Analisis Statistika Deskriptif
Dataset Netflix Movies & TV Shows

Eksplorasi karakteristik konten Netflix menggunakan metode Statistika Deskriptif 
<span class="badge">📅 2026-03-11</span>
<span class="badge">📊 8.807 Judul</span>
<span class="badge">🔢 12 Variabel</span>
<span class="badge">🎓 Statistika Deskriptif II</span>

👥 Kelompok 4 — UNTIRTA 2025

Agni Tri Tania Siboro (3337250050)

Dina Wulandari (3337250144)

Findy Aprilia (3337250107)

Nayla Mesa Ayu (3337250145)

Putri Kaira Langi (3337250082)

1 Pendahuluan

1.1 Latar Belakang

Netflix merupakan salah satu platform streaming terbesar di dunia dengan jutaan pelanggan aktif di lebih dari 190 negara. Dataset ini berisi informasi konten Netflix berupa film dan serial TV, mencakup jenis konten, rating, tahun rilis, dan durasi.

Analisis ini menggunakan metode Statistika Deskriptif untuk mengeksplorasi karakteristik data secara menyeluruh, meliputi ukuran pemusatan, penyebaran, dan visualisasi distribusi data.

1.2 Sumber Data

8.807
Total Judul
12
Variabel
2
Tipe Konten
190+
Negara
  • Dataset: Netflix Movies and TV Shows
  • Sumber: Kaggle — Shivamb
  • Lisensi: CC0: Public Domain

1.3 Variabel yang Dianalisis

Jenis Variabel Keterangan
🏷️ Kategorik type Jenis konten: Movie atau TV Show
🏷️ Kategorik rating Rating usia (TV-MA, TV-14, PG-13, dll.)
🔢 Numerik release_year Tahun rilis konten
🔢 Numerik duration_minutes Durasi film dalam menit

2 Persiapan Data

2.1 Load Library

# Jika belum terinstall, jalankan baris ini lebih dulu:
# install.packages(c("ggplot2", "dplyr", "scales", "knitr", "kableExtra"))

library(ggplot2)
library(dplyr)
library(knitr)
library(kableExtra)

2.2 Import Dataset

# Download file netflix_titles.csv dari:
# https://www.kaggle.com/datasets/shivamb/netflix-shows
# Lalu simpan di folder yang sama dengan file .Rmd ini

netflix <- read.csv("netflix_titles.csv",
                    stringsAsFactors = FALSE,
                    na.strings = c("", "NA"))

# Lihat struktur data
cat("Dimensi data:", nrow(netflix), "baris x", ncol(netflix), "kolom\n")
## Dimensi data: 8807 baris x 12 kolom
head(netflix[, c("type","title","rating","release_year","duration")], 5)

2.3 Pembersihan Data

# Fungsi modus
modus <- function(x) {
  x <- x[!is.na(x)]
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}

# Cleaning dan transformasi data
netflix_clean <- netflix %>%
  filter(!is.na(type), !is.na(rating), !is.na(release_year)) %>%
  mutate(
    duration_value   = as.numeric(gsub("[^0-9]", "", duration)),
    duration_minutes = ifelse(type == "Movie", duration_value, NA),
    rating_group = case_when(
      rating %in% c("G","TV-G","TV-Y","TV-Y7","TV-Y7-FV") ~ "Anak-Anak",
      rating %in% c("PG","TV-PG","PG-13","TV-14")         ~ "Remaja",
      rating %in% c("R","TV-MA","NC-17","NR","UR")         ~ "Dewasa",
      TRUE ~ "Lainnya"
    )
  )

cat("✅ Data siap dianalisis:", nrow(netflix_clean), "baris\n")
## ✅ Data siap dianalisis: 8803 baris

3 Analisis Data Kategorik

3.1 Variabel 1: type — Jenis Konten

3.1.1 Tabel Frekuensi

tabel_type <- netflix_clean %>%
  count(type) %>%
  mutate(Persentase = round(n / sum(n) * 100, 2)) %>%
  rename(Kategori = type, Frekuensi = n)

kable(tabel_type, caption = "Tabel Frekuensi Jenis Konten Netflix",
      align = c("l","c","c")) %>%
  kable_styling(bootstrap_options = c("striped","hover","bordered"),
                full_width = FALSE, position = "center") %>%
  row_spec(0, background = "#E50914", color = "white", bold = TRUE)
Tabel Frekuensi Jenis Konten Netflix
Kategori Frekuensi Persentase
Movie 6129 69.62
TV Show 2674 30.38

