Pemprogram Sains Data

UTS Pemprograman Sains Data

1 Check Sheet

  1. Data Check Sheet
  1. Frequency Tabulation
# Hitung frekuensi tiap jenis cacat produk
library(dplyr)
library(DT)

# Asumsikan data dummy sebelumnya sudah dibuat dan bernama 'bread_data'

defect_summary <- bread_data %>%
  count(Jenis_Cacat, sort = TRUE) %>%
  rename(Frekuensi = n)

# Tampilkan tabel ringkasan
datatable(
  defect_summary,
  options = list(
    scrollCollapse = TRUE,
    searching = FALSE,
    paging = FALSE
  ),
  rownames = FALSE,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left;
             font-size: 18px; font-weight: bold;',
    'Check Sheet: Frekuensi Jenis Cacat Produk'
  ),
  class = 'stripe hover compact'
)
  1. Visualisasi
library(dplyr)

reason_summary <- bread_data %>%
  group_by(Jenis_Cacat) %>%
  summarise(Frequency = sum(Jumlah_Cacat, na.rm = TRUE)) %>%
  arrange(desc(Frequency)) %>%
  rename(Reason = Jenis_Cacat)
library(plotly)
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.4.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
# Visualisasi horizontal bar chart
plot_ly(reason_summary,
        x = ~Frequency,
        y = ~reorder(Reason, Frequency),
        type = 'bar',
        orientation = 'h',
        marker = list(
          color = ~Frequency,
          colorscale = 'Viridis',
          showscale = TRUE
        )
) %>%
  layout(
    title = list(text = "Check Sheet: Frekuensi Jenis Cacat Produk Roti", font = list(size = 18)),
    xaxis = list(title = "Frekuensi"),
    yaxis = list(title = "Jenis Cacat"),
    margin = list(l = 120)
  )

2 Histogram

# Install and load necessary libraries
if (!require("plotly")) install.packages("plotly")
library(plotly)

# Gunakan data aktual dari bread_data
actual_data <- bread_data$Berat_Batch_kg

# Hitung kurva densitas
density_data <- density(actual_data)

# Buat histogram menggunakan plotly
histogram_plot <- plot_ly(
  x = actual_data,
  type = 'histogram',
  marker = list(color = 'lightblue', line = list(color = 'black', width = 1)),
  name = 'Histogram Berat Batch (kg)',
  nbinsx = 30,
  opacity = 0.6,
  showlegend = TRUE
) %>%
  # Tambahkan kurva densitas
  add_trace(
    x = density_data$x,
    y = density_data$y * length(actual_data) * diff(range(actual_data)) / 30,
    type = 'scatter',
    mode = 'lines',
    name = 'Kurva Densitas',
    line = list(color = 'black', width = 3),
    showlegend = TRUE
  ) %>%
  layout(
    title = 'Histogram Berat Batch (kg) dengan Kurva Densitas',
    xaxis = list(title = 'Berat Batch (kg)', showgrid = FALSE),
    yaxis = list(title = 'Frekuensi / Densitas', showgrid = FALSE),
    bargap = 0.1,
    plot_bgcolor = 'white',
    paper_bgcolor = 'white',
    showlegend = TRUE,
    legend = list(
      orientation = 'v',
      x = 0.98,
      xanchor = 'right',
      y = 0.98,
      yanchor = 'top',
      bgcolor = 'rgba(255,255,255,0.8)',
      bordercolor = 'black',
      borderwidth = 0.3
    )
  )

# Tampilkan plot
histogram_plot
## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...
## Warning: 'scatter' objects don't have these attributes: 'nbinsx'
## Valid attributes include:
## 'cliponaxis', 'connectgaps', 'customdata', 'customdatasrc', 'dx', 'dy', 'error_x', 'error_y', 'fill', 'fillcolor', 'fillpattern', 'groupnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'marker', 'meta', 'metasrc', 'mode', 'name', 'opacity', 'orientation', 'selected', 'selectedpoints', 'showlegend', 'stackgaps', 'stackgroup', 'stream', 'text', 'textfont', 'textposition', 'textpositionsrc', 'textsrc', 'texttemplate', 'texttemplatesrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'

