Data_Tubes <- read_excel("G:/My Drive/Semester 5/Analisis Runtun Waktu/Analisis Runtun Waktu/Tubes ARW/Data Tubes.xlsx")
Data_Tubes$Data <- gsub("\\.", "", Data_Tubes$Data)
Data_Tubes$Data <- as.numeric(Data_Tubes$Data)
print(Data_Tubes)
## # A tibble: 60 × 3
## Tahun Bulan Data
## <dbl> <dbl> <dbl>
## 1 2020 1 120255000
## 2 2020 2 146131000
## 3 2020 3 164074000
## 4 2020 4 64987000
## 5 2020 5 6849000
## 6 2020 6 66456000
## 7 2020 7 152926000
## 8 2020 8 131194000
## 9 2020 9 60919000
## 10 2020 10 99546000
## # ℹ 50 more rows
sum(is.na(Data_Tubes$Data))
## [1] 0
cat("=== Statistik Deskriptif ===\n")
## === Statistik Deskriptif ===
summary(Data_Tubes$Data)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2130000 33859606 54850833 66046398 93062500 164074000
ts_data <- ts(Data_Tubes$Data, start = c(2020, 1), frequency = 12)
training <- Data_Tubes[1:48, ]
testing <- Data_Tubes[49:60, ]
ts_training <- ts(training$Data, start = c(2020, 1), frequency = 12)
ts_testing <- ts(testing$Data, start = c(2024, 1), frequency = 12)
df_training <- data.frame(
Tahun = as.Date(zoo::as.yearmon(time(ts_training))),
Nilai = as.numeric(ts_training),
Tipe = "Training"
)
df_testing <- data.frame(
Tahun = as.Date(zoo::as.yearmon(time(ts_testing))),
Nilai = as.numeric(ts_testing),
Tipe = "Testing"
)
df_plot <- bind_rows(df_training, df_testing)
ggplot(df_plot, aes(x = Tahun, y = Nilai, color = Tipe, linetype = Tipe)) +
geom_line(size = 0.5) +
scale_color_manual(values = c("Training" = "blue", "Testing" = "blue")) +
scale_linetype_manual(values = c("Training" = "solid", "Testing" = "dashed")) +
geom_vline(xintercept = as.Date("2024-01-01"), linetype = "dotted", color = "gray40") +
labs(title = "Plot Deret Waktu (Training vs Testing)",
x = "Tahun", y = "Nilai") +
theme_minimal(base_size = 12) +
theme(legend.title = element_blank())
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#boxplot tahunan
training$Periode <- "2020–2023"
ggplot(training, aes(x = Periode, y = Data)) +
geom_boxplot(fill = "lightgreen", color = "darkgreen",
outlier.colour = "red", outlier.size = 2) +
labs(
title = "Boxplot Data Training (2020–2023)",
x = "",
y = "Nilai Denda Pelanggaran"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5, size = 14),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold")
)
#boxplot per tahun
training$Tahun <- rep(2020:2023, each = 12)
ggplot(training, aes(x = factor(Tahun), y = Data)) +
geom_boxplot(fill = "skyblue", color = "darkblue", outlier.colour = "red", outlier.size = 2) +
labs(title = "Boxplot Data Training per Tahun (2020–2023)",
x = "Tahun",
y = "Nilai Denda Pelanggaran") +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5, size = 14),
axis.title = element_text(face = "bold")
)
#boxplot perbulan
training$Index <- 1:nrow(training)
training$Tahun <- 2020 + (training$Index - 1) %/% 12
training$BulanNum <- ((training$Index - 1) %% 12) + 1
training$Bulan <- month.abb[training$BulanNum]
training$Bulan <- factor(training$Bulan, levels = month.abb)
ggplot(training, aes(x = Bulan, y = Data)) +
geom_boxplot(fill = "gold", color = "darkorange",
outlier.colour = "red", outlier.size = 2) +
labs(title = "Boxplot Data Training per Bulan (2020–2023)",
x = "Bulan",
y = "Nilai Denda Pelanggaran") +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5, size = 14),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)
)
model_des_fit <- holt(ts_training, h = length(ts_testing),
initial = "optimal", seasonal = "none")
nilai_fitted_des <- ts(fitted(model_des_fit),
start = start(ts_training),
frequency = frequency(ts_training))
print("--- Ringkasan Model Double Exponential Smoothing (Holt's Method) ---")