3.1.2 Pie Chart

ggplot(tabel_type, aes(x = "", y = Frekuensi, fill = Kategori)) +
  geom_bar(stat = "identity", width = 1, color = "white", linewidth = 1.2) +
  coord_polar("y") +
  geom_text(aes(label = paste0(Persentase, "%")),
            position = position_stack(vjust = 0.5),
            color = "white", size = 7, fontface = "bold") +
  scale_fill_manual(values = c("Movie" = "#E50914", "TV Show" = "#221F1F")) +
  labs(title = "Proporsi Jenis Konten di Netflix",
       fill = "Jenis Konten") +
  theme_void() +
  theme(
    plot.title      = element_text(hjust = 0.5, size = 15, face = "bold",
                                   color = "#141414", margin = margin(b=12)),
    legend.text     = element_text(size = 11),
    legend.title    = element_text(size = 11, face = "bold"),
    plot.background = element_rect(fill = "#fafafa", color = NA)
  )

🎬 Interpretasi Pie Chart menunjukkan konten Netflix didominasi oleh Movie (~69%) dibandingkan TV Show (~31%). Nilai modus variabel type adalah “Movie”. Secara inferensia, dominasi film mencerminkan strategi Netflix yang lebih berfokus pada konten film untuk menarik pelanggan baru di berbagai pasar global.

3.2 Variabel 2: rating — Rating Konten

3.2.1 Tabel Frekuensi

tabel_rating <- netflix_clean %>%
  count(rating) %>%
  arrange(desc(n)) %>%
  mutate(Persentase = round(n / sum(n) * 100, 2)) %>%
  rename(Rating = rating, Frekuensi = n)

kable(tabel_rating, caption = "Tabel Frekuensi Rating Konten Netflix",
      align = c("l","c","c")) %>%
  kable_styling(bootstrap_options = c("striped","hover","bordered"),
                full_width = FALSE, position = "center") %>%
  row_spec(0, background = "#E50914", color = "white", bold = TRUE) %>%
  row_spec(1, bold = TRUE, background = "#fff5f5")
Tabel Frekuensi Rating Konten Netflix
Rating Frekuensi Persentase
TV-MA 3207 36.43
TV-14 2160 24.54
TV-PG 863 9.80
R 799 9.08
PG-13 490 5.57
TV-Y7 334 3.79
TV-Y 307 3.49
PG 287 3.26
TV-G 220 2.50
NR 80 0.91
G 41 0.47
TV-Y7-FV 6 0.07
NC-17 3 0.03
UR 3 0.03
66 min 1 0.01
74 min 1 0.01
84 min 1 0.01

3.2.2 Bar Chart

netflix_clean %>%
  count(rating) %>%
  arrange(desc(n)) %>%
  ggplot(aes(x = reorder(rating, n), y = n, fill = n)) +
  geom_bar(stat = "identity", color = "white", width = 0.75) +
  geom_text(aes(label = n), hjust = -0.2, size = 3.5, color = "#333") +
  coord_flip() +
  scale_fill_gradient(low = "#ffb3b3", high = "#E50914") +
  labs(title = "Distribusi Rating Konten di Netflix",
       x = "Rating", y = "Jumlah Konten") +
  theme_minimal(base_size = 12) +
  theme(
    plot.title      = element_text(hjust = 0.5, size = 14, face = "bold", color = "#141414"),
    panel.grid.major.y = element_blank(),
    legend.position = "none",
    axis.text       = element_text(color = "#444"),
    plot.background = element_rect(fill = "#fafafa", color = NA)
  )

📊 Interpretasi Bar Chart menunjukkan TV-MA (dewasa) sebagai rating paling dominan, diikuti TV-14 dan TV-PG. Nilai modus = TV-MA. Secara inferensia, Netflix menargetkan penonton dewasa sebagai segmen pasar utama, yang tercermin dari mayoritas konten berrating dewasa.

