Update app.py

app.py

@@ -475,9 +475,38 @@ if 'temuan_kode_distrik' in df_local.columns:
 else:
     st.error("Column 'temuan_kode_distrik' not found in the data. Cannot determine PG/UM areas.")
     st.stop()
+# =================== OBJECTIVE 2 — Active vs Inactive Locations (Treemap with Color Gradient) ===================
+st.markdown(
+    """
+    <style>
+    .section-title {
+        text-align: center;
+        font-size: 1.5rem;
+        font-weight: 600;
+        color: #2c3e50;
+        margin-bottom: 1.2rem;
+    }
+    .ai-insight {
+        background-color: #f8f9fa;
+        padding: 12px;
+        border-left: 4px solid #27ae60;
+        border-radius: 0 4px 4px 0;
+        font-size: 0.95rem;
+        line-height: 1.5;
+        margin-top: 1rem;
+    }
+    </style>
+    <h3 class='section-title'>OBJECTIVE 2 — Active vs Inactive Locations: Who Leads?</h3>
+    """,
+    unsafe_allow_html=True
+)
 
-# =================== 2. Treemap: Distribusi Temuan per Area (nama_lokasi_full) - PERBAIKAN ===================
-st.markdown("<h3 class='section-title'>OBJECTIVE 2 - Active vs Inactive Locations: Who Leads?</h3>", unsafe_allow_html=True)
+df_local = df_filtered.copy()
+if df_local.empty:
+    st.warning("No data available after filtering.")
+    st.stop()
+
+df_local['created_month'] = df_local['created_at'].dt.to_period('M')
 
 # Hitung temuan per bulan per lokasi
 findings_by_location_month = df_local.groupby(['created_month', 'nama_lokasi_full']).size().reset_index(name='findings_count')
@@ -490,51 +519,34 @@ merged_loc = merged_loc.fillna({'findings_count': 0, 'unique_creators': 0})
 # Filter untuk menghindari pembagian dengan nol
 merged_loc = merged_loc[merged_loc['unique_creators'] > 0]
 # Hitung rasio (ignore NaN)
-# Pembagian oleh 0 akan menghasilkan inf, jadi kita ganti inf dengan NaN
 merged_loc['ratio'] = merged_loc['findings_count'] / merged_loc['unique_creators']
 merged_loc['ratio'] = merged_loc['ratio'].replace([np.inf, -np.inf], np.nan)
 
 # Rata-rata bulanan per lokasi
-# Group by nama_lokasi_full dan ambil mean dari rasio
-# mean() akan mengabaikan NaN secara default
 avg_ratio_per_location = merged_loc.groupby('nama_lokasi_full')['ratio'].mean().reset_index(name='avg_monthly_ratio')
 
 # Filter hasil akhir untuk menghindari NaN
 avg_ratio_per_location = avg_ratio_per_location.dropna(subset=['avg_monthly_ratio'])
 
