Regresi dummy
Tugas Minggu 5
Import Data
library(readxl)
library(tidyverse)
data <- read_excel("C:/Users/Admin/Downloads/PSD Kelompok 3 (3).xlsx")
names(data)[names(data) == "Jumlah Ulasan"] <- "J.Ulasan"
head(data)## # A tibble: 6 × 6
## Brand HARGA LOKASI J.Ulasan RAM Penyimpanan
## <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 iphone 4388000 Jabodetabek 10 4 256
## 2 iphone 9458000 Jabodetabek 3071 4 128
## 3 iphone 9409000 Jabodetabek 2713 4 128
## 4 iphone 5047000 Luar Jawa 87 4 64
## 5 iphone 11287000 Jabodetabek 567 6 128
## 6 iphone 21447000 Jabodetabek 732 8 256Pada tahap ini, Kami akan mengambil data hasil scrapping pada Minggu 3. Data ini adalah Data Penjualan Handphone, dengan Harga sebagai peubah Respon dan 5 Peubah Penjelas. Peubah Penjelas terdiri dari Brand, Lokasi, Ram, Penyimpanan.
Pada Tahap ini Toko tidak kami masukkan ke model atas dasar pertimbangan pengelompokkan yang rumit untuk dilakukan sehingga pada bagian ini kami tidak memasukkan peubah Toko terlebih dahulu dan memodelkan dengan 5 Peubah Penjelas
Melakukan Releveling Pada Data Kategorik
data$Brand <- relevel(as.factor(data$Brand), ref="iphone")
data$LOKASI <- relevel(as.factor(data$LOKASI), ref="Jabodetabek")
data$RAM <- relevel(as.factor(as.numeric(data$RAM)), ref = "6")
data$Penyimpanan <- relevel(as.factor(as.numeric(data$Penyimpanan)), ref = "128")
data$J.Ulasan <- as.numeric(data$J.Ulasan)Pada tahapan ini , kami melakukan Re-level pada data-data kategorik yaitu Brand, Lokasi , RAM dan Penyimpanan. RAM dan Penyimpanan memang berbentuk data numerik, namun dalam substansinya, angka-angka pada RAM dan Penyimpanan sudah disetting dalam beberapa kategori sehingga dapat dikatakan bahwa data ini lebih ke Kategorik daripada Numerik.
Pada Tahap ini, Kami juga menentukan Reference yaitu Untuk Brand Kami menerapkan Reference di kategori Iphone, lalu untuk Lokasi kamu menerapkan reference di Jabodetabek, 6 Untuk Ram dan 128 untuk Penyimpanan.
Terakhir kami menyiapkan data ulasan sebagai numeric. Hal ini dilakukan agar data tetap dibaca sebagai numerik pada model.
Eksplorasi
Untuk Visualisasi Sebelum Ke Model, Dapat dilihat bahwa Kecenderungan Harga menjulur ke kanan. Hal ini mengartikan bahwa harga Handphone menyebar di daerah 5 Jutaan. Kemudian data mulai turun secara signifikan sampai ke harga puluhan juta. Sebaran data hasil histogram ini juga mengindikasikan adanya outlier karena data tidak terdistribusi secara merata, untuk itu kami akan melakukan pendeteksian pencilan setelah pemodelan.
Pemodelan
##
## Call:
## lm(formula = HARGA ~ Brand + LOKASI + RAM + Penyimpanan + J.Ulasan,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12853595 -1607087 -586536 1435603 14613759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.102e+07 1.678e+05 65.685 < 2e-16 ***
## Brandasus -7.748e+06 4.688e+05 -16.529 < 2e-16 ***
## Brandinfinix -9.997e+06 2.215e+05 -45.142 < 2e-16 ***
## Brandoppo -8.896e+06 2.553e+05 -34.848 < 2e-16 ***
## BrandRealme -8.830e+06 2.508e+05 -35.211 < 2e-16 ***
## BrandSamsung -7.186e+06 1.737e+05 -41.364 < 2e-16 ***
## Brandvivo -8.731e+06 1.980e+05 -44.103 < 2e-16 ***
## Brandxiaomi -9.110e+06 1.778e+05 -51.228 < 2e-16 ***
## LOKASIBali 3.884e+05 4.681e+05 0.830 0.406761
## LOKASIDI Yogyakarta -6.591e+05 7.483e+05 -0.881 0.378479
## LOKASIJawa Barat -1.784e+04 2.283e+05 -0.078 0.937735
## LOKASIJawa Tengah -1.905e+05 4.780e+05 -0.399 0.690263
## LOKASIJawa Timur 3.799e+05 1.773e+05 2.142 0.032244 *
## LOKASILuar Jawa 4.378e+05 1.671e+05 2.620 0.008841 **
## RAM1 -1.117e+07 1.327e+06 -8.413 < 2e-16 ***
## RAM2 -8.142e+06 5.753e+05 -14.153 < 2e-16 ***
## RAM3 -6.381e+06 3.622e+05 -17.618 < 2e-16 ***
## RAM4 -2.780e+06 1.822e+05 -15.259 < 2e-16 ***
## RAM8 9.669e+05 1.661e+05 5.820 6.50e-09 ***
## RAM12 4.762e+06 2.669e+05 17.842 < 2e-16 ***
## RAM16 8.399e+06 6.345e+05 13.237 < 2e-16 ***
## RAM18 1.271e+07 2.100e+06 6.053 1.59e-09 ***
## RAM24 1.567e+06 1.742e+06 0.899 0.368505
## Penyimpanan8 -3.033e+06 2.928e+06 -1.036 0.300349
## Penyimpanan16 2.474e+06 1.156e+06 2.140 0.032437 *
## Penyimpanan32 3.742e+06 6.041e+05 6.194 6.63e-10 ***
## Penyimpanan64 -5.358e+05 1.917e+05 -2.795 0.005226 **
## Penyimpanan256 5.122e+05 1.379e+05 3.714 0.000207 ***
## Penyimpanan512 2.456e+06 2.494e+05 9.848 < 2e-16 ***
## J.Ulasan -3.379e+02 2.808e+02 -1.203 0.228918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2870000 on 3091 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6239
## F-statistic: 179.4 on 29 and 3091 DF, p-value: < 2.2e-16Didapatkan Model Sebagai Berikut ini , Dimana R-Squared 62%. Didapat pula bahwa mayoritas peubah yang digunaka signfikan dalam taraf kepercayaan 95 %. Namun ada beberapa peubah juga yang tidak signifkan seperti Jumlah Ulasan.