4 Analisis Data Numerik

4.1 Variabel 3: release_year — Tahun Rilis

4.1.1 Statistik Deskriptif

year_data <- netflix_clean$release_year

stats_year <- data.frame(
  Statistik = c("Mean","Median","Modus","Q1 (Kuartil 1)","Q3 (Kuartil 3)",
                "Range","Varians","Standar Deviasi","Minimum","Maksimum"),
  Nilai = c(
    round(mean(year_data, na.rm=TRUE), 2),
    median(year_data, na.rm=TRUE),
    modus(year_data),
    quantile(year_data, 0.25, na.rm=TRUE),
    quantile(year_data, 0.75, na.rm=TRUE),
    diff(range(year_data, na.rm=TRUE)),
    round(var(year_data, na.rm=TRUE), 2),
    round(sd(year_data, na.rm=TRUE), 2),
    min(year_data, na.rm=TRUE),
    max(year_data, na.rm=TRUE)
  )
)

kable(stats_year, caption = "Statistik Deskriptif: Tahun Rilis",
      align = c("l","c")) %>%
  kable_styling(bootstrap_options = c("striped","hover","bordered"),
                full_width = FALSE, position = "center") %>%
  row_spec(0, background = "#E50914", color = "white", bold = TRUE) %>%
  row_spec(c(1,2,3), bold = TRUE, background = "#fff5f5")
Statistik Deskriptif: Tahun Rilis
Statistik Nilai
Mean 2014.18
Median 2017.00
Modus 2018.00
Q1 (Kuartil 1) 2013.00
Q3 (Kuartil 3) 2019.00
Range 96.00
Varians 77.81
Standar Deviasi 8.82
Minimum 1925.00
Maksimum 2021.00

4.1.2 Histogram

ggplot(netflix_clean, aes(x = release_year)) +
  geom_histogram(binwidth = 3, fill = "#E50914", color = "white", alpha = 0.9) +
  geom_vline(xintercept = mean(year_data, na.rm=TRUE),
             color = "#2563eb", linetype = "dashed", linewidth = 1.2) +
  geom_vline(xintercept = median(year_data, na.rm=TRUE),
             color = "#16a34a", linetype = "solid", linewidth = 1.2) +
  annotate("text", x = mean(year_data, na.rm=TRUE) - 3, y = 700,
           label = paste("Mean =", round(mean(year_data, na.rm=TRUE),1)),
           color = "#2563eb", size = 3.8, fontface = "bold") +
  annotate("text", x = median(year_data, na.rm=TRUE) + 4, y = 600,
           label = paste("Median =", median(year_data, na.rm=TRUE)),
           color = "#16a34a", size = 3.8, fontface = "bold") +
  labs(title = "Histogram Tahun Rilis Konten Netflix",
       x = "Tahun Rilis", y = "Frekuensi") +
  theme_minimal(base_size = 12) +
  theme(
    plot.title      = element_text(hjust = 0.5, size = 14, face = "bold", color = "#141414"),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "#fafafa", color = NA)
  )

📈 Interpretasi Histogram menunjukkan distribusi miring ke kiri (negative skew), dengan konten terbanyak dirilis pada 2015–2021. Nilai Mean < Median mengkonfirmasi kemiringan kiri akibat konten lama sebagai outlier. Secara inferensia, tren ini menunjukkan Netflix terus menambah konten baru secara agresif untuk bersaing di pasar streaming global.

4.1.3 Density Plot

ggplot(netflix_clean, aes(x = release_year, fill = type)) +
  geom_density(alpha = 0.65, color = NA) +
  scale_fill_manual(values = c("Movie" = "#E50914", "TV Show" = "#221F1F")) +
  labs(title = "Density Plot Tahun Rilis Berdasarkan Jenis Konten",
       x = "Tahun Rilis", y = "Densitas", fill = "Jenis Konten") +
  theme_minimal(base_size = 12) +
  theme(
    plot.title      = element_text(hjust = 0.5, size = 14, face = "bold", color = "#141414"),
    legend.position = "top",
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "#fafafa", color = NA)
  )

📈 Interpretasi Density Plot menunjukkan pola distribusi Movie dan TV Show yang serupa, dengan puncak di sekitar 2018–2020. TV Show memiliki ekor lebih panjang ke kiri. Secara inferensia, kedua jenis konten mengalami lonjakan produksi signifikan dalam satu dekade terakhir seiring meningkatnya popularitas layanan streaming.

