# Data jumlah pengunjung perpustakaan (orang/bulan)
pengunjung <- c(
  210, 195, 220, 205, 230, 215,
  240, 225, 250, 235, 245, 260,
  255, 265, 270, 280, 275, 290
)

# Objek deret waktu bulanan
pengunjung_ts <- ts(
  pengunjung,
  start = c(2023, 1),
  frequency = 12
)

# Data frame untuk visualisasi dan tabel
tanggal <- seq.Date(
  from = as.Date("2023-01-01"),
  by = "month",
  length.out = length(pengunjung)
)

data_pengunjung <- data.frame(
  tanggal = tanggal,
  periode = format(tanggal, "%b %Y"),
  tahun = format(tanggal, "%Y"),
  bulan = format(tanggal, "%b"),
  pengunjung = as.numeric(pengunjung_ts)
)

kable(
  data_pengunjung,
  col.names = c("Tanggal", "Periode", "Tahun", "Bulan", "Pengunjung"),
  caption = "Data jumlah pengunjung Perpustakaan Daerah Kabupaten Serang"
)

ggplot(data_pengunjung, aes(x = tanggal, y = pengunjung)) +
  geom_line(linewidth = 0.9) +
  geom_point(size = 2.2) +
  geom_smooth(
    method = "lm",
    formula = y ~ x,
    se = FALSE,
    linetype = "dashed",
    linewidth = 0.8
  ) +
  scale_x_date(date_breaks = "3 months", date_labels = "%b\n%Y") +
  labs(
    title = "Jumlah Pengunjung Perpustakaan Daerah Kabupaten Serang",
    subtitle = "Januari 2023 – Juni 2024",
    x = "Periode",
    y = "Jumlah pengunjung (orang)"
  ) +
  theme_minimal(base_size = 12)

# SMA orde 3
m <- 3

sma3 <- zoo::rollmean(
  pengunjung_ts,
  k = m,
  align = "right",
  fill = NA
)

data_pengunjung$sma3 <- as.numeric(sma3)

kable(
  data_pengunjung,
  col.names = c("Tanggal", "Periode", "Tahun", "Bulan", "Aktual", "SMA(3)"),
  digits = 2,
  caption = "Hasil Single Moving Average orde 3"
)

plot_sma <- ggplot(data_pengunjung, aes(x = tanggal)) +
  geom_line(aes(y = pengunjung), linewidth = 0.9) +
  geom_point(aes(y = pengunjung), size = 1.8) +
  geom_line(
    aes(y = sma3),
    linewidth = 1,
    na.rm = TRUE,
    linetype = "solid"
  ) +
  geom_point(
    aes(y = sma3),
    size = 1.8,
    na.rm = TRUE
  ) +
  scale_x_date(date_breaks = "3 months", date_labels = "%b\n%Y") +
  labs(
    title = "Perbandingan Data Aktual dan SMA(3)",
    subtitle = "SMA(3) digunakan untuk meredam fluktuasi jangka pendek",
    x = "Periode",
    y = "Jumlah pengunjung (orang)"
  ) +
  theme_minimal(base_size = 12)

plot_sma

enam_bulan <- tail(data_pengunjung, 6)

enam_bulan$sisih <- enam_bulan$pengunjung - enam_bulan$sma3
enam_bulan$abs_sisih <- abs(enam_bulan$sisih)

kable(
  enam_bulan[, c("periode", "pengunjung", "sma3", "sisih")],
  digits = 2,
  col.names = c("Periode", "Aktual", "SMA(3)", "Aktual - SMA(3)"),
  caption = "Data aktual dan SMA(3) pada enam bulan terakhir"
)

mae_sma6 <- mean(enam_bulan$abs_sisih, na.rm = TRUE)
bias_sma6 <- mean(enam_bulan$sisih, na.rm = TRUE)

sma_summary <- data.frame(
  Ukuran = c(
    "Rata-rata selisih absolut",
    "Rata-rata aktual - SMA(3)"
  ),
  Nilai = c(mae_sma6, bias_sma6)
)

kable(
  sma_summary,
  digits = 2,
  col.names = c("Ukuran", "Nilai"),
  caption = "Ringkasan selisih SMA(3) pada enam bulan terakhir"
)

