 28 3.9875045 6.1157867 9.891828 4.9097501 4.0497691 4.2126018
## 29 3.4736300 3.9132112 7.780262 4.5142598 2.3942561 2.2820438
## 30 3.8092071 5.1326985 8.916623 4.8566303 4.4647139 3.2047071
## 31 3.9348939 5.0125463 10.097141 4.8771939 3.5511690 3.1594916
## 32 3.2892949 4.3730420 8.346304 4.2997462 3.0199438 3.2083863
## 33 4.4694683 6.4492657 11.607255 6.5174041 4.5138288 3.9426865
## 34 1.6823365 2.5083157 5.136792 2.2047411 1.9140495 1.6622059
## 35 3.0715869 4.1442266 6.820916 4.4808324 3.1121053 3.2957919
## 36 3.5732294 3.7947416 8.117110 3.5818548 2.4801501 2.3593838
## 37 2.9215555 4.4313259 8.473929 4.4380105 3.4300446 3.3255877
## 38 3.1287303 3.7731360 6.868813 3.8198651 2.7677285 2.7387945
## 39 2.6047166 3.1325144 6.176668 3.3150584 2.0912911 1.7697416
## 40 2.9522857 3.0535426 6.319922 3.0124292 3.0597854 1.8267376
## 41 2.3940416 2.9617209 5.255129 2.8262042 2.4896256 3.0285369
## 42 1.0282069 2.2634846 4.339061 2.2206077 0.9807297 0.7841281
## 43 1.5191535 2.1229300 4.329598 2.1296217 1.6446564 1.2359000
## 44 1.3140987 1.0827228 3.156508 1.0674423 1.3909427 1.4135780
## 45 2.1443189 2.5008185 4.666068 2.5671570 1.0320744 0.9670496
## 46 1.6621376 2.0510177 4.058100 1.8399199 2.2019972 2.1858922
## 47 0.4774177 1.3522249 2.063620 1.5054035 0.1485434 0.2474795
## 48 1.5550817 2.0898163 3.748932 2.1441348 1.3044979 1.1955064
## 49 1.4083750 1.2201568 2.664881 1.1696607 1.5136162 1.4947235
## 50 0.9258007 1.4146223 2.172253 1.3966620 0.9397813 0.9728047
## 51 0.8285104 1.2177630 1.892572 1.0742473 0.9237027 1.0006133
## 52 1.7939540 2.7539697 5.510482 2.7921321 1.7768026 1.8679837
## TANAH ABANG JOHAR BARU PENJARINGAN TANJUNG PRIOK KOJA CILINCING
## 4 1.814509 1.863759 7.716940 9.926094 8.559961 9.890082
## 5 7.598677 4.982716 6.459291 9.229485 7.457742 9.335629
## 6 4.548436 4.525511 11.968771 16.555061 13.941452 16.573262
## 7 4.730695 3.860496 9.488334 13.561282 10.979993 13.330980
## 8 6.243163 5.092016 15.528465 20.643317 17.143162 21.412283
## 9 4.791500 3.838111 6.789119 8.935636 7.080200 8.572947
## 10 7.921685 5.459208 17.041022 22.685207 19.214291 23.337186
## 11 6.468714 6.500834 7.667701 9.109460 7.481364 9.174796
## 12 2.833226 2.020682 9.584246 12.310273 10.872464 12.297087
## 13 7.588188 5.823163 6.673616 9.579300 7.556320 9.587013
## 14 13.159256 9.692361 20.606138 27.774208 23.143202 28.564360
## 15 8.320145 6.613359 15.343706 21.784952 17.126935 21.468508
## 16 2.930327 2.981526 16.396525 21.371016 18.199335 21.808578
## 17 9.671067 7.088746 17.976989 24.028205 19.696967 23.735045
## 18 9.723222 7.601148 17.408093 24.401535 19.574090 24.363060
## 19 8.537582 6.611268 23.149496 31.375180 25.406750 31.269726
## 20 7.560217 5.604793 22.196549 30.202597 24.220350 30.052385
## 21 9.087430 7.300454 23.255730 31.690893 26.387359 32.509882
## 22 4.660167 3.663230 18.334278 24.153953 19.989471 23.708880
## 23 8.166089 5.159426 21.306611 29.571764 24.269025 30.226810
## 24 10.265107 8.508470 17.964164 23.634164 19.778100 24.494488
## 25 5.169843 3.532488 15.839240 21.975640 17.591782 22.685103
## 26 5.893022 4.883499 17.285619 21.481744 18.014134 23.070889
## 27 7.783714 5.356123 15.381821 21.490996 17.631471 