4.2 Variabel 4: duration_minutes — Durasi Film

movie_data <- netflix_clean %>%
  filter(type == "Movie", !is.na(duration_minutes))

cat("🎬 Jumlah data film:", nrow(movie_data), "\n")
## 🎬 Jumlah data film: 6126

4.2.1 Statistik Deskriptif

dur_data <- movie_data$duration_minutes

stats_dur <- data.frame(
  Statistik = c("Mean","Median","Modus","Q1 (Kuartil 1)","Q3 (Kuartil 3)",
                "Range","Varians","Standar Deviasi","Minimum","Maksimum"),
  Nilai = c(
    round(mean(dur_data, na.rm=TRUE), 2),
    median(dur_data, na.rm=TRUE),
    modus(dur_data),
    quantile(dur_data, 0.25, na.rm=TRUE),
    quantile(dur_data, 0.75, na.rm=TRUE),
    diff(range(dur_data, na.rm=TRUE)),
    round(var(dur_data, na.rm=TRUE), 2),
    round(sd(dur_data, na.rm=TRUE), 2),
    min(dur_data, na.rm=TRUE),
    max(dur_data, na.rm=TRUE)
  )
)

kable(stats_dur, caption = "Statistik Deskriptif: Durasi Film (menit)",
      align = c("l","c")) %>%
  kable_styling(bootstrap_options = c("striped","hover","bordered"),
                full_width = FALSE, position = "center") %>%
  row_spec(0, background = "#E50914", color = "white", bold = TRUE) %>%
  row_spec(c(1,2,3), bold = TRUE, background = "#fff5f5")
Statistik Deskriptif: Durasi Film (menit)
Statistik Nilai
Mean 99.58
Median 98.00
Modus 90.00
Q1 (Kuartil 1) 87.00
Q3 (Kuartil 3) 114.00
Range 309.00
Varians 799.94
Standar Deviasi 28.28
Minimum 3.00
Maksimum 312.00

4.2.2 Histogram

ggplot(movie_data, aes(x = duration_minutes)) +
  geom_histogram(binwidth = 10, fill = "#E50914", color = "white", alpha = 0.9) +
  geom_vline(xintercept = mean(dur_data, na.rm=TRUE),
             color = "#2563eb", linetype = "dashed", linewidth = 1.2) +
  geom_vline(xintercept = median(dur_data, na.rm=TRUE),
             color = "#16a34a", linetype = "solid", linewidth = 1.2) +
  annotate("text", x = mean(dur_data, na.rm=TRUE) + 12, y = 450,
           label = paste("Mean =", round(mean(dur_data, na.rm=TRUE),1)),
           color = "#2563eb", size = 3.8, fontface = "bold") +
  annotate("text", x = median(dur_data, na.rm=TRUE) - 20, y = 380,
           label = paste("Median =", median(dur_data, na.rm=TRUE)),
           color = "#16a34a", size = 3.8, fontface = "bold") +
  labs(title = "Histogram Durasi Film Netflix",
       x = "Durasi (Menit)", y = "Frekuensi") +
  theme_minimal(base_size = 12) +
  theme(
    plot.title      = element_text(hjust = 0.5, size = 14, face = "bold", color = "#141414"),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "#fafafa", color = NA)
  )

🎬 Interpretasi Histogram durasi film menunjukkan distribusi mendekati simetris dengan puncak di 90–100 menit. Terdapat outlier di sisi kanan (film >180 menit, kemungkinan dokumenter/film epik). Secara inferensia, durasi rata-rata sesuai standar industri perfilman internasional (90–120 menit).

4.2.3 Density Plot

ggplot(movie_data, aes(x = duration_minutes)) +
  geom_density(fill = "#E50914", alpha = 0.65, color = "#b0070f") +
  geom_vline(xintercept = mean(dur_data, na.rm=TRUE),
             color = "#2563eb", linetype = "dashed", linewidth = 1.2) +
  geom_vline(xintercept = median(dur_data, na.rm=TRUE),
             color = "#16a34a", linetype = "solid", linewidth = 1.2) +
  annotate("text", x = mean(dur_data, na.rm=TRUE) + 15, y = 0.014,
           label = paste("Mean =", round(mean(dur_data, na.rm=TRUE),1)),
           color = "#2563eb", size = 3.8, fontface = "bold") +
  annotate("text", x = median(dur_data, na.rm=TRUE) - 20, y = 0.012,
           label = paste("Median =", median(dur_data, na.rm=TRUE)),
           color = "#16a34a", size = 3.8, fontface = "bold") +
  labs(title = "Density Plot Durasi Film Netflix",
       x = "Durasi (Menit)", y = "Densitas") +
  theme_minimal(base_size = 12) +
  theme(
    plot.title      = element_text(hjust = 0.5, size = 14, face = "bold", color = "#141414"),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "#fafafa", color = NA)
  )

