Pengelolaan Titanic Dataset
Juliandini
2026-10-04
1. Pendahuluan
1.1 Latar Belakang
Tragedi tenggelamnya Titanic pada tahun 1912 merupakan salah satu peristiwa yang dapat dikaji melalui pendekatan data. Titanic Dataset yang diperoleh dari Kaggle memuat berbagai informasi mengenai penumpang, termasuk usia, jenis kelamin, dan status keselamatan. Beberapa penelitian sebelumnya menunjukkan adanya perbedaan tingkat keselamatan penumpang berdasarkan karakteristik tersebut (Fakhri & Winursito, 2024).
Pada pengelolaan ini, Titanic Dataset dipersiapkan untuk digunakan dalam memprediksi penumpang yang selamat dari bencana berdasarkan faktor usia dan jenis kelamin. Pengelolaan data dilakukan melalui beberapa tahapan, yaitu pemeriksaan data, pemilihan variabel, pembersihan data, penanganan missing value, dan encoding data. Tahapan tersebut dilakukan untuk menghasilkan data yang bersih dan terstruktur sehingga siap digunakan untuk analisis dan prediksi pada tahap selanjutnya.
1.2 Rumus Masalah
- Bagaimana kondisi awal Titanic Dataset sebelum dilakukan pengolahan?
- Apa saja permasalahan yang ditemukan pada Titanic Dataset?
- Bagaimana proses pembersihan dan pengolahan data dilakukan?
- Bagaimana karakteristik data setelah dilakukan cleaning?
- Apakah data memenuhi asumsi normalitas dan homogenitas varians?
1.3 Tujuan
- Mengidentifikasi permasalahan yang terdapat pada Titanic Dataset sebelum dilakukan pengolahan.
- Melakukan proses pembersihan dan penyesuaian data agar dataset lebih siap digunakan untuk analisis.
- Menangani nilai kosong pada variabel yang digunakan dalam analisis.
- Mengetahui karakteristik data penumpang Titanic setelah dilakukan proses pengolahan.
- Mengetahui kondisi data berdasarkan hasil pengujian normalitas dan homogenitas varians.
2. Pengenalan Dataset
2.1 Sumber dan Gambaran Umum Data
Data yang digunakan dalam analisis ini merupakan Titanic Dataset yang diperoleh dari Kaggle (Elyamadad, n.d.). Dataset berisi informasi mengenai penumpang kapal Titanic beserta karakteristik dan status keselamatannya, mencakup nama, jenis kelamin, umur, kelas tiket, jumlah keluarga yang ikut, harga tiket, kabin, pelabuhan keberangkatan, serta status keselamatan. Data tersebut digunakan untuk melihat karakteristik penumpang serta hubungan antara beberapa karakteristik penumpang dengan status keselamatan. Berdasarkan dataset yang digunakan, terdapat 891 data penumpang dengan 12 variabel.
2.2 Struktur dan Ukuran Data
library(readxl)
file_mentah <- "D:/data titanic/data set titanicc_2.xlsx"
df_mentah <- as.data.frame(read_excel(file_mentah, sheet = 1))library(car)
cat("Jumlah baris :", nrow(df_mentah), "\n")## Jumlah baris : 891cat("Jumlah variabel :", ncol(df_mentah), "\n\n")## Jumlah variabel : 12str(df_mentah)## 'data.frame': 891 obs. of 12 variables:
## $ PassengerId: num 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : num 0 1 1 1 0 0 0 0 1 1 ...
## $ Pclass : num 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : num 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : num 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 725 712833 7925 531 805 ...
## $ Cabin : chr NA "C85" NA "C123" ...
## $ Embarked : chr "S" "C" "S" "S" ...Berdasarkan data mentah, terdapat 891 data penumpang dengan 12 variabel. Setiap baris mewakili seorang penumpang.
2.3 Penjelasan Variabel
info_var <- data.frame(
Variabel = c("PassengerId", "Survived", "Pclass", "Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"),
Keterangan = c("ID penumpang", "Status keselamatan", "Kelas tiket", "Nama penumpang", "Jenis kelamin", "Umur", "Jumlah saudara/pasangan", "Jumlah orang tua/anak", "Nomor tiket", "Harga tiket", "Kode kabin", "Pelabuhan keberangkatan"),
Jenis = c("Kualitatif", "Kualitatif", "Kualitatif", "Kualitatif", "Kualitatif", "Kuantitatif kontinu", "Kuantitatif diskrit", "Kuantitatif diskrit", "Kualitatif", "Kuantitatif kontinu", "Kualitatif", "Kualitatif"),
Skala = c("Nominal", "Nominal", "Ordinal", "Nominal", "Nominal", "Rasio", "Rasio", "Rasio", "Nominal", "Rasio", "Nominal", "Nominal"))
knitr::kable(info_var, caption = "Identifikasi dan penjelasan variabel")| Variabel | Keterangan | Jenis | Skala |
|---|---|---|---|
| PassengerId | ID penumpang | Kualitatif | Nominal |
| Survived | Status keselamatan | Kualitatif | Nominal |
| Pclass | Kelas tiket | Kualitatif | Ordinal |
| Name | Nama penumpang | Kualitatif | Nominal |
| Sex | Jenis kelamin | Kualitatif | Nominal |
| Age | Umur | Kuantitatif kontinu | Rasio |
| SibSp | Jumlah saudara/pasangan | Kuantitatif diskrit | Rasio |
| Parch | Jumlah orang tua/anak | Kuantitatif diskrit | Rasio |
| Ticket | Nomor tiket | Kualitatif | Nominal |
| Fare | Harga tiket | Kuantitatif kontinu | Rasio |
| Cabin | Kode kabin | Kualitatif | Nominal |
| Embarked | Pelabuhan keberangkatan | Kualitatif | Nominal |
2.4 Kondisi Data Sebelum Diolah
library(dplyr)
knitr::kable( head(dplyr::select(df_mentah, -Name, -Ticket, -Cabin), 8), caption = "Delapan baris pertama data mentah (kolom Name, Ticket, dan Cabin tidak ditampilkan agar tabel muat)")| PassengerId | Survived | Pclass | Sex | Age | SibSp | Parch | Fare | Embarked |
|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 3 | male | 22 | 1 | 0 | 725 | S |
| 2 | 1 | 1 | female | 38 | 1 | 0 | 712833 | C |
| 3 | 1 | 3 | female | 26 | 0 | 0 | 7925 | S |
| 4 | 1 | 1 | female | 35 | 1 | 0 | 531 | S |
| 5 | 0 | 3 | male | 35 | 0 | 0 | 805 | S |
| 6 | 0 | 3 | male | NA | 0 | 0 | 84583 | Q |
| 7 | 0 | 1 | male | 54 | 0 | 0 | 518625 | S |
| 8 | 0 | 3 | male | 2 | 3 | 1 | 21075 | S |
Pada data mentah terlihat bahwa variabel Age masih
memiliki nilai kosong dan nilai Fare tersimpan tanpa
desimal. Permasalahan ini dibahas rinci pada Bagian 3.
3. Identifikasi Masalah Data
3.1 Pemeriksaan Missing Value
colSums(is.na(df_mentah))## PassengerId Survived Pclass Name Sex Age
## 0 0 0 0 0 177
## SibSp Parch Ticket Fare Cabin Embarked
## 0 0 0 0 687 2Hasil pemeriksaan menunjukkan terdapat 177 data kosong pada variabel Age, 687 data kosong pada variabel Cabin, dan 2 data kosong pada variabel Embarked
3.2 Pemeriksaan Format dan Tipe Data
tipe_data <- data.frame(Variabel = names(df_mentah), Tipe_di_R = sapply(df_mentah, function(x) class(x)[1]), Jenis_Seharusnya = info_var$Jenis, row.names = NULL)
knitr::kable( tipe_data, caption = "Tipe data tiap variabel pada data mentah", format = "html")| Variabel | Tipe_di_R | Jenis_Seharusnya |
|---|---|---|
| PassengerId | numeric | Kualitatif |
| Survived | numeric | Kualitatif |
| Pclass | numeric | Kualitatif |
| Name | character | Kualitatif |
| Sex | character | Kualitatif |
| Age | numeric | Kuantitatif kontinu |
| SibSp | numeric | Kuantitatif diskrit |
| Parch | numeric | Kuantitatif diskrit |
| Ticket | character | Kualitatif |
| Fare | numeric | Kuantitatif kontinu |
| Cabin | character | Kualitatif |
| Embarked | character | Kualitatif |
Variabel Ticket perlu diperiksa karena penulisannya
tidak konsisten.
tk_angka <- grepl("^[0-9]+$", df_mentah$Ticket)
tk_huruf <- grepl("[A-Za-z]", df_mentah$Ticket)
tk_spasi <- grepl(" ", df_mentah$Ticket)
knitr::kable(data.frame(Pola = c("Angka saja", "Mengandung huruf", "Mengandung spasi"), Jumlah = c(sum(tk_angka), sum(tk_huruf), sum(tk_spasi))), caption = "Pola penulisan nomor tiket", format = "html")| Pola | Jumlah |
|---|---|
| Angka saja | 661 |
| Mengandung huruf | 230 |
| Mengandung spasi | 226 |
knitr::kable(data.frame(Contoh_Tiket = head(unique(df_mentah$Ticket[tk_huruf]), 6)), caption = "Contoh nomor tiket yang mengandung huruf", format = "html")| Contoh_Tiket |
|---|
| A/5 21171 |
| PC 17599 |
| STON/O2. 3101282 |
| PP 9549 |
| A/5. 2151 |
| PC 17601 |
Dari 891 tiket, 661 berisi angka saja dan 230 mengandung huruf,
sedangkan 226 tiket mengandung spasi. Hal ini menunjukkan format
penulisan tiket tidak konsisten (campuran huruf, angka, dan spasi).
