TBP 2 Komstat
Nurul M0725029 Zharfa M0725078 Nisa M0725085 Selsa M0725099
2026-04-12
Import
Pada tahap ini dilakukan proses impor data dari file CSV ke dalam R. Data yang digunakan terdiri dari tiga dataset, yaitu data kualitas wine merah, data user knowledge modelling, dan data daily demand forecasting. Proses ini bertujuan agar data dapat dibaca dan diolah lebih lanjut dalam analisis statistik.
data1=read.csv("C:/Users/LENOVO/OneDrive/COLLEGE/SEMESTER 2/Komputasi Statistika/winequality-red.csv", sep=";")data2=read.csv("C:/Users/LENOVO/OneDrive/COLLEGE/SEMESTER 2/Komputasi Statistika/data user knowledge modellingg.csv")data3=read.csv("C:/Users/LENOVO/OneDrive/COLLEGE/SEMESTER 2/Komputasi Statistika/data daily demand forecasting.csv", sep = ";")Eksplorasi Data
Eksplorasi data merupakan tahap awal dalam analisis yang bertujuan untuk memahami struktur, karakteristik, dan kondisi data sebelum dianalisis lebih lanjut. Pada tahap ini dilakukan peninjauan data menggunakan fungsi seperti head, str, dim, dan summary untuk melihat isi, tipe, serta ringkasan statistik data, sehingga dapat membantu menentukan metode analisis yang tepat.
Eksplorasi Data Wine Quality
Eksplorasi data dilakukan untuk memahami struktur, karakteristik, dan ringkasan statistik dari data wine quality. Langkah ini penting agar dapat mengetahui distribusi data serta variabel yang akan dianalisis lebih lanjut.
head(data1)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5tail(data1)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1594 6.8 0.620 0.08 1.9 0.068
## 1595 6.2 0.600 0.08 2.0 0.090
## 1596 5.9 0.550 0.10 2.2 0.062
## 1597 6.3 0.510 0.13 2.3 0.076
## 1598 5.9 0.645 0.12 2.0 0.075
## 1599 6.0 0.310 0.47 3.6 0.067
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1594 28 38 0.99651 3.42 0.82 9.5
## 1595 32 44 0.99490 3.45 0.58 10.5
## 1596 39 51 0.99512 3.52 0.76 11.2
## 1597 29 40 0.99574 3.42 0.75 11.0
## 1598 32 44 0.99547 3.57 0.71 10.2
## 1599 18 42 0.99549 3.39 0.66 11.0
## quality
## 1594 6
## 1595 5
## 1596 6
## 1597 6
## 1598 5
## 1599 6str(data1)## 'data.frame': 1599 obs. of 12 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...dim(data1)## [1] 1599 12names(data1)## [1] "fixed.acidity" "volatile.acidity" "citric.acid"
## [4] "residual.sugar" "chlorides" "free.sulfur.dioxide"
## [7] "total.sulfur.dioxide" "density" "pH"
## [10] "sulphates" "alcohol" "quality"summary(data1)## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 4.60 Min. :0.1200 Min. :0.000 Min. : 0.900
## 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090 1st Qu.: 1.900
## Median : 7.90 Median :0.5200 Median :0.260 Median : 2.200
## Mean : 8.32 Mean :0.5278 Mean :0.271 Mean : 2.539
## 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420 3rd Qu.: 2.600
## Max. :15.90 Max. :1.5800 Max. :1.000 Max. :15.500
## chlorides free.sulfur.dioxide total.sulfur.dioxide density
## Min. :0.01200 Min. : 1.00 Min. : 6.00 Min. :0.9901
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00 1st Qu.:0.9956
## Median :0.07900 Median :14.00 Median : 38.00 Median :0.9968
## Mean :0.08747 Mean :15.87 Mean : 46.47 Mean :0.9967
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00 3rd Qu.:0.9978
## Max. :0.61100 Max. :72.00 Max. :289.00 Max. :1.0037
## pH sulphates alcohol quality
## Min. :2.740 Min. :0.3300 Min. : 8.40 Min. :3.000
## 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50 1st Qu.:5.000
## Median :3.310 Median :0.6200 Median :10.20 Median :6.000
## Mean :3.311 Mean :0.6581 Mean :10.42 Mean :5.636
## 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :4.010 Max. :2.0000 Max. :14.90 Max. :8.000Eksplorasi Data User Knowledge Modelling
Pada bagian ini dilakukan eksplorasi terhadap data user knowledge modelling untuk melihat pola data, tipe variabel, serta ringkasan statistik yang dapat membantu dalam analisis selanjutnya.