## [1] "--- Ringkasan Model Double Exponential Smoothing (Holt's Method) ---"
summary(model_des_fit)
##
## Forecast method: Holt's method
##
## Model Information:
## Holt's method
##
## Call:
## holt(y = ts_training, h = length(ts_testing), initial = "optimal",
## seasonal = "none")
##
## Smoothing parameters:
## alpha = 0.3681
## beta = 1e-04
##
## Initial states:
## l = 122128399.7153
## b = -3780854.6827
##
## sigma: 49224990
##
## AIC AICc BIC
## 1891.985 1893.413 1901.341
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 5590486 47129340 35856275 -111.0776 154.1508 0.5740707 0.03568187
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Jan 2024 36038588 -27045775 99122951 -60440620 132517796
## Feb 2024 32284569 -34939549 99508686 -70525847 135094984
## Mar 2024 28530550 -42594833 99655933 -80246337 137307436
## Apr 2024 24776530 -50048943 99602003 -89659155 139212216
## May 2024 21022511 -57330383 99375405 -98807899 140852922
## Jun 2024 17268492 -64461514 98998498 -107726766 142263750
## Jul 2024 13514473 -71460260 98489205 -116443166 143472111
## Aug 2024 9760453 -78341249 97862156 -124979473 144500380
## Sep 2024 6006434 -85116605 97129474 -133354229 145367097
## Oct 2024 2252415 -91796510 96301340 -141583004 146087834
## Nov 2024 -1501604 -98389612 95386404 -149679025 146675817
## Dec 2024 -5255623 -104903331 94392084 -157653641 147142394
# --- Plot hasil DES ---
autoplot(ts_training, series = "Data Aktual (2020-2023)") +
autolayer(nilai_fitted_des, series = "Garis DES (Smoothing)") +
labs(
title = "Double Exponential Smoothing (DES) (2020-2023)",
subtitle = paste(
"Alpha Optimal:", round(model_des_fit$model$par["alpha"], 4),
"| Beta Optimal:", round(model_des_fit$model$par["beta"], 4)
),
y = "Jumlah Denda (Rp)",
x = "Waktu"
) +
guides(colour = guide_legend(title = "Deret Waktu")) +
theme(legend.position = "bottom")
cat("=== UJI STASIONERITAS ADF TEST ===\n")
## === UJI STASIONERITAS ADF TEST ===
adf_test_result <- adf.test(ts_training)
print(adf_test_result)
##
## Augmented Dickey-Fuller Test
##
## data: ts_training
## Dickey-Fuller = -2.7682, Lag order = 3, p-value = 0.2673
## alternative hypothesis: stationary
t <- 1:length(ts_training)
lm_trend <- lm(ts_training ~ t)
summary(lm_trend)
##
## Call:
## lm(formula = ts_training ~ t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72820639 -39235110 -12320695 41268270 90725432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83708653 14122442 5.927 3.72e-07 ***
## t -807803 501766 -1.610 0.114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48160000 on 46 degrees of freedom
## Multiple R-squared: 0.05334, Adjusted R-squared: 0.03276
## F-statistic: 2.592 on 1 and 46 DF, p-value: 0.1143
plot(ts_training, main="Regresi Tren", ylab="Denda")
lines(ts_training, col="black")
lines(lm_trend$fitted.values, col="red", lwd=2)
t_future <- (length(ts_training)+1):(length(ts_training)+12)
reg_pred <- predict(lm_trend, newdata = list(t = t_future))
# ====== ACF ======
acf(ts_data, main = "ACF Original Data")
# Nilai ACF
acf_values <- acf(ts_data, plot = FALSE)
cat("\n=== Nilai ACF ===\n")
##
## === Nilai ACF ===
print(acf_values)
##
## Autocorrelations of series 'ts_data', by lag
##
## 0.0000 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333
## 1.000 0.354 0.143 0.085 0.035 0.114 0.014 0.058 -0.208 -0.160 -0.344
## 0.9167 1.0000 1.0833 1.1667 1.2500 1.3333 1.4167
## -0.356 -0.199 -0.291 -0.167 -0.223 -0.153 -0.036
# ====== PACF ======
pacf(ts_data, main = "PACF Original Data")
# Nilai PACF
pacf_values <- pacf(ts_data, plot = FALSE)
cat("\n=== Nilai PACF ===\n")
##
## === Nilai PACF ===
print(pacf_values)