Karena Ticket tidak digunakan dalam analisis, masalah ini
tidak perlu diperbaiki.
3.3 Pemilihan Variabel yang Digunakan
Dari 12 variabel, analisis difokuskan pada empat variabel yang sesuai dengan tujuan analisis.
pilih_var <- data.frame(Variabel = c("PassengerId", "Survived", "Sex", "Age"), Peran = c("Kunci identitas penumpang (key untuk integrasi data)", "Status keselamatan (0 = tidak selamat, 1 = selamat)", "Jenis kelamin penumpang (male/female)", "Umur penumpang (tahun)"))
knitr::kable(pilih_var,caption = "Variabel yang dianalisis", align = c("c", "l"), format = "html")| Variabel | Peran |
|---|---|
| PassengerId | Kunci identitas penumpang (key untuk integrasi data) |
| Survived | Status keselamatan (0 = tidak selamat, 1 = selamat) |
| Sex | Jenis kelamin penumpang (male/female) |
| Age | Umur penumpang (tahun) |
3.4 Pemeriksaan Nilai yang Tidak Sesuai
Nilai Fare
fare_max <- max(df_mentah$Fare, na.rm = TRUE)
out_fare <- df_mentah[ df_mentah$Fare == fare_max, c("PassengerId", "Name", "Fare")]
knitr::kable(out_fare, caption = "Penumpang dengan Fare tertinggi (outlier) pada data mentah", format = "html")| PassengerId | Name | Fare | |
|---|---|---|---|
| 259 | 259 | Ward, Miss. Anna | 5123292 |
| 680 | 680 | Cardeza, Mr. Thomas Drake Martinez | 5123292 |
| 738 | 738 | Lesurer, Mr. Gustave J | 5123292 |
Nilai Fare tertinggi terdapat pada 3 penumpang
(PassengerId 259, 680, 738) dengan nilai yang sama, yaitu 5.123292^{6}.
Nilai ini jauh di atas penumpang lain sehingga tergolong outlier.
Selain itu, seluruh nilai Fare pada data mentah
tersimpan tanpa desimal (semua nilai bilangan bulat: TRUE), sehingga
nilainya tampak jauh lebih besar dari harga tiket yang wajar.
4. Proses Pembersihan Data
4.1 Penghapusan Variabel yang Tidak Digunakan
data_variabel <- df_mentah[ , !(names(df_mentah) %in% c("Pclass", "Name", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"))]
knitr::kable( head(data_variabel, 8), caption = "Variabel yang dipilih untuk analisis", format = "html")| PassengerId | Survived | Sex | Age |
|---|---|---|---|
| 1 | 0 | male | 22 |
| 2 | 1 | female | 38 |
| 3 | 1 | female | 26 |
| 4 | 1 | female | 35 |
| 5 | 0 | male | 35 |
| 6 | 0 | male | NA |
| 7 | 0 | male | 54 |
| 8 | 0 | male | 2 |
Dari 12 variabel yang terdapat pada data awal, 8 variabel dihapus, yaitu Pclass, Name, SibSp, Parch, Ticket, Fare, Cabin, dan Embarked. Dengan demikian, terdapat 4 variabel yang dipertahankan, yaitu PassengerId, Survived, Sex, dan Age.
4.2 Penanganan Nilai Kosong
Pembersihan data dilakukan dengan melengkapi nilai Age
yang kosong menggunakan sumber luar, yaitu Encyclopedia Titanica dan
Titanic Universe. Hasil pembersihan tersebut disimpan pada file
data_set_titanic__ensiklopedia_sudah_cleaning.xlsx, yang
dibaca berikut ini.
Tahapan pembersihan data yang dilakukan adalah sebagai berikut.
tahapan <- data.frame(Tahap = 1:5, Kegiatan = c(
"Pilih kolom Age",
"Cek area kosong",
"Imputasi data",
"Mencari median",
"Validasi" ),
Penjelasan = c(
"Fokus pada variabel yang akan dianalisis.",
"Ditemukan 177 data kosong (missing value) khusus di kolom Age.",
"Melengkapi data kosong di variabel Age dari sumber Encyclopedia Titanica dan Titanic Universe.",
"Missing value yang masih ada dilengkapi dengan nilai median variabel Age.",
"Cek kembali apakah masih terdapat missing value."))
knitr::kable (tahapan, caption = "Tahapan Data Cleaning", align = c("c", "l", "l"), format = "html")| Tahap | Kegiatan | Penjelasan |
|---|---|---|
| 1 | Pilih kolom Age | Fokus pada variabel yang akan dianalisis. |
| 2 | Cek area kosong | Ditemukan 177 data kosong (missing value) khusus di kolom Age. |
| 3 | Imputasi data | Melengkapi data kosong di variabel Age dari sumber Encyclopedia Titanica dan Titanic Universe. |
| 4 | Mencari median | Missing value yang masih ada dilengkapi dengan nilai median variabel Age. |
| 5 | Validasi | Cek kembali apakah masih terdapat missing value. |
Missing value pada kolom Age ditangani dengan melakukan imputasi berdasarkan informasi usia penumpang dari Encyclopedia Titanic dan Titanic Universe. Dari 177 data yang kosong, sebanyak 175 data berhasil diimputasi berdasarkan informasi tersebut, sedangkan 2 data yang tersisa diisi menggunakan nilai median Age yang diolah dengan Excel.
file_bersih <- "D:/data titanic/data set titanic ensiklopedia sudah cleaning.xlsx"
df_bersih_awal <- as.data.frame(read_excel(file_bersih, sheet = 1))4.3 Kondisi Data Setelah Dibersihkan
knitr::kable(head(df_bersih_awal["Age"], 10), caption = "Sepuluh baris pertama variabel Age setelah cleaning", format = "html")| Age |
|---|
| 22 |
| 38 |
| 26 |
| 35 |
| 35 |
| 29 |
| 54 |
| 2 |
| 27 |
| 14 |
cat("Jumlah baris :", nrow(df_bersih_awal), "\n")## Jumlah baris : 891cat("Jumlah variabel :", ncol(df_bersih_awal), "\n")## Jumlah variabel : 125. Pemeriksaan Data Setelah Cleaning
5.1 Pemeriksaan Nilai Kosong
na_setelah <- colSums(is.na(df_bersih_awal))
knitr::kable( data.frame( Variabel = names(na_setelah), Jumlah_Kosong = as.integer(na_setelah)), caption = "Jumlah nilai kosong setelah cleaning", format = "html")| Variabel | Jumlah_Kosong |
|---|---|
| PassengerId | 0 |
| Survived | 0 |
| Pclass | 0 |
| Name | 0 |
| Sex | 0 |
| Age | 0 |
| SibSp | 0 |
| Parch | 0 |
| Ticket | 0 |
| Fare | 0 |
| Cabin | 687 |
| Embarked | 2 |
Total nilai kosong setelah cleaning adalah 689. Masih terdapat missing value yang perlu ditangani.
5.2 Pemeriksaan Tipe Data
knitr::kable( data.frame( Variabel = names(df_bersih_awal), Tipe_di_R = sapply(df_bersih_awal, function(x) class(x)[1]), row.names = NULL), caption = "Tipe data setelah cleaning", format = "html")| Variabel | Tipe_di_R |
|---|---|
| PassengerId | numeric |
| Survived | numeric |
| Pclass | numeric |
| Name | character |
| Sex | character |
| Age | numeric |
| SibSp | numeric |
| Parch | numeric |
| Ticket | character |
| Fare | numeric |
| Cabin | character |
| Embarked | character |
Variabel Sex bertipe teks (kategorik) sehingga perlu
ditransformasi pada tahap berikutnya.
6. Pengolahan dan Visualisasi Data
6.1 Integrasi Data
Data karakteristik yang terdiri dari PassengerId,
Sex, dan Age serta data keselamatan yang
terdiri dari PassengerId dan Survived
diintegrasikan menjadi satu dataset. Proses integrasi dilakukan
menggunakan PassengerId sebagai primary key (kunci
penghubung) karena setiap penumpang memiliki PassengerId
yang menjadi identitas unik. Penggabungan dilakukan dengan fungsi
left_join(), sehingga seluruh data pada tabel karakteristik
tetap dipertahankan, kemudian informasi Survived dari tabel
keselamatan dicocokkan berdasarkan PassengerId yang sama.