22.477132
## 28 7.176181 6.406050 15.497095 19.376246 16.033868 20.390069
## 29 5.189185 4.220230 15.985201 21.094128 17.232494 21.313049
## 30 6.064304 5.072480 10.214489 12.686872 10.548392 13.317682
## 31 6.966140 4.904308 16.005037 21.230863 16.838906 20.837335
## 32 6.258226 5.463587 9.975773 12.983993 11.224906 13.994969
## 33 8.289637 5.936752 20.239569 27.358307 22.340295 26.920370
## 34 3.260801 2.901626 12.008677 15.303348 13.575728 17.209172
## 35 5.052921 3.980122 15.664161 21.042950 17.742562 21.080358
## 36 5.389910 4.928271 12.412856 17.920072 14.769182 18.214884
## 37 6.556886 5.021756 14.998842 19.359535 16.103028 20.771143
## 38 4.623361 3.524089 7.437388 9.736428 7.748293 10.037524
## 39 5.096166 3.569333 9.129101 12.579273 10.670871 12.755725
## 40 4.783741 3.360290 11.835642 15.097763 12.469598 16.564075
## 41 3.925728 3.916383 5.272707 8.379148 6.811227 7.620987
## 42 3.244652 1.847551 10.240766 12.758800 11.377721 14.282005
## 43 3.281347 2.250210 9.096438 13.597190 10.125788 12.969556
## 44 2.144507 2.386343 7.872057 9.212438 8.089764 9.752983
## 45 3.613757 1.997936 13.361508 20.032595 15.875577 19.516640
## 46 3.024467 3.331714 6.855877 8.529969 6.952616 8.567815
## 47 1.173107 1.162742 7.447605 10.514258 9.255605 10.472074
## 48 2.701035 2.194834 6.729926 8.247326 6.239057 8.491360
## 49 2.578888 1.559976 6.801939 8.806421 8.261289 8.869819
## 50 1.852642 1.317831 10.100158 13.991929 10.367278 13.984684
## 51 1.701086 1.220645 6.708290 8.903475 8.015602 8.867035
## 52 3.553712 2.697774 8.808010 11.429563 9.649222 12.372259
## KELAPA GADING CENGKARENG GROGOL PETAMBURAN TAMAN SARI TAMBORA KEBON JERUK
## 4 7.797633 10.881092 4.966204 3.0157159 5.925516 7.256608
## 5 6.187483 18.106610 7.765215 4.0415853 8.862211 10.755437
## 6 12.050722 9.337106 3.275362 2.1686517 4.693458 6.211646
## 7 9.514959 14.821671 7.711128 4.0612753 8.433560 9.989465
## 8 15.096036 13.531057 4.934035 3.0427353 6.057608 8.673361
## 9 6.090028 9.778075 4.407662 1.9653101 5.227065 5.814265
## 10 16.969047 21.860011 10.017793 5.8152518 10.834206 13.439039
## 11 5.653543 17.124026 5.808105 4.1360471 8.368626 10.601274
## 12 8.873172 15.268535 7.321575 3.0236227 9.056345 9.437113
## 13 6.266646 14.307142 6.155913 3.3169197 6.891648 9.059390
## 14 19.934695 24.502889 10.766331 6.7038153 12.893322 14.623304
## 15 15.033079 21.405743 8.645180 5.1718247 10.930093 13.001580
## 16 15.091774 20.538811 9.202177 3.9710390 11.070379 12.799093
## 17 16.713533 18.420195 8.101483 4.4490140 9.101459 11.311023
## 18 16.499796 16.315690 6.904687 4.7743597 9.007093 9.749304
## 19 22.511000 19.614356 8.516971 4.3583762 10.057066 11.640266
## 20 20.278668 20.791741 8.170248 5.0194632 9.943230 12.683931
## 21 23.028173 15.371093 6.255367 3.6486819 8.420425 10.054363
## 22 16.719797 15.883801 7.226567 3.9938854 8.148379 9.604364
## 23 20.990815 15.365362 6.156338 3.4151194 7.515549 9.558112
## 24 16.775330 14.231856 6.315366 3.1879402 7.435512 9.165562
## 25 15.607576 15.140927 6.403027 3.4937481 7.345499 9.672844
## 26 15.523593 14.013925 6.038301 2.9959408 7.021177 9.021391
## 27 14.970505 15.298189 6.022578 4.0760388 8.046790 9.205032
## 28 14.345544 13.215336 