-# Plot Treemap
+# Plot Treemap dengan gradasi warna
 if not avg_ratio_per_location.empty:
-    # Tambahkan kolom untuk warna berdasarkan kriteria
-    def categorize_risk(r):
-        if r > 1.3:
-            return 'High Activity (> 1.3)' # Warna Hijau
-        elif r > 1.0:
-            return 'Medium Activity (1.0 - 1.3)' # Warna Kuning
-        else:
-            return 'Low Activity (<= 1.0)' # Warna Merah
-
-    avg_ratio_per_location['Activity_Category'] = avg_ratio_per_location['avg_monthly_ratio'].apply(categorize_risk)
-
-    # Peta warna
-    color_map = {
-        'High Activity (> 1.3)': '#4CAF50',      # Hijau
-        'Medium Activity (1.0 - 1.3)': '#FFB300', # Kuning
-        'Low Activity (<= 1.0)': '#D32F2F'      # Merah
-    }
-
-    # Gunakan treemap plot dengan ukuran mencerminkan rata-rata rasio dan warna berdasarkan kategori aktivitas
+    # Gunakan color_continuous_scale untuk gradasi warna: merah → kuning → hijau
     fig_treemap = px.treemap(
         avg_ratio_per_location,
         path=['nama_lokasi_full'],  # Path untuk hierarki (hanya satu level di sini)
         values='avg_monthly_ratio', # Nilai yang menentukan ukuran area
         title='Avg Monthly Finding by Location',
         labels={'avg_monthly_ratio': 'Avg Monthly Finding/Person Ratio', 'nama_lokasi_full': 'Location'},
-        color='Activity_Category', # Warna berdasarkan kategori aktivitas
-        color_discrete_map=color_map
+        color='avg_monthly_ratio', # Warna berdasarkan nilai rasio (bukan kategori)
+        color_continuous_scale=[
+            [0.0, '#D32F2F'],  # Merah untuk rendah
+            [0.5, '#FFB300'],  # Kuning untuk sedang
+            [1.0, '#4CAF50']   # Hijau untuk tinggi
+        ]
     )
     # Format hover
     fig_treemap.update_traces(
-        hovertemplate="<b>%{label}</b><br>Avg Ratio: %{value:.2f}<br>Activity Level: %{color}<extra></extra>"
+        hovertemplate="<b>%{label}</b><br>Avg Ratio: %{value:.2f}<extra></extra>"
     )
     fig_treemap.update_layout(height=600)
     st.plotly_chart(fig_treemap, use_container_width=True)
@@ -548,14 +560,14 @@ if not avg_ratio_per_location.empty:
         st.markdown("### Insight")
         insight_text = (
             f"<div class='ai-insight'>"
-            f"The treemap visualizes the average finding-to-person ratio per location, indicating reporting activity levels. "
-            f"Locations with <span style='color:#4CAF50; font-weight:bold;'>green</span> color have a high ratio reporting"
-            f"Those with <span style='color:#FFB300; font-weight:bold;'>yellow</span> color have a medium ratio, indicating area with moderate reporting. "
-            f"Locations with <span style='color:#D32F2F; font-weight:bold;'>red</span> color have a low ratio  indicate lower activity levels or potentially under-reporting. "
+            f"The treemap visualizes the average finding-to-person ratio per location using a <strong>color gradient</strong>, indicating reporting activity levels. "
+            f"Locations with <span style='color:#4CAF50; font-weight:bold;'>green</span> color have a high ratio, indicating high reporting activity or exposure. "
+            f"Those with <span style='color:#FFB300; font-weight:bold;'>yellow</span> color have a medium ratio, indicating moderate reporting. "
+            f"Locations with <span style='color:#D32F2F; font-weight:bold;'>red</span> color have a low ratio, indicating lower activity levels or potentially under-reporting. "
             f"<strong>{top_location['nama_lokasi_full']}</strong> shows the highest activity level "
-            f"(<strong>{top_location['avg_monthly_ratio']:.2f}</strong>, color: {top_location['Activity_Category']}). "
+            f"(<strong>{top_location['avg_monthly_ratio']:.2f}</strong>). "
             f"<strong>{low_location['nama_lokasi_full']}</strong> shows the lowest activity level "
-            f"(<strong>{low_location['avg_monthly_ratio']:.2f}</strong>, color: {low_location['Activity_Category']}). "
+            f"(<strong>{low_location['avg_monthly_ratio']:.2f}</strong>). "
             f"Areas with high activity (green) warrant investigation into the underlying causes of frequent findings. "
             f"Areas with low activity (red) should be reviewed to ensure reporting completeness and identify any hidden risks."
             f"</div>"
@@ -563,303 +575,475 @@ if not avg_ratio_per_location.empty:
         st.markdown(insight_text, unsafe_allow_html=True)
 else:
     st.warning("No data available for location ratio calculation or all ratios are NaN.")
+# =================== OBJECTIVE 2 — Active vs Inactive Locations (Treemap with Color Gradient) ===================
+st.markdown(
+    """
+    <style>
+    .section-title {
+        text-align: center;
+        font-size: 1.5rem;
+        font-weight: 600;
+        color: #2c3e50;
+        margin-bottom: 1.2rem;
+    }
+    .ai-insight {
+        background-color: #f8f9fa;
+        padding: 12px;
+        border-left: 4px solid #27ae60;
+        border-radius: 0 4px 4px 0;
+        font-size: 0.95rem;
+        line-height: 1.5;
+        margin-top: 1rem;
+    }
+    </style>
+    <h3 class='section-title'>OBJECTIVE 3 — Active vs Inactive Division: Who Leads?</h3>
+    """,
+    unsafe_allow_html=True
+)
 