##
## Partial autocorrelations of series 'ts_data', by lag
##
## 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333 0.9167
## 0.354 0.020 0.032 -0.008 0.114 -0.073 0.072 -0.299 0.015 -0.368 -0.102
## 1.0000 1.0833 1.1667 1.2500 1.3333 1.4167
## -0.104 -0.154 -0.069 -0.087 -0.102 0.098
# ====== UJI ADF ======
adf_test <- adf.test(ts_data)
cat("\n=== Uji ADF (Mean Stasioner?) ===\n")
##
## === Uji ADF (Mean Stasioner?) ===
print(adf_test)
##
## Augmented Dickey-Fuller Test
##
## data: ts_data
## Dickey-Fuller = -3.2627, Lag order = 3, p-value = 0.08607
## alternative hypothesis: stationary
# ====== CEK STASIONER VARIANSI (MANUAL) ======
cat("\n=== Uji Stasioner Variansi (Manual Check) ===\n")
##
## === Uji Stasioner Variansi (Manual Check) ===
n <- length(ts_data)
half <- floor(n/2)
bagian_awal <- ts_data[1:half]
bagian_akhir <- ts_data[(half+1):n]
var_awal <- var(bagian_awal)
var_akhir <- var(bagian_akhir)
mean_awal <- mean(bagian_awal)
mean_akhir <- mean(bagian_akhir)
cat("Mean Bagian Awal :", round(mean_awal, 3), "\n")
## Mean Bagian Awal : 74138807
cat("Mean Bagian Akhir :", round(mean_akhir, 3), "\n\n")
## Mean Bagian Akhir : 57953989
cat("Varians Bagian Awal :", round(var_awal, 3), "\n")
## Varians Bagian Awal : 2.866654e+15
cat("Varians Bagian Akhir :", round(var_akhir, 3), "\n\n")
## Varians Bagian Akhir : 1.2262e+15
if(abs(var_awal - var_akhir) / var_awal < 0.20){
cat("Kesimpulan: Variansi relatif stabil (stasioner variansi)\n")
} else {
cat("Kesimpulan: Variansi berubah signifikan → tidak stasioner variansi\n")
}
## Kesimpulan: Variansi berubah signifikan → tidak stasioner variansi
cat("=== LANGKAH 1: Box-Cox Sebelum Transformasi ===\n")
## === LANGKAH 1: Box-Cox Sebelum Transformasi ===
ts_sqrt <- sqrt(ts_data)
# Plot data asli dan data setelah transformasi
plot(ts_data, type="l", col="darkred", lwd=2,
main="Data Asli (Sebelum Transformasi)",
ylab="Nilai", xlab="Time")
plot(ts_sqrt, type="l", col="darkblue", lwd=2,
main="Data Setelah Transformasi Akar Kuadrat",
ylab="Nilai", xlab="Time")
# --- LANGKAH 2: ACF & PACF Setelah Transformasi ---
par(mfrow=c(1,2))
acf(ts_sqrt, main="ACF - Setelah Transformasi Akar Kuadrat")
pacf(ts_sqrt, main="PACF - Setelah Transformasi Akar Kuadrat")
par(mfrow=c(1,1))
# Simpan nilai numerik ACF & PACF
acf_vals <- acf(ts_sqrt, plot = FALSE)
pacf_vals <- pacf(ts_sqrt, plot = FALSE)
cat("\n=== Nilai Numerik ACF ===\n")
##
## === Nilai Numerik ACF ===
print(acf_vals)
##
## Autocorrelations of series 'ts_sqrt', by lag
##
## 0.0000 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333
## 1.000 0.378 0.201 0.094 0.034 0.089 -0.036 0.058 -0.194 -0.183 -0.311
## 0.9167 1.0000 1.0833 1.1667 1.2500 1.3333 1.4167
## -0.373 -0.221 -0.321 -0.163 -0.228 -0.180 0.016
cat("\n=== Nilai Numerik PACF ===\n")
##
## === Nilai Numerik PACF ===
print(pacf_vals)
##
## Partial autocorrelations of series 'ts_sqrt', by lag
##
## 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333 0.9167
## 0.378 0.068 -0.003 -0.015 0.089 -0.111 0.102 -0.281 -0.036 -0.259 -0.172
## 1.0000 1.0833 1.1667 1.2500 1.3333 1.4167
## -0.046 -0.196 -0.030 -0.133 -0.123 0.138
# --- LANGKAH 3: Uji ADF Setelah Transformasi ---
adf_sqrt <- adf.test(ts_sqrt)
print(adf_sqrt)
##
## Augmented Dickey-Fuller Test
##
## data: ts_sqrt
## Dickey-Fuller = -3.2305, Lag order = 3, p-value = 0.09117
## alternative hypothesis: stationary
cat("\nInterpretasi:\n")
##
## Interpretasi:
if(adf_sqrt$p.value < 0.05){
cat("→ p-value < 0.05 → Data SUDAH stasioner mean setelah transformasi akar.\n")
} else {
cat("→ p-value ≥ 0.05 → Data BELUM stasioner mean → perlu differencing.\n")