3 Pareto Chart

# Load libraries
library(dplyr)
library(plotly)
library(RColorBrewer)

# Filter data hanya untuk produk cacat
defective_data <- bread_data %>%
  filter(Jumlah_Cacat > 0)

# Summarize frekuensi jenis cacat
pareto_data <- defective_data %>%
  count(Jenis_Cacat, sort = TRUE) %>%
  mutate(
    cum_freq = cumsum(n) / sum(n) * 100  # persentase kumulatif
  )

# Generate warna berbeda untuk setiap jenis cacat
colors <- RColorBrewer::brewer.pal(n = max(3, min(12, nrow(pareto_data))), name = "Set3")

# Buat objek plotly
fig <- plot_ly()

# Tambahkan Bar Chart (jumlah cacat)
fig <- fig %>% add_bars(
  x = ~reorder(pareto_data$Jenis_Cacat, -pareto_data$n),
  y = ~pareto_data$n,
  name = 'Jumlah Cacat',
  marker = list(color = colors),
  yaxis = "y1"
)

# Tambahkan garis kumulatif
fig <- fig %>% add_lines(
  x = ~reorder(pareto_data$Jenis_Cacat, -pareto_data$n),
  y = ~pareto_data$cum_freq,
  name = 'Kumulatif (%)',
  yaxis = "y2",
  line = list(color = 'red', dash = 'dash')
)

# Tambahkan garis cutoff 80%
fig <- fig %>% add_lines(
  x = ~reorder(pareto_data$Jenis_Cacat, -pareto_data$n),
  y = rep(80, nrow(pareto_data)),
  name = 'Cut-off 80%',
  yaxis = "y2",
  line = list(color = 'green', dash = 'dot')
)

# Layout akhir
fig <- fig %>% layout(
  title = "Pareto Chart - Jenis Cacat Produk",
  xaxis = list(
    title = "Jenis Cacat",
    tickangle = -45
  ),
  yaxis = list(title = "Jumlah Cacat"),
  yaxis2 = list(
    title = "Kumulatif (%)",
    overlaying = "y",
    side = "right",
    range = c(0, 100)
  ),
  legend = list(x = 0.8, y = 0.75),
  shapes = list(
    list(
      type = "line",
      x0 = -0.5,
      x1 = nrow(pareto_data) - 0.5,
      y0 = 80,
      y1 = 80,
      yref = "y2",
      line = list(color = "green", width = 2, dash = "dot")
    )
  )
)

# Tampilkan chart
fig

4 Fishbone

library(DiagrammeR)
## Warning: package 'DiagrammeR' was built under R version 4.4.3
library(DiagrammeRsvg)
## Warning: package 'DiagrammeRsvg' was built under R version 4.4.3
library(rsvg)
## Warning: package 'rsvg' was built under R version 4.4.3
## Linking to librsvg 2.57.0
library(knitr)

graph <- grViz("
digraph fishbone {
  graph [layout = dot, rankdir = LR]
  node [fontname=Helvetica, fontsize=25, style=filled]

  Problem [label='Cacat Produk Roti', shape=ellipse, fillcolor=lightcoral, width=5.0, height=1.2]

  # Kategori utama
  node [shape=diamond, width=2.5, height=1.0, fillcolor='#FFD700']
  Man [label='Man']
  Machine [label='Machine']
  Method [label='Method']
  Material [label='Material']
  Environment [label='Environment']
  Measurement [label='Measurement']