5.913925 2.5259337 6.544538 8.640125
## 29 14.402849 14.276553 6.122915 3.0115004 7.202554 9.311772
## 30 8.626241 14.388735 6.051703 3.4121875 7.283933 8.996978
## 31 14.960302 11.486311 5.069725 3.1172880 6.093087 7.344821
## 32 8.996391 13.341603 6.352867 2.8902497 7.222500 8.179234
## 33 19.562862 20.809759 7.805308 5.0359453 9.916389 12.677862
## 34 10.579740 11.706485 4.717722 3.1589265 6.628444 7.126715
## 35 14.871039 12.546727 6.598067 2.7320551 7.166488 7.853720
## 36 12.634257 16.208113 6.438839 3.7015986 7.730776 10.247153
## 37 13.749303 12.208667 4.915097 3.3550095 6.177058 6.905203
## 38 7.023174 9.314546 4.469738 2.0487869 5.361884 6.223440
## 39 9.443942 12.308784 5.584199 3.2975124 6.459895 7.842632
## 40 10.592900 9.995684 3.581267 1.9361064 4.705838 5.626186
## 41 6.061137 8.181633 4.111514 1.8677648 5.024264 5.641177
## 42 8.907970 8.772825 4.307510 2.4500871 4.269160 5.487546
## 43 9.697208 8.566972 3.945247 1.9748028 4.308445 5.173456
## 44 6.464267 6.306216 1.969433 0.9760087 3.232220 3.356526
## 45 14.506680 8.628306 4.943642 2.3798375 4.341691 5.589779
## 46 6.096039 7.837092 2.580866 1.9693319 3.828719 5.015998
## 47 7.846983 7.654253 3.003213 1.6753467 3.863328 4.485802
## 48 5.287920 6.954859 3.252434 1.9991683 3.966264 3.952379
## 49 7.112277 5.975396 2.921768 0.9326546 2.923660 4.050833
## 50 9.369071 8.771565 4.028655 2.3691955 4.375180 5.495995
## 51 7.101912 7.866881 2.561577 2.0684573 3.936580 4.746568
## 52 7.823020 7.488640 3.274420 1.6380951 3.824911 4.804600
## KALI DERES PALMERAH KEMBANGAN TEBET SETIA BUDI MAMPANG PRAPATAN
## 4 9.689800 4.788788 5.634666 7.794808 4.165918 4.712867
## 5 13.866841 6.978564 8.953155 9.074499 4.928396 6.255586
## 6 7.515603 4.044252 4.418142 8.204857 4.770354 5.756670
## 7 11.985705 6.097065 8.138614 8.414678 3.786579 5.577799
## 8 10.901227 5.238673 6.585915 8.950338 4.279464 6.056132
## 9 7.897391 4.035030 4.963560 5.805414 2.702877 3.864353
## 10 17.504700 8.663027 11.151584 10.770779 5.298327 6.377237
## 11 12.686254 6.852489 8.384732 5.959680 3.741256 4.981175
## 12 12.241228 7.055546 7.328509 3.997571 2.687515 3.140879
## 13 11.592811 6.037980 7.083355 4.461611 2.286934 3.403746
## 14 18.984975 10.603278 11.856933 9.984359 4.423335 6.574843
## 15 16.477492 9.158037 10.350135 8.204526 3.642117 5.820936
## 16 16.098949 8.952835 9.971732 7.522137 4.455017 5.019908
## 17 14.332953 7.059809 9.189510 6.580073 2.522483 3.692468
## 18 13.076434 6.906464 8.665942 9.497635 5.741814 6.387812
## 19 14.852667 8.094573 9.222116 10.004170 4.383224 5.882524
## 20 15.570681 7.989437 10.566494 11.138213 5.288134 7.025128
## 21 12.052787 6.399562 8.289006 8.801171 3.924910 5.773711
## 22 12.019633 7.071390 7.535087 7.061549 3.071901 4.013400
## 23 12.086475 6.350690 7.549119 9.757059 5.254980 6.488638
## 24 11.054633 6.179343 7.570522 10.364806 4.820841 7.181340
## 25 11.602244 6.308421 7.664535 11.908007 6.163025 7.763408
## 26 10.958191 5.972705 7.090096 9.914302 4.733603 5.881629
## 27 12.311052 6.083871 7.015883 12.477242 6.276690 8.163543
## 28 10.279401 5.899363 6.857849 13.860707 6.861688 8.944690
## 29 11.167769 6.038706 7.242096 15.812282 7.688878 9.681128
## 30 