Dengan demikian, diperoleh data yang memuat karakteristik penumpang
sekaligus status keselamatannya.
library(dplyr)
tabel_karakteristik <- df_bersih_awal %>% select(PassengerId, Sex, Age)
tabel_karakteristik## PassengerId Sex Age
## 1 1 male 22.00
## 2 2 female 38.00
## 3 3 female 26.00
## 4 4 female 35.00
## 5 5 male 35.00
## 6 6 male 29.00
## 7 7 male 54.00
## 8 8 male 2.00
## 9 9 female 27.00
## 10 10 female 14.00
## 11 11 female 4.00
## 12 12 female 58.00
## 13 13 male 20.00
## 14 14 male 39.00
## 15 15 female 14.00
## 16 16 female 55.00
## 17 17 male 2.00
## 18 18 male 24.00
## 19 19 female 31.00
## 20 20 female 22.00
## 21 21 male 35.00
## 22 22 male 34.00
## 23 23 female 15.00
## 24 24 male 28.00
## 25 25 female 8.00
## 26 26 female 38.00
## 27 27 male 26.00
## 28 28 male 19.00
## 29 29 female 23.00
## 30 30 male 23.00
## 31 31 male 40.00
## 32 32 female 57.00
## 33 33 female 19.00
## 34 34 male 66.00
## 35 35 male 28.00
## 36 36 male 42.00
## 37 37 male 20.00
## 38 38 male 21.00
## 39 39 female 18.00
## 40 40 female 14.00
## 41 41 female 40.00
## 42 42 female 27.00
## 43 43 male 27.00
## 44 44 female 3.00
## 45 45 female 19.00
## 46 46 male 30.00
## 47 47 male 21.00
## 48 48 female 27.00
## 49 49 male 16.00
## 50 50 female 18.00
## 51 51 male 7.00
## 52 52 male 21.00
## 53 53 female 49.00
## 54 54 female 29.00
## 55 55 male 65.00
## 56 56 male 46.00
## 57 57 female 21.00
## 58 58 male 28.50
## 59 59 female 5.00
## 60 60 male 11.00
## 61 61 male 22.00
## 62 62 female 38.00
## 63 63 male 45.00
## 64 64 male 4.00
## 65 65 male 64.00
## 66 66 male 8.00
## 67 67 female 29.00
## 68 68 male 19.00
## 69 69 female 17.00
## 70 70 male 26.00
## 71 71 male 32.00
## 72 72 female 16.00
## 73 73 male 21.00
## 74 74 male 26.00
## 75 75 male 32.00
## 76 76 male 25.00
## 77 77 male 23.00
## 78 78 male 28.00
## 79 79 male 0.83
## 80 80 female 30.00
## 81 81 male 22.00
## 82 82 male 29.00
## 83 83 female 31.00
## 84 84 male 28.00
## 85 85 female 17.00
## 86 86 female 33.00
## 87 87 male 16.00
## 88 88 male 20.00
## 89 89 female 23.00
## 90 90 male 24.00
## 91 91 male 29.00
## 92 92 male 20.00
## 93 93 male 46.00
## 94 94 male 26.00
## 95 95 male 59.00
## 96 96 male 23.00
## 97 97 male 71.00
## 98 98 male 23.00
## 99 99 female 34.00
## 100 100 male 34.00
## 101 101 female 28.00
## 102 102 male 29.00
## 103 103 male 21.00
## 104 104 male 33.00
## 105 105 male 37.00
## 106 106 male 28.00
## 107 107 female 21.00
## 108 108 male 30.00
## 109 109 male 38.00
## 110 110 female 47.00
## 111 111 male 47.00
## 112 112 female 14.50
## 113 113 male 22.00
## 114 114 female 20.00
## 115 115 female 17.00
## 116 116 male 21.00
## 117 117 male 70.50
## 118 118 male 29.00
## 119 119 male 24.00
## 120 120 female 2.00
## 121 121 male 21.00
## 122 122 male 19.00
## 123 123 male 32.50
## 124 124 female 32.50
## 125 125 male 54.00
## 126 126 male 12.00
## 127 127 male 19.00
## 128 128 male 24.00
## 129 129 female 2.00
## 130 130 male 45.00
## 131 131 male 33.00
## 132 132 male 20.00
## 133 133 female 47.00
## 134 134 female 29.00
## 135 135 male 25.00
## 136 136 male 23.00
## 137 137 female 19.00
## 138 138 male 37.00
## 139 139 male 16.00
## 140 140 male 24.00
## 141 141 female 40.00
## 142 142 female 22.00
## 143 143 female 24.00
## 144 144 male 19.00
## 145 145 male 18.00
## 146 146 male 19.00
## 147 147 male 27.00
## 148 148 female 9.00
## 149 149 male 36.50
## 150 150 male 42.00
## 151 151 male 51.00
## 152 152 female 22.00
## 153 153 male 55.50
## 154 154 male 40.50
## 155 155 male 27.00
## 156 156 male 51.00
## 157 157 female 16.00
## 158 158 male 30.00
## 159 159 male 37.00
## 160 160 male 44.00
## 161 161 male 44.00
## 162 162 female 40.00
## 163 163 male 26.00
## 164 164 male 17.00
## 165 165 male 1.00
## 166 166 male 9.00
## 167 167 female 48.00
## 168 168 female 45.00
## 169 169 male 48.00
## 170 170 male 28.00
## 171 171 male 61.00
## 172 172 male 4.00
## 173 173 female 1.00
## 174 174 male 21.00
## 175 175 male 56.00
## 176 176 male 18.00
## 177 177 male 5.00
## 178 178 female 50.00
## 179 179 male 30.00
## 180 180 male 36.00
## 181 181 female 8.00
## 182 182 male 40.00
## 183 183 male 9.00
## 184 184 male 1.00
## 185 185 female 4.00
## 186 186 male 39.00
## 187 187 female 26.00
## 188 188 male 45.00
## 189 189 male 40.00
## 190 190 male 36.00
## 191 191 female 32.00
## 192 192 male 19.00
## 193 193 female 19.00
## 194 194 male 3.00
## 195 195 female 44.00
## 196 196 female 58.00
## 197 197 male 29.00
## 198 198 male 42.00
## 199 199 female 22.00
## 200 200 female 24.00
## 201 201 male 28.00
## 202 202 male 17.00
## 203 203 male 34.00
## 204 204 male 45.50
## 205 205 male 18.00
## 206 206 female 2.00
## 207 207 male 32.00
## 208 208 male 26.00
## 209 209 female 16.00
## 210 210 male 40.00
## 211 211 male 24.00
## 212 212 female 35.00
## 213 213 male 22.00
## 214 214 male 30.00
## 215 215 male 22.00
## 216 216 female 31.00
## 217 217 female 27.00
## 218 218 male 42.00
## 219 219 female 32.00
## 220 220 male 30.00
## 221 221 male 16.00
## 222 222 male 27.00
## 223 223 male 51.00
## 224 224 male 22.00
## 225 225 male 38.00
## 226 226 male 22.00
## 227 227 male 19.00
## 228 228 male 20.50
## 229 229 male 18.00
## 230 230 female 12.00
## 231 231 female 35.00
## 232 232 male 29.00
## 233 233 male 59.00
## 234 234 female 5.00
## 235 235 male 24.00
## 236 236 female 22.00
## 237 237 male 44.00
## 238 238 female 8.00
## 239 239 male 19.00
## 240 240 male 33.00
## 241 241 female 19.00
## 242 242 female 19.00
## 243 243 male 29.00
## 244 244 male 22.00
## 245 245 male 30.00
## 246 246 male 44.00
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## 891 891 male 32.00tabel_keselamatan <- df_bersih_awal %>% select(PassengerId, Survived)
tabel_keselamatan## PassengerId Survived
## 1 1 0
## 2 2 1
## 3 3 1
## 4 4 1
## 5 5 0
## 6 6 0
## 7 7 0
## 8 8 0
## 9 9 1
## 10 10 1
## 11 11 1
## 12 12 1
## 13 13 0
## 14 14 0
## 15 15 0
## 16 16 1
## 17 17 0
## 18 18 1
## 19 19 0
## 20 20 1
## 21 21 0
## 22 22 1
## 23 23 1
## 24 24 1
## 25 25 0
## 26 26 1
## 27 27 0
## 28 28 0
## 29 29 1
## 30 30 0
## 31 31 0
## 32 32 1
## 33 33 1
## 34 34 0
## 35 35 0
## 36 36 0
## 37 37 1
## 38 38 0
## 39 39 0
## 40 40 1
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## 42 42 0
## 43 43 0
## 44 44 1
## 45 45 1
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## 47 47 0
## 48 48 1
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## 50 50 0
## 51 51 0
## 52 52 0
## 53 53 1
## 54 54 1
## 55 55 0
## 56 56 1
## 57 57 1
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## 59 59 1
## 60 60 0
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## 62 62 1
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## 891 891 0data_integrasi <- tabel_karakteristik %>% left_join(tabel_keselamatan, by = "PassengerId")
data_integrasi## PassengerId Sex Age Survived