-import plotly.express as px
-import numpy as np
+df_local = df_filtered.copy()
+if df_local.empty:
+    st.warning("No data available after filtering.")
+    st.stop()
+
+df_local['created_month'] = df_local['created_at'].dt.to_period('M')
+
+# Hitung temuan per bulan per lokasi
+findings_by_location_month = df_local.groupby(['created_month', 'nama']).size().reset_index(name='findings_count')
+# Hitung jumlah orang unik per bulan per lokasi
+creators_by_location_month = df_local.groupby(['created_month', 'nama'])['creator_nid'].nunique().reset_index(name='unique_creators')
+# Gabung
+merged_loc = findings_by_location_month.merge(creators_by_location_month, on=['created_month', 'nama'], how='outer')
+# Isi NaN dengan 0 untuk kolom yang mungkin hilang dari merge
+merged_loc = merged_loc.fillna({'findings_count': 0, 'unique_creators': 0})
+# Filter untuk menghindari pembagian dengan nol
+merged_loc = merged_loc[merged_loc['unique_creators'] > 0]
+# Hitung rasio (ignore NaN)
+merged_loc['ratio'] = merged_loc['findings_count'] / merged_loc['unique_creators']
+merged_loc['ratio'] = merged_loc['ratio'].replace([np.inf, -np.inf], np.nan)
+
+# Rata-rata bulanan per lokasi
+avg_ratio_per_location = merged_loc.groupby('nama')['ratio'].mean().reset_index(name='avg_monthly_ratio')
+
+# Filter hasil akhir untuk menghindari NaN
+avg_ratio_per_location = avg_ratio_per_location.dropna(subset=['avg_monthly_ratio'])
 
+# Plot Treemap dengan gradasi warna
+if not avg_ratio_per_location.empty:
+    # Gunakan color_continuous_scale untuk gradasi warna: merah → kuning → hijau
+    fig_treemap = px.treemap(
+        avg_ratio_per_location,
+        path=['nama'],  # Path untuk hierarki (hanya satu level di sini)
+        values='avg_monthly_ratio', # Nilai yang menentukan ukuran area
+        title='Avg Monthly Finding by Division',
+        labels={'avg_monthly_ratio': 'Avg Monthly Finding/Person Ratio', 'nama': 'Location'},
+        color='avg_monthly_ratio', # Warna berdasarkan nilai rasio (bukan kategori)
+        color_continuous_scale=[
+            [0.0, '#D32F2F'],  # Merah untuk rendah
+            [0.5, '#FFB300'],  # Kuning untuk sedang
+            [1.0, '#4CAF50']   # Hijau untuk tinggi
+        ]
+    )
+    # Format hover
+    fig_treemap.update_traces(
+        hovertemplate="<b>%{label}</b><br>Avg Ratio: %{value:.2f}<extra></extra>"
+    )
+    fig_treemap.update_layout(height=600)
+    st.plotly_chart(fig_treemap, use_container_width=True)
+
+    # AI Insight untuk Treemap Lokasi (Business-focused)
+    if not avg_ratio_per_location.empty:
+        # Temukan lokasi dengan rasio tertinggi dan terendah
+        top_location = avg_ratio_per_location.loc[avg_ratio_per_location['avg_monthly_ratio'].idxmax()]
+        low_location = avg_ratio_per_location.loc[avg_ratio_per_location['avg_monthly_ratio'].idxmin()]
+
+        st.markdown("### Insight")
+        insight_text = (
+            f"<div class='ai-insight'>"
+            f"The treemap visualizes the average finding-to-person ratio per location using a <strong>color gradient</strong>, indicating reporting activity levels. "
+            f"Locations with <span style='color:#4CAF50; font-weight:bold;'>green</span> color have a high ratio, indicating high reporting activity or exposure. "
+            f"Those with <span style='color:#FFB300; font-weight:bold;'>yellow</span> color have a medium ratio, indicating moderate reporting. "
+            f"Locations with <span style='color:#D32F2F; font-weight:bold;'>red</span> color have a low ratio, indicating lower activity levels or potentially under-reporting. "
+            f"<strong>{top_location['nama']}</strong> shows the highest activity level "
+            f"(<strong>{top_location['avg_monthly_ratio']:.2f}</strong>). "
+            f"<strong>{low_location['nama']}</strong> shows the lowest activity level "
+            f"(<strong>{low_location['avg_monthly_ratio']:.2f}</strong>). "
+            f"Areas with high activity (green) warrant investigation into the underlying causes of frequent findings. "
+            f"Areas with low activity (red) should be reviewed to ensure reporting completeness and identify any hidden risks."
+            f"</div>"
+        )
+        st.markdown(insight_text, unsafe_allow_html=True)
+else:
+    st.warning("No data available for location ratio calculation or all ratios are NaN.")
+
+import streamlit as st
 import plotly.express as px
 import numpy as np
-# =================== 3. Reporter & Executor Analysis (3a, 3b, 3c, 3d) ===================
-st.markdown("<h3 class='section-title'>OBJECTIVE 3 - Frequency & Response Time: Who Reports Well? Who Executes Well?</h3>", unsafe_allow_html=True)
+import pandas as pd
 