11.280716 5.946584 7.127792 13.924360 6.920520 8.867164
## 31 8.457526 5.109212 5.104836 14.273253 7.160501 8.913605
## 32 10.429073 6.232122 7.568268 10.256347 5.138276 7.330317
## 33 15.799688 8.043260 10.073590 16.150020 8.413668 10.211469
## 34 9.306974 4.963939 4.874051 15.801860 7.129243 10.653233
## 35 9.713058 5.187007 6.247311 16.293324 7.933460 9.360735
## 36 12.468236 6.852789 8.475191 16.249479 7.344111 9.639175
## 37 9.245350 4.942264 5.901324 13.139108 6.433000 8.295886
## 38 7.369682 4.083835 4.870910 9.136242 3.997391 5.034616
## 39 10.212073 5.624525 6.428071 8.007799 3.614107 4.770858
## 40 6.803320 3.950375 4.579773 8.527128 4.344756 5.546428
## 41 6.553221 3.977102 4.806100 6.970574 2.760208 5.027900
## 42 7.869108 4.307944 4.465376 6.252120 3.382122 4.050596
## 43 5.927083 2.999510 4.301063 5.120404 1.919561 3.250441
## 44 4.187404 2.280266 3.417025 3.923170 2.293608 3.129348
## 45 8.057917 4.626498 4.323315 6.181788 2.828807 3.983096
## 46 5.559597 2.595110 3.980196 4.863127 2.991781 4.115201
## 47 5.462918 2.787458 3.773255 4.746786 1.874907 2.686813
## 48 6.424769 3.313945 3.872201 5.273793 2.295529 3.228568
## 49 4.761663 2.959353 2.851875 7.343595 3.006137 4.164566
## 50 6.308370 3.000143 4.474891 7.885438 3.723357 4.817090
## 51 6.063575 2.962812 4.180922 9.956029 4.905136 5.856772
## 52 5.842042 3.033796 3.705958 6.791611 2.750530 3.693118
## PASAR MINGGU KEBAYORAN LAMA CILANDAK KEBAYORAN BARU PANCORAN JAGAKARSA
## 4 10.008908 10.019858 6.976372 4.712867 4.949326 10.008908
## 5 12.149986 12.124634 7.953909 6.255586 6.129468 12.149986
## 6 11.402303 11.326314 7.535092 5.756670 5.673111 11.402303
## 7 9.937811 9.856198 7.376506 5.577799 5.593743 9.937811
## 8 12.509416 12.545393 8.004127 6.056132 6.075007 12.509416
## 9 6.053286 5.950237 4.795423 3.864353 3.841365 6.053286
## 10 14.557792 14.701613 9.723224 6.377237 7.393214 14.557792
## 11 9.101214 8.956192 5.238942 4.981175 4.766675 9.101214
## 12 5.655058 5.571818 4.013975 3.140879 3.453371 5.655058
## 13 5.765037 5.782961 4.450981 3.403746 3.185270 5.765037
## 14 13.058145 13.121071 9.063767 6.574843 6.543647 13.058145
## 15 10.240050 10.170375 7.382217 5.820936 5.788697 10.240050
## 16 10.595809 10.521109 6.405357 5.019908 5.153089 10.595809
## 17 7.469367 7.306081 5.549327 3.692468 4.638825 7.469367
## 18 13.327262 13.458505 8.511657 6.387812 6.302221 13.327262
## 19 12.309901 12.224778 8.965223 5.882524 6.990091 12.309901
## 20 14.243260 14.324831 10.053275 7.025128 6.913584 14.243260
## 21 10.797273 10.794341 7.798074 5.773711 6.043189 10.797273
## 22 9.316500 9.336188 6.077776 4.013400 4.922422 9.316500
## 23 12.355192 12.347968 8.690964 6.488638 6.309305 12.355192
## 24 13.604361 13.668100 9.373809 7.181340 7.344439 13.604361
## 25 14.743861 14.772754 9.816031 7.763408 7.735710 14.743861
## 26 12.675060 12.672430 8.872545 5.881629 6.961364 12.675060
## 27 15.364517 15.424169 10.214277 8.163543 8.120849 15.364517
## 28 18.165425 18.164572 12.104369 8.944690 9.079523 18.165425
## 29 18.742859 18.674759 13.496345 9.681128 10.640219 18.742859
## 30 17.248904 17.332320 11.883795 8.867164 8.829420 17.248904
## 31 18.136026 18.159568 12.189862 8.913605 9.244788 