head(data2)## STG SCG STR LPR PEG UNS
## 1 0.00 0.10 0.50 0.26 0.05 Very Low
## 2 0.05 0.05 0.55 0.60 0.14 Low
## 3 0.08 0.18 0.63 0.60 0.85 High
## 4 0.20 0.20 0.68 0.67 0.85 High
## 5 0.22 0.22 0.90 0.30 0.90 High
## 6 0.14 0.14 0.70 0.50 0.30 Lowtail(data2)## STG SCG STR LPR PEG UNS
## 140 0.68 0.61 0.34 0.31 0.23 Low
## 141 0.90 0.78 0.62 0.32 0.89 High
## 142 0.85 0.82 0.66 0.83 0.83 High
## 143 0.56 0.60 0.77 0.13 0.32 Low
## 144 0.66 0.68 0.81 0.57 0.57 Middle
## 145 0.68 0.64 0.79 0.97 0.24 Middlestr(data2)## 'data.frame': 145 obs. of 6 variables:
## $ STG: num 0 0.05 0.08 0.2 0.22 0.14 0.16 0.12 0.2 0.16 ...
## $ SCG: num 0.1 0.05 0.18 0.2 0.22 0.14 0.16 0.12 0.2 0.25 ...
## $ STR: num 0.5 0.55 0.63 0.68 0.9 0.7 0.8 0.75 0.88 0.01 ...
## $ LPR: num 0.26 0.6 0.6 0.67 0.3 0.5 0.5 0.68 0.77 0.1 ...
## $ PEG: num 0.05 0.14 0.85 0.85 0.9 0.3 0.5 0.15 0.8 0.07 ...
## $ UNS: chr "Very Low" "Low" "High" "High" ...dim(data2)## [1] 145 6names(data2)## [1] "STG" "SCG" "STR" "LPR" "PEG" "UNS"summary(data2)## STG SCG STR LPR
## Min. :0.0000 Min. :0.0000 Min. :0.0100 Min. :0.0000
## 1st Qu.:0.1600 1st Qu.:0.2000 1st Qu.:0.2300 1st Qu.:0.2400
## Median :0.2700 Median :0.2900 Median :0.4100 Median :0.3200
## Mean :0.3211 Mean :0.3564 Mean :0.4392 Mean :0.4289
## 3rd Qu.:0.4100 3rd Qu.:0.5200 3rd Qu.:0.6600 3rd Qu.:0.6700
## Max. :0.9000 Max. :0.9000 Max. :0.9100 Max. :0.9900
## PEG UNS
## Min. :0.0100 Length:145
## 1st Qu.:0.2400 Class :character
## Median :0.3200 Mode :character
## Mean :0.4525
## 3rd Qu.:0.6600
## Max. :0.9900Eksplorasi Data Daily Demand Forecasting
Eksplorasi data dilakukan untuk memahami variabel-variabel dalam data daily demand forecasting, termasuk jumlah observasi, tipe data, serta distribusi nilai yang tersedia.