Periksa Asumsi
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
Dari Plot Residual Vs Fitted, Asumsi Homoskedastisitas tidak terpenuhi karena banyaknya residual data hasil tidak menyebar di nilai tengah dimana y= 0 . Sehingga dapat disimpulkan bahwa terjadi Heteroskedasitas yang artinya ragam sisanya tidak homogen.
Dari Plot QQ-Residual , walaupun beberapa data menyebar normal di tengah, namun, mayoritas data di ujung dan di belakannya tidak berada di garis kenormalan data. Sehinga dapat dikatakan data tidak menyebar normal namun harus tetap dilakukan uji secara formal. Data ini bisa berpotensi tidak normal karena adanya pencilan. Data tidak menyebar mengikuti garis lurus. Bagian atas yang melenceng dari garis menunjukkan bahwa sisaan model menjulur ke kanan.
Dari Plot Fitted Values Vs Standarad Residual dapat dilihat bahwa data cenderung menyebar dan membentuk pola, hal ini mengindikasikan bahwa nilai harapan sisaan pada data tidak saling bebas . karena adanya pola yang terbentuk.
## Warning: package 'car' was built under R version 4.3.2## Loading required package: carData## Warning: package 'carData' was built under R version 4.3.2##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## some## GVIF Df GVIF^(1/(2*Df))
## Brand 2.494361 7 1.067466
## LOKASI 1.130748 6 1.010293
## RAM 7.462211 9 1.118131
## Penyimpanan 4.322606 6 1.129741
## J.Ulasan 1.067401 1 1.033151VIF pada Seluruh Data Berada dibawah 10, yang artinya cukup bukti untuk mengatakan tidak ada multikolinearitas.
Uji Formal
##
## Shapiro-Wilk normality test
##
## data: residuals(model)
## W = 0.91741, p-value < 2.2e-16## Warning: package 'lmtest' was built under R version 4.3.3## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.3.2##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## studentized Breusch-Pagan test
##
## data: model
## BP = 630.37, df = 29, p-value < 2.2e-16##
## Approximate runs rest
##
## data: model$residuals
## Runs = 1579, p-value = 0.5309
## alternative hypothesis: two.sidedDapat dilihat bahwa data tidak menyebar normal dan terjadi heteroskedastitsitas. Namun dari autokorelasinya menggunakan run.test terpenuhi
## Warning: package 'olsrr' was built under R version 4.3.2##
## Attaching package: 'olsrr'## The following object is masked from 'package:datasets':
##
## rivers
# Menghapus layer teks yang biasanya mengandung label urutan data
p$layers <- p$layers[sapply(p$layers, function(x) class(x$geom)[1] != "GeomText")]
# Tampilkan plot tanpa label urutan data
p
Jika dilihat masih banyak data yang merupakan outlier dan menjadi titik Leverage, sehingga mungkin saja data-data ini menyebabkan distribusi menjadi tidak normal dan merambat pada pelanggaran asumsi lainnya
Uji Coba Transformasi untuk penanganan
##
## Call:
## lm(formula = HARGA ~ Brand + LOKASI + RAM + Penyimpanan + J.Ulasan,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12853595 -1607087 -586536 1435603 14613759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.102e+07 1.678e+05 65.685 < 2e-16 ***
## Brandasus -7.748e+06 4.688e+05 -16.529 < 2e-16 ***
## Brandinfinix -9.997e+06 2.215e+05 -45.142 < 2e-16 ***
## Brandoppo -8.896e+06 2.553e+05 -34.848 < 2e-16 ***
## BrandRealme -8.830e+06 2.508e+05 -35.211 < 2e-16 ***
## BrandSamsung -7.186e+06 1.737e+05 -41.364 < 2e-16 ***
## Brandvivo -8.731e+06 1.980e+05 -44.103 < 2e-16 ***
## Brandxiaomi -9.110e+06 1.778e+05 -51.228 < 2e-16 ***
## LOKASIBali 3.884e+05 4.681e+05 0.830 0.406761
## LOKASIDI Yogyakarta -6.591e+05 7.483e+05 -0.881 0.378479
## LOKASIJawa Barat -1.784e+04 2.283e+05 -0.078 0.937735
## LOKASIJawa Tengah -1.905e+05 4.780e+05 -0.399 0.690263
## LOKASIJawa Timur 3.799e+05 1.773e+05 2.142 0.032244 *
## LOKASILuar Jawa 4.378e+05 1.671e+05 2.620 0.008841 **
## RAM1 -1.117e+07 1.327e+06 -8.413 < 2e-16 ***
## RAM2 -8.142e+06 5.753e+05 -14.153 < 2e-16 ***
## RAM3 -6.381e+06 3.622e+05 -17.618 < 2e-16 ***
## RAM4 -2.780e+06 1.822e+05 -15.259 < 2e-16 ***
## RAM8 9.669e+05 1.661e+05 5.820 6.50e-09 ***
## RAM12 4.762e+06 2.669e+05 17.842 < 2e-16 ***
## RAM16 8.399e+06 6.345e+05 13.237 < 2e-16 ***
## RAM18 1.271e+07 2.100e+06 6.053 1.59e-09 ***
## RAM24 1.567e+06 1.742e+06 0.899 0.368505
## Penyimpanan8 -3.033e+06 2.928e+06 -1.036 0.300349
## Penyimpanan16 2.474e+06 1.156e+06 2.140 0.032437 *
## Penyimpanan32 3.742e+06 6.041e+05 6.194 6.63e-10 ***
## Penyimpanan64 -5.358e+05 1.917e+05 -2.795 0.005226 **
## Penyimpanan256 5.122e+05 1.379e+05 3.714 0.000207 ***
## Penyimpanan512 2.456e+06 2.494e+05 9.848 < 2e-16 ***
## J.Ulasan -3.379e+02 2.808e+02 -1.203 0.228918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2870000 on 3091 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6239
## F-statistic: 179.4 on 29 and 3091 DF, p-value: < 2.2e-16Didapatkan R-Squared yang lebih tinggi daripada model sebelum di Transformasi, namun tujuannya adalah melihat apakah bisa untuk menyelesaikan permasalahan asumsi maka langsung kita cek
Pengecekan
