LAPORAN PRAKTIKUM PERTEMUAN 1
Febry Widyan Adha
2026-09-21
BAB I PENDAHULUAN
1.1 Latar Belakang Masalah
Tuliskan latar belakang penelitian di sini.
1.2 Rumusan Masalah
Tuliskan pertanyaan penelitian yang ingin dijawab.
1.3 Tujuan
Tuliskan tujuan penelitian .
BAB II TINJAUAN PUSTAKA
2.1 Landasan Teori 1
Jelaskan teori pertama yang mendukung penelitian ini. pada sintaks baris ke-91 itu include_grahics(“masukkan nama file gambarnya”) pastikan file gambar dan file rmd ini berada dalam 1 folder yang sama.
Berikut contoh menampilkan gambar dari komputer pribadi:
Gambar 1. Contoh encoding kategori
Gambar 2. Perbandingan metode scaling
2.2 Landasan Teori 2
pagar 1 untuk judul besar, pagar 2 untuk sub judul. kalo mau nambahkan equation gunakan dolarnya 1 je untuk inline seperti ini contohnya \(a=5%\) sedangkan kalo mau equationnya ditengah itu gunakan tanda dollar sebanyak 2 seperti contoh dibawah ini.
\[ I = \frac{n}{W} \frac{\sum_i \sum_j w_{ij}(x_i - \bar{x})(x_j - \bar{x})}{\sum_i (x_i - \bar{x})^2} \] sedangkan untuk tulisan italik gunain tanda * 1 saja. contoh ini teksnya akan jadi italik.
untuk menulis sintaks R nya teman-teman bisa mengklik huruf C yang warna hijau nggak jauh dari tab run dan disebalh tanda arah atas
BAB III METODE PENELITIAN
3.1 Sumber Data
seperti biasa tuliskan disini ya
3.2 Variabel Penelitian
Daftar dan definisikan setiap variabel yang digunakan
3.3 Langkah-langkah Analisis
Jelaskan prosedur analisis mulai dari tahap awal hingga akhir.
BAB IV HASIL DAN PEMBAHASAN
4.1 Library
kolom kotak abu ini bagian tempat kalian menuliskan sintaksnya ya, sedangkan tombol pause warna hijau dikanan atas itu untuk runingnya tapi bisa jg makai ctrl+enter
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## MAE, RMSE
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## calibration
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## lift## # A tibble: 6 × 5
## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 23 95 5 NA
## 2 1 24 90 1 NA
## 3 1 26.8 77 1 NA
## 4 1 29.6 62 2 2
## 5 1 30.8 56 1 7
## 6 1 31 55 1 7## tibble [743 × 5] (S3: tbl_df/tbl/data.frame)
## $ Hujan : num [1:743] 1 1 1 1 1 1 1 2 2 2 ...
## $ Suhu : num [1:743] 23 24 26.8 29.6 30.8 31 30.4 30.9 30.2 29.7 ...
## $ Kelembapan : num [1:743] 95 90 77 62 56 55 57 58 62 62 ...
## $ Keadaan_Cuaca : num [1:743] 5 1 1 2 1 1 3 2 2 2 ...
## $ Kecepatan_Angin: num [1:743] NA NA NA 2 7 7 9 10 8 7 ...jangan lupa jelaskan statistik deskriptifnya itu apa maksudnya ya.apa yang dibutuhkan untuk Data preprocessing.