  # Subkategori
  node [shape=ellipse, width=2.5, height=0.6, fillcolor='#90EE90']
  
  # Man
  Man1 [label='Kurangnya pelatihan operator']
  Man2 [label='Kelelahan saat shift malam']

  # Machine
  Machine1 [label='Suhu mesin tidak stabil']
  Machine2 [label='Mesin sering rusak']

  # Method
  Method1 [label='Prosedur standar tidak diikuti']
  Method2 [label='Kontrol kualitas tidak konsisten']

  # Material
  Material1 [label='Bahan baku tidak segar']
  Material2 [label='Komposisi adonan tidak tepat']

  # Environment
  Environment1 [label='Kelembaban tinggi di musim hujan']
  Environment2 [label='Ventilasi pabrik buruk']

  # Measurement
  Measurement1 [label='Alat ukur kalibrasi buruk']
  Measurement2 [label='Data produksi tidak akurat']

  # Hubungkan kategori ke masalah
  Man -> Problem
  Machine -> Problem
  Method -> Problem
  Material -> Problem
  Environment -> Problem
  Measurement -> Problem

  # Hubungkan subkategori ke kategori
  Man1 -> Man
  Man2 -> Man

  Machine1 -> Machine
  Machine2 -> Machine

  Method1 -> Method
  Method2 -> Method

  Material1 -> Material
  Material2 -> Material

  Environment1 -> Environment
  Environment2 -> Environment

  Measurement1 -> Measurement
  Measurement2 -> Measurement
}
")

# Simpan sebagai gambar
dir.create("images/bab8", recursive = TRUE, showWarnings = FALSE)
svg_code <- export_svg(graph)
rsvg_png(charToRaw(svg_code), file = "images/bab8/fishbone_roti_qc.png", width = 3000, height = 3000)
rsvg_pdf(charToRaw(svg_code), file = "images/bab8/fishbone_roti_qc.pdf")

# Tampilkan dalam laporan
knitr::include_graphics("images/bab8/fishbone_roti_qc.png")

5 Scatter Diagram

# Pastikan package sudah terinstal
# install.packages("plotly") # Jika belum terinstal

library(plotly)

# Scatter plot suhu mesin vs jumlah cacat
correlation_value <- cor(bread_data$Suhu_Mesin_C, bread_data$Jumlah_Cacat)

fig <- plot_ly(
  data = bread_data,
  x = ~Suhu_Mesin_C,
  y = ~Jumlah_Cacat,
  type = 'scatter',
  mode = 'markers',
  marker = list(color = 'black', size = 7)
) %>%
  add_lines(
    x = bread_data$Suhu_Mesin_C,
    y = predict(lm(Jumlah_Cacat ~ Suhu_Mesin_C, data = bread_data)),
    line = list(color = 'red', dash = 'solid')
  ) %>%
  layout(
    title = paste("Scatter Plot: Suhu Mesin vs Jumlah Cacat\nKorelasi:", round(correlation_value, 2)),
    xaxis = list(title = "Suhu Mesin (°C)"),
    yaxis = list(title = "Jumlah Cacat")
  )

# Tampilkan plot
fig
## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...

6 Control Chart

library(plotly)
library(dplyr)

# Ambil data berat batch dan urutkan berdasarkan tanggal
berat_data <- bread_data %>%
  arrange(Tanggal) %>%
  mutate(Hari = row_number()) %>%
  select(Hari, Tanggal, Berat = Berat_Batch_kg)

# Tentukan batas kendali berdasarkan rata-rata dan standar deviasi
CL <- mean(berat_data$Berat)
SD <- sd(berat_data$Berat)
UCL <- CL + 3 * SD
LCL <- CL - 3 * SD

# Tandai outlier
berat_data <- berat_data %>%
  mutate(Outlier = ifelse(Berat > UCL | Berat < LCL, "Ya", "Tidak"))

# Buat plot kontrol chart
plot_ly(berat_data, x = ~Hari, y = ~Berat, type = 'scatter', mode = 'lines+markers',
        line = list(color = 'red'), 
        marker = list(size = 8, color = ifelse(berat_data$Outlier == "Ya", "red", "blue")),
        hoverinfo = 'text',
        text = ~paste("Tanggal:", Tanggal, "<br>Berat:", Berat, "kg")) %>%
  add_lines(y = rep(CL, nrow(berat_data)), name = "CL", line = list(color = 'green', dash = 'dot')) %>%
  add_lines(y = rep(UCL, nrow(berat_data)), name = "UCL", line = list(color = 'red', dash = 'dot')) %>%
  add_lines(y = rep(LCL, nrow(berat_data)), name = "LCL", line = list(color = 'red', dash = 'dot')) %>%
  layout(title = "Control Chart – Konsistensi Berat Batch Produk Roti",
         xaxis = list(title = "Urutan Batch (Hari Produksi)"),
         yaxis = list(title = "Berat Batch (kg)"),
         legend = list(orientation = 'h', x = 0.3, y = -0.2))
## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...
## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...
## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...