-# 3a & 3b: Reporter Frequency & Executor Lead Time by nama (Average Monthly Rate per Division)
-col_3a, col_3b = st.columns(2)
+# =================== OBJECTIVE 3 - Frequency & Response Time ===================
+st.markdown(
+    """
+    <style>
+    .section-title {
+        text-align: center;
+        font-size: 1.5rem;
+        font-weight: 600;
+        color: #2c3e50;
+        margin-bottom: 1.2rem;
+    }
+    .ai-insight {
+        background-color: #f8f9fa;
+        padding: 12px;
+        border-left: 4px solid #27ae60;
+        border-radius: 0 4px 4px 0;
+        font-size: 0.95rem;
+        line-height: 1.5;
+        margin-top: 1rem;
+    }
+    </style>
+    <h3 class='section-title'>OBJECTIVE 3 — Frequency & Response Time: Who Reports Well? Who Executes Well?</h3>
+    """,
+    unsafe_allow_html=True
+)
 
-with col_3a:
-    st.markdown("<h5>3a. Average Finding by Division (Reporter)</h5>", unsafe_allow_html=True)
-    if 'nama' in df_local.columns:
-        # Hitung temuan per bulan per nama
-        findings_by_nama_month = df_local.groupby(['created_month', 'nama']).size().reset_index(name='findings_count')
-        # Hitung jumlah orang unik per bulan per nama
-        creators_by_nama_month = df_local.groupby(['created_month', 'nama'])['creator_nid'].nunique().reset_index(name='unique_creators')
-        # Gabung
-        merged_rep = findings_by_nama_month.merge(creators_by_nama_month, on=['created_month', 'nama'], how='outer')
-        # Isi NaN dengan 0 untuk kolom yang mungkin hilang dari merge
-        merged_rep = merged_rep.fillna({'findings_count': 0, 'unique_creators': 0})
-        # Filter untuk menghindari pembagian dengan nol
-        merged_rep = merged_rep[merged_rep['unique_creators'] > 0]
-        # Hitung rasio (ignore NaN)
-        merged_rep['ratio'] = merged_rep['findings_count'] / merged_rep['unique_creators']
-        merged_rep['ratio'] = merged_rep['ratio'].replace([np.inf, -np.inf], np.nan)
+df_local = df_filtered.copy()
+if df_local.empty:
+    st.warning("No data available after filtering.")
+    st.stop()
 
-        # Rata-rata bulanan per nama
-        avg_ratio_per_nama = merged_rep.groupby('nama')['ratio'].mean().reset_index(name='avg_monthly_ratio')
+df_local['created_month'] = df_local['created_at'].dt.to_period('M')
 