18.136026
## 32 13.265714 13.259684 9.319873 7.330317 7.137090 13.265714
## 33 21.237799 22.334226 14.256423 10.211469 11.224558 21.237799
## 34 19.953020 19.460495 14.053236 10.653233 11.363944 19.953020
## 35 18.780886 19.350752 12.774103 9.360735 10.649901 18.780886
## 36 21.243743 21.201379 14.180989 9.639175 10.797981 21.243743
## 37 17.569939 17.584893 11.061301 8.295886 9.263043 17.569939
## 38 10.935310 10.903963 7.296023 5.034616 5.928413 10.935310
## 39 10.096490 10.241681 7.051978 4.770858 4.759977 10.096490
## 40 11.010004 10.999285 7.621817 5.546428 5.486551 11.010004
## 41 8.412872 8.328153 6.134043 5.027900 4.787328 8.412872
## 42 7.617986 7.707600 5.237212 4.050596 4.132877 7.617986
## 43 6.594874 6.622195 4.168408 3.250441 3.281890 6.594874
## 44 5.002615 4.959137 4.013637 3.129348 3.066631 5.002615
## 45 7.933234 7.911056 5.121376 3.983096 3.874122 7.933234
## 46 6.989492 6.995385 5.102354 4.115201 4.159721 6.989492
## 47 5.783851 5.795505 3.552573 2.686813 2.719876 5.783851
## 48 6.184588 6.187180 4.389839 3.228568 3.245360 6.184588
## 49 9.507084 9.544346 6.196889 4.164566 5.109386 9.507084
## 50 9.089029 9.027239 6.784874 4.817090 4.665919 9.089029
## 51 13.400557 13.490838 8.946105 5.856772 7.226714 13.400557
## 52 6.715147 6.630115 5.650549 3.693118 3.441580 6.715147
## PESANGGRAHAN MATRAMAN PULO GADUNG JATINEGARA KRAMAT JATI PASAR REBO
## 4 6.988603 5.467202 9.500522 9.817194 9.907767 7.070196
## 5 9.070718 8.118637 11.972754 13.206123 11.657862 7.741519
## 6 8.402049 7.020569 11.654400 11.214884 11.526922 7.540210
## 7 7.346672 8.282927 13.566691 15.019573 12.577045 9.680921
## 8 9.202056 7.894013 11.456763 12.113566 11.996593 7.393263
## 9 5.447082 5.790901 10.139342 9.773532 8.785670 6.993649
## 10 10.907073 12.053784 17.529886 19.053725 17.805682 12.548083
## 11 6.574298 9.841956 16.768272 17.338599 15.192635 10.794549
## 12 3.776352 7.475970 11.918919 12.412486 12.004669 8.379543
## 13 4.511532 5.878895 9.985683 9.977588 8.925717 6.728394
## 14 9.928522 8.239763 12.432000 14.300806 13.137295 9.187609
## 15 8.571019 12.248796 20.671676 21.946207 20.885085 14.864405
## 16 7.371712 10.791569 18.350727 19.106667 16.244502 12.554298
## 17 6.383844 9.533919 15.264974 15.635340 14.547580 9.876937
## 18 9.602809 10.758568 17.630571 18.408511 16.975123 12.096192
## 19 9.914483 10.215424 15.408445 16.486330 15.515591 11.575161
## 20 10.996187 11.736028 19.045495 20.388472 18.816904 12.876400
## 21 8.792345 10.126260 15.407888 16.661187 15.030591 11.324476
## 22 7.082064 8.071085 12.248169 13.233705 12.329445 8.001859
## 23 9.655356 9.137493 15.017127 15.959561 13.999401 10.461760
## 24 10.506418 7.949760 11.980917 12.803566 12.530816 8.700774
## 25 11.687011 11.548318 18.862345 19.941889 17.697971 12.317643
## 26 9.767881 7.010890 11.398189 12.295803 10.292916 8.084260
## 27 12.361063 10.383344 16.676131 17.560800 17.312986 11.141496
## 28 13.006470 10.974785 17.088648 18.253798 15.288967 12.169546
## 29 14.519896 12.271824 20.278373 22.152891 20.555933 14.217415
## 30 12.716144 10.298856 16.632518 16.802019 14.902798 10.738444
## 31 13.076939 11.135158 17.916633 19.082371 17.411131 12.228301
## 32 10.467217 