7 Flowchart

Logo

---
title: "Pemprogram Sains Data"
subtitle: "UTS Pemprograman Sains Data"
author: 
  - "Nova Sitorus 52240023"
  - "Olivia"
date:  "`r format(Sys.Date(), '%B %d, %Y')`"
output:
  rmdformats::readthedown:   # https://github.com/juba/rmdformats
    self_contained: true
    thumbnails: true
    lightbox: true
    gallery: true
    number_sections: true
    lib_dir: libs
    df_print: "paged"
    code_folding: "show"
    code_download: yes
    css: "style/style css.css"
---

# Check Sheet

1. Data Check Sheet
```{r echo=FALSE, warning=FALSE, message=FALSE}
# Pastikan semua package terinstal
packages <- c("dplyr", "stringi", "lubridate", "DT")
new_packages <- packages[!(packages %in% installed.packages()[, "Package"])]
if(length(new_packages)) install.packages(new_packages)

# Panggil library yang dibutuhkan
library(dplyr)
library(stringi)
library(lubridate)
library(DT)

# Buat data dummy produksi roti
set.seed(42)
n <- 300

dates <- seq.Date(from = as.Date("2024-01-01"), to = as.Date("2024-12-31"), by = "day")
sample_dates <- sample(dates, n, replace = TRUE)

# Simulasi data produksi roti
bread_types <- c("Roti Tawar", "Roti Manis", "Roti Gandum", "Roti Isi Coklat", "Roti Isi Keju")
defect_types <- c("Gosong", "Tidak Mengembang", "Isi Tidak Merata", "Bentuk Tidak Normal", "Kemas Rusak", "Tidak Ada Cacat")
locations <- c("Jakarta", "Bandung", "Surabaya", "Medan", "Makassar")
shifts <- c("Pagi", "Siang", "Malam")
machines <- paste0("MC-", sprintf("%03d", 1:20))

# Tambahan variabel untuk QC
batch_weights <- round(rnorm(n, mean = 50, sd = 5), 1)  # Berat per batch (kg)
defect_qty <- sample(0:30, n, replace = TRUE)
prod_qty <- sample(950:1050, n, replace = TRUE)
process_time <- round(rnorm(n, mean = 45, sd = 7), 1)  # Waktu proses (menit)
temp_machine <- round(rnorm(n, mean = 180, sd = 10), 1)  # Suhu mesin (°C)

# Musim produksi
season <- case_when(
  month(sample_dates) %in% c(11, 12, 1, 2) ~ "Musim Hujan",
  month(sample_dates) %in% c(6, 7, 8, 9) ~ "Musim Kemarau",
  TRUE ~ "Musim Peralihan"
)

# Dataset produksi roti
bread_data <- tibble(
  Batch_ID = stri_rand_strings(n, 10),
  Tanggal = sample_dates,
  Lokasi = sample(locations, n, replace = TRUE),
  Shift = sample(shifts, n, replace = TRUE),
  Jenis_Roti = sample(bread_types, n, replace = TRUE),
  ID_Mesin = sample(machines, n, replace = TRUE),
  Berat_Batch_kg = batch_weights,
  Jumlah_Produksi = prod_qty,
  Jenis_Cacat = sample(defect_types, n, replace = TRUE),
  Jumlah_Cacat = defect_qty,
  Waktu_Proses_menit = process_time,
  Suhu_Mesin_C = temp_machine,
  Musim = season
)

# Tampilkan tabel interaktif
datatable(
  bread_data,
  extensions = 'Buttons',
  options = list(
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    scrollY = "400px",
    scrollCollapse = TRUE,
    paging = FALSE
  ),
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left; font-size: 18px; font-weight: bold;',
    "Data Dummy Produksi Roti & Kecacatan Produk"
  ),
  class = 'stripe hover compact'
)
```