# Orde DMA
m <- 3

# Moving average tahap pertama
Mp <- zoo::rollmean(
  pengunjung_ts,
  k = m,
  align = "right",
  fill = NA
)

# Moving average tahap kedua
Mpp <- zoo::rollmean(
  Mp,
  k = m,
  align = "right",
  fill = NA
)

# Komponen level dan tren
a_t <- 2 * Mp - Mpp
b_t <- (2 / (m - 1)) * (Mp - Mpp)

data_dma <- data.frame(
  tanggal = tanggal,
  periode = format(tanggal, "%b %Y"),
  aktual = as.numeric(pengunjung_ts),
  M_prime = as.numeric(Mp),
  M_double_prime = as.numeric(Mpp),
  a_t = as.numeric(a_t),
  b_t = as.numeric(b_t)
)

kable(
  data_dma,
  digits = 2,
  col.names = c(
    "Tanggal", "Periode", "Aktual",
    "M't", "M''t", "a_t", "b_t"
  ),
  caption = "Perhitungan lengkap Double Moving Average orde 3"
)

dma_last <- tail(na.omit(data_dma), 1)

kable(
  dma_last[, c("periode", "aktual", "M_prime", "M_double_prime", "a_t", "b_t")],
  digits = 2,
  col.names = c(
    "Periode", "Aktual", "M't", "M''t", "a_t", "b_t"
  ),
  caption = "Komponen DMA pada observasi terakhir"
)

a_last <- dma_last$a_t
b_last <- dma_last$b_t

forecast_1 <- a_last + b_last * 1
forecast_2 <- a_last + b_last * 2

forecast_dma <- data.frame(
  Periode = c("Juli 2024", "Agustus 2024"),
  Horizon = c(1, 2),
  Ramalan_DMA = c(forecast_1, forecast_2)
)

kable(
  forecast_dma,
  digits = 2,
  col.names = c("Periode", "p", "Ramalan DMA (orang)"),
  caption = "Ramalan jumlah pengunjung menggunakan DMA(3)"
)

comparison <- data.frame(
  Periode = c("Juli 2024", "Agustus 2024"),
  `SMA(3)` = c(sma_forecast, sma_forecast),
  `DMA(3)` = c(forecast_1, forecast_2),
  `Selisih DMA - SMA` = c(
    forecast_1 - sma_forecast,
    forecast_2 - sma_forecast
  )
)

kable(
  comparison,
  digits = 2,
  col.names = c(
    "Periode",
    "SMA(3)",
    "DMA(3)",
    "DMA(3) - SMA(3)"
  ),
  caption = "Perbandingan ramalan SMA(3) dan DMA(3)"
)

historis <- data.frame(
  tanggal = tanggal,
  nilai = as.numeric(pengunjung_ts),
  jenis = "Aktual"
)

forecast_dates <- seq.Date(
  from = as.Date("2024-07-01"),
  by = "month",
  length.out = 2
)

forecast_df <- data.frame(
  tanggal = forecast_dates,
  SMA = c(sma_forecast, sma_forecast),
  DMA = c(forecast_1, forecast_2)
)

ggplot() +
  geom_line(
    data = historis,
    aes(x = tanggal, y = nilai),
    linewidth = 0.9
  ) +
  geom_point(
    data = historis,
    aes(x = tanggal, y = nilai),
    size = 1.8
  ) +
  geom_line(
    data = forecast_df,
    aes(x = tanggal, y = SMA),
    linewidth = 1,
    linetype = "dashed"
  ) +
  geom_point(
    data = forecast_df,
    aes(x = tanggal, y = SMA),
    size = 2.2
  ) +
  geom_line(
    data = forecast_df,
    aes(x = tanggal, y = DMA),
    linewidth = 1
  ) +
  geom_point(
    data = forecast_df,
    aes(x = tanggal, y = DMA),
    size = 2.2
  ) +
  geom_vline(
    xintercept = as.numeric(as.Date("2024-06-01")),
    linetype = "dotted"
  ) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b\n%Y"
  ) +
  labs(
    title = "Perbandingan Forecast SMA(3) dan DMA(3)",
    subtitle = "Forecast dimulai setelah observasi terakhir: Juni 2024",
    x = "Periode",
    y = "Jumlah pengunjung (orang)"
  ) +
  theme_minimal(base_size = 12)