11.287875 18.323677 19.715070 17.677398 12.644708
## 33 15.351133 12.562339 19.223009 20.093461 17.991770 13.426081
## 34 16.065796 11.120198 17.981243 19.136465 18.381270 12.366004
## 35 14.191007 9.502487 15.163252 15.467531 13.467230 9.574373
## 36 15.550959 11.782174 18.956998 20.698361 18.652922 13.292898
## 37 13.276391 6.361886 10.382299 9.902802 9.176916 7.112400
## 38 7.980225 6.040973 10.245589 10.519047 9.766043 6.537713
## 39 7.508822 5.725555 8.732754 9.243465 8.727368 5.647285
## 40 8.708694 3.668108 5.731825 6.598409 5.080881 4.020894
## 41 6.856660 4.444900 7.166749 7.679949 7.253081 4.753894
## 42 6.433544 4.066989 6.191785 6.974858 6.089554 4.105001
## 43 5.240158 3.750346 5.529850 6.406579 5.473205 3.638105
## 44 4.116559 2.949170 3.893163 3.847688 3.912690 2.912074
## 45 6.085956 3.019184 5.185337 5.812494 5.223261 2.895145
## 46 5.016246 4.692833 7.704338 8.185647 6.913663 5.870365
## 47 4.676453 2.647580 4.030402 3.968224 3.342935 2.160595
## 48 5.110231 3.813555 5.737674 5.549273 5.084782 3.844883
## 49 7.470236 2.634228 4.770410 5.754141 4.711211 3.929836
## 50 7.625393 4.420161 7.549807 6.975390 7.024588 4.854855
## 51 9.986303 4.206882 6.042509 6.447489 5.714902 4.091406
## 52 5.409401 4.948763 8.444260 9.843956 8.547215 6.567830
## CAKUNG DUREN SAWIT MAKASAR CIRACAS CIPAYUNG
## 4 15.121886 12.686388 6.999941 8.736450 7.421343
## 5 20.405761 16.189111 7.731970 11.266278 10.122240
## 6 18.713829 15.683414 7.626129 10.463816 9.359663
## 7 21.633238 17.409039 9.795704 12.329965 10.223196
## 8 19.403359 15.521066 7.160816 10.504215 9.983356
## 9 15.878441 13.183609 7.188200 9.031826 7.820271
## 10 29.545550 23.378058 11.392179 16.823598 14.621119
## 11 27.986184 22.245773 11.211621 15.676665 14.383704
## 12 19.446389 16.089331 8.205074 10.465723 9.888099
## 13 15.668228 13.162632 6.594491 9.162039 7.681009
## 14 22.267068 17.375104 9.255419 12.798732 10.258352
## 15 33.822233 28.043749 13.952360 18.820980 16.977698
## 16 28.512344 23.256564 11.825684 16.179395 14.313931
## 17 26.327658 20.524859 10.214233 14.951854 13.462559
## 18 28.143311 22.319433 11.582309 15.475511 13.650299
## 19 27.016545 22.103457 11.242828 14.736726 13.687600
## 20 30.394985 25.032530 13.027238 17.590554 14.845714
## 21 26.784381 21.376023 11.235795 14.110952 13.218479
## 22 19.918106 16.104421 8.074299 11.177853 9.922812
## 23 24.464416 20.476225 10.554929 14.369798 12.107294
## 24 21.448862 16.878316 8.441428 11.246822 10.822344
## 25 29.772137 24.880885 12.540417 16.602683 14.348676
## 26 18.728543 14.760572 7.744477 11.014048 9.262921
## 27 28.215729 23.483603 11.495217 15.284888 13.612549
## 28 27.661031 21.574683 10.936146 15.972326 13.853108
## 29 33.754207 27.462949 14.890490 19.115053 16.283643
## 30 26.708740 21.918087 10.134613 14.564394 13.245928
## 31 30.201904 23.692589 12.269640 17.344951 15.150088
## 32 30.455148 24.550624 12.547252 17.260454 14.954787
## 33 31.504367 25.798058 13.307081 17.096960 15.241951
## 34 29.831448 24.414416 12.465361 17.208825 15.187601
## 35 23.499469 19.364420 9.715650 13.658323 11.319116
## 36 31.369349 26.108576 13.476375 17.947597 14.864660
## 37 16.575153 13.422727 6.645493 9.657322 9.172800
## 