R Notebook

SIMULASI UNTUK DISTRIBUSI DISKRIT

Distribusi Binomial

set.seed(123)
n <- 500

n_trials <- 10
p_success <- 0.5
binomial_data <- rbinom(n, size = n_trials, prob = p_success)

hist(binomial_data, breaks = 30, main = "Histogram Distribusi Binomial", 
     xlab = "Jumlah Sukses", col = "aquamarine")

## Distribusi Poisson

set.seed(123)
n <- 500

lambda_val <- 3
poisson_data <- rpois(n, lambda = lambda_val)

hist(poisson_data, breaks = 30, main = "Histogram Distribusi Poisson", 
     xlab = "Jumlah Kejadian", col = "aquamarine")

# SIMULASI UNTUK DISTRIBUSI DISKRIT ## Distribusi Uniform

uniform_data <- runif(n, min = 0, max = 1)

hist(uniform_data, breaks = 30, main = "Histogram Distribusi Uniform", 
     xlab = "Nilai", col = "lightyellow")

## Distribusi Normal

mu <- 0
sigma <- 1
normal_data <- rnorm(n, mean = mu, sd = sigma)

# Visualisasi
hist(normal_data, breaks = 30, main = "Histogram Distribusi Normal", 
     xlab = "Nilai", col = "lightyellow")

## Distribusi Eksponensial

rate_val <- 1
exp_data <- rexp(n, rate = rate_val)

# Visualisasi
hist(exp_data, breaks = 30, main = "Histogram Distribusi Eksponensial", 
     xlab = "Nilai", col = "lightyellow")

# Kasus Simulasi Logistik Variabel Diskrit ## Distribusi Poisson

set.seed(123)
n_hari <- 30

lambda_truk <- 12
data_truk <- rpois(n_hari, lambda = lambda_truk)

print("Data jumlah truk harian:")

## [1] "Data jumlah truk harian:"

print(data_truk)

##  [1] 10 11 17 12 12 17 13  7  6 16 13 13 12 10 18 13  5  9  8 10  8  9 11  7  8
## [26] 10  7 15 15 14

hist(data_truk, 
     main = "Simulasi Kedatangan Truk Harian (Poisson)", 
     xlab = "Jumlah Truk", 
     ylab = "Frekuensi (Hari)",
     col = "steelblue", 
     border = "white")

Distribusi Poisson

Misal: Tiap batch ada 50 paket, peluang sortir benar adalah 98% (0.98).

n_paket_per_batch <- 50
p_benar <- 0.98
n_batch_simulasi <- 100 # Simulasi untuk 100 batch sortir

data_sortir_benar <- rbinom(n_batch_simulasi, size = n_paket_per_batch, prob = p_benar)

hist(data_sortir_benar, 
     main = "Simulasi Akurasi Sortir per 50 Paket (Binomial)", 
     xlab = "Jumlah Paket yang Benar Disortir", 
     ylab = "Frekuensi (Batch)",
     col = "steelblue",
     border = "white")