-        # Filter hasil akhir untuk menghindari NaN
-        avg_ratio_per_nama = avg_ratio_per_nama.dropna(subset=['avg_monthly_ratio'])
-        if not avg_ratio_per_nama.empty:
-            # Tambahkan kolom untuk warna KE DATAFRAME
-            # Urutkan untuk menentukan 5 teratas
-            avg_ratio_per_nama_sorted = avg_ratio_per_nama.sort_values('avg_monthly_ratio', ascending=True)
-            top_5_indices = avg_ratio_per_nama_sorted.tail(5).index
-            # Buat warna default, lalu ubah untuk top 5
-            avg_ratio_per_nama_sorted['color'] = '#1f77b4' # Warna default plotly
-            avg_ratio_per_nama_sorted.loc[avg_ratio_per_nama_sorted.index.isin(top_5_indices), 'color'] = '#4CAF50' # Warna hijau untuk top 5
-
-            # Pilihan sorting
-            sort_option_3a = st.selectbox("Sort 3a by:", ["Lowest First", "Highest First"], key='sort_3a')
-            if sort_option_3a == "Highest First":
-                avg_ratio_per_nama_sorted = avg_ratio_per_nama_sorted.sort_values('avg_monthly_ratio', ascending=False)
-            # Jika "Lowest First", sudah diurutkan ascending di atas
-
-            fig_rep_nama = px.bar(
-                avg_ratio_per_nama_sorted,
-                x='avg_monthly_ratio',
-                y='nama',
-                orientation='h',
-                title='Avg Monthly Finding by Division',
-                labels={'avg_monthly_ratio': 'Avg Monthly Finding/Person Ratio', 'nama': 'Division'},
-                color='color', # Gunakan nama kolom yang ditambahkan
-                color_discrete_map={c: c for c in avg_ratio_per_nama_sorted['color'].unique()}, # Peta warna
-                text=avg_ratio_per_nama_sorted['avg_monthly_ratio'].apply(lambda x: f'{x:.2f}') # Format 2 angka desimal
-            )
-            # Hapus legend untuk warna karena tidak informatif
-            fig_rep_nama.update_layout(yaxis={'categoryorder': 'total ascending'}, height=500, showlegend=False)
-            fig_rep_nama.update_traces(textposition='auto') # Posisi teks otomatis
-            st.plotly_chart(fig_rep_nama, use_container_width=True)
-
-            # AI Insight for 3a
-            top_nama = avg_ratio_per_nama_sorted.iloc[-1] if not avg_ratio_per_nama_sorted.empty else None
-            low_nama = avg_ratio_per_nama_sorted.iloc[0] if not avg_ratio_per_nama_sorted.empty else None
-            if top_nama is not None and low_nama is not None:
-                st.markdown("### Insight")
-                insight_text = (
-                    f"<div class='ai-insight'>"
-                    f"The division <strong>{top_nama['nama']}</strong> has the highest average finding-to-person ratio "
-                    f"(<strong>{top_nama['avg_monthly_ratio']:.2f}</strong>), indicating potentially high reporting activity or exposure. "
-                    f"Conversely, <strong>{low_nama['nama']}</strong> has the lowest ratio "
-                    f"(<strong>{low_nama['avg_monthly_ratio']:.2f}</strong>), suggesting lower activity or potentially under-reporting. "
-                    f"Monitor high-ratio divisions for potential systemic issues and verify reporting completeness in low-ratio ones."
-                    f"</div>"
-                )
-                st.markdown(insight_text, unsafe_allow_html=True)
-        else:
-            st.warning("No data or all ratios are NaN for reporter analysis by division.")
-    else:
-        st.warning("Column 'nama' not available for reporter analysis (3a).")
 
-with col_3b:
-    st.markdown("<h5>3b. Average  by Division (Executor)</h5>", unsafe_allow_html=True)
-    if 'nama' in df_local.columns and 'days_to_close' in df_local.columns:
-        # Hitung rata-rata lead time per nama per bulan
-        leadtime_by_nama_month = df_local.groupby(['created_month', 'nama'])['days_to_close'].mean().reset_index(name='avg_leadtime')
-        # Rata-rata bulanan keseluruhan per nama
-        avg_leadtime_nama = leadtime_by_nama_month.groupby('nama')['avg_leadtime'].mean().reset_index(name='avg_monthly_leadtime')
+# ─── Helper: Hitung rasio per nama (Reporter) ────────────────────────────────
+def compute_reporter_ratio_by_nama(df):
+    if 'nama' not in df.columns:
+        return pd.DataFrame()
+    
+    findings_by_nama_month = df.groupby(['created_month', 'nama']).size().reset_index(name='findings_count')
+    creators_by_nama_month = df.groupby(['created_month', 'nama'])['creator_nid'].nunique().reset_index(name='unique_creators')
+    merged_rep = findings_by_nama_month.merge(creators_by_nama_month, on=['created_month', 'nama'], how='outer')
+    merged_rep = merged_rep.fillna({'findings_count': 0, 'unique_creators': 0})
+    merged_rep = merged_rep[merged_rep['unique_creators'] > 0]
+    merged_rep['ratio'] = merged_rep['findings_count'] / merged_rep['unique_creators']
+    merged_rep['ratio'] = merged_rep['ratio'].replace([np.inf, -np.inf], np.nan)
+    avg_ratio_per_nama = merged_rep.groupby('nama')['ratio'].mean().reset_index(name='avg_monthly_ratio')
+    avg_ratio_per_nama = avg_ratio_per_nama.dropna(subset=['avg_monthly_ratio'])
+    return avg_ratio_per_nama
+
+
+# ─── Helper: Hitung lead time per nama (Executor) ───────────────────────────
+def compute_executor_leadtime_by_nama(df):
+    if 'nama' not in df.columns or 'days_to_close' not in df.columns:
+        return pd.DataFrame()
+    
+    leadtime_by_nama_month = df.groupby(['created_month', 'nama'])['days_to_close'].mean().reset_index(name='avg_leadtime')
+    avg_leadtime_nama = leadtime_by_nama_month.groupby('nama')['avg_leadtime'].mean().reset_index(name='avg_monthly_leadtime')
+    avg_leadtime_nama = avg_leadtime_nama.dropna(subset=['avg_monthly_leadtime'])
+    return avg_leadtime_nama
 