2. Frequency Tabulation
```{r echo=TRUE}
# Hitung frekuensi tiap jenis cacat produk
library(dplyr)
library(DT)

# Asumsikan data dummy sebelumnya sudah dibuat dan bernama 'bread_data'

defect_summary <- bread_data %>%
  count(Jenis_Cacat, sort = TRUE) %>%
  rename(Frekuensi = n)

# Tampilkan tabel ringkasan
datatable(
  defect_summary,
  options = list(
    scrollCollapse = TRUE,
    searching = FALSE,
    paging = FALSE
  ),
  rownames = FALSE,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left;
             font-size: 18px; font-weight: bold;',
    'Check Sheet: Frekuensi Jenis Cacat Produk'
  ),
  class = 'stripe hover compact'
)
```

3. Visualisasi

```{r echo=TRUE}
library(dplyr)

reason_summary <- bread_data %>%
  group_by(Jenis_Cacat) %>%
  summarise(Frequency = sum(Jumlah_Cacat, na.rm = TRUE)) %>%
  arrange(desc(Frequency)) %>%
  rename(Reason = Jenis_Cacat)
library(plotly)

# Visualisasi horizontal bar chart
plot_ly(reason_summary,
        x = ~Frequency,
        y = ~reorder(Reason, Frequency),
        type = 'bar',
        orientation = 'h',
        marker = list(
          color = ~Frequency,
          colorscale = 'Viridis',
          showscale = TRUE
        )
) %>%
  layout(
    title = list(text = "Check Sheet: Frekuensi Jenis Cacat Produk Roti", font = list(size = 18)),
    xaxis = list(title = "Frekuensi"),
    yaxis = list(title = "Jenis Cacat"),
    margin = list(l = 120)
  )

```

# Histogram
```{r echo=TRUE}
# Install and load necessary libraries
if (!require("plotly")) install.packages("plotly")
library(plotly)

# Gunakan data aktual dari bread_data
actual_data <- bread_data$Berat_Batch_kg

# Hitung kurva densitas
density_data <- density(actual_data)

# Buat histogram menggunakan plotly
histogram_plot <- plot_ly(
  x = actual_data,
  type = 'histogram',
  marker = list(color = 'lightblue', line = list(color = 'black', width = 1)),
  name = 'Histogram Berat Batch (kg)',
  nbinsx = 30,
  opacity = 0.6,
  showlegend = TRUE
) %>%
  # Tambahkan kurva densitas
  add_trace(
    x = density_data$x,
    y = density_data$y * length(actual_data) * diff(range(actual_data)) / 30,
    type = 'scatter',
    mode = 'lines',
    name = 'Kurva Densitas',
    line = list(color = 'black', width = 3),
    showlegend = TRUE
  ) %>%
  layout(
    title = 'Histogram Berat Batch (kg) dengan Kurva Densitas',
    xaxis = list(title = 'Berat Batch (kg)', showgrid = FALSE),
    yaxis = list(title = 'Frekuensi / Densitas', showgrid = FALSE),
    bargap = 0.1,
    plot_bgcolor = 'white',
    paper_bgcolor = 'white',
    showlegend = TRUE,
    legend = list(
      orientation = 'v',
      x = 0.98,
      xanchor = 'right',
      y = 0.98,
      yanchor = 'top',
      bgcolor = 'rgba(255,255,255,0.8)',
      bordercolor = 'black',
      borderwidth = 0.3
    )
  )

# Tampilkan plot
histogram_plot
```