4.2 Handling Missing Value
## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0 0 0 9 314## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0.00000000 0.00000000 0.00000000 0.01211306 0.42261104## # A tibble: 9 × 5
## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 26 92 NA NA
## 2 1 23.8 98 NA 5
## 3 1 23.3 97 NA NA
## 4 2 28.8 79 NA 12
## 5 2 24.6 92 NA NA
## 6 1 22.8 98 NA 4
## 7 2 25.5 96 NA NA
## 8 2 27 80 NA 4
## 9 2 31 57 NA 6## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0 0 0 0 0## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0 0 0 0 314df_imp2 <- data
df_imp2$Kecepatan_Angin[is.na(df_imp2$Kecepatan_Angin)] <- mean(
df_imp2$Kecepatan_Angin, na.rm = TRUE )
colSums(is.na(df_imp2)) ## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0 0 0 9 0df_imp3 <- data
df_imp3$Kecepatan_Angin[is.na(df_imp3$Kecepatan_Angin)] <- median(
df_imp3$Kecepatan_Angin, na.rm = TRUE )
colSums(is.na(df_imp1)) ## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0 0 0 0 314# Interpolasi
df_imp4 <- data
df_imp4$Keadaan_Cuaca <- na.approx(df_imp4$Keadaan_Cuaca, na.rm = FALSE)
colSums(is.na(df_imp4)) ## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0 0 0 0 314median_value <- median(df_imp4$Kecepatan_Angin, na.rm = TRUE)
df_imp4$Kecepatan_Angin[is.na(df_imp4$Kecepatan_Angin)] <- median_value
colSums(is.na(df_imp4)) ## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## 0 0 0 0 04.3 Kardinalitas
## # A tibble: 10 × 5
## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 23 95 5 6
## 2 1 24 90 1 6
## 3 1 26.8 77 1 6
## 4 1 29.6 62 2 2
## 5 1 30.8 56 1 7
## 6 1 31 55 1 7
## 7 1 30.4 57 3 9
## 8 2 30.9 58 2 10
## 9 2 30.2 62 2 8
## 10 2 29.7 62 2 7## [1] 5.0 1.0 2.0 3.0 14.0 60.0 61.0 21.0 29.0 10.0 16.0 62.0 63.0 65.0 15.0
## [16] 17.0 13.0 95.0 91.0 49.0 97.0 56.0 1.5bins <- c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
labels <- 0:9
df_imp4$Keadaan_Cuaca_reduced <- cut(
df_imp4$Keadaan_Cuaca,
breaks = bins,
labels = labels,
right = TRUE,
include.lowest = TRUE
)
cat("--- Hasil Perbandingan ---\n")## --- Hasil Perbandingan ---## # A tibble: 10 × 6
## Hujan Suhu Kelembapan Keadaan_Cuaca Kecepatan_Angin Keadaan_Cuaca_reduced
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
## 1 2 30.4 68 2 11 0
## 2 2 25.4 89 2 6 0
## 3 2 25.7 92 13 6 1
## 4 2 25.5 90 2 7 0
## 5 2 23.9 97 95 6 9
## 6 2 26.4 89 2 6 0
## 7 2 23.5 98 21 6 2
## 8 1 23.5 98 61 5 6
## 9 2 25.7 86 2 3 0
## 10 1 23.8 97 91 6 9##
## --- Pengecekan Kardinalitas ---## Jumlah kategori di "Keadaan_Cuaca" asli : 23cat('Jumlah kategori di "Keadaan_Cuaca_reduced" :', length(unique(df_imp4$Keadaan_Cuaca_reduced)), "\n")## Jumlah kategori di "Keadaan_Cuaca_reduced" : 7##
## Kategori unik yang baru (reduced):## [1] 0 1 5 6 2 9 4
## Levels: 0 1 2 3 4 5 6 7 8 94.4 Splitting Data
set.seed(200)
split_stratify <- initial_split(df_imp4, prop = 0.8, strata = Hujan)
X_train <- training(split_stratify) %>% select(-Hujan)
X_test <- testing(split_stratify) %>% select(-Hujan)
y_train <- training(split_stratify)$Hujan