##
## Shapiro-Wilk normality test
##
## data: residuals(modelt)
## W = 0.99178, p-value = 2.147e-12##
## studentized Breusch-Pagan test
##
## data: modelt
## BP = 313.04, df = 29, p-value < 2.2e-16# Seleksi Peubah
## Warning: package 'leaps' was built under R version 4.3.3regfit.full <- regsubsets(HARGA ~ Brand+ LOKASI + RAM + Penyimpanan+ J.Ulasan, data=data , nvmax = 29)
reg.summary <- summary(regfit.full)
summary(regfit.full)## Subset selection object
## Call: regsubsets.formula(HARGA ~ Brand + LOKASI + RAM + Penyimpanan +
## J.Ulasan, data = data, nvmax = 29)
## 29 Variables (and intercept)
## Forced in Forced out
## Brandasus FALSE FALSE
## Brandinfinix FALSE FALSE
## Brandoppo FALSE FALSE
## BrandRealme FALSE FALSE
## BrandSamsung FALSE FALSE
## Brandvivo FALSE FALSE
## Brandxiaomi FALSE FALSE
## LOKASIBali FALSE FALSE
## LOKASIDI Yogyakarta FALSE FALSE
## LOKASIJawa Barat FALSE FALSE
## LOKASIJawa Tengah FALSE FALSE
## LOKASIJawa Timur FALSE FALSE
## LOKASILuar Jawa FALSE FALSE
## RAM1 FALSE FALSE
## RAM2 FALSE FALSE
## RAM3 FALSE FALSE
## RAM4 FALSE FALSE
## RAM8 FALSE FALSE
## RAM12 FALSE FALSE
## RAM16 FALSE FALSE
## RAM18 FALSE FALSE
## RAM24 FALSE FALSE
## Penyimpanan8 FALSE FALSE
## Penyimpanan16 FALSE FALSE
## Penyimpanan32 FALSE FALSE
## Penyimpanan64 FALSE FALSE
## Penyimpanan256 FALSE FALSE
## Penyimpanan512 FALSE FALSE
## J.Ulasan FALSE FALSE
## 1 subsets of each size up to 29
## Selection Algorithm: exhaustive
## Brandasus Brandinfinix Brandoppo BrandRealme BrandSamsung Brandvivo
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " "*" " " " " " " " "
## 4 ( 1 ) " " "*" " " " " " " "*"
## 5 ( 1 ) " " "*" " " "*" " " "*"
## 6 ( 1 ) " " "*" "*" "*" "*" "*"
## 7 ( 1 ) " " "*" "*" "*" "*" "*"
## 8 ( 1 ) " " "*" "*" "*" "*" "*"
## 9 ( 1 ) " " "*" "*" "*" "*" "*"
## 10 ( 1 ) " " "*" "*" "*" "*" "*"
## 11 ( 1 ) " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*"
## 16 ( 1 ) "*" "*" "*" "*" "*" "*"
## 17 ( 1 ) "*" "*" "*" "*" "*" "*"
## 18 ( 1 ) "*" "*" "*" "*" "*" "*"
## 19 ( 1 ) "*" "*" "*" "*" "*" "*"
## 20 ( 1 ) "*" "*" "*" "*" "*" "*"
## 21 ( 1 ) "*" "*" "*" "*" "*" "*"
## 22 ( 1 ) "*" "*" "*" "*" "*" "*"
## 23 ( 1 ) "*" "*" "*" "*" "*" "*"
## 24 ( 1 ) "*" "*" "*" "*" "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*"
## Brandxiaomi LOKASIBali LOKASIDI Yogyakarta LOKASIJawa Barat
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) "*" " " " " " "
## 3 ( 1 ) "*" " " " " " "
## 4 ( 1 ) "*" " " " " " "
## 5 ( 1 ) "*" " " " " " "
## 6 ( 1 ) "*" " " " " " "
## 7 ( 1 ) "*" " " " " " "
## 8 ( 1 ) "*" " " " " " "
## 9 ( 1 ) "*" " " " " " "
## 10 ( 1 ) "*" " " " " " "
## 11 ( 1 ) "*" " " " " " "
## 12 ( 1 ) "*" " " " " " "
## 13 ( 1 ) "*" " " " " " "
## 14 ( 1 ) "*" " " " " " "
## 15 ( 1 ) "*" " " " " " "
## 16 ( 1 ) "*" " " " " " "
## 17 ( 1 ) "*" " " " " " "
## 18 ( 1 ) "*" " " " " " "
## 19 ( 1 ) "*" " " " " " "
## 20 ( 1 ) "*" " " " " " "
## 21 ( 1 ) "*" " " " " " "
## 22 ( 1 ) "*" " " " " " "
## 23 ( 1 ) "*" " " " " " "
## 24 ( 1 ) "*" " " " " " "
## 25 ( 1 ) "*" " " " " " "
## 26 ( 1 ) "*" " " "*" " "
## 27 ( 1 ) "*" "*" "*" " "
## 28 ( 1 ) "*" "*" "*" " "
## 29 ( 1 ) "*" "*" "*" "*"
## LOKASIJawa Tengah LOKASIJawa Timur LOKASILuar Jawa RAM1 RAM2 RAM3
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " "*"