head(data3)## Week.of.the.month..first.week..second..third..fourth.or.fifth.week
## 1 1
## 2 1
## 3 1
## 4 2
## 5 2
## 6 2
## Day.of.the.week..Monday.to.Friday. Non.urgent.order Urgent.order Order.type.A
## 1 4 316.307 223.270 61.543
## 2 5 128.633 96.042 38.058
## 3 6 43.651 84.375 21.826
## 4 2 171.297 127.667 41.542
## 5 3 90.532 113.526 37.679
## 6 4 110.925 96.360 30.792
## Order.type.B Order.type.C Fiscal.sector.orders
## 1 175.586 302.448 0.000
## 2 56.037 130.580 0.000
## 3 25.125 82.461 1.386
## 4 113.294 162.284 18.156
## 5 56.618 116.220 6.459
## 6 50.704 125.868 79.000
## Orders.from.the.traffic.controller.sector Banking.orders..1.
## 1 65556 44914
## 2 40419 21399
## 3 11992 3452
## 4 49971 33703
## 5 48534 19646
## 6 52042 8773
## Banking.orders..2. Banking.orders..3. Target..Total.orders.
## 1 188411 14793 539.577
## 2 89461 7679 224.675
## 3 21305 14947 129.412
## 4 69054 18423 317.120
## 5 16411 20257 210.517
## 6 47522 24966 207.364tail(data3)## Week.of.the.month..first.week..second..third..fourth.or.fifth.week
## 55 4
## 56 5
## 57 5
## 58 5
## 59 5
## 60 5
## Day.of.the.week..Monday.to.Friday. Non.urgent.order Urgent.order
## 55 6 134.425 79.084
## 56 2 158.716 158.133
## 57 3 150.784 133.069
## 58 4 193.534 109.639
## 59 5 196.555 108.395
## 60 6 192.116 121.106
## Order.type.A Order.type.B Order.type.C Fiscal.sector.orders
## 55 36.748 71.353 105.408 0.000
## 56 59.131 92.639 165.079 0.000
## 57 54.224 115.746 116.442 2.559
## 58 58.378 142.382 102.687 274.000
## 59 76.763 96.478 131.709 0.000
## 60 107.568 121.152 103.180 18.678
## Orders.from.the.traffic.controller.sector Banking.orders..1.
## 55 33970 28701
## 56 32027 33282
## 57 51235 34421
## 58 28364 88404
## 59 37011 109931
## 60 27328 108072
## Banking.orders..2. Banking.orders..3. Target..Total.orders.
## 55 65199 11023 213.509
## 56 128269 9287 316.849
## 57 87708 11354 286.412
## 58 91367 15003 303.447
## 59 50112 12957 304.950
## 60 56015 10690 331.900str(data3)## 'data.frame': 60 obs. of 13 variables:
## $ Week.of.the.month..first.week..second..third..fourth.or.fifth.week: int 1 1 1 2 2 2 2 2 3 3 ...
## $ Day.of.the.week..Monday.to.Friday. : int 4 5 6 2 3 4 5 6 2 3 ...
## $ Non.urgent.order : num 316.3 128.6 43.7 171.3 90.5 ...
## $ Urgent.order : num 223.3 96 84.4 127.7 113.5 ...
## $ Order.type.A : num 61.5 38.1 21.8 41.5 37.7 ...
## $ Order.type.B : num 175.6 56 25.1 113.3 56.6 ...
## $ Order.type.C : num 302.4 130.6 82.5 162.3 116.2 ...
## $ Fiscal.sector.orders : num 0 0 1.39 18.16 6.46 ...
## $ Orders.from.the.traffic.controller.sector : int 65556 40419 11992 49971 48534 52042 46573 35033 66612 58224 ...
## $ Banking.orders..1. : int 44914 21399 3452 33703 19646 8773 33597 26278 19461 7742 ...
## $ Banking.orders..2. : int 188411 89461 21305 69054 16411 47522 48269 56665 103376 82395 ...
## $ Banking.orders..3. : int 14793 7679 14947 18423 20257 24966 20973 18502 10458 11948 ...
## $ Target..Total.orders. : num 540 225 129 317 211 ...dim(data3)## [1] 60 13names(data3)## [1] "Week.of.the.month..first.week..second..third..fourth.or.fifth.week"