38 15.986643 12.797577 7.011490 9.135194 7.499947
## 39 14.471993 11.638809 5.478071 8.225849 6.491780
## 40 10.607099 8.782657 3.988285 6.119590 5.141679
## 41 11.135860 8.678624 4.857532 6.651101 5.594350
## 42 10.498689 8.385670 4.005732 6.009453 4.922393
## 43 9.574445 7.601258 3.605939 5.636597 4.727587
## 44 6.886467 4.879781 2.920265 3.925465 2.970730
## 45 8.160478 7.469861 2.893741 5.281028 4.242953
## 46 12.540253 9.794054 5.998512 6.669001 5.580280
## 47 6.525860 4.657545 1.867240 3.484645 3.452375
## 48 8.583357 8.083257 4.089121 5.070022 4.701933
## 49 9.040405 6.357613 3.748313 4.908383 3.712790
## 50 11.111217 9.457558 4.918927 5.923049 5.429514
## 51 10.304536 8.344927 4.074987 5.205472 5.332878
## 52 14.522357 11.588359 6.725597 8.530279 6.974541plot(fitmodel2, n_predict = 30)
(n_predict = predict(fitmodel2, n = 10))## GAMBIR SAWAH BESAR KEMAYORAN SENEN CEMPAKA PUTIH MENTENG
## 521 1.457987 2.101526 4.354345 2.071998 1.352186 1.399514
## 5211 1.346502 1.936180 3.736105 2.038683 1.280791 1.311450
## 52111 1.548457 2.273479 4.452235 2.275609 1.517146 1.562666
## 521111 1.517212 2.196850 4.422250 2.194631 1.446774 1.481152
## 5211111 1.451625 2.104361 4.167055 2.155525 1.380763 1.417218
## 52111111 1.488173 2.170467 4.272307 2.193212 1.438013 1.480698
## 521111111 1.494961 2.174576 4.318399 2.189312 1.436376 1.475133
## 5211111111 1.479808 2.146680 4.264238 2.178961 1.413219 1.450463
## 52111111111 1.486098 2.163380 4.275606 2.187388 1.429962 1.469099
## 521111111111 1.485875 2.160223 4.282866 2.183633 1.425578 1.465756
## TANAH ABANG JOHAR BARU PENJARINGAN TANJUNG PRIOK KOJA
## 521 2.896999 2.182339 7.067132 8.807994 8.345615
## 5211 2.732383 1.919029 8.238754 10.445602 9.246876
## 52111 3.090928 2.248835 7.489221 9.334685 8.783865
## 521111 3.035471 2.242791 8.276869 10.526158 9.439934
## 5211111 2.920799 2.113265 7.638068 9.534197 8.905132
## 52111111 2.982636 2.164243 8.118188 10.309132 9.326454
## 521111111 2.995137 2.192739 7.773285 9.731092 9.027551
## 5211111111 2.967949 2.158307 8.026644 10.175050 9.253297
## 52111111111 2.981561 2.169642 7.841444 9.836913 9.087424
## 521111111111 2.978008 2.170721 7.977446 10.097016 9.211946
## CILINCING KELAPA GADING CENGKARENG GROGOL PETAMBURAN TAMAN SARI
## 521 9.552868 6.669658 7.927196 3.371469 1.909460
## 5211 11.546676 7.226060 8.023790 3.110484 2.013904
## 52111 10.270606 6.867048 7.735643 3.178233 1.845037
## 521111 11.606382 7.399105 7.718475 3.214390 1.846643
## 5211111 10.528476 6.919631 7.884450 3.187899 1.927327
## 52111111 11.357391 7.313590 7.848246 3.188952 1.899054
## 521111111 10.751013 7.011746 7.775054 3.193890 1.871120
## 5211111111 11.207158 7.255437 7.821387 3.193253 1.895640
## 52111111111 10.866794 7.061921 7.831756 3.190652 1.897305
## 521111111111 11.122762 7.219002 7.814538 3.193118 1.887325
## TAMBORA KEBON JERUK KALI DERES PALMERAH KEMBANGAN TEBET
## 521 3.970046 5.085359 5.953275 3.012579 4.034247 7.079845
## 5211 3.958631 5.024792 6.073612 3.015225 4.130723 6.511433
## 52111 3.900281 4.857185 5.953158 3.018937 3.872251 6.730883
## 521111 3.911235 4.888477 5.887586 3.006250 3.914494 6.660656
## 5211111 3.933576 4.984340 5.981450 3.012817 4.043741 