}
## → p-value ≥ 0.05 → Data BELUM stasioner mean → perlu differencing.
ts_sqrt_diff <- diff(ts_sqrt)
cat("\n=== Data Setelah Differencing (Transformasi Akar + Diff 1) ===\n")
##
## === Data Setelah Differencing (Transformasi Akar + Diff 1) ===
print(head(ts_sqrt_diff))
## Feb Mar Apr May Jun Jul
## 2020 1122.3815 720.6718 -4747.6859 -5444.3921 5534.9955 4214.2703
cat("\n=== ACF & PACF Setelah Differencing ===\n")
##
## === ACF & PACF Setelah Differencing ===
par(mfrow=c(1,2))
acf(ts_sqrt_diff, main="ACF - Setelah Differencing")
pacf(ts_sqrt_diff, main="PACF - Setelah Differencing")
par(mfrow=c(1,1))
acf_diff_vals <- acf(ts_sqrt_diff, plot = FALSE)
pacf_diff_vals <- pacf(ts_sqrt_diff, plot = FALSE)
cat("\n=== Nilai Numerik ACF Setelah Differencing ===\n")
##
## === Nilai Numerik ACF Setelah Differencing ===
print(acf_diff_vals)
##
## Autocorrelations of series 'ts_sqrt_diff', by lag
##
## 0.0000 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333
## 1.000 -0.371 -0.062 -0.012 -0.066 0.118 -0.203 0.288 -0.195 0.101 -0.028
## 0.9167 1.0000 1.0833 1.1667 1.2500 1.3333 1.4167
## -0.176 0.217 -0.218 0.189 -0.101 -0.110 0.164
cat("\n=== Nilai Numerik PACF Setelah Differencing ===\n")
##
## === Nilai Numerik PACF Setelah Differencing ===
print(pacf_diff_vals)
##
## Partial autocorrelations of series 'ts_sqrt_diff', by lag
##
## 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333 0.9167
## -0.371 -0.231 -0.154 -0.185 -0.005 -0.235 0.153 -0.098 0.086 -0.014 -0.163
## 1.0000 1.0833 1.1667 1.2500 1.3333 1.4167
## 0.021 -0.163 -0.017 -0.065 -0.256 -0.032
cat("\n=== Uji ADF Setelah Differencing ===\n")
##
## === Uji ADF Setelah Differencing ===
adf_diff <- adf.test(ts_sqrt_diff)
## Warning in adf.test(ts_sqrt_diff): p-value smaller than printed p-value
print(adf_diff)
##
## Augmented Dickey-Fuller Test
##
## data: ts_sqrt_diff
## Dickey-Fuller = -5.9242, Lag order = 3, p-value = 0.01
## alternative hypothesis: stationary
cat("\nInterpretasi ADF:\n")
##
## Interpretasi ADF:
if(adf_diff$p.value < 0.05){
cat("→ p-value < 0.05 → Data SUDAH stasioner mean setelah differencing.\n")
} else {
cat("→ Data MASIH belum stasioner, mungkin perlu differencing tambahan.\n")