## [2] "Day.of.the.week..Monday.to.Friday."
## [3] "Non.urgent.order"
## [4] "Urgent.order"
## [5] "Order.type.A"
## [6] "Order.type.B"
## [7] "Order.type.C"
## [8] "Fiscal.sector.orders"
## [9] "Orders.from.the.traffic.controller.sector"
## [10] "Banking.orders..1."
## [11] "Banking.orders..2."
## [12] "Banking.orders..3."
## [13] "Target..Total.orders."summary(data3)## Week.of.the.month..first.week..second..third..fourth.or.fifth.week
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :3.017
## 3rd Qu.:4.000
## Max. :5.000
## Day.of.the.week..Monday.to.Friday. Non.urgent.order Urgent.order
## Min. :2.000 Min. : 43.65 Min. : 77.37
## 1st Qu.:3.000 1st Qu.:125.35 1st Qu.:100.89
## Median :4.000 Median :151.06 Median :113.11
## Mean :4.033 Mean :172.55 Mean :118.92
## 3rd Qu.:5.000 3rd Qu.:194.61 3rd Qu.:132.11
## Max. :6.000 Max. :435.30 Max. :223.27
## Order.type.A Order.type.B Order.type.C Fiscal.sector.orders
## Min. : 21.83 Min. : 25.12 Min. : 74.37 Min. : 0.000
## 1st Qu.: 39.46 1st Qu.: 74.92 1st Qu.:113.63 1st Qu.: 1.243
## Median : 47.17 Median : 99.48 Median :127.99 Median : 7.832
## Mean : 52.11 Mean :109.23 Mean :139.53 Mean : 77.396
## 3rd Qu.: 58.46 3rd Qu.:132.17 3rd Qu.:160.11 3rd Qu.: 20.361
## Max. :118.18 Max. :267.34 Max. :302.45 Max. :865.000
## Orders.from.the.traffic.controller.sector Banking.orders..1.
## Min. :11992 Min. : 3452
## 1st Qu.:34994 1st Qu.: 20130
## Median :44312 Median : 32528
## Mean :44504 Mean : 46641
## 3rd Qu.:52112 3rd Qu.: 45119
## Max. :71772 Max. :210508
## Banking.orders..2. Banking.orders..3. Target..Total.orders.
## Min. : 16411 Min. : 7679 Min. :129.4
## 1st Qu.: 50681 1st Qu.:12610 1st Qu.:238.2
## Median : 67181 Median :18012 Median :288.0
## Mean : 79401 Mean :23115 Mean :300.9
## 3rd Qu.: 94788 3rd Qu.:31048 3rd Qu.:334.2
## Max. :188411 Max. :73839 Max. :616.5Visualisasi Data
Visualisasi data merupakan tahap untuk menyajikan data dalam bentuk grafik agar lebih mudah dipahami dan dianalisis. Melalui visualisasi seperti histogram, scatter plot, dan boxplot, dapat dilihat distribusi data, hubungan antar variabel, serta perbandingan antar kelompok, sehingga membantu dalam menarik kesimpulan awal sebelum dilakukan analisis lebih lanjut.
Visualisasi Data Wine Quality
Visualisasi dilakukan untuk melihat distribusi kadar alkohol serta hubungan antara kadar alkohol dan kualitas wine. Grafik histogram digunakan untuk melihat distribusi data, sedangkan scatter plot digunakan untuk melihat hubungan antar variabel.
a) Histogram Distribusi Kadar Alkohol
hist(data1$alcohol,
main = "Distribusi Kadar Alkohol",
xlab = "Kadar Alkohol",
ylab = "Frekuensi",
col = "skyblue",
border = "black")
abline(v = mean(data1$alcohol), col = "red", lwd = 2, lty = 2)
legend("topright", legend = "Mean", col = "red", lty = 2, bty = "n")
b) Scatter Plot Hubungan Alkohol dan Kualitas Wine
plot(data1$alcohol, data1$quality,
main = "Hubungan Alkohol dan Kualitas Wine",
xlab = "Alkohol",
ylab = "Kualitas",
col = "blue",
pch = 16)
abline(lm(data1$quality ~ data1$alcohol), col = "red", lwd = 2)
legend("topright", legend = "Tren", col = "red", lty = 1, bty = "n")
c) Boxplot Perbandingan Alkohol berdasarkan Kualitas
boxplot(data1$total.sulfur.dioxide,
main = "Perbandingan Alkohol berdasarkan Kualitas",
xlab = "Kualitas",
ylab = "Alkohol",
col = "lightgreen")
Visualisasi Data User Knowledge Modelling
Visualisasi ini bertujuan untuk melihat hubungan antara proses belajar dan nilai ujian, distribusi nilai ujian, serta perbandingan nilai berdasarkan tingkat pemahaman pengguna.