-        # Filter hasil akhir untuk menghindari NaN
-        avg_leadtime_nama = avg_leadtime_nama.dropna(subset=['avg_monthly_leadtime'])
-        if not avg_leadtime_nama.empty:
-            # Tambahkan kolom untuk warna KE DATAFRAME
-            # Urutkan untuk menentukan 5 teratas
-            avg_leadtime_nama_sorted = avg_leadtime_nama.sort_values('avg_monthly_leadtime', ascending=True)
-            top_5_indices = avg_leadtime_nama_sorted.tail(5).index
-            # Buat warna default, lalu ubah untuk top 5
-            avg_leadtime_nama_sorted['color'] = '#1f77b4' # Warna default plotly
-            avg_leadtime_nama_sorted.loc[avg_leadtime_nama_sorted.index.isin(top_5_indices), 'color'] = '#D32F2F' # Warna merah untuk top 5
-
-            # Pilihan sorting
-            sort_option_3b = st.selectbox("Sort 3b by:", ["Fastest First", "Slowest First"], key='sort_3b')
-            if sort_option_3b == "Slowest First":
-                avg_leadtime_nama_sorted = avg_leadtime_nama_sorted.sort_values('avg_monthly_leadtime', ascending=False)
-            # Jika "Fastest First", sudah diurutkan ascending di atas
-
-            fig_exec_nama = px.bar(
-                avg_leadtime_nama_sorted,
-                x='avg_monthly_leadtime',
-                y='nama',
-                orientation='h',
-                title='Avg Monthly Lead Time by Division',
-                labels={'avg_monthly_leadtime': 'Avg Lead Time (Days)', 'nama': 'Division'},
-                color='color', # Gunakan nama kolom yang ditambahkan
-                color_discrete_map={c: c for c in avg_leadtime_nama_sorted['color'].unique()}, # Peta warna
-                text=avg_leadtime_nama_sorted['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}') # Format 2 angka desimal
-            )
-            # Hapus legend untuk warna karena tidak informatif
-            fig_exec_nama.update_layout(yaxis={'categoryorder': 'total ascending'}, height=500, showlegend=False)
-            fig_exec_nama.update_traces(textposition='auto') # Posisi teks otomatis
-            st.plotly_chart(fig_exec_nama, use_container_width=True)
-
-            # AI Insight for 3b
-            top_nama = avg_leadtime_nama_sorted.iloc[-1] if not avg_leadtime_nama_sorted.empty else None
-            low_nama = avg_leadtime_nama_sorted.iloc[0] if not avg_leadtime_nama_sorted.empty else None
-            if top_nama is not None and low_nama is not None:
-                st.markdown("### Insight")
-                insight_text = (
-                    f"<div class='ai-insight'>"
-                    f"The division <strong>{top_nama['nama']}</strong> has the highest average lead time "
-                    f"(<strong>{top_nama['avg_monthly_leadtime']:.2f} days</strong>), indicating slower resolution. "
-                    f"<strong>{low_nama['nama']}</strong> has the fastest average resolution "
-                    f"(<strong>{low_nama['avg_monthly_leadtime']:.2f} days</strong>). "
-                    f"Focus on improving SLA compliance in divisions with longer lead times."
-                    f"</div>"
-                )
-                st.markdown(insight_text, unsafe_allow_html=True)
-        else:
-            st.warning("No data or all lead times are NaN for executor analysis by division.")
+
+# ─── Helper: Hitung rasio per creator_name ──────────────────────────────────
+def compute_reporter_rate_by_creator(df):
+    if 'creator_name' not in df.columns:
+        return pd.DataFrame()
+    
+    findings_by_creator_month = df.groupby(['created_month', 'creator_name']).size().reset_index(name='findings_count')
+    active_months_by_creator = findings_by_creator_month.groupby('creator_name')['created_month'].nunique().reset_index(name='active_months')
+    total_findings_by_creator = findings_by_creator_month.groupby('creator_name')['findings_count'].sum().reset_index()
+    merged_rep_creator = total_findings_by_creator.merge(active_months_by_creator, on='creator_name', how='outer')
+    merged_rep_creator = merged_rep_creator.fillna({'findings_count': 0, 'active_months': 0})
+    merged_rep_creator = merged_rep_creator[merged_rep_creator['active_months'] > 0]
+    merged_rep_creator['avg_monthly_rate'] = merged_rep_creator['findings_count'] / merged_rep_creator['active_months']
+    merged_rep_creator['avg_monthly_rate'] = merged_rep_creator['avg_monthly_rate'].replace([np.inf, -np.inf], np.nan)
+    avg_rate_per_creator = merged_rep_creator.dropna(subset=['avg_monthly_rate'])
+    return avg_rate_per_creator
+
+
+# ─── Helper: Hitung lead time per nama_pic ──────────────────────────────────
+def compute_executor_leadtime_by_pic(df):
+    if 'nama_pic' not in df.columns or 'days_to_close' not in df.columns:
+        return pd.DataFrame()
+    
+    leadtime_by_executor_month = df.groupby(['created_month', 'nama_pic'])['days_to_close'].mean().reset_index(name='avg_leadtime')
+    active_months_by_executor = leadtime_by_executor_month.groupby('nama_pic')['created_month'].nunique().reset_index(name='active_months')
+    total_leadtime_by_executor = leadtime_by_executor_month.groupby('nama_pic')['avg_leadtime'].sum().reset_index()
+    merged_exec_pic = total_leadtime_by_executor.merge(active_months_by_executor, on='nama_pic', how='outer')
+    merged_exec_pic = merged_exec_pic.fillna({'avg_leadtime': 0, 'active_months': 0})
+    merged_exec_pic = merged_exec_pic[merged_exec_pic['active_months'] > 0]
+    merged_exec_pic['avg_monthly_leadtime'] = merged_exec_pic['avg_leadtime'] / merged_exec_pic['active_months']
+    merged_exec_pic['avg_monthly_leadtime'] = merged_exec_pic['avg_monthly_leadtime'].replace([np.inf, -np.inf], np.nan)
+    avg_leadtime_per_executor = merged_exec_pic.dropna(subset=['avg_monthly_leadtime'])
+    return avg_leadtime_per_executor
+
+
+# ─── Data untuk 3a & 3c ──────────────────────────────────────────────────────
+avg_ratio_per_nama = compute_reporter_ratio_by_nama(df_local)
+avg_rate_per_creator = compute_reporter_rate_by_creator(df_local)
+
+# ─── Data untuk 3b & 3d ──────────────────────────────────────────────────────
+avg_leadtime_nama = compute_executor_leadtime_by_nama(df_local)
+avg_leadtime_per_executor = compute_executor_leadtime_by_pic(df_local)
+
+
+# ─── Layout: 2 Baris — 3a & 3c di baris pertama, 3b & 3d di baris kedua ─────
+# Baris 1: 3a & 3c
+col_3a, col_3c = st.columns(2)
+
+with col_3a:
+    st.markdown("<h5>3a. Average Finding by Division (Reporter)</h5>", unsafe_allow_html=True)
+    if avg_ratio_per_nama.empty:
+        st.warning("No data for reporter analysis by division.")
     else:
-        st.warning("Columns 'nama' or 'days_to_close' not available for executor analysis (3b).")
+        sort_option_3a = st.selectbox("Show 3a:", ["Top 10", "Bottom 10"], key='sort_3a')
+        
+        # Urutkan data dari tertinggi ke terendah
+        sorted_data_all = avg_ratio_per_nama.sort_values('avg_monthly_ratio', ascending=False)
+        
+        # Ambil Top 10 atau Bottom 10
+        if sort_option_3a == "Top 10":
+            sorted_data = sorted_data_all.head(10)
+        else:
+            sorted_data = sorted_data_all.tail(10)
+        
+        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
+        sorted_data = sorted_data.sort_values('avg_monthly_ratio', ascending=False).reset_index(drop=True)
+        sorted_data = sorted_data.iloc[::-1]  # ← Balik posisi data
 