6.638238
## 52111111 3.922889 4.945901 5.987220 3.016389 3.967663 6.635989
## 521111111 3.917775 4.904249 5.936591 3.010902 3.933032 6.639294
## 5211111111 3.924675 4.940446 5.956941 3.013340 3.987327 6.632454
## 52111111111 3.922031 4.943504 5.965216 3.012899 3.978843 6.636947
## 521111111111 3.922272 4.930399 5.959367 3.013257 3.958260 6.632875
## SETIA BUDI MAMPANG PRAPATAN PASAR MINGGU KEBAYORAN LAMA CILANDAK
## 521 3.470777 4.024218 9.526687 9.812737 6.107108
## 5211 2.882996 3.811822 6.991113 6.794508 5.561383
## 52111 3.205629 3.936201 8.536929 8.752607 5.812615
## 521111 3.057631 3.945108 7.650051 7.500642 5.759555
## 5211111 3.123172 3.918514 8.156553 8.289941 5.742006
## 52111111 3.080689 3.922532 7.800849 7.708068 5.741618
## 521111111 3.110106 3.929245 8.049812 8.128887 5.749100
## 5211111111 3.089786 3.921952 7.873644 7.820269 5.740543
## 52111111111 3.103726 3.927774 7.996797 8.043226 5.747356
## 521111111111 3.094013 3.923365 7.909846 7.880855 5.741946
## KEBAYORAN BARU PANCORAN JAGAKARSA PESANGGRAHAN MATRAMAN
## 521 4.024218 4.366760 9.526687 6.352363 4.652950
## 5211 3.811822 3.597726 6.991113 5.414220 4.541703
## 52111 3.936201 3.931958 8.536929 5.845906 4.545450
## 521111 3.945108 3.809774 7.650051 5.709089 4.595390
## 5211111 3.918514 3.840472 8.156553 5.727632 4.596328
## 52111111 3.922532 3.816569 7.800849 5.704683 4.566647
## 521111111 3.929245 3.835437 8.049812 5.727653 4.587827
## 5211111111 3.921952 3.820942 7.873644 5.707934 4.580646
## 52111111111 3.927774 3.831357 7.996797 5.722860 4.581800
## 521111111111 3.923365 3.823728 7.909846 5.711515 4.581125
## PULO GADUNG JATINEGARA KRAMAT JATI PASAR REBO CAKUNG DUREN SAWIT
## 521 7.572918 7.946246 7.028632 5.433845 12.58667 9.986486
## 5211 7.299579 7.844690 7.157959 5.158005 12.36371 9.912194
## 52111 7.429069 8.240358 7.183396 5.512371 12.60682 10.032749
## 521111 7.463235 8.054365 7.201913 5.421358 12.61290 10.052067
## 5211111 7.468575 8.116819 7.236853 5.401288 12.62101 10.080117
## 52111111 7.426792 8.104249 7.169165 5.421198 12.56688 10.011389
## 521111111 7.456855 8.098051 7.223127 5.421462 12.61331 10.064276
## 5211111111 7.446391 8.103267 7.193214 5.412105 12.58967 10.038705
## 52111111111 7.448225 8.104866 7.207665 5.419711 12.60021 10.049045
## 521111111111 7.447224 8.100027 7.199910 5.416789 12.59376 10.043421
## MAKASAR CIRACAS CIPAYUNG
## 521 5.167000 7.082515 6.318972
## 5211 5.368618 6.808981 6.210368
## 52111 5.472975 7.151232 6.233284
## 521111 5.380122 7.083980 6.290369
## 5211111 5.460715 7.068450 6.283807
## 52111111 5.403613 7.069939 6.254706
## 521111111 5.429692 7.081312 6.280416
## 5211111111 5.422637 7.070551 6.266429
## 52111111111 5.424136 7.076645 6.273812
## 521111111111 5.422199 7.073786 6.268948Kesimpulan
Dipilih model GSTAR (1; 1) sebagai model GSTAR untuk data penderita DBD di Provinsi DKI Jakarta tahun 2013 untuk setiap kecamatan di Provinsi DKI Jakarta, karena memiliki nilai MSE terkecil. Hasil prediksi yang diperoleh untuk penderita Demam Berdarah di setiap kecamatan di Provinsi DKI Jakarta dalam beberapa minggu ke depan menggunakan model GSTAR (1;1) yang kemudian akan dianalisis lebih lanjut menggunakan Analisis Spasial.