## 1 1 male 22.00 0
## 2 2 female 38.00 1
## 3 3 female 26.00 1
## 4 4 female 35.00 1
## 5 5 male 35.00 0
## 6 6 male 29.00 0
## 7 7 male 54.00 0
## 8 8 male 2.00 0
## 9 9 female 27.00 1
## 10 10 female 14.00 1
## 11 11 female 4.00 1
## 12 12 female 58.00 1
## 13 13 male 20.00 0
## 14 14 male 39.00 0
## 15 15 female 14.00 0
## 16 16 female 55.00 1
## 17 17 male 2.00 0
## 18 18 male 24.00 1
## 19 19 female 31.00 0
## 20 20 female 22.00 1
## 21 21 male 35.00 0
## 22 22 male 34.00 1
## 23 23 female 15.00 1
## 24 24 male 28.00 1
## 25 25 female 8.00 0
## 26 26 female 38.00 1
## 27 27 male 26.00 0
## 28 28 male 19.00 0
## 29 29 female 23.00 1
## 30 30 male 23.00 0
## 31 31 male 40.00 0
## 32 32 female 57.00 1
## 33 33 female 19.00 1
## 34 34 male 66.00 0
## 35 35 male 28.00 0
## 36 36 male 42.00 0
## 37 37 male 20.00 1
## 38 38 male 21.00 0
## 39 39 female 18.00 0
## 40 40 female 14.00 1
## 41 41 female 40.00 0
## 42 42 female 27.00 0
## 43 43 male 27.00 0
## 44 44 female 3.00 1
## 45 45 female 19.00 1
## 46 46 male 30.00 0
## 47 47 male 21.00 0
## 48 48 female 27.00 1
## 49 49 male 16.00 0
## 50 50 female 18.00 0
## 51 51 male 7.00 0
## 52 52 male 21.00 0
## 53 53 female 49.00 1
## 54 54 female 29.00 1
## 55 55 male 65.00 0
## 56 56 male 46.00 1
## 57 57 female 21.00 1
## 58 58 male 28.50 0
## 59 59 female 5.00 1
## 60 60 male 11.00 0
## 61 61 male 22.00 0
## 62 62 female 38.00 1
## 63 63 male 45.00 0
## 64 64 male 4.00 0
## 65 65 male 64.00 0
## 66 66 male 8.00 1
## 67 67 female 29.00 1
## 68 68 male 19.00 0
## 69 69 female 17.00 1
## 70 70 male 26.00 0
## 71 71 male 32.00 0
## 72 72 female 16.00 0
## 73 73 male 21.00 0
## 74 74 male 26.00 0
## 75 75 male 32.00 1
## 76 76 male 25.00 0
## 77 77 male 23.00 0
## 78 78 male 28.00 0
## 79 79 male 0.83 1
## 80 80 female 30.00 1
## 81 81 male 22.00 0
## 82 82 male 29.00 1
## 83 83 female 31.00 1
## 84 84 male 28.00 0
## 85 85 female 17.00 1
## 86 86 female 33.00 1
## 87 87 male 16.00 0
## 88 88 male 20.00 0
## 89 89 female 23.00 1
## 90 90 male 24.00 0
## 91 91 male 29.00 0
## 92 92 male 20.00 0
## 93 93 male 46.00 0
## 94 94 male 26.00 0
## 95 95 male 59.00 0
## 96 96 male 23.00 0
## 97 97 male 71.00 0
## 98 98 male 23.00 1
## 99 99 female 34.00 1
## 100 100 male 34.00 0
## 101 101 female 28.00 0
## 102 102 male 29.00 0
## 103 103 male 21.00 0
## 104 104 male 33.00 0
## 105 105 male 37.00 0
## 106 106 male 28.00 0
## 107 107 female 21.00 1
## 108 108 male 30.00 1
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## 110 110 female 47.00 1
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## 112 112 female 14.50 0
## 113 113 male 22.00 0
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## 115 115 female 17.00 0
## 116 116 male 21.00 0
## 117 117 male 70.50 0
## 118 118 male 29.00 0
## 119 119 male 24.00 0
## 120 120 female 2.00 0
## 121 121 male 21.00 0
## 122 122 male 19.00 0
## 123 123 male 32.50 0
## 124 124 female 32.50 1
## 125 125 male 54.00 0
## 126 126 male 12.00 1
## 127 127 male 19.00 0
## 128 128 male 24.00 1
## 129 129 female 2.00 1
## 130 130 male 45.00 0
## 131 131 male 33.00 0
## 132 132 male 20.00 0
## 133 133 female 47.00 0
## 134 134 female 29.00 1
## 135 135 male 25.00 0
## 136 136 male 23.00 0
## 137 137 female 19.00 1
## 138 138 male 37.00 0
## 139 139 male 16.00 0
## 140 140 male 24.00 0
## 141 141 female 40.00 0
## 142 142 female 22.00 1
## 143 143 female 24.00 1
## 144 144 male 19.00 0
## 145 145 male 18.00 0
## 146 146 male 19.00 0
## 147 147 male 27.00 1
## 148 148 female 9.00 0
## 149 149 male 36.50 0
## 150 150 male 42.00 0
## 151 151 male 51.00 0
## 152 152 female 22.00 1
## 153 153 male 55.50 0
## 154 154 male 40.50 0
## 155 155 male 27.00 0
## 156 156 male 51.00 0
## 157 157 female 16.00 1
## 158 158 male 30.00 0
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## 160 160 male 44.00 0
## 161 161 male 44.00 0
## 162 162 female 40.00 1
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## 164 164 male 17.00 0
## 165 165 male 1.00 0
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## 168 168 female 45.00 0
## 169 169 male 48.00 0
## 170 170 male 28.00 0
## 171 171 male 61.00 0
## 172 172 male 4.00 0
## 173 173 female 1.00 1
## 174 174 male 21.00 0
## 175 175 male 56.00 0
## 176 176 male 18.00 0
## 177 177 male 5.00 0
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## 179 179 male 30.00 0
## 180 180 male 36.00 0
## 181 181 female 8.00 0
## 182 182 male 40.00 0
## 183 183 male 9.00 0
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## 191 191 female 32.00 1
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## 193 193 female 19.00 1
## 194 194 male 3.00 1
## 195 195 female 44.00 1
## 196 196 female 58.00 1
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## 199 199 female 22.00 1
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## 201 201 male 28.00 0
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## 203 203 male 34.00 0
## 204 204 male 45.50 0
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## 251 251 male 19.00 0
## 252 252 female 29.00 0
## 253 253 male 62.00 0
## 254 254 male 30.00 0
## 255 255 female 41.00 0
## 256 256 female 29.00 1
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## 258 258 female 30.00 1
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## 260 260 female 50.00 1
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## 262 262 male 3.00 1
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## 264 264 male 40.00 0
## 265 265 female 22.00 0
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## 270 270 female 35.00 1
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## 286 286 male 33.00 0
## 287 287 male 30.00 1
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## 289 289 male 42.00 1
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## 293 293 male 36.00 0
## 294 294 female 24.00 0
## 295 295 male 24.00 0
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## 297 297 male 23.50 0
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## 304 304 female 46.00 1
## 305 305 male 28.00 0
## 306 306 male 0.92 1
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## 891 891 male 32.00 0nrow(data_integrasi)## [1] 891ncol(data_integrasi)## [1] 4Hasil integrasi memuat 891 baris dan 4 kolom.
6.2 Transformasi Data Kategorik
Transformasi Data Kategorik I (Label Encoding)
Variabel Sex diubah menjadi angka: 1 = male dan 0 =
female.
df_encoded <- data_integrasi %>% dplyr::mutate(sex = ifelse(Sex == "male", 1, 0))
knitr::kable(head(df_encoded, 10), caption = "Data setelah label encoding (kolom sex)", format = "html")| PassengerId | Sex | Age | Survived | sex |
|---|---|---|---|---|
| 1 | male | 22 | 0 | 1 |
| 2 | female | 38 | 1 | 0 |
| 3 | female | 26 | 1 | 0 |
| 4 | female | 35 | 1 | 0 |
| 5 | male | 35 | 0 | 1 |
| 6 | male | 29 | 0 | 1 |
| 7 | male | 54 | 0 | 1 |
| 8 | male | 2 | 0 | 1 |
| 9 | female | 27 | 1 | 0 |
| 10 | female | 14 | 1 | 0 |
Transformasi Data Kategorik II (One-Hot Encoding)
Setiap kategori Sex diubah menjadi kolom indikator
(0/1).
df_onehot <- data_integrasi %>% dplyr::mutate(Sex_male = ifelse(Sex == "male", 1, 0), Sex_female = ifelse(Sex == "female", 1, 0)) %>% dplyr::select(PassengerId, Sex_male, Sex_female)
knitr::kable(head(df_onehot, 10), caption = "Data setelah one-hot encoding", format = "html")| PassengerId | Sex_male | Sex_female |
|---|---|---|
| 1 | 1 | 0 |
| 2 | 0 | 1 |
| 3 | 0 | 1 |
| 4 | 0 | 1 |
| 5 | 1 | 0 |
| 6 | 1 | 0 |
| 7 | 1 | 0 |
| 8 | 1 | 0 |
| 9 | 0 | 1 |
| 10 | 0 | 1 |
6.3 Transformasi Data Numerik
Pemeriksaan Statistik Awal
statistik_awal <- data_integrasi %>%
dplyr::summarise(Min_Age = min(Age, na.rm = TRUE),
Max_Age = max(Age, na.rm = TRUE),
Mean_Age = mean(Age, na.rm = TRUE),
SD_Age = sd(Age, na.rm = TRUE))
knitr::kable(as.data.frame(statistik_awal), caption = "Statistik awal variabel Age", digits = 2, format = "html")| Min_Age | Max_Age | Mean_Age | SD_Age |
|---|---|---|---|
| 0.42 | 80 | 29.6 | 14.15 |
Normalisasi Min-Max
Normalisasi Min-Max mengubah nilai Age ke rentang 0 sampai 1.