-# 3c & 3d: Reporter Frequency & Executor Lead Time by creator_name and nama_pic (Average Monthly Rate per Person)
-col_3c, col_3d = st.columns(2)
+        
+        # Tambahkan warna untuk top 5 dari data yang ditampilkan
+        sorted_data['color'] = '#1f77b4'
+        top_5_indices = sorted_data.head(5).index
+        sorted_data.loc[top_5_indices, 'color'] = '#4CAF50'
+        
+        fig_rep_nama = px.bar(
+            sorted_data,
+            x='avg_monthly_ratio',
+            y='nama',
+            orientation='h',
+            title='Avg Monthly Finding by Division',
+            labels={'avg_monthly_ratio': 'Avg Monthly Finding/Person Ratio', 'nama': 'Division'},
+            color='color',
+            color_discrete_map={c: c for c in sorted_data['color'].unique()},
+            text=sorted_data['avg_monthly_ratio'].apply(lambda x: f'{x:.2f}')
+        )
+        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
+        fig_rep_nama.update_layout(
+            yaxis={
+                'categoryorder': 'array',
+                'categoryarray': sorted_data['nama'].tolist()
+            },
+            height=500,
+            showlegend=False
+        )
+        fig_rep_nama.update_traces(textposition='auto')
+        st.plotly_chart(fig_rep_nama, use_container_width=True)
+        
+        # Insight
+        top = sorted_data.iloc[0] if sort_option_3a == "Top 10" else sorted_data_all.iloc[-1]
+        low = sorted_data.iloc[-1] if sort_option_3a == "Top 10" else sorted_data_all.iloc[0]
+        insight_text = (
+            f"<div class='ai-insight'>"
+            f"The division <strong>{top['nama']}</strong> has the highest average finding-to-person ratio "
+            f"(<strong>{top['avg_monthly_ratio']:.2f}</strong>). "
+            f"<strong>{low['nama']}</strong> has the lowest ratio "
+            f"(<strong>{low['avg_monthly_ratio']:.2f}</strong>). "
+            f"Monitor high-ratio divisions for potential systemic issues and verify reporting completeness in low-ratio ones."
+            f"</div>"
+        )
+        st.markdown(insight_text, unsafe_allow_html=True)
 