🎬 Interpretasi Density Plot memperlihatkan kurva unimodal dengan puncak di sekitar 90 menit. Ekor panjang ke kanan menunjukkan kemiringan positif ringan akibat film berdurasi sangat panjang. Secara inferensia, distribusi ini mendekati normal sehingga uji parametrik dapat diterapkan untuk analisis lebih lanjut.

4.2.4 Boxplot per Kelompok Rating

ggplot(movie_data %>% filter(rating_group != "Lainnya"),
       aes(x = rating_group, y = duration_minutes, fill = rating_group)) +
  geom_boxplot(outlier.colour = "#E50914", outlier.shape = 16,
               outlier.size = 2, alpha = 0.85, width = 0.55) +
  stat_summary(fun = mean, geom = "point", shape = 23,
               size = 4, fill = "white", color = "black") +
  scale_fill_manual(values = c("Anak-Anak" = "#FFD700",
                               "Remaja"    = "#FF8C00",
                               "Dewasa"    = "#E50914")) +
  labs(title = "Boxplot Durasi Film Netflix per Kelompok Rating",
       subtitle = "◇ Titik putih = Mean  |  ● Titik merah = Outlier",
       x = "Kelompok Rating", y = "Durasi (Menit)") +
  theme_minimal(base_size = 12) +
  theme(
    plot.title      = element_text(hjust = 0.5, size = 14, face = "bold", color = "#141414"),
    plot.subtitle   = element_text(hjust = 0.5, color = "#666", size = 10),
    legend.position = "none",
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "#fafafa", color = NA)
  )

📦 Interpretasi Boxplot menunjukkan perbandingan distribusi durasi film per kelompok rating:
  • Film Dewasa memiliki median durasi tertinggi dengan IQR lebar (variasi besar)
  • Film Anak-Anak cenderung lebih pendek dan konsisten (IQR sempit)
  • Outlier muncul di semua kategori, terutama film berdurasi sangat panjang
  • Titik putih (mean) berada di atas median, mengkonfirmasi kemiringan kanan

Secara inferensia, perbedaan durasi antar kelompok dapat diuji lanjut dengan uji ANOVA untuk mengetahui apakah perbedaannya signifikan secara statistik.

5 Identifikasi Bentuk Sebaran & Deteksi Outlier

cat("===== Identifikasi Bentuk Sebaran =====\n\n")
## ===== Identifikasi Bentuk Sebaran =====
cat(">> Release Year:\n")
## >> Release Year:
cat("   Mean   :", round(mean(year_data, na.rm=TRUE), 2), "\n")
##    Mean   : 2014.18
cat("   Median :", median(year_data, na.rm=TRUE), "\n")
##    Median : 2017
cat("   Modus  :", modus(year_data), "\n")
##    Modus  : 2018
if (mean(year_data, na.rm=TRUE) < median(year_data, na.rm=TRUE)) {
  cat("   Kesimpulan: Mean < Median -> MIRING KE KIRI (Negative Skew)\n\n")
} else {
  cat("   Kesimpulan: Mean > Median -> MIRING KE KANAN (Positive Skew)\n\n")
}
##    Kesimpulan: Mean < Median -> MIRING KE KIRI (Negative Skew)
cat(">> Duration (menit):\n")
## >> Duration (menit):
cat("   Mean   :", round(mean(dur_data, na.rm=TRUE), 2), "\n")
##    Mean   : 99.58
cat("   Median :", median(dur_data, na.rm=TRUE), "\n")
##    Median : 98
cat("   Modus  :", modus(dur_data), "\n")
##    Modus  : 90
if (mean(dur_data, na.rm=TRUE) < median(dur_data, na.rm=TRUE)) {
  cat("   Kesimpulan: Mean < Median -> MIRING KE KIRI (Negative Skew)\n")
} else {
  cat("   Kesimpulan: Mean > Median -> MIRING KE KANAN (Positive Skew)\n")
}
##    Kesimpulan: Mean > Median -> MIRING KE KANAN (Positive Skew)
cat("===== Deteksi Outlier: Durasi Film =====\n\n")
## ===== Deteksi Outlier: Durasi Film =====
Q1_dur  <- quantile(dur_data, 0.25, na.rm=TRUE)
Q3_dur  <- quantile(dur_data, 0.75, na.rm=TRUE)
IQR_dur <- Q3_dur - Q1_dur
BB      <- Q1_dur - 1.5 * IQR_dur
BA      <- Q3_dur + 1.5 * IQR_dur