}
## → p-value < 0.05 → Data SUDAH stasioner mean setelah differencing.
cat("\n=== Uji ARCH / Heteroskedastisitas ===\n")
##
## === Uji ARCH / Heteroskedastisitas ===
library(FinTS)
arch_test <- ArchTest(ts_sqrt_diff)
print(arch_test)
##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: ts_sqrt_diff
## Chi-squared = 4.651, df = 12, p-value = 0.9686
cat("\nInterpretasi ARCH:\n")
##
## Interpretasi ARCH:
if(arch_test$p.value > 0.05){
cat("→ p-value > 0.05 → TIDAK ada heteroskedastisitas → variansi sudah stasioner.\n")
} else {
cat("→ p-value ≤ 0.05 → Ada heteroskedastisitas → variansi BELUM stasioner.\n")
}
## → p-value > 0.05 → TIDAK ada heteroskedastisitas → variansi sudah stasioner.
ts_sqrt <- ts(ts_sqrt, start = c(2020, 1), frequency = 12)
# ------------------------------
# 2. ARIMA Manual (tanpa auto.arima)
# ------------------------------
cat("=== ARIMA MANUAL TANPA auto.arima ===\n")
## === ARIMA MANUAL TANPA auto.arima ===
m_arima_011 <- arima(ts_sqrt, order=c(0,1,1))
m_arima_111 <- arima(ts_sqrt, order=c(1,1,1))
m_arima_012 <- arima(ts_sqrt, order=c(0,1,2))
m_arima_211 <- arima(ts_sqrt, order=c(2,1,1))
m_arima_110 <- arima(ts_sqrt, order=c(1,1,0))
# ------------------------------
# 3. Bandingkan AIC
# ------------------------------
cat("\n=== AIC SETIAP MODEL ===\n")
##
## === AIC SETIAP MODEL ===
AIC(m_arima_011, m_arima_111, m_arima_012, m_arima_211, m_arima_110)
## df AIC
## m_arima_011 2 1111.151
## m_arima_111 3 1109.548
## m_arima_012 3 1112.100
## m_arima_211 4 1110.993
## m_arima_110 2 1116.817
# ------------------------------
# 4. Pilih model terbaik
# ------------------------------
model_best <- m_arima_110
cat("\n=== MODEL TERPILIH ===\n")
##
## === MODEL TERPILIH ===
print(model_best)
##
## Call:
## arima(x = ts_sqrt, order = c(1, 1, 0))
##
## Coefficients:
## ar1
## -0.3655
## s.e. 0.1198
##
## sigma^2 estimated as 9074748: log likelihood = -556.41, aic = 1116.82
# ------------------------------
# 5. Diagnostik Residual
# ------------------------------
par(mfrow=c(2,2))
plot(model_best$residuals, main="Residual Plot")
acf(model_best$residuals, main="ACF Residual")
pacf(model_best$residuals, main="PACF Residual")
hist(model_best$residuals, main="Histogram Residual", xlab="Residual")
par(mfrow=c(1,1))
# ------------------------------
# 6. Uji Ljung-Box (independensi residual)
# ------------------------------
cat("\n=== UJI LJUNG-BOX ===\n")
##
## === UJI LJUNG-BOX ===
print(Box.test(model_best$residuals, lag=12, type="Ljung"))
##
## Box-Ljung test
##
## data: model_best$residuals
## X-squared = 12.462, df = 12, p-value = 0.4093
# ------------------------------
# 7. Uji ARCH (heteroskedastisitas)
# ------------------------------
cat("\n=== UJI ARCH (HETEROSKEDASTISITAS) ===\n")
##
## === UJI ARCH (HETEROSKEDASTISITAS) ===
arch_test <- ArchTest(model_best$residuals, lags=12)
print(arch_test)
##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: model_best$residuals
## Chi-squared = 6.3834, df = 12, p-value = 0.8955
# ------------------------------
# 8. Forecast Tahun 2024 (12 bulan)
# ------------------------------
cat("\n=== FORECAST TAHUN 2024 (12 BULAN) ===\n")
##
## === FORECAST TAHUN 2024 (12 BULAN) ===
forecast_2024 <- forecast(model_best, h = 12)
print(forecast_2024)