# Pareto Chart
```{r echo=TRUE}
# Load libraries
library(dplyr)
library(plotly)
library(RColorBrewer)

# Filter data hanya untuk produk cacat
defective_data <- bread_data %>%
  filter(Jumlah_Cacat > 0)

# Summarize frekuensi jenis cacat
pareto_data <- defective_data %>%
  count(Jenis_Cacat, sort = TRUE) %>%
  mutate(
    cum_freq = cumsum(n) / sum(n) * 100  # persentase kumulatif
  )

# Generate warna berbeda untuk setiap jenis cacat
colors <- RColorBrewer::brewer.pal(n = max(3, min(12, nrow(pareto_data))), name = "Set3")

# Buat objek plotly
fig <- plot_ly()

# Tambahkan Bar Chart (jumlah cacat)
fig <- fig %>% add_bars(
  x = ~reorder(pareto_data$Jenis_Cacat, -pareto_data$n),
  y = ~pareto_data$n,
  name = 'Jumlah Cacat',
  marker = list(color = colors),
  yaxis = "y1"
)

# Tambahkan garis kumulatif
fig <- fig %>% add_lines(
  x = ~reorder(pareto_data$Jenis_Cacat, -pareto_data$n),
  y = ~pareto_data$cum_freq,
  name = 'Kumulatif (%)',
  yaxis = "y2",
  line = list(color = 'red', dash = 'dash')
)

# Tambahkan garis cutoff 80%
fig <- fig %>% add_lines(
  x = ~reorder(pareto_data$Jenis_Cacat, -pareto_data$n),
  y = rep(80, nrow(pareto_data)),
  name = 'Cut-off 80%',
  yaxis = "y2",
  line = list(color = 'green', dash = 'dot')
)

# Layout akhir
fig <- fig %>% layout(
  title = "Pareto Chart - Jenis Cacat Produk",
  xaxis = list(
    title = "Jenis Cacat",
    tickangle = -45
  ),
  yaxis = list(title = "Jumlah Cacat"),
  yaxis2 = list(
    title = "Kumulatif (%)",
    overlaying = "y",
    side = "right",
    range = c(0, 100)
  ),
  legend = list(x = 0.8, y = 0.75),
  shapes = list(
    list(
      type = "line",
      x0 = -0.5,
      x1 = nrow(pareto_data) - 0.5,
      y0 = 80,
      y1 = 80,
      yref = "y2",
      line = list(color = "green", width = 2, dash = "dot")
    )
  )
)

# Tampilkan chart
fig

```


# Fishbone
```{r echo=TRUE}
library(DiagrammeR)
library(DiagrammeRsvg)
library(rsvg)
library(knitr)

graph <- grViz("
digraph fishbone {
  graph [layout = dot, rankdir = LR]
  node [fontname=Helvetica, fontsize=25, style=filled]

  Problem [label='Cacat Produk Roti', shape=ellipse, fillcolor=lightcoral, width=5.0, height=1.2]

  # Kategori utama
  node [shape=diamond, width=2.5, height=1.0, fillcolor='#FFD700']
  Man [label='Man']
  Machine [label='Machine']
  Method [label='Method']
  Material [label='Material']
  Environment [label='Environment']
  Measurement [label='Measurement']

  # Subkategori
  node [shape=ellipse, width=2.5, height=0.6, fillcolor='#90EE90']
  
  # Man
  Man1 [label='Kurangnya pelatihan operator']
  Man2 [label='Kelelahan saat shift malam']

  # Machine
  Machine1 [label='Suhu mesin tidak stabil']
  Machine2 [label='Mesin sering rusak']

  # Method
  Method1 [label='Prosedur standar tidak diikuti']
  Method2 [label='Kontrol kualitas tidak konsisten']

  # Material
  Material1 [label='Bahan baku tidak segar']
  Material2 [label='Komposisi adonan tidak tepat']

  # Environment
  Environment1 [label='Kelembaban tinggi di musim hujan']
  Environment2 [label='Ventilasi pabrik buruk']

  # Measurement
  Measurement1 [label='Alat ukur kalibrasi buruk']
  Measurement2 [label='Data produksi tidak akurat']

  # Hubungkan kategori ke masalah
  Man -> Problem
  Machine -> Problem
  Method -> Problem
  Material -> Problem
  Environment -> Problem
  Measurement -> Problem

  # Hubungkan subkategori ke kategori
  Man1 -> Man
  Man2 -> Man

  Machine1 -> Machine
  Machine2 -> Machine

  Method1 -> Method
  Method2 -> Method

  Material1 -> Material
  Material2 -> Material

  Environment1 -> Environment
  Environment2 -> Environment

  Measurement1 -> Measurement
  Measurement2 -> Measurement
}
")

# Simpan sebagai gambar
dir.create("images/bab8", recursive = TRUE, showWarnings = FALSE)
svg_code <- export_svg(graph)
rsvg_png(charToRaw(svg_code), file = "images/bab8/fishbone_roti_qc.png", width = 3000, height = 3000)
rsvg_pdf(charToRaw(svg_code), file = "images/bab8/fishbone_roti_qc.pdf")

# Tampilkan dalam laporan
knitr::include_graphics("images/bab8/fishbone_roti_qc.png")