cat("Q1 (Kuartil 1) :", Q1_dur, "menit\n")
## Q1 (Kuartil 1) : 87 menit
cat("Q3 (Kuartil 3) :", Q3_dur, "menit\n")
## Q3 (Kuartil 3) : 114 menit
cat("IQR            :", IQR_dur, "menit\n")
## IQR            : 27 menit
cat("Batas Bawah    :", BB, "menit\n")
## Batas Bawah    : 46.5 menit
cat("Batas Atas     :", BA, "menit\n")
## Batas Atas     : 154.5 menit
cat("Outlier bawah  :", sum(dur_data < BB, na.rm=TRUE), "film\n")
## Outlier bawah  : 248 film
cat("Outlier atas   :", sum(dur_data > BA, na.rm=TRUE), "film\n")
## Outlier atas   : 201 film

6 Rekapitulasi Statistik Deskriptif

rekap <- data.frame(
  Variabel = c("Release Year", "Duration (menit)"),
  Mean     = c(round(mean(year_data, na.rm=TRUE),2), round(mean(dur_data, na.rm=TRUE),2)),
  Median   = c(median(year_data, na.rm=TRUE),        median(dur_data, na.rm=TRUE)),
  Modus    = c(modus(year_data),                     modus(dur_data)),
  Q1       = c(quantile(year_data,0.25,na.rm=TRUE),  quantile(dur_data,0.25,na.rm=TRUE)),
  Q3       = c(quantile(year_data,0.75,na.rm=TRUE),  quantile(dur_data,0.75,na.rm=TRUE)),
  Range    = c(diff(range(year_data,na.rm=TRUE)),    diff(range(dur_data,na.rm=TRUE))),
  Varians  = c(round(var(year_data,na.rm=TRUE),2),   round(var(dur_data,na.rm=TRUE),2)),
  Std_Dev  = c(round(sd(year_data,na.rm=TRUE),2),    round(sd(dur_data,na.rm=TRUE),2))
)

kable(rekap, caption = "Rekapitulasi Statistik Deskriptif",
      col.names = c("Variabel","Mean","Median","Modus","Q1","Q3","Range","Varians","Std. Deviasi")) %>%
  kable_styling(bootstrap_options = c("striped","hover","bordered"),
                full_width = TRUE) %>%
  row_spec(0, background = "#E50914", color = "white", bold = TRUE) %>%
  column_spec(1, bold = TRUE, background = "#fff5f5")
Rekapitulasi Statistik Deskriptif
Variabel Mean Median Modus Q1 Q3 Range Varians Std. Deviasi
Release Year 2014.18 2017 2018 2013 2019 96 77.81 8.82
Duration (menit) 99.58 98 90 87 114 309 799.94 28.28

7 Kesimpulan

✅ Ringkasan Temuan
  1. Jenis Konten (type): Netflix didominasi Movie (~69%) vs TV Show (~31%). Modus = Movie.
  2. Rating Konten: TV-MA paling dominan, menunjukkan Netflix menargetkan penonton dewasa.
  3. Tahun Rilis: Mayoritas konten dirilis 2015–2021. Distribusi miring kiri (Mean < Median) akibat konten lama. Standar deviasi ±8–10 tahun.
  4. Durasi Film: Rata-rata durasi ±90–100 menit, distribusi mendekati simetris dengan kemiringan kanan ringan. Film dewasa umumnya berdurasi lebih panjang.
  5. Inferensia: Perbedaan durasi antar kelompok rating dapat diuji dengan uji ANOVA atau Kruskal-Wallis untuk menentukan signifikansinya.

8 Referensi

Dibuat dengan R Markdown  |  Kelompok 4 – Statistika Deskriptif II  |  UNTIRTA 2025
Di-upload ke RPubs • 2026-03-11