y_test <- testing(split_stratify)$Hujan
dim(X_test)## [1] 149 54.5 Handling Outlier
## Handling Outlier
list_num <- c("Suhu", "Kelembapan", "Kecepatan_Angin")
list_outlier <- c()
list_lower_bound <- c()
list_upper_bound <- c()
for (i in list_num) {
Q1 <- quantile(X_train[[i]], 0.25, na.rm = TRUE)
Q3 <- quantile(X_train[[i]], 0.75, na.rm = TRUE)
IQR_val <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_val
upper_bound <- Q3 + 1.5 * IQR_val
list_lower_bound <- c(list_lower_bound, lower_bound)
list_upper_bound <- c(list_upper_bound, upper_bound)
num_outliers_lower <- sum(X_train[[i]] < lower_bound, na.rm = TRUE)
num_outliers_upper <- sum(X_train[[i]] > upper_bound, na.rm = TRUE)
total_outliers <- num_outliers_lower + num_outliers_upper
list_outlier <- c(list_outlier, total_outliers)
}
outliers <- data.frame(
Kolom = list_num,
Jumlah_Outlier = list_outlier,
Lower_Bound = list_lower_bound,
Upper_Bound = list_upper_bound
)
outliers## Kolom Jumlah_Outlier Lower_Bound Upper_Bound
## 1 Suhu 0 17.4625 35.7625
## 2 Kelembapan 0 46.5000 122.5000
## 3 Kecepatan_Angin 106 2.6250 9.6250df_num <- X_train[, list_num]
nilai_skew <- c()
nilai_skew_normal <- c()
for (i in colnames(df_num)) {
skew_val <- skewness(X_train[[i]], na.rm = TRUE)
if (skew_val >= -0.5 && skew_val <= 0.5) {
nilai_skew_normal <- c(nilai_skew_normal, i)
} else {
nilai_skew <- c(nilai_skew, i)
}
}
cat("kolom yang mempunyai nilai skewness sedang :", nilai_skew, "\n")## kolom yang mempunyai nilai skewness sedang : Kelembapan Kecepatan_Angin## kolom yang mempunyai nilai skewness normal : Suhuoutliers_indexed <- outliers %>% filter(Kolom == "Kecepatan_Angin")
lower_kecepatan <- outliers_indexed$Lower_Bound
upper_kecepatan <- outliers_indexed$Upper_Bound
outliers_KecepAngin <- ifelse(
X_train$Kecepatan_Angin > upper_kecepatan, TRUE,
ifelse(X_train$Kecepatan_Angin < lower_kecepatan, TRUE, FALSE)
)
# Trimming
X_train_trimmed1 <- X_train[!outliers_KecepAngin, ]
cat("Size dataset - Before trimming :", dim(X_train), "\n")## Size dataset - Before trimming : 594 5## Size dataset - After trimming : 488 5# Capping
X_train_capped <- X_train
X_train_capped$Kecepatan_Angin <- Winsorize(
X_train$Kecepatan_Angin,
val = c(lower_bound, upper_bound)
)
X_test_capped <- X_test
X_test_capped$Kecepatan_Angin <- Winsorize(
X_test$Kecepatan_Angin,
val = c(lower_bound, upper_bound)
)
diagnostic_plots <- function(df, variable) {
p1 <- ggplot(df, aes(x = .data[[variable]])) +
geom_histogram(bins = 30, fill = "#008080", color = "black") +
ggtitle("Histogram") +
theme_minimal()
p2 <- ggplot(df, aes(y = .data[[variable]])) +
geom_boxplot(fill = "#008080") +
ggtitle("Boxplot") +
theme_minimal()
grid.arrange(p1, p2, ncol = 2)
}
for (col in list_num) {
cat(col, "- Before Capping\n")
diagnostic_plots(X_train, col)
cat("\n", col, "- After Capping\n")
diagnostic_plots(X_train_capped, col)
}## Suhu - Before Capping
##
## Suhu - After Capping
## Kelembapan - Before Capping
##
## Kelembapan - After Capping
## Kecepatan_Angin - Before Capping
##
## Kecepatan_Angin - After Capping
4.6 Scalling Data