min_max <- function(x) {
(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
}
data_transformasi <- data_integrasi %>%
dplyr::mutate(Age_MinMax = min_max(Age))
knitr::kable(head(dplyr::select(data_transformasi, PassengerId, Age, Age_MinMax), 6), caption = "Contoh hasil normalisasi Min-Max", digits = 4, format = "html")| PassengerId | Age | Age_MinMax |
|---|---|---|
| 1 | 22 | 0.2712 |
| 2 | 38 | 0.4722 |
| 3 | 26 | 0.3214 |
| 4 | 35 | 0.4345 |
| 5 | 35 | 0.4345 |
| 6 | 29 | 0.3591 |
ringkas_minmax <- data_transformasi %>%
dplyr::summarise(Min = min(Age_MinMax, na.rm = TRUE), Max = max(Age_MinMax, na.rm = TRUE),
Mean = mean(Age_MinMax, na.rm = TRUE), SD = sd(Age_MinMax, na.rm = TRUE))
knitr::kable(as.data.frame(ringkas_minmax), caption = "Ringkasan Age_MinMax", digits = 4, format = "html")| Min | Max | Mean | SD |
|---|---|---|---|
| 0 | 1 | 0.3667 | 0.1779 |
Setelah normalisasi, nilai minimum Age_MinMax adalah 0 dan maksimum 1.
Standarisasi Z-Score
Standarisasi Z-score mengubah Age sehingga memiliki rata-rata 0 dan standar deviasi 1.
data_transformasi <- data_transformasi %>%
dplyr::mutate(Age_Z = as.numeric(scale(Age)))
knitr::kable(head(dplyr::select(data_transformasi, PassengerId, Age, Age_Z), 6), caption = "Contoh hasil standarisasi Z-score", digits = 4, format = "html")| PassengerId | Age | Age_Z |
|---|---|---|
| 1 | 22 | -0.5371 |
| 2 | 38 | 0.5934 |
| 3 | 26 | -0.2545 |
| 4 | 35 | 0.3814 |
| 5 | 35 | 0.3814 |
| 6 | 29 | -0.0425 |
ringkas_z <- data_transformasi %>%
dplyr::summarise(Mean_Z = mean(Age_Z, na.rm = TRUE), SD_Z = sd(Age_Z, na.rm = TRUE))
knitr::kable(as.data.frame(ringkas_z), caption = "Ringkasan Age_Z", digits = 4, format = "html")| Mean_Z | SD_Z |
|---|---|
| 0 | 1 |
Hasil standarisasi memiliki rata-rata 0 dan standar deviasi 1.
Pemeriksaan Keamanan Fungsi Invers
Transformasi invers membagi 1 dengan Age pangkat tertentu, sehingga nilai Age = 0 akan menyebabkan pembagian dengan nol. Karena itu dilakukan pemeriksaan terlebih dahulu.
cek_invers <- data_transformasi %>%
dplyr::summarise(min_val = min(Age, na.rm = TRUE),
jumlah_nol = sum(Age == 0, na.rm = TRUE))
knitr::kable(as.data.frame(cek_invers), caption = "Pemeriksaan nilai nol pada Age", digits = 2, format = "html")| min_val | jumlah_nol |
|---|---|
| 0.42 | 0 |
Nilai Age terkecil adalah 0.42 dan jumlah Age bernilai nol adalah 0. Dengan demikian transformasi invers aman dilakukan.
Transformasi Invers Kuadrat dan Invers Kubik
data_transformasi <- data_transformasi %>%
dplyr::mutate(Age_Inv_Sq = 1 / (Age^2),
Age_Inv_Cubic = 1 / (Age^3))
knitr::kable(head(dplyr::select(data_transformasi, PassengerId, Age, Age_Inv_Sq, Age_Inv_Cubic), 6), caption = "Contoh hasil transformasi invers kuadrat dan invers kubik", digits = 6, format = "html")| PassengerId | Age | Age_Inv_Sq | Age_Inv_Cubic |
|---|---|---|---|
| 1 | 22 | 0.002066 | 9.4e-05 |
| 2 | 38 | 0.000693 | 1.8e-05 |
| 3 | 26 | 0.001479 | 5.7e-05 |
| 4 | 35 | 0.000816 | 2.3e-05 |
| 5 | 35 | 0.000816 | 2.3e-05 |
| 6 | 29 | 0.001189 | 4.1e-05 |
ringkas_invsq <- data_transformasi %>% dplyr::summarise(Min = min(Age_Inv_Sq, na.rm = TRUE), Max = max(Age_Inv_Sq, na.rm = TRUE), Mean = mean(Age_Inv_Sq, na.rm = TRUE), SD = sd(Age_Inv_Sq, na.rm = TRUE))
knitr::kable(as.data.frame(ringkas_invsq), caption = "Ringkasan Age_Inv_Sq", digits = 6, format = "html")| Min | Max | Mean | SD |
|---|---|---|---|
| 0.000156 | 5.668934 | 0.03214 | 0.250727 |
Visualisasi Sebelum Transformasi
library(ggplot2)
ggplot(data_transformasi, aes(x = Age)) +
geom_histogram(bins = 20, fill = "steelblue", color = "white") +
labs(title = "Distribusi Age Sebelum Transformasi", x = "Age", y = "Frekuensi") +
theme_minimal()
Perbandingan Semua Transformasi
tabel_perbandingan <- data_transformasi %>%
dplyr::summarise(
Age_Min = min(Age, na.rm = TRUE), Age_Max = max(Age, na.rm = TRUE),
Age_Mean = mean(Age, na.rm = TRUE), Age_SD = sd(Age, na.rm = TRUE),
MinMax_Min = min(Age_MinMax, na.rm = TRUE), MinMax_Max = max(Age_MinMax, na.rm = TRUE),
MinMax_Mean = mean(Age_MinMax, na.rm = TRUE), MinMax_SD = sd(Age_MinMax, na.rm = TRUE),
Z_Min = min(Age_Z, na.rm = TRUE), Z_Max = max(Age_Z, na.rm = TRUE),
Z_Mean = mean(Age_Z, na.rm = TRUE), Z_SD = sd(Age_Z, na.rm = TRUE),
InvSq_Min = min(Age_Inv_Sq, na.rm = TRUE), InvSq_Max = max(Age_Inv_Sq, na.rm = TRUE),
InvSq_Mean = mean(Age_Inv_Sq, na.rm = TRUE), InvSq_SD = sd(Age_Inv_Sq, na.rm = TRUE),
InvCubic_Min = min(Age_Inv_Cubic, na.rm = TRUE), InvCubic_Max = max(Age_Inv_Cubic, na.rm = TRUE),
InvCubic_Mean = mean(Age_Inv_Cubic, na.rm = TRUE), InvCubic_SD = sd(Age_Inv_Cubic, na.rm = TRUE)
)
tp <- as.data.frame(tabel_perbandingan)
banding_semua <- data.frame(
Variabel = c("Age (asli)", "Age_MinMax", "Age_Z", "Age_Inv_Sq", "Age_Inv_Cubic"),
Min = c(tp$Age_Min, tp$MinMax_Min, tp$Z_Min, tp$InvSq_Min, tp$InvCubic_Min),
Max = c(tp$Age_Max, tp$MinMax_Max, tp$Z_Max, tp$InvSq_Max, tp$InvCubic_Max),
Mean = c(tp$Age_Mean, tp$MinMax_Mean, tp$Z_Mean, tp$InvSq_Mean, tp$InvCubic_Mean),
SD = c(tp$Age_SD, tp$MinMax_SD, tp$Z_SD, tp$InvSq_SD, tp$InvCubic_SD)
)
knitr::kable(banding_semua, caption = "Perbandingan statistik Age asli dan hasil transformasi", digits = 4, format = "html")| Variabel | Min | Max | Mean | SD |
|---|---|---|---|---|
| Age (asli) | 0.4200 | 80.0000 | 29.6018 | 14.1536 |
| Age_MinMax | 0.0000 | 1.0000 | 0.3667 | 0.1779 |
| Age_Z | -2.0618 | 3.5608 | 0.0000 | 1.0000 |
| Age_Inv_Sq | 0.0002 | 5.6689 | 0.0321 | 0.2507 |
| Age_Inv_Cubic | 0.0000 | 13.4975 | 0.0396 | 0.4949 |
Transformasi Min-Max dan Z-score hanya mengubah skala, sedangkan transformasi invers mengubah urutan dan bentuk sebaran: usia kecil menghasilkan nilai yang sangat besar (maksimum Age_Inv_Sq 5.67 dan Age_Inv_Cubic 13.5), sedangkan usia besar menghasilkan nilai mendekati nol.