## 10 ( 1 ) " " " " " " " " "*" "*"
## 11 ( 1 ) " " " " " " " " "*" "*"
## 12 ( 1 ) " " " " " " " " "*" "*"
## 13 ( 1 ) " " " " " " " " "*" "*"
## 14 ( 1 ) " " " " " " "*" "*" "*"
## 15 ( 1 ) " " " " " " "*" "*" "*"
## 16 ( 1 ) " " " " " " "*" "*" "*"
## 17 ( 1 ) " " " " " " "*" "*" "*"
## 18 ( 1 ) " " " " " " "*" "*" "*"
## 19 ( 1 ) " " " " " " "*" "*" "*"
## 20 ( 1 ) " " " " "*" "*" "*" "*"
## 21 ( 1 ) " " "*" "*" "*" "*" "*"
## 22 ( 1 ) " " "*" "*" "*" "*" "*"
## 23 ( 1 ) " " "*" "*" "*" "*" "*"
## 24 ( 1 ) " " "*" "*" "*" "*" "*"
## 25 ( 1 ) " " "*" "*" "*" "*" "*"
## 26 ( 1 ) " " "*" "*" "*" "*" "*"
## 27 ( 1 ) " " "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*"
## RAM4 RAM8 RAM12 RAM16 RAM18 RAM24 Penyimpanan8 Penyimpanan16
## 1 ( 1 ) " " " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " " " " " " "
## 9 ( 1 ) "*" " " "*" " " " " " " " " " "
## 10 ( 1 ) "*" " " "*" " " " " " " " " " "
## 11 ( 1 ) "*" " " "*" " " " " " " " " " "
## 12 ( 1 ) "*" " " "*" "*" " " " " " " " "
## 13 ( 1 ) "*" " " "*" "*" " " " " " " " "
## 14 ( 1 ) "*" " " "*" "*" " " " " " " " "
## 15 ( 1 ) "*" "*" "*" "*" " " " " " " " "
## 16 ( 1 ) "*" "*" "*" "*" " " " " " " " "
## 17 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 18 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 19 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 20 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 21 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 22 ( 1 ) "*" "*" "*" "*" "*" " " " " "*"
## 23 ( 1 ) "*" "*" "*" "*" "*" " " " " "*"
## 24 ( 1 ) "*" "*" "*" "*" "*" " " "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## Penyimpanan32 Penyimpanan64 Penyimpanan256 Penyimpanan512 J.Ulasan
## 1 ( 1 ) " " " " " " "*" " "
## 2 ( 1 ) " " " " " " "*" " "
## 3 ( 1 ) " " " " " " "*" " "
## 4 ( 1 ) " " " " " " "*" " "
## 5 ( 1 ) " " " " " " "*" " "
## 6 ( 1 ) " " " " " " " " " "
## 7 ( 1 ) " " "*" " " " " " "
## 8 ( 1 ) " " "*" " " " " " "
## 9 ( 1 ) " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " "
## 11 ( 1 ) " " " " " " "*" " "
## 12 ( 1 ) " " " " " " " " " "
## 13 ( 1 ) " " " " " " "*" " "
## 14 ( 1 ) " " " " " " "*" " "
## 15 ( 1 ) " " " " " " "*" " "
## 16 ( 1 ) "*" " " " " "*" " "
## 17 ( 1 ) "*" " " " " "*" " "
## 18 ( 1 ) "*" " " "*" "*" " "
## 19 ( 1 ) "*" "*" "*" "*" " "
## 20 ( 1 ) "*" "*" "*" "*" " "
## 21 ( 1 ) "*" "*" "*" "*" " "
## 22 ( 1 ) "*" "*" "*" "*" " "
## 23 ( 1 ) "*" "*" "*" "*" "*"
## 24 ( 1 ) "*" "*" "*" "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*"## [1] 0.09240237 0.13856492 0.17996271 0.22998662 0.27180073 0.33017864
## [7] 0.40556025 0.46406155 0.51016061 0.53845534 0.56307456 0.58207056
## [13] 0.59511227 0.60330756 0.61001694 0.61525926 0.61958078 0.62152828
## [19] 0.62252194 0.62318433 0.62365766 0.62410094 0.62415064 0.62415792
## [25] 0.62413529 0.62410899 0.62407385 0.62397130 0.62385039## [1] 24## (Intercept) Brandasus Brandinfinix Brandoppo
## 1.102538e+07 -7.643575e+06 -1.000860e+07 -8.898051e+06
## BrandRealme BrandSamsung Brandvivo Brandxiaomi
## -8.830088e+06 -7.192492e+06 -8.728234e+06 -9.105534e+06
## LOKASIJawa Timur LOKASILuar Jawa RAM1 RAM2
## 3.830273e+05 4.408839e+05 -1.117701e+07 -8.169859e+06
## RAM3 RAM4 RAM8 RAM12
## -6.385813e+06 -2.787857e+06 9.568391e+05 4.737387e+06
## RAM16 RAM18 Penyimpanan8 Penyimpanan16
## 8.300965e+06 1.256514e+07 -3.012885e+06 2.487205e+06
## Penyimpanan32 Penyimpanan64 Penyimpanan256 Penyimpanan512
## 3.748907e+06 -5.397252e+05 5.161355e+05 2.492005e+06
## J.Ulasan
## -3.327357e+02## [1] 4408.72093 4025.65492 3682.31799 3267.74290 2921.42153 2438.19702
## [7] 1814.56096 1330.98154 950.28778 717.04507 514.33213 358.21288
## [13] 251.37716 184.62932 130.19089 87.89424 53.22162 38.14977
## [19] 30.95178 26.48923 23.58780 20.93643 21.52792 22.46879
## [25] 23.65580 24.87283 26.16250 28.00610 30.00000## [1] 22## [1] 96327.73 96162.49 96006.54 95807.52 95630.86 95366.96 94990.34 94663.40
## [9] 94379.43 94191.24 94017.78 93876.92 93776.18 93710.83 93656.15 93612.56
## [17] 93576.01 93558.85 93549.57 93543.03 93538.06 93533.34 93531.90 93530.83
## [25] 93530.00 93529.20 93528.48 93528.32 93528.32## [1] 29## (Intercept) Brandasus Brandinfinix Brandoppo
## 1.102039e+07 -7.748241e+06 -9.996847e+06 -8.896092e+06
## BrandRealme BrandSamsung Brandvivo Brandxiaomi
## -8.829891e+06 -7.185516e+06 -8.730847e+06 -9.109551e+06
## LOKASIBali LOKASIDI Yogyakarta LOKASIJawa Barat LOKASIJawa Tengah
## 3.883858e+05 -6.590816e+05 -1.783686e+04 -1.904907e+05
## LOKASIJawa Timur LOKASILuar Jawa RAM1 RAM2
## 3.798504e+05 4.377656e+05 -1.116853e+07 -8.141921e+06
## RAM3 RAM4 RAM8 RAM12
## -6.381449e+06 -2.780065e+06 9.668636e+05 4.762440e+06
## RAM16 RAM18 RAM24 Penyimpanan8
## 8.398689e+06 1.271118e+07 1.567127e+06 -3.032647e+06
## Penyimpanan16 Penyimpanan32 Penyimpanan64 Penyimpanan256
## 2.473774e+06 3.741696e+06 -5.357751e+05 5.121893e+05
## Penyimpanan512 J.Ulasan
## 2.456009e+06 -3.379496e+02## [1] -287.5025 -443.3781 -590.0422 -779.4395 -946.6506 -1200.4091
## [7] -1565.9870 -1882.2796 -2155.9450 -2334.5980 -2498.6373 -2630.3238
## [13] -2722.2266 -2779.0053 -2825.2022 -2860.4001 -2888.6141 -2897.5927
## [19] -2898.7579 -2897.2001 -2894.0840 -2890.7236 -2884.0979 -2877.1204
## [25] -2869.8948 -2862.6391 -2855.3103 -2847.4223 -2839.3826## [1] 19## (Intercept) Brandasus Brandinfinix Brandoppo BrandRealme
## 11094447.8 -7667292.8 -9972189.6 -8951201.6 -8835170.6
## BrandSamsung Brandvivo Brandxiaomi RAM1 RAM2
## -7169694.4 -8736884.5 -9083600.1 -10723015.7 -7959902.8
## RAM3 RAM4 RAM8 RAM12 RAM16
## -6375916.6 -2763403.3 959749.5 4713574.5 8300264.8
## RAM18 Penyimpanan32 Penyimpanan64 Penyimpanan256 Penyimpanan512
## 12552487.1 3611103.6 -578264.1 495172.0 2434857.9#Kesimpulan
method.sub <- data.frame(kriteria_pemilihan_model = c("R2-adjusted", "Cp", "AIC", "BIC"), model_terpilih = c(24,22, 29, 19))
colnames(method.sub) <- c("Kriteria Pemilihan Model", "Model Terpilih")
method.sub## Kriteria Pemilihan Model Model Terpilih
## 1 R2-adjusted 24
## 2 Cp 22
## 3 AIC 29
## 4 BIC 19#membuat plot
par(mfrow=c(2,2))
#plot Adj R2
plot(reg.summary$adjr2 ,xlab="Model",
ylab="R2-Adjusted",type="l", main="Plot R2-Adjusted")
points(24, reg.summary$adjr2[24], col="red",cex=2,pch=20)
#plot Cp
plot(reg.summary$cp ,xlab="Model",
ylab="Cp",type="l", main="Plot Cp")
points(22, reg.summary$cp[22], col="red",cex=2,pch=20)
#plot AIC
plot(aic, xlab="Model",
ylab="AIC",type="l", main="Plot AIC")
points(29, aic[29], col="red",cex=2,pch=20)
#plot BIC
plot(reg.summary$bic ,xlab="Model",
ylab="BIC",type="l", main="Plot BIC")
points(19, reg.summary$bic[19], col="red",cex=2,pch=20)
regfit.fwd <- regsubsets(HARGA ~ Brand+ LOKASI + RAM + Penyimpanan+ J.Ulasan, data=data , nvmax = 29, method = "forward")
reg.summary.fwd <- summary(regfit.fwd)
reg.summary.fwd## Subset selection object
## Call: regsubsets.formula(HARGA ~ Brand + LOKASI + RAM + Penyimpanan +
## J.Ulasan, data = data, nvmax = 29, method = "forward")
## 29 Variables (and intercept)
## Forced in Forced out
## Brandasus FALSE FALSE
## Brandinfinix FALSE FALSE
## Brandoppo FALSE FALSE
## BrandRealme FALSE FALSE
## BrandSamsung FALSE FALSE
## Brandvivo FALSE FALSE
## Brandxiaomi FALSE FALSE
## LOKASIBali FALSE FALSE
## LOKASIDI Yogyakarta FALSE FALSE
## LOKASIJawa Barat FALSE FALSE
## LOKASIJawa Tengah FALSE FALSE
## LOKASIJawa Timur FALSE FALSE
## LOKASILuar Jawa FALSE FALSE
## RAM1 FALSE FALSE
## RAM2 FALSE FALSE
## RAM3 FALSE FALSE
## RAM4 FALSE FALSE
## RAM8 FALSE FALSE
## RAM12 FALSE FALSE
## RAM16 FALSE FALSE
## RAM18 FALSE FALSE
## RAM24 FALSE FALSE
## Penyimpanan8 FALSE FALSE
## Penyimpanan16 FALSE FALSE
## Penyimpanan32 FALSE FALSE
## Penyimpanan64 FALSE FALSE
## Penyimpanan256 FALSE FALSE
## Penyimpanan512 FALSE FALSE
## J.Ulasan FALSE FALSE
## 1 subsets of each size up to 29
## Selection Algorithm: forward
## Brandasus Brandinfinix Brandoppo BrandRealme BrandSamsung Brandvivo
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " "*" " " " " " " " "
## 4 ( 1 ) " " "*" " " " " " " "*"
## 5 ( 1 ) " " "*" " " "*" " " "*"
## 6 ( 1 ) " " "*" " " "*" "*" "*"
## 7 ( 1 ) " " "*" "*" "*" "*" "*"
## 8 ( 1 ) " " "*" "*" "*" "*" "*"
## 9 ( 1 ) " " "*" "*" "*" "*" "*"
## 10 ( 1 ) " " "*" "*" "*" "*" "*"
## 11 ( 1 ) " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*"
## 16 ( 1 ) "*" "*" "*" "*" "*" "*"
## 17 ( 1 ) "*" "*" "*" "*" "*" "*"
## 18 ( 1 ) "*" "*" "*" "*" "*" "*"
## 19 ( 1 ) "*" "*" "*" "*" "*" "*"
## 20 ( 1 ) "*" "*" "*" "*" "*" "*"
## 21 ( 1 ) "*" "*" "*" "*" "*" "*"
## 22 ( 1 ) "*" "*" "*" "*" "*" "*"
## 23 ( 1 ) "*" "*" "*" "*" "*" "*"
## 24 ( 1 ) "*" "*" "*" "*" "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*"
## Brandxiaomi LOKASIBali LOKASIDI Yogyakarta LOKASIJawa Barat
## 1 ( 1 ) " " " " " " " "
## 2 ( 1 ) "*" " " " " " "
## 3 ( 1 ) "*" " " " " " "
## 4 ( 1 ) "*" " " " " " "
## 5 ( 1 ) "*" " " " " " "
## 6 ( 1 ) "*" " " " " " "
## 7 ( 1 ) "*" " " " " " "
## 8 ( 1 ) "*" " " " " " "
## 9 ( 1 ) "*" " " " " " "
## 10 ( 1 ) "*" " " " " " "
## 11 ( 1 ) "*" " " " " " "
## 12 ( 1 ) "*" " " " " " "
## 13 ( 1 ) "*" " " " " " "
## 14 ( 1 ) "*" " " " " " "
## 15 ( 1 ) "*" " " " " " "
## 16 ( 1 ) "*" " " " " " "
## 17 ( 1 ) "*" " " " " " "
## 18 ( 1 ) "*" " " " " " "
## 19 ( 1 ) "*" " " " " " "
## 20 ( 1 ) "*" " " " " " "
## 21 ( 1 ) "*" " " " " " "
## 22 ( 1 ) "*" " " " " " "
## 23 ( 1 ) "*" " " " " " "
## 24 ( 1 ) "*" " " " " " "
## 25 ( 1 ) "*" " " " " " "
## 26 ( 1 ) "*" " " "*" " "
## 27 ( 1 ) "*" "*" "*" " "
## 28 ( 1 ) "*" "*" "*" " "
## 29 ( 1 ) "*" "*" "*" "*"
## LOKASIJawa Tengah LOKASIJawa Timur LOKASILuar Jawa RAM1 RAM2 RAM3
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " "*"