```

# Scatter Diagram

```{r echo=TRUE}
# Pastikan package sudah terinstal
# install.packages("plotly") # Jika belum terinstal

library(plotly)

# Scatter plot suhu mesin vs jumlah cacat
correlation_value <- cor(bread_data$Suhu_Mesin_C, bread_data$Jumlah_Cacat)

fig <- plot_ly(
  data = bread_data,
  x = ~Suhu_Mesin_C,
  y = ~Jumlah_Cacat,
  type = 'scatter',
  mode = 'markers',
  marker = list(color = 'black', size = 7)
) %>%
  add_lines(
    x = bread_data$Suhu_Mesin_C,
    y = predict(lm(Jumlah_Cacat ~ Suhu_Mesin_C, data = bread_data)),
    line = list(color = 'red', dash = 'solid')
  ) %>%
  layout(
    title = paste("Scatter Plot: Suhu Mesin vs Jumlah Cacat\nKorelasi:", round(correlation_value, 2)),
    xaxis = list(title = "Suhu Mesin (°C)"),
    yaxis = list(title = "Jumlah Cacat")
  )

# Tampilkan plot
fig
```

# Control Chart
```{r echo=TRUE}
library(plotly)
library(dplyr)

# Ambil data berat batch dan urutkan berdasarkan tanggal
berat_data <- bread_data %>%
  arrange(Tanggal) %>%
  mutate(Hari = row_number()) %>%
  select(Hari, Tanggal, Berat = Berat_Batch_kg)

# Tentukan batas kendali berdasarkan rata-rata dan standar deviasi
CL <- mean(berat_data$Berat)
SD <- sd(berat_data$Berat)
UCL <- CL + 3 * SD
LCL <- CL - 3 * SD

# Tandai outlier
berat_data <- berat_data %>%
  mutate(Outlier = ifelse(Berat > UCL | Berat < LCL, "Ya", "Tidak"))

# Buat plot kontrol chart
plot_ly(berat_data, x = ~Hari, y = ~Berat, type = 'scatter', mode = 'lines+markers',
        line = list(color = 'red'), 
        marker = list(size = 8, color = ifelse(berat_data$Outlier == "Ya", "red", "blue")),
        hoverinfo = 'text',
        text = ~paste("Tanggal:", Tanggal, "<br>Berat:", Berat, "kg")) %>%
  add_lines(y = rep(CL, nrow(berat_data)), name = "CL", line = list(color = 'green', dash = 'dot')) %>%
  add_lines(y = rep(UCL, nrow(berat_data)), name = "UCL", line = list(color = 'red', dash = 'dot')) %>%
  add_lines(y = rep(LCL, nrow(berat_data)), name = "LCL", line = list(color = 'red', dash = 'dot')) %>%
  layout(title = "Control Chart – Konsistensi Berat Batch Produk Roti",
         xaxis = list(title = "Urutan Batch (Hari Produksi)"),
         yaxis = list(title = "Berat Batch (kg)"),
         legend = list(orientation = 'h', x = 0.3, y = -0.2))

```


# Flowchart
<img src="flowchart.jpg" alt="Logo" style="width:500px; display: block; margin: auto;"/>