tabel_age <- dplyr::select(data_transformasi, PassengerId, Age, Age_MinMax, Age_Z, Age_Inv_Sq, Age_Inv_Cubic)
knitr::kable(head(tabel_age, 10), caption = "Tabel hasil: Age dan seluruh hasil transformasinya", digits = 4, format = "html")| PassengerId | Age | Age_MinMax | Age_Z | Age_Inv_Sq | Age_Inv_Cubic |
|---|---|---|---|---|---|
| 1 | 22 | 0.2712 | -0.5371 | 0.0021 | 0.0001 |
| 2 | 38 | 0.4722 | 0.5934 | 0.0007 | 0.0000 |
| 3 | 26 | 0.3214 | -0.2545 | 0.0015 | 0.0001 |
| 4 | 35 | 0.4345 | 0.3814 | 0.0008 | 0.0000 |
| 5 | 35 | 0.4345 | 0.3814 | 0.0008 | 0.0000 |
| 6 | 29 | 0.3591 | -0.0425 | 0.0012 | 0.0000 |
| 7 | 54 | 0.6733 | 1.7238 | 0.0003 | 0.0000 |
| 8 | 2 | 0.0199 | -1.9502 | 0.2500 | 0.1250 |
| 9 | 27 | 0.3340 | -0.1838 | 0.0014 | 0.0001 |
| 10 | 14 | 0.1706 | -1.1023 | 0.0051 | 0.0004 |
Variabel Tambahan untuk Analisis
Untuk keperluan visualisasi dan analisis keselamatan, dibuat variabel
Status (label keselamatan) dan Kelompok_Umur.
Batas kelompok umur ditentukan oleh peneliti: Anak (0-12), Remaja
(13-17), Dewasa (18-59), dan Lansia (60 ke atas).
df_final <- df_encoded %>% dplyr::mutate(Sex_male = ifelse(Sex == "male", 1, 0), Sex_female = ifelse(Sex == "female", 1, 0), Status = factor(ifelse(Survived == 1, "Selamat", "Tidak Selamat"), levels = c("Tidak Selamat", "Selamat")), Kelompok_Umur = cut(Age, breaks = c(-Inf, 12, 17, 59, Inf), labels = c("Anak (0-12)", "Remaja (13-17)", "Dewasa (18-59)", "Lansia (60+)")))
warna_sex <- c(female = "pink", male = "blue")
warna_status <- c("Tidak Selamat" = "red", "Selamat" = "green")
knitr::kable(head(df_final, 8), caption = "Contoh dataset akhir", format = "html")| PassengerId | Sex | Age | Survived | sex | Sex_male | Sex_female | Status | Kelompok_Umur |
|---|---|---|---|---|---|---|---|---|
| 1 | male | 22 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 2 | female | 38 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 3 | female | 26 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 4 | female | 35 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 5 | male | 35 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 6 | male | 29 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 7 | male | 54 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 8 | male | 2 | 0 | 1 | 1 | 0 | Tidak Selamat | Anak (0-12) |
6.3 Ringkasan Statistik
ringkas_age <- data.frame(Statistik = c("Jumlah Data", "Mean", "Median", "Standar Deviasi", "Kuartil 1 (Q1)", "Kuartil 3 (Q3)", "Minimum", "Maksimum"),
Nilai = c(
length(df_final$Age),
mean(df_final$Age, na.rm = TRUE),
median(df_final$Age, na.rm = TRUE),
sd(df_final$Age, na.rm = TRUE),
quantile(df_final$Age, 0.25, na.rm = TRUE),
quantile(df_final$Age, 0.75, na.rm = TRUE),
min(df_final$Age, na.rm = TRUE),
max(df_final$Age, na.rm = TRUE)))
knitr::kable(ringkas_age, caption = "Statistik deskriptif variabel Age", format = "html")| Statistik | Nilai |
|---|---|
| Jumlah Data | 891.00000 |
| Mean | 29.60176 |
| Median | 28.00000 |
| Standar Deviasi | 14.15357 |
| Kuartil 1 (Q1) | 20.00000 |
| Kuartil 3 (Q3) | 38.00000 |
| Minimum | 0.42000 |
| Maksimum | 80.00000 |
Data usia berjumlah 891 dengan rata-rata 29.6 tahun dan median 28 tahun. Usia termuda 0.42 tahun dan tertua 80 tahun. Tidak terdapat missing value pada variabel Age.
ringkas_sex <- df_final %>% dplyr::group_by(Sex) %>% dplyr::summarise(
Jumlah = dplyr::n(),
Mean_Age = mean(Age, na.rm = TRUE),
Median = median(Age, na.rm = TRUE),
Varians = var(Age, na.rm = TRUE),
Std_Deviasi = sd(Age, na.rm = TRUE),
.groups = "drop")
ringkas_sex <- as.data.frame(ringkas_sex)
knitr::kable(ringkas_sex, caption = "Ringkasan usia berdasarkan jenis kelamin", digits = 2, format = "html")| Sex | Jumlah | Mean_Age | Median | Varians | Std_Deviasi |
|---|---|---|---|---|---|
| female | 314 | 27.94 | 27 | 195.64 | 13.99 |
| male | 577 | 30.51 | 28 | 200.88 | 14.17 |
rf <- ringkas_sex[ringkas_sex$Sex == "female", ]
rm_ <- ringkas_sex[ringkas_sex$Sex == "male", ]Penumpang laki-laki (male) berjumlah 577 orang, lebih banyak daripada perempuan (female) yang berjumlah 314 orang. Rata-rata usia laki-laki adalah 30.51 tahun, sedangkan perempuan 27.94 tahun, sehingga laki-laki cenderung lebih tua. Varians usia laki-laki sebesar 200.88, sedangkan perempuan sebesar 195.64, yang menunjukkan penyebaran usia laki-laki sedikit lebih besar.
6.4 Gambaran Distribusi Data
Histogram
library(ggplot2)
ggplot(df_final, aes(x = Age)) + geom_histogram(bins = 20, fill = "steelblue", color = "white", alpha = 0.9) + labs(title = "Distribusi Usia Penumpang", x = "Usia (Tahun)", y = "Total Penumpang") + theme_minimal()
Histogram digunakan untuk melihat bentuk distribusi usia secara visual. Usia memiliki konsentrasi terbesar pada usia muda hingga dewasa dan penyebaran ke arah usia yang lebih tinggi.
Density Plot
ggplot(df_final, aes(x = Age)) + geom_density() + labs(title = "Density Plot Usia Penumpang", x = "Usia", y = "Density")
Density plot digunakan untuk melihat pola distribusi secara lebih halus. Distribusi usia tidak sepenuhnya simetris dan terdapat kecenderungan penyimpangan dari bentuk normal.
Q-Q Plot
ggplot(df_final, aes(sample = Age)) + stat_qq() + stat_qq_line() + labs(title = "Q-Q Plot Usia Penumpang", x = "Kuantil Teoritis", y = "Kuantil Sampel (Usia)")
Pada Q-Q plot, jika data berdistribusi normal maka titik-titik akan mengikuti garis acuan. Pada data Age, titik-titik tidak seluruhnya mengikuti garis, terutama pada bagian ujung distribusi. Hal ini menunjukkan adanya penyimpangan dari distribusi normal pada bagian tail.
Boxplot Usia Berdasarkan Jenis Kelamin
ggplot(df_final, aes(x = Sex, y = Age, fill = Sex)) + geom_boxplot() + scale_fill_manual(values = warna_sex, name = "Jenis Kelamin") + labs(title = "Boxplot Usia Berdasarkan Jenis Kelamin", x = "Jenis Kelamin", y = "Usia")
Boxplot menunjukkan penyebaran usia antara laki-laki dan perempuan relatif mirip. Pada kelompok laki-laki terlihat beberapa pencilan pada usia tinggi.
6.4 Visualisasi Variabel Kategorik
Distribusi Jenis Kelamin
ggplot(df_final, aes(x = Sex, fill = Sex)) + geom_bar(width = 0.6) + geom_text(stat = "count", aes(label = after_stat(count)), vjust = -0.4) + scale_fill_manual(values = warna_sex) + labs(title = "Distribusi Jenis Kelamin Penumpang", x = "Jenis Kelamin", y = "Jumlah Penumpang") + theme_minimal() + theme(legend.position = "none")
Penumpang laki-laki berjumlah 577 orang (64.76%) dan perempuan 314 orang (35.24%).
Distribusi Status Keselamatan
ggplot(df_final, aes(x = Status, fill = Status)) +
geom_bar(width = 0.6) +
geom_text(stat = "count", aes(label = after_stat(count)), vjust = -0.4) +
scale_fill_manual(values = warna_status) +
labs(title = "Distribusi Status Keselamatan Penumpang", x = "Status", y = "Jumlah Penumpang") +
theme_minimal() +
theme(legend.position = "none")
Dari 891 penumpang, 342 selamat (38.38%) dan 549 tidak selamat (61.62%).