## 10 ( 1 ) " " " " " " " " " " "*"
## 11 ( 1 ) " " " " " " " " "*" "*"
## 12 ( 1 ) " " " " " " " " "*" "*"
## 13 ( 1 ) " " " " " " " " "*" "*"
## 14 ( 1 ) " " " " " " "*" "*" "*"
## 15 ( 1 ) " " " " " " "*" "*" "*"
## 16 ( 1 ) " " " " " " "*" "*" "*"
## 17 ( 1 ) " " " " " " "*" "*" "*"
## 18 ( 1 ) " " " " " " "*" "*" "*"
## 19 ( 1 ) " " " " " " "*" "*" "*"
## 20 ( 1 ) " " " " "*" "*" "*" "*"
## 21 ( 1 ) " " "*" "*" "*" "*" "*"
## 22 ( 1 ) " " "*" "*" "*" "*" "*"
## 23 ( 1 ) " " "*" "*" "*" "*" "*"
## 24 ( 1 ) " " "*" "*" "*" "*" "*"
## 25 ( 1 ) " " "*" "*" "*" "*" "*"
## 26 ( 1 ) " " "*" "*" "*" "*" "*"
## 27 ( 1 ) " " "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*"
## RAM4 RAM8 RAM12 RAM16 RAM18 RAM24 Penyimpanan8 Penyimpanan16
## 1 ( 1 ) " " " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " " " "
## 8 ( 1 ) "*" " " " " " " " " " " " " " "
## 9 ( 1 ) "*" " " " " " " " " " " " " " "
## 10 ( 1 ) "*" " " "*" " " " " " " " " " "
## 11 ( 1 ) "*" " " "*" " " " " " " " " " "
## 12 ( 1 ) "*" " " "*" " " " " " " " " " "
## 13 ( 1 ) "*" " " "*" "*" " " " " " " " "
## 14 ( 1 ) "*" " " "*" "*" " " " " " " " "
## 15 ( 1 ) "*" "*" "*" "*" " " " " " " " "
## 16 ( 1 ) "*" "*" "*" "*" " " " " " " " "
## 17 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 18 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 19 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 20 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 21 ( 1 ) "*" "*" "*" "*" "*" " " " " " "
## 22 ( 1 ) "*" "*" "*" "*" "*" " " " " "*"
## 23 ( 1 ) "*" "*" "*" "*" "*" " " " " "*"
## 24 ( 1 ) "*" "*" "*" "*" "*" " " "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*"
## Penyimpanan32 Penyimpanan64 Penyimpanan256 Penyimpanan512 J.Ulasan
## 1 ( 1 ) " " " " " " "*" " "
## 2 ( 1 ) " " " " " " "*" " "
## 3 ( 1 ) " " " " " " "*" " "
## 4 ( 1 ) " " " " " " "*" " "
## 5 ( 1 ) " " " " " " "*" " "
## 6 ( 1 ) " " " " " " "*" " "
## 7 ( 1 ) " " " " " " "*" " "
## 8 ( 1 ) " " " " " " "*" " "
## 9 ( 1 ) " " " " " " "*" " "
## 10 ( 1 ) " " " " " " "*" " "
## 11 ( 1 ) " " " " " " "*" " "
## 12 ( 1 ) " " " " " " "*" " "
## 13 ( 1 ) " " " " " " "*" " "
## 14 ( 1 ) " " " " " " "*" " "
## 15 ( 1 ) " " " " " " "*" " "
## 16 ( 1 ) "*" " " " " "*" " "
## 17 ( 1 ) "*" " " " " "*" " "
## 18 ( 1 ) "*" " " "*" "*" " "
## 19 ( 1 ) "*" "*" "*" "*" " "
## 20 ( 1 ) "*" "*" "*" "*" " "
## 21 ( 1 ) "*" "*" "*" "*" " "
## 22 ( 1 ) "*" "*" "*" "*" " "
## 23 ( 1 ) "*" "*" "*" "*" "*"
## 24 ( 1 ) "*" "*" "*" "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*"## [1] 0.09240237 0.13856492 0.17996271 0.22998662 0.27180073 0.31971277
## [7] 0.39810804 0.45942116 0.50886123 0.53707813 0.56307456 0.57849925
## [13] 0.59511227 0.60330756 0.61001694 0.61525926 0.61958078 0.62152828
## [19] 0.62252194 0.62318433 0.62365766 0.62410094 0.62415064 0.62415792
## [25] 0.62413529 0.62410899 0.62407385 0.62397130 0.62385039## [1] 24## [1] 4408.72093 4025.65492 3682.31799 3267.74290 2921.42153 2524.83997
## [7] 1876.23513 1369.37291 961.03449 728.43189 514.33213 387.72148
## [13] 251.37716 184.62932 130.19089 87.89424 53.22162 38.14977
## [19] 30.95178 26.48923 23.58780 20.93643 21.52792 22.46879
## [25] 23.65580 24.87283 26.16250 28.00610 30.00000## [1] 22## (Intercept) Brandasus Brandinfinix Brandoppo
## 10990755.0 -7623740.3 -10000888.1 -8891565.1
## BrandRealme BrandSamsung Brandvivo Brandxiaomi
## -8809399.0 -7184141.8 -8714781.0 -9091447.5
## LOKASIJawa Timur LOKASILuar Jawa RAM1 RAM2
## 392234.8 465815.4 -11162498.7 -8281926.7
## RAM3 RAM4 RAM8 RAM12
## -6426797.3 -2795097.7 952428.6 4725554.2
## RAM16 RAM18 Penyimpanan16 Penyimpanan32
## 8296919.9 12547133.4 2486487.0 3804857.7
## Penyimpanan64 Penyimpanan256 Penyimpanan512
## -542625.9 523738.7 2500352.0## [1] 96327.73 96162.49 96006.54 95807.52 95630.86 95415.73 95029.53 94690.52
## [9] 94387.77 94200.61 94017.78 93903.69 93776.18 93710.83 93656.15 93612.56
## [17] 93576.01 93558.85 93549.57 93543.03 93538.06 93533.34 93531.90 93530.83
## [25] 93530.00 93529.20 93528.48 93528.32 93528.32## [1] 29## [1] -287.5025 -443.3781 -590.0422 -779.4395 -946.6506 -1152.0209
## [7] -1527.1037 -1855.3729 -2147.6770 -2325.2990 -2498.6373 -2603.7673
## [13] -2722.2266 -2779.0053 -2825.2022 -2860.4001 -2888.6141 -2897.5927
## [19] -2898.7579 -2897.2001 -2894.0840 -2890.7236 -2884.0979 -2877.1204
## [25] -2869.8948 -2862.6391 -2855.3103 -2847.4223 -2839.3826## [1] 19Backward
regfit.backward <- regsubsets(HARGA ~ Brand+ LOKASI + RAM + Penyimpanan+ J.Ulasan, data=data , nvmax = 29, method = "backward")