## Scalling
nilai_skew <- c()
nilai_skew_normal <- c()
for (i in colnames(df_num)) {
skew_val <- skewness(X_train_capped[[i]], na.rm = TRUE)
if (skew_val >= -0.5 && skew_val <= 0.5) {
nilai_skew_normal <- c(nilai_skew_normal, i)
} else {
nilai_skew <- c(nilai_skew, i)
}
}
cat("kolom yang mempunyai nilai skewness sedang :", nilai_skew, "\n")## kolom yang mempunyai nilai skewness sedang : Kelembapan## kolom yang mempunyai nilai skewness normal : Suhu Kecepatan_Angin# Standard scaler
mean_val <- sapply(X_train_capped[nilai_skew_normal], mean, na.rm = TRUE)
sd_val <- sapply(X_train_capped[nilai_skew_normal], sd, na.rm = TRUE)
for (col in nilai_skew_normal) {
X_train_capped[[col]] <- (X_train_capped[[col]] - mean_val[col]) / sd_val[col]
}
for (col in nilai_skew_normal) {
X_test_capped[[col]] <- (X_test_capped[[col]] - mean_val[col]) / sd_val[col]
}
# Robust scaler
median_val <- sapply(X_train_capped[nilai_skew], median, na.rm = TRUE)
iqr_val <- sapply(X_train_capped[nilai_skew], IQR, na.rm = TRUE)
for (col in nilai_skew) {
X_train_capped[[col]] <- (X_train_capped[[col]] - median_val[col]) / iqr_val[col]
}
for (col in nilai_skew) {
X_test_capped[[col]] <- (X_test_capped[[col]] - median_val[col]) / iqr_val[col]
}
X_train_scale <- X_train_capped
X_test_scale <- X_test_capped4.7 Encoding
## Encoding - One Hot Encoder
list_cat <- c("Keadaan_Cuaca_reduced")
resep_encode <- recipe(~ Keadaan_Cuaca_reduced, data = X_train_scale) %>%
step_dummy(all_of(list_cat), one_hot = TRUE) %>%
prep(training = X_train_scale)
X_train_encoded <- bake(resep_encode, new_data = NULL)
X_test_encoded <- bake(resep_encode, new_data = X_test_scale)
X_train_encoded## # A tibble: 594 × 10
## Keadaan_Cuaca_reduced_X0 Keadaan_Cuaca_reduced_X1 Keadaan_Cuaca_reduced_X2
## <dbl> <dbl> <dbl>
## 1 1 0 0
## 2 1 0 0
## 3 1 0 0
## 4 1 0 0
## 5 1 0 0
## 6 1 0 0
## 7 1 0 0
## 8 1 0 0
## 9 1 0 0
## 10 1 0 0
## # ℹ 584 more rows
## # ℹ 7 more variables: Keadaan_Cuaca_reduced_X3 <dbl>,
## # Keadaan_Cuaca_reduced_X4 <dbl>, Keadaan_Cuaca_reduced_X5 <dbl>,
## # Keadaan_Cuaca_reduced_X6 <dbl>, Keadaan_Cuaca_reduced_X7 <dbl>,
## # Keadaan_Cuaca_reduced_X8 <dbl>, Keadaan_Cuaca_reduced_X9 <dbl>## # A tibble: 149 × 10
## Keadaan_Cuaca_reduced_X0 Keadaan_Cuaca_reduced_X1 Keadaan_Cuaca_reduced_X2
## <dbl> <dbl> <dbl>
## 1 1 0 0
## 2 1 0 0
## 3 1 0 0
## 4 1 0 0
## 5 1 0 0
## 6 1 0 0
## 7 1 0 0
## 8 1 0 0
## 9 1 0 0
## 10 0 0 0
## # ℹ 139 more rows
## # ℹ 7 more variables: Keadaan_Cuaca_reduced_X3 <dbl>,
## # Keadaan_Cuaca_reduced_X4 <dbl>, Keadaan_Cuaca_reduced_X5 <dbl>,
## # Keadaan_Cuaca_reduced_X6 <dbl>, Keadaan_Cuaca_reduced_X7 <dbl>,
## # Keadaan_Cuaca_reduced_X8 <dbl>, Keadaan_Cuaca_reduced_X9 <dbl>BAB V KESIMPULAN
5.1 Kesimpulan
tuliskan kesimpulan apa yang bisa diambil dari hasil processing data tersebut
DAFTAR PUSTAKA
Kalian bisa buat referensi seperti laporan praktikum biasa dengan menggunakan word terus ditempelkan kesini atau bisa juga menggunakan sistasi langsung dari rmaakrdown ini.