6.6 Analisis Status Keselamatan
6.6.1 Berdasarkan Jenis Kelamin
rate_sex <- df_final %>% dplyr::group_by(Sex) %>% dplyr::summarise(
Total = dplyr::n(),
Selamat = sum(Survived == 1),
Tidak_Selamat = sum(Survived == 0),
Tingkat_Keselamatan_Persen = 100 * mean(Survived), .groups = "drop")
rate_sex <- as.data.frame(rate_sex)
knitr::kable(rate_sex, caption = "Status keselamatan berdasarkan jenis kelamin", digits = 1, format = "html")| Sex | Total | Selamat | Tidak_Selamat | Tingkat_Keselamatan_Persen |
|---|---|---|---|---|
| female | 314 | 233 | 81 | 74.2 |
| male | 577 | 109 | 468 | 18.9 |
ggplot(df_final, aes(x = Sex, fill = Status)) + geom_bar(position = "fill", width = 0.6) + scale_y_continuous(labels = function(x) paste0(100 * x, "%")) + scale_fill_manual(values = warna_status, name = "Status") + labs(title = "Proporsi Keselamatan Berdasarkan Jenis Kelamin", x = "Jenis Kelamin", y = "Proporsi") + theme_minimal()
uji_sex <- chisq.test(table(df_final$Sex, df_final$Survived))
r_f <- rate_sex$Tingkat_Keselamatan_Persen[rate_sex$Sex == "female"]
r_m <- rate_sex$Tingkat_Keselamatan_Persen[rate_sex$Sex == "male"]Tingkat keselamatan perempuan adalah 74.2%, sedangkan laki-laki 18.9%. Uji tambahan chi-square (tidak terdapat pada PPT) menghasilkan \(\chi^2\) = 260.72 dengan p-value = 0, sehingga terdapat perbedaan status keselamatan yang signifikan antara laki-laki dan perempuan pada taraf signifikansi 5%.
6.6.2 Berdasarkan Umur
ringkas_umur_status <- df_final %>% dplyr::group_by(Status) %>% dplyr::summarise(
Jumlah = dplyr::n(),
Mean_Age = mean(Age),
Median_Age = median(Age),
Std_Deviasi = sd(Age), .groups = "drop")
knitr::kable(as.data.frame(ringkas_umur_status), caption = "Ringkasan usia berdasarkan status keselamatan", digits = 2, format = "html")| Status | Jumlah | Mean_Age | Median_Age | Std_Deviasi |
|---|---|---|---|---|
| Tidak Selamat | 549 | 30.25 | 28 | 13.84 |
| Selamat | 342 | 28.56 | 28 | 14.61 |
ggplot(df_final, aes(x = Age, fill = Status)) + geom_density(alpha = 0.5) + scale_fill_manual(values = warna_status, name = "Status") + labs(title = "Distribusi Usia Berdasarkan Status Keselamatan", x = "Usia (Tahun)", y = "Density") + theme_minimal()
rate_umur <- df_final %>%
dplyr::group_by(Kelompok_Umur) %>%
dplyr::summarise(Total = dplyr::n(), Selamat = sum(Survived == 1),
Tingkat_Keselamatan_Persen = 100 * mean(Survived), .groups = "drop")
rate_umur <- as.data.frame(rate_umur)
knitr::kable(rate_umur, caption = "Tingkat keselamatan berdasarkan kelompok umur", digits = 1, format = "html")| Kelompok_Umur | Total | Selamat | Tingkat_Keselamatan_Persen |
|---|---|---|---|
| Anak (0-12) | 79 | 44 | 55.7 |
| Remaja (13-17) | 51 | 22 | 43.1 |
| Dewasa (18-59) | 731 | 269 | 36.8 |
| Lansia (60+) | 30 | 7 | 23.3 |
ggplot(rate_umur, aes(x = Kelompok_Umur, y = Tingkat_Keselamatan_Persen)) +
geom_col(fill = "steelblue", width = 0.6) +
geom_text(aes(label = paste0(round(Tingkat_Keselamatan_Persen, 1), "%")), vjust = -0.4) +
ylim(0, 100) +
labs(title = "Tingkat Keselamatan Berdasarkan Kelompok Umur", x = "Kelompok Umur", y = "Tingkat Keselamatan (%)") +
theme_minimal()
uji_umur <- chisq.test(table(df_final$Kelompok_Umur, df_final$Survived))Rata-rata usia penumpang yang selamat adalah 28.56 tahun dan yang tidak selamat 30.25 tahun. Berdasarkan kelompok umur, tingkat keselamatan tertinggi terdapat pada kelompok Anak (0-12) (55.7%) dan terendah pada kelompok Lansia (60+) (23.3%). Uji tambahan chi-square antara kelompok umur dan status keselamatan menghasilkan p-value = 0.0027, sehingga terdapat perbedaan status keselamatan antar kelompok umur pada taraf signifikansi 5%. Batas kelompok umur ditentukan peneliti sehingga hasil dapat berubah jika batas diubah.
6.6.3 Kombinasi Jenis Kelamin dan Kelompok Umur
rate_kombinasi <- df_final %>% dplyr::group_by(Sex, Kelompok_Umur) %>% dplyr::summarise(Total = dplyr::n(), Selamat = sum(Survived == 1), Tingkat_Keselamatan_Persen = 100 * mean(Survived), .groups = "drop")
rate_kombinasi <- as.data.frame(rate_kombinasi)
knitr::kable(rate_kombinasi, caption = "Tingkat keselamatan berdasarkan jenis kelamin dan kelompok umur", digits = 1, format = "html")| Sex | Kelompok_Umur | Total | Selamat | Tingkat_Keselamatan_Persen |
|---|---|---|---|---|
| female | Anak (0-12) | 38 | 20 | 52.6 |
| female | Remaja (13-17) | 25 | 20 | 80.0 |
| female | Dewasa (18-59) | 246 | 189 | 76.8 |
| female | Lansia (60+) | 5 | 4 | 80.0 |
| male | Anak (0-12) | 41 | 24 | 58.5 |
| male | Remaja (13-17) | 26 | 2 | 7.7 |
| male | Dewasa (18-59) | 485 | 80 | 16.5 |
| male | Lansia (60+) | 25 | 3 | 12.0 |
ggplot(rate_kombinasi, aes(x = Kelompok_Umur, y = Tingkat_Keselamatan_Persen, fill = Sex)) + geom_col(position = position_dodge(width = 0.8), width = 0.7) + geom_text(
aes(label = paste0(round(Tingkat_Keselamatan_Persen, 1), "%")),
position = position_dodge(width = 0.8),
vjust = -0.4,
size = 3) +
scale_fill_manual(values = warna_sex, name = "Jenis Kelamin") + ylim(0, 100) + labs(
title = "Tingkat Keselamatan Berdasarkan Jenis Kelamin dan Kelompok Umur",
x = "Kelompok Umur",
y = "Tingkat Keselamatan (%)") + theme_minimal()
kel <- levels(df_final$Kelompok_Umur)
ambil <- function(s, k) {
rate_kombinasi$Tingkat_Keselamatan_Persen[rate_kombinasi$Sex == s & rate_kombinasi$Kelompok_Umur == k]
}
n_ambil <- function(s, k) {
rate_kombinasi$Total[rate_kombinasi$Sex == s & rate_kombinasi$Kelompok_Umur == k]
}Pada perempuan, tingkat keselamatan berkisar antara 52.6% dan 80% di semua kelompok umur. Pada laki-laki, tingkat keselamatan kelompok anak mencapai 58.5%, tetapi turun menjadi 7.7% pada remaja, 16.5% pada dewasa, dan 12% pada lansia. Perlu diperhatikan bahwa jumlah penumpang pada beberapa kelompok sangat kecil (misalnya perempuan lansia hanya 5 orang), sehingga persentasenya perlu ditafsirkan dengan hati-hati.
7. Pengujian Asumsi
7.1 Pengujian Normalitas
Uji Shapiro-Wilk
Normalitas variabel Age diuji dengan uji Shapiro-Wilk pada taraf signifikansi 5%.
Hipotesis: - H0: data Age berdistribusi normal. - H1: data Age tidak berdistribusi normal.
shapiro.test(df_bersih_awal$Age)##
## Shapiro-Wilk normality test
##
## data: df_bersih_awal$Age
## W = 0.97916, p-value = 5.666e-10Bisa. Berdasarkan hasil pada foto:
- W = 0,97916
- p-value = 5,666 × 10⁻¹⁰
- p-value < 0,05, sehingga H₀ ditolak.
Interpretasi yang langsung bisa kamu masukkan:
Uji Shapiro-Wilk menghasilkan nilai W = 0,97916 dengan p-value = 5,666 × 10⁻¹⁰. Karena p-value < 0,05, maka H₀ ditolak, sehingga variabel Age tidak berdistribusi normal.
Namun, sesuai konsep EDA, hasil ini tidak berarti bahwa data tersebut buruk. Data usia memang dapat memiliki distribusi yang tidak normal. Normalitas harus dilihat berdasarkan tujuan analisis dan metode statistik yang akan digunakan.
7.2 Pengujian Homogenitas
Homogenitas varians Age antara kelompok laki-laki dan perempuan diuji dengan Levene’s Test pada taraf signifikansi 5%.
- H0: varians usia kelompok laki-laki dan perempuan sama (homogen).