a) Histogram Distribusi Nilai Ujian
hist(data2$PEG,
main = "Distribusi Nilai Ujian",
xlab = "Nilai Ujian (PEG)",
ylab = "Frekuensi",
col = "lightblue")
abline(v = mean(data2$PEG), col = "red", lwd = 2, lty = 2)
legend("topright", legend = "Mean", col = "red", lty = 2, bty = "n")
b) Scatter Plot Hubungan Nilai Ujian dengan Proses Belajar
plot(data2$LPR, data2$PEG,
main = "Hubungan Nilai Ujian dengan Proses Belajar",
xlab = "Proses Belajar",
ylab = "Nilai Ujian",
col = "blue",
pch = 16)
abline(lm(data2$LPR ~ data2$PEG), col = "red", lwd = 2)
legend("topright", legend = "Tren", col = "red", lty = 1, bty = "n")
c) Boxplot Perbandingan Nilai Ujian Berdasarkan Tingkat Pemahaman
boxplot(PEG ~ UNS, data = data2,
main = "Perbandingan Nilai Ujian Berdasarkan Tingkat Pemahaman",
xlab = "Tingkat Pemahaman Pengguna",
ylab = "Nilai Ujian (PEG)",
col = c("red", "yellow", "green", "blue"))
Visualisasi Data Daily Demand Forecasting
Visualisasi ini digunakan untuk melihat distribusi pesanan penting, hubungan antara total pesanan banking dengan target pesanan, serta tren total target pesanan dari waktu ke waktu.
a) Histogram Distribusi Pesanan Penting
hist(data3$Urgent.order,
col = "lightblue",
main = "Distribusi Pesanan Penting",
xlab = "Jumlah Pesanan Penting")
abline(h = mean(data3$Urgent.order), col = "red", lwd = 2, lty = 2)
legend("topright", legend = "Mean", col = "red", lty = 2, bty = "n")
b) Scatter Plot Hubungan Total Pesanan Banking dengan Target
total_banking <- data3$Banking.orders..1. +
data3$Banking.orders..2. +
data3$Banking.orders..3.
plot(total_banking, data3$Target..Total.orders.,
col = "red",
pch = 16,
main = "Hubungan Total Pesanan Banking dengan Target",
xlab = "Total Pesanan Banking",
ylab = "Total Target Pesanan")
grid()
abline(lm(data3$Target..Total.orders. ~ total_banking), col = "blue", lwd = 2)
legend("bottomright", legend = "Tren", col = "blue", lty = 1, bty = "n")
c) Plot Tren Total Target Pesanan
plot(data3$Target..Total.orders.,
type = "l",
col = "purple",
main = "Tren Total Target Pesanan",
xlab = "Urutan Data",
ylab = "Total Target Pesanan")
abline(h = mean(data3$Target..Total.orders.), col = "red", lwd = 2, lty = 2)
legend("topright", legend = "Mean", col = "red", lty = 2, bty = "n")
Uji Hipotesis
Uji hipotesis merupakan tahap analisis untuk menguji dugaan terhadap suatu data menggunakan metode statistik. Pada tahap ini ditentukan hipotesis nol (H₀) dan hipotesis alternatif (H₁), kemudian keputusan diambil berdasarkan nilai p-value. Jika p-value lebih kecil dari tingkat signifikansi (misalnya 0,05), maka H₀ ditolak.