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Jan 2025 7686.923 3826.3354 11547.51 1782.6639 13591.18
## Feb 2025 7596.489 3024.2595 12168.72 603.8673 14589.11
## Mar 2025 7629.538 2179.9093 13079.17 -704.9501 15964.03
## Apr 2025 7617.460 1501.1194 13733.80 -1736.6760 16971.60
## May 2025 7621.874 875.9137 14367.84 -2695.1822 17938.93
## Jun 2025 7620.261 308.3706 14932.15 -3562.3105 18802.83
## Jul 2025 7620.851 -219.5236 15461.23 -4369.9671 19611.67
## Aug 2025 7620.635 -713.6424 15954.91 -5125.5425 20366.81
## Sep 2025 7620.714 -1180.1862 16421.61 -5839.1013 21080.53
## Oct 2025 7620.685 -1623.1757 16864.55 -6516.5801 21757.95
## Nov 2025 7620.696 -2045.8969 17287.29 -7163.0820 22404.47
## Dec 2025 7620.692 -2450.8881 17692.27 -7782.4606 23023.84
forecast_original_scale <- forecast_2024$mean^2
cat("\nForecast di skala asli (kuadrat dari transformasi akar):\n")
##
## Forecast di skala asli (kuadrat dari transformasi akar):
print(forecast_original_scale)
## Jan Feb Mar Apr May Jun Jul Aug
## 2025 59088780 57706648 58209856 58025704 58092969 58068382 58077367 58074083
## Sep Oct Nov Dec
## 2025 58075283 58074845 58075005 58074946
plot(forecast_2024,
main = "Forecast ARIMA Manual (Transformasi Akar)",
xlab = "Tahun",
ylab = "Nilai",
xlim = c(2020, 2025))
# ------------------------------
# 9. Uji Normalitas Residual (Kolmogorov-Smirnov)
# ------------------------------
cat("\n=== UJI KOLMOGOROV–SMIRNOV (Normalitas) ===\n")
##
## === UJI KOLMOGOROV–SMIRNOV (Normalitas) ===
resid <- model_best$residuals
ks <- ks.test(resid, "pnorm", mean(resid), sd(resid))
print(ks)
##
## Exact one-sample Kolmogorov-Smirnov test
##
## data: resid
## D = 0.063033, p-value = 0.9588
## alternative hypothesis: two-sided
if (ks$p.value > 0.05) {
cat("→ Residual NORMAL (p > 0.05)\n")
} else {
cat("→ Residual TIDAK NORMAL (p ≤ 0.05)\n")
}
## → Residual NORMAL (p > 0.05)
rmse <- function(a,b) sqrt(mean((a-b)^2))
mape <- function(a,b) mean(abs((a-b)/a)) * 100
smape <- function(a,b) mean(2 * abs(a-b) / (abs(a)+abs(b))) * 100
actual_sqrt <- ts_sqrt[2:length(ts_sqrt)]
pred_sqrt <- ts_sqrt[2:length(ts_sqrt)] - model_best$residuals
## Warning in `-.default`(ts_sqrt[2:length(ts_sqrt)], model_best$residuals):
## longer object length is not a multiple of shorter object length
n <- min(length(actual_sqrt), length(pred_sqrt))
actual_sqrt <- actual_sqrt[1:n]
pred_sqrt <- pred_sqrt[1:n]
eval_sqrt <- data.frame(
Model = c("Model_Best (Transformasi Akar)"),
RMSE = c(rmse(actual_sqrt, pred_sqrt)),
MAPE = c(mape(actual_sqrt, pred_sqrt)),
SMAPE = c(smape(actual_sqrt, pred_sqrt))
)
cat("\n=== Evaluasi di Skala Transformasi Akar ===\n")
##
## === Evaluasi di Skala Transformasi Akar ===
print(eval_sqrt)
## Model RMSE MAPE SMAPE
## 1 Model_Best (Transformasi Akar) 3011.388 37.60024 38.41022
actual_orig <- actual_sqrt^2
pred_orig <- pred_sqrt^2
eval_orig <- data.frame(
Model = c("Model_Best (Skala Asli)"),
RMSE = c(rmse(actual_orig, pred_orig)),
MAPE = c(mape(actual_orig, pred_orig)),
SMAPE = c(smape(actual_orig, pred_orig))
)
cat("\n=== Evaluasi di Skala Asli (Kuadrat dari Transformasi Akar) ===\n")
##
## === Evaluasi di Skala Asli (Kuadrat dari Transformasi Akar) ===
print(eval_orig)
## Model RMSE MAPE SMAPE
## 1 Model_Best (Skala Asli) 51658362 81.59334 62.9538