- H1: varians usia kelompok laki-laki dan perempuan berbeda (tidak homogen).
car::leveneTest(Age ~ factor(Sex), data = df_final)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.2307 0.6311
## 889Berdasarkan hasil Levene’s Test tersebut:
- F value = 0,2307
- p-value = 0,6311
- Karena p-value > 0,05, maka H₀ tidak ditolak.
Interpretasi yang bisa langsung kamu masukkan:
Levene’s Test menghasilkan nilai F = 0,2307 dengan p-value = 0,6311. Karena p-value > 0,05, maka H₀ tidak ditolak, sehingga varians variabel Age antara laki-laki dan perempuan dapat dianggap homogen.
7.3 Interpretasi Hasil Pengujian
ringkas_uji <- data.frame(
Asumsi = c(
"Normalitas", "Normalitas", "Normalitas",
"Homogenitas", "Homogenitas", "Homogenitas"),
Bukti = c(
"Histogram dan density plot",
"Q-Q plot",
"Uji Shapiro-Wilk",
"Boxplot",
"Levene's Test",
"Kesimpulan"),
Hasil = c(
"Bentuk distribusi usia tidak sepenuhnya normal.",
"Titik menyimpang dari garis acuan, terutama pada bagian ujung.",
"p-value = 5,666e-10 (< 0,05), Age tidak berdistribusi normal.",
"Penyebaran usia laki-laki dan perempuan relatif mirip.",
"p-value = 0,6311 (> 0,05).",
"Varians Age berdasarkan Sex dapat dianggap homogen."))
knitr::kable(ringkas_uji, caption = "Ringkasan pengujian asumsi", align = c("c", "l", "l"), format = "html")| Asumsi | Bukti | Hasil |
|---|---|---|
| Normalitas | Histogram dan density plot | Bentuk distribusi usia tidak sepenuhnya normal. |
| Normalitas | Q-Q plot | Titik menyimpang dari garis acuan, terutama pada bagian ujung. |
| Normalitas | Uji Shapiro-Wilk | p-value = 5,666e-10 (< 0,05), Age tidak berdistribusi normal. |
| Homogenitas | Boxplot | Penyebaran usia laki-laki dan perempuan relatif mirip. |
| Homogenitas | Levene’s Test | p-value = 0,6311 (> 0,05). |
| Homogenitas | Kesimpulan | Varians Age berdasarkan Sex dapat dianggap homogen. |
8. Hasil Dataset Akhir
8.1 Bentuk dan Ukuran Dataset Akhir
cat("Jumlah baris :", nrow(df_final), "\n")## Jumlah baris : 891cat("Jumlah variabel :", ncol(df_final), "\n")## Jumlah variabel : 9cat("Nama variabel :", paste(names(df_final), collapse = ", "), "\n")## Nama variabel : PassengerId, Sex, Age, Survived, sex, Sex_male, Sex_female, Status, Kelompok_Umur8.2 Kondisi Data Setelah Pengolahan
str(df_final)## 'data.frame': 891 obs. of 9 variables:
## $ PassengerId : num 1 2 3 4 5 6 7 8 9 10 ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 29 54 2 27 14 ...
## $ Survived : num 0 1 1 1 0 0 0 0 1 1 ...
## $ sex : num 1 0 0 0 1 1 1 1 0 0 ...
## $ Sex_male : num 1 0 0 0 1 1 1 1 0 0 ...
## $ Sex_female : num 0 1 1 1 0 0 0 0 1 1 ...
## $ Status : Factor w/ 2 levels "Tidak Selamat",..: 1 2 2 2 1 1 1 1 2 2 ...
## $ Kelompok_Umur: Factor w/ 4 levels "Anak (0-12)",..: 3 3 3 3 3 3 3 1 3 2 ...cat("\nTotal missing value:", sum(is.na(df_final)), "\n")##
## Total missing value: 0Dataset akhir terdiri atas 891 baris dengan total missing value 0.
Dataset memuat variabel asli (PassengerId,
Sex, Age, Survived) serta
variabel hasil transformasi (sex, Sex_male,
Sex_female, Status,
Kelompok_Umur).
8.3 Tampilan Dataset Akhir
knitr::kable(head(df_final, 15), caption = "Lima belas baris pertama dataset akhir")| PassengerId | Sex | Age | Survived | sex | Sex_male | Sex_female | Status | Kelompok_Umur |
|---|---|---|---|---|---|---|---|---|
| 1 | male | 22 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 2 | female | 38 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 3 | female | 26 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 4 | female | 35 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 5 | male | 35 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 6 | male | 29 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 7 | male | 54 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 8 | male | 2 | 0 | 1 | 1 | 0 | Tidak Selamat | Anak (0-12) |
| 9 | female | 27 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 10 | female | 14 | 1 | 0 | 0 | 1 | Selamat | Remaja (13-17) |
| 11 | female | 4 | 1 | 0 | 0 | 1 | Selamat | Anak (0-12) |
| 12 | female | 58 | 1 | 0 | 0 | 1 | Selamat | Dewasa (18-59) |
| 13 | male | 20 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 14 | male | 39 | 0 | 1 | 1 | 0 | Tidak Selamat | Dewasa (18-59) |
| 15 | female | 14 | 0 | 0 | 0 | 1 | Tidak Selamat | Remaja (13-17) |
9. Penyimpanan Data
9.1 Penyimpanan Dataset Hasil Cleaning
write.csv(df_bersih_awal, "titanic_hasil_cleaning.csv", row.names = FALSE)
write.csv(df_final, "titanic_dataset_akhir.csv", row.names = FALSE)
file_tersimpan <- c("titanic_hasil_cleaning.csv", "titanic_dataset_akhir.csv")
knitr::kable(data.frame(File = file_tersimpan, Tersimpan = file.exists(file_tersimpan)),caption = "File hasil penyimpanan (disimpan di folder yang sama dengan file .Rmd)", format = "html")| File | Tersimpan |
|---|---|
| titanic_hasil_cleaning.csv | TRUE |
| titanic_dataset_akhir.csv | TRUE |
9.2 Format Dataset yang Digunakan
Dataset disimpan dalam format CSV (pemisah koma) sehingga dapat
dibuka kembali di R, Excel, maupun perangkat lunak analisis lain.
titanic_hasil_cleaning.csv berisi empat variabel hasil
cleaning, sedangkan titanic_dataset_akhir.csv berisi
dataset akhir setelah integrasi dan transformasi.
10. Kesimpulan
10.1 Hasil Pengolahan Data
Titanic Dataset mentah terdiri atas 891 penumpang dan 12 variabel.
Permasalahan yang ditemukan meliputi nilai kosong pada Age
(177 data), Cabin (687 data), dan Embarked (2
data), penulisan nomor tiket yang tidak konsisten, nilai
Fare tertinggi (outlier) pada 3 penumpang, serta nilai
Age dan Fare pada data mentah yang kehilangan
tanda desimal.
Setelah proses pembersihan, variabel yang digunakan dipersempit
menjadi empat, yaitu PassengerId, Survived,
Sex, dan Age. Sebanyak 177 nilai kosong pada
variabel Age telah dilengkapi, sehingga tidak terdapat lagi
missing value pada data. Selanjutnya, data karakteristik dan data
keselamatan diintegrasikan menggunakan left_join().
Variabel Sex kemudian ditransformasi menggunakan label
encoding dan one-hot encoding sebagai tahap persiapan data untuk
analisis selanjutnya.
10.2 Hasil Pengujian
Data usia penumpang memiliki rata-rata 29.6 tahun, median 28 tahun,
dan standar deviasi 14.15. Uji Shapiro-Wilk menghasilkan p-value 5,666 ×
10⁻¹⁰, sehingga variabel Age tidak berdistribusi normal.
Levene’s Test menghasilkan p-value 0,6311, sehingga varians
Age antara laki-laki dan perempuan dapat dianggap
homogen.
Pada eksplorasi keselamatan, tingkat keselamatan perempuan (74.2%) jauh lebih tinggi dibandingkan laki-laki (18.9%), dan tingkat keselamatan menurun seiring bertambahnya kelompok umur dengan nilai tertinggi pada kelompok Anak (0-12).
10.3 Kesimpulan Akhir
Titanic Dataset yang telah dibersihkan dan ditransformasi siap digunakan untuk analisis lanjutan. Hasil eksplorasi menunjukkan bahwa jenis kelamin dan umur berkaitan dengan status keselamatan penumpang, sehingga keduanya layak digunakan sebagai variabel penjelas untuk memprediksi keselamatan. Meskipun variabel Age tidak berdistribusi normal, hal ini tidak berarti data buruk; pemilihan metode analisis lanjutan sebaiknya mempertimbangkan kondisi tersebut dan tujuan analisis. Hubungan yang ditemukan bersifat deskriptif dan tidak membuktikan sebab-akibat.
Daftar Pustaka
- Titanic Dataset [https://www.kaggle.com/datasets/elyamadad/titanic-dataset]
- Eaton, J. P., & Haas, C. A. (1994). Titanic: Triumph and Tragedy. [LENGKAPI: penerbit dan kota terbit]
- Encyclopedia Titanica. [https://www.encyclopedia-titanica.org]
- Fakhri, & Winursito. (2024).[https://ejurnal.kampusakademik.co.id/index.php/jssr/article/view/759]
- Titanic Universe. [https://titanicuniverse.com/]