reg.summary.backward <- summary(regfit.backward)
reg.summary.backward## Subset selection object
## Call: regsubsets.formula(HARGA ~ Brand + LOKASI + RAM + Penyimpanan +
## J.Ulasan, data = data, nvmax = 29, method = "backward")
## 29 Variables (and intercept)
## Forced in Forced out
## Brandasus FALSE FALSE
## Brandinfinix FALSE FALSE
## Brandoppo FALSE FALSE
## BrandRealme FALSE FALSE
## BrandSamsung FALSE FALSE
## Brandvivo FALSE FALSE
## Brandxiaomi FALSE FALSE
## LOKASIBali FALSE FALSE
## LOKASIDI Yogyakarta FALSE FALSE
## LOKASIJawa Barat FALSE FALSE
## LOKASIJawa Tengah FALSE FALSE
## LOKASIJawa Timur FALSE FALSE
## LOKASILuar Jawa FALSE FALSE
## RAM1 FALSE FALSE
## RAM2 FALSE FALSE
## RAM3 FALSE FALSE
## RAM4 FALSE FALSE
## RAM8 FALSE FALSE
## RAM12 FALSE FALSE
## RAM16 FALSE FALSE
## RAM18 FALSE FALSE
## RAM24 FALSE FALSE
## Penyimpanan8 FALSE FALSE
## Penyimpanan16 FALSE FALSE
## Penyimpanan32 FALSE FALSE
## Penyimpanan64 FALSE FALSE
## Penyimpanan256 FALSE FALSE
## Penyimpanan512 FALSE FALSE
## J.Ulasan FALSE FALSE
## 1 subsets of each size up to 29
## Selection Algorithm: backward
## Brandasus Brandinfinix Brandoppo BrandRealme BrandSamsung Brandvivo
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " "*" " " " " " " " "
## 3 ( 1 ) " " "*" " " " " " " "*"
## 4 ( 1 ) " " "*" " " "*" " " "*"
## 5 ( 1 ) " " "*" " " "*" "*" "*"
## 6 ( 1 ) " " "*" "*" "*" "*" "*"
## 7 ( 1 ) " " "*" "*" "*" "*" "*"
## 8 ( 1 ) " " "*" "*" "*" "*" "*"
## 9 ( 1 ) " " "*" "*" "*" "*" "*"
## 10 ( 1 ) " " "*" "*" "*" "*" "*"
## 11 ( 1 ) "*" "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*"
## 16 ( 1 ) "*" "*" "*" "*" "*" "*"
## 17 ( 1 ) "*" "*" "*" "*" "*" "*"
## 18 ( 1 ) "*" "*" "*" "*" "*" "*"
## 19 ( 1 ) "*" "*" "*" "*" "*" "*"
## 20 ( 1 ) "*" "*" "*" "*" "*" "*"
## 21 ( 1 ) "*" "*" "*" "*" "*" "*"
## 22 ( 1 ) "*" "*" "*" "*" "*" "*"
## 23 ( 1 ) "*" "*" "*" "*" "*" "*"
## 24 ( 1 ) "*" "*" "*" "*" "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*"
## Brandxiaomi LOKASIBali LOKASIDI Yogyakarta LOKASIJawa Barat
## 1 ( 1 ) "*" " " " " " "
## 2 ( 1 ) "*" " " " " " "
## 3 ( 1 ) "*" " " " " " "
## 4 ( 1 ) "*" " " " " " "
## 5 ( 1 ) "*" " " " " " "
## 6 ( 1 ) "*" " " " " " "
## 7 ( 1 ) "*" " " " " " "
## 8 ( 1 ) "*" " " " " " "
## 9 ( 1 ) "*" " " " " " "
## 10 ( 1 ) "*" " " " " " "
## 11 ( 1 ) "*" " " " " " "
## 12 ( 1 ) "*" " " " " " "
## 13 ( 1 ) "*" " " " " " "
## 14 ( 1 ) "*" " " " " " "
## 15 ( 1 ) "*" " " " " " "
## 16 ( 1 ) "*" " " " " " "
## 17 ( 1 ) "*" " " " " " "
## 18 ( 1 ) "*" " " " " " "
## 19 ( 1 ) "*" " " " " " "
## 20 ( 1 ) "*" " " " " " "
## 21 ( 1 ) "*" " " " " " "
## 22 ( 1 ) "*" " " " " " "
## 23 ( 1 ) "*" " " " " " "
## 24 ( 1 ) "*" " " " " " "
## 25 ( 1 ) "*" " " " " " "
## 26 ( 1 ) "*" " " "*" " "
## 27 ( 1 ) "*" "*" "*" " "
## 28 ( 1 ) "*" "*" "*" " "
## 29 ( 1 ) "*" "*" "*" "*"
## LOKASIJawa Tengah LOKASIJawa Timur LOKASILuar Jawa RAM1 RAM2 RAM3
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " "*"
## 9 ( 1 ) " " " " " " " " " " "*"
## 10 ( 1 ) " " " " " " " " " " "*"
## 11 ( 1 ) " " " " " " " " " " "*"
## 12 ( 1 ) " " " " " " " " "*" "*"
## 13 ( 1 ) " " " " " " " " "*" "*"
## 14 ( 1 ) " " " " " " "*" "*" "*"
## 15 ( 1 ) " " " " " " "*" "*" "*"
## 16 ( 1 ) " " " " " " "*" "*" "*"
## 17 ( 1 ) " " " " " " "*" "*" "*"
## 18 ( 1 ) " " " " " " "*" "*" "*"
## 19 ( 1 ) " " " " " " "*" "*" "*"
## 20 ( 1 ) " " " " "*" "*" "*" "*"
## 21 ( 1 ) " " "*" "*" "*" "*" "*"
## 22 ( 1 ) " " "*" "*" "*" "*" "*"
## 23 ( 1 ) " " "*" "*" "*" "*" "*"
## 24 ( 1 ) " " "*" "*" "*" "*" "*"
## 25 ( 1 ) " " "*" "*" "*" "*" "*"
## 26 ( 1 ) " " "*" "*" "*" "*" "*"
## 27 ( 1 ) " " "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*" "*"
## RAM4 RAM8 RAM12 RAM16 RAM18 RAM24 Penyimpanan8 Penyimpanan16
## 1 ( 1 ) " " " " " " " " " " " " " " " "
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## Penyimpanan32 Penyimpanan64 Penyimpanan256 Penyimpanan512 J.Ulasan