Uji Hipotesis Data Wine Quality
Pada data wine quality dilakukan uji normalitas menggunakan Shapiro-Wilk untuk mengetahui apakah data berdistribusi normal. Karena data tidak normal, digunakan uji korelasi Kendall Tau untuk menguji hubungan antara kadar alkohol dan kualitas wine.
a) Uji Normalitas
shapiro.test(data1$alcohol)##
## Shapiro-Wilk normality test
##
## data: data1$alcohol
## W = 0.92884, p-value < 2.2e-16shapiro.test(data1$quality)##
## Shapiro-Wilk normality test
##
## data: data1$quality
## W = 0.85759, p-value < 2.2e-16b) Uji Korelasi Kendall Tau
# Hipotesis
# $H_0: \tau = 0$
# $H_1: \tau \neq 0$
hasil_wine <- cor.test(data1$alcohol, data1$quality, method = "kendall")
cat("p-value =", hasil_wine$p.value, "\n")## p-value = 5.678405e-84cat("Keputusan:",
ifelse(hasil_wine$p.value < 0.05,
"TOLAK H0",
"GAGAL TOLAK H0"))## Keputusan: TOLAK H0cat("Interpretasi:",
ifelse(hasil_wine$p.value < 0.05,
"Terdapat cukup bukti adanya hubungan antara kadar alkohol dan kualitas wine",
"Tidak terdapat cukup bukti adanya hubungan antara kadar alkohol dan kualitas wine"))## Interpretasi: Terdapat cukup bukti adanya hubungan antara kadar alkohol dan kualitas wineUji Hipotesis Data User Knowledge Modelling
Pada data user knowledge modelling dilakukan uji normalitas, kemudian dilanjutkan dengan uji Kruskal-Wallis karena data tidak berdistribusi normal. Uji ini digunakan untuk mengetahui apakah terdapat perbedaan nilai ujian berdasarkan tingkat pemahaman.
a) Uji Normalitas
shapiro.test(data2$PEG)##
## Shapiro-Wilk normality test
##
## data: data2$PEG
## W = 0.91809, p-value = 2.372e-07b) Uji Kruskal-Wallis
#Hipotesis
# Hipotesis
# $H_0: M_1 = M_2 = M_3 = M_4$
# $H_1: \text{minimal ada satu } M_i \neq M_j$
hasil_user <- kruskal.test(PEG ~ UNS, data = data2)
cat("p-value =", hasil_user$p.value, "\n")## p-value = 1.41204e-26cat("Keputusan:",
ifelse(hasil_user$p.value < 0.05,
"TOLAK H0",
"GAGAL TOLAK H0"), "\n")## Keputusan: TOLAK H0cat("Interpretasi:",
ifelse(hasil_user$p.value < 0.05,
"Terdapat cukup bukti adanya perbedaan median nilai PEG antar tingkat pemahaman",
"Tidak terdapat cukup bukti adanya perbedaan median nilai PEG antar tingkat pemahaman"))## Interpretasi: Terdapat cukup bukti adanya perbedaan median nilai PEG antar tingkat pemahamanUji Hipotesis Data Daily Demand Forecasting
Pada data daily demand forecasting dilakukan uji normalitas dan dilanjutkan dengan uji korelasi Spearman untuk mengetahui hubungan antara total pesanan banking dan target total pesanan.
a) Uji Normalitas
shapiro.test(data3$Target..Total.orders.)##
## Shapiro-Wilk normality test
##
## data: data3$Target..Total.orders.
## W = 0.8901, p-value = 5.843e-05b) Uji Korelasi Spearman
#Hipotesis
# Hipotesis
# $H_0: \rho_s = 0$
# $H_1: \rho_s \neq 0$
total_banking <- data3$Banking.orders..1. +
data3$Banking.orders..2. +
data3$Banking.orders..3.
hasil_ddf <- cor.test(total_banking,
data3$Target..Total.orders.,
method = "spearman")
cat("p-value =", hasil_ddf$p.value, "\n")## p-value = 0cat("Keputusan:",
ifelse(hasil_ddf$p.value < 0.05,
"TOLAK H0",
"GAGAL TOLAK H0"), "\n")## Keputusan: TOLAK H0cat("Interpretasi:",
ifelse(hasil_ddf$p.value < 0.05,
"Terdapat cukup bukti adanya hubungan antara total banking orders dan target orders",
"Tidak terdapat cukup bukti adanya hubungan antara total banking orders dan target orders"))## Interpretasi: Terdapat cukup bukti adanya hubungan antara total banking orders dan target orders