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## 29 ( 1 ) "*" "*" "*" "*" "*"## [1] 0.04693347 0.09940683 0.14617087 0.19386141 0.24431969 0.33017864
## [7] 0.40519491 0.46221234 0.51016061 0.52429590 0.55192693 0.58207056
## [13] 0.59511227 0.60330756 0.61001694 0.61525926 0.61958078 0.62152828
## [19] 0.62252194 0.62318433 0.62365766 0.62410094 0.62415064 0.62415792
## [25] 0.62413529 0.62410899 0.62407385 0.62397130 0.62385039## [1] 24library(readxl)
library(tidyverse)
data <- read_excel("C:/Users/Admin/Downloads/PSD Kelompok 3 (3).xlsx")
names(data)[names(data) == "Jumlah Ulasan"] <- "J.Ulasan"
head(data)## # A tibble: 6 × 6
## Brand HARGA LOKASI J.Ulasan RAM Penyimpanan
## <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 iphone 4388000 Jabodetabek 10 4 256
## 2 iphone 9458000 Jabodetabek 3071 4 128
## 3 iphone 9409000 Jabodetabek 2713 4 128
## 4 iphone 5047000 Luar Jawa 87 4 64
## 5 iphone 11287000 Jabodetabek 567 6 128
## 6 iphone 21447000 Jabodetabek 732 8 256data$Brand_iphone <- ifelse(data$Brand == "iphone", 1, 0)
data$Brand_samsung <- ifelse(data$Brand == "samsung", 1, 0)
data$Brand_xiaomi <- ifelse(data$Brand == "xiaomi", 1, 0)
data$J.Ulasan <- as.numeric(data$J.Ulasan)
# Membersihkan data lokasi
data$LOKASI <- gsub("^di ", "", data$LOKASI)
# Membuat dummy DKI Jakarta vs Lainnya
data$dummy <- ifelse(data$LOKASI == "Jabodetabek", 2, 3)
data$dummyram6 <- ifelse(data$RAM == 6, 1, 0)
data$Brand <- relevel(as.factor(data$Brand), ref="iphone")
data$LOKASI <- relevel(as.factor(data$dummy), ref= "2")
data$RAM <- relevel(as.factor(as.numeric(data$RAM)), ref = "1")
data$Penyimpanan <- relevel(as.factor(as.numeric(data$Penyimpanan)), ref = "128")
data$J.Ulasan <- as.numeric(data$J.Ulasan)##
## Call:
## lm(formula = HARGA ~ Brand + dummy + dummyram6 + Penyimpanan +
## J.Ulasan, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11520286 -1620775 -447706 877252 18761573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8665481.7 361873.2 23.946 < 2e-16 ***
## Brandasus -2193998.8 459488.7 -4.775 1.88e-06 ***
## Brandinfinix -7863486.7 240782.0 -32.658 < 2e-16 ***
## Brandoppo -6994171.3 287121.2 -24.360 < 2e-16 ***
## BrandRealme -6834263.3 280762.4 -24.342 < 2e-16 ***
## BrandSamsung -4840441.0 178770.5 -27.076 < 2e-16 ***
## Brandvivo -6344122.5 212745.1 -29.820 < 2e-16 ***
## Brandxiaomi -7298562.3 193542.3 -37.710 < 2e-16 ***
## dummy 130104.9 135962.0 0.957 0.33868
## dummyram6 434934.9 172327.7 2.524 0.01166 *
## Penyimpanan8 -8829886.1 3353000.3 -2.633 0.00849 **
## Penyimpanan16 -2875859.9 1273404.2 -2.258 0.02399 *
## Penyimpanan32 -2731171.9 612496.6 -4.459 8.52e-06 ***
## Penyimpanan64 -2791235.0 197462.1 -14.136 < 2e-16 ***
## Penyimpanan256 1690847.0 150751.4 11.216 < 2e-16 ***
## Penyimpanan512 5209691.8 259351.3 20.087 < 2e-16 ***
## J.Ulasan -455.2 326.5 -1.394 0.16340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3349000 on 3104 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.4907, Adjusted R-squared: 0.4881
## F-statistic: 186.9 on 16 and 3104 DF, p-value: < 2.2e-16library(leaps)
subset <-regsubsets(HARGA ~ Brand+dummy + dummyram6 + Penyimpanan+J.Ulasan, data=data, nvmax = 20)
summary(subset)## Subset selection object
## Call: regsubsets.formula(HARGA ~ Brand + dummy + dummyram6 + Penyimpanan +
## J.Ulasan, data = data, nvmax = 20)
## 16 Variables (and intercept)
## Forced in Forced out
## Brandasus FALSE FALSE
## Brandinfinix FALSE FALSE
## Brandoppo FALSE FALSE
## BrandRealme FALSE FALSE
## BrandSamsung FALSE FALSE
## Brandvivo FALSE FALSE
## Brandxiaomi FALSE FALSE
## dummy FALSE FALSE
## dummyram6 FALSE FALSE
## Penyimpanan8 FALSE FALSE
## Penyimpanan16 FALSE FALSE
## Penyimpanan32 FALSE FALSE
## Penyimpanan64 FALSE FALSE
## Penyimpanan256 FALSE FALSE
## Penyimpanan512 FALSE FALSE
## J.Ulasan FALSE FALSE
## 1 subsets of each size up to 16
## Selection Algorithm: exhaustive
## Brandasus Brandinfinix Brandoppo BrandRealme BrandSamsung Brandvivo
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## Brandxiaomi dummy dummyram6 Penyimpanan8 Penyimpanan16 Penyimpanan32
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## Penyimpanan64 Penyimpanan256 Penyimpanan512 J.Ulasan
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## 16 ( 1 ) "*" "*" "*" "*"## [1] 0.09240237 0.13856492 0.17996271 0.22998662 0.27180073 0.33017864
## [7] 0.40556025 0.45744159 0.47777285 0.48155627 0.48509134 0.48614543
## [13] 0.48708469 0.48784095 0.48809742 0.48808352## [1] 15