 with col_3c:
     st.markdown("<h5>3c. Average Finding Rate per Reporter (Name)</h5>", unsafe_allow_html=True)
-    if 'creator_name' in df_local.columns:
-        # Hitung temuan per bulan per creator_name
-        findings_by_creator_month = df_local.groupby(['created_month', 'creator_name']).size().reset_index(name='findings_count')
-        # Hitung jumlah bulan aktif per creator_name
-        active_months_by_creator = findings_by_creator_month.groupby('creator_name')['created_month'].nunique().reset_index(name='active_months')
-        # Gabung untuk mendapatkan total temuan per creator
-        total_findings_by_creator = findings_by_creator_month.groupby('creator_name')['findings_count'].sum().reset_index()
-        # Gabung semua
-        merged_rep_creator = total_findings_by_creator.merge(active_months_by_creator, on='creator_name', how='outer')
-        # Isi NaN dengan 0
-        merged_rep_creator = merged_rep_creator.fillna({'findings_count': 0, 'active_months': 0})
-        # Filter untuk menghindari pembagian dengan nol (jika seseorang tidak aktif sepanjang periode)
-        merged_rep_creator = merged_rep_creator[merged_rep_creator['active_months'] > 0]
-        # Hitung rata-rata bulanan (ignore NaN)
-        merged_rep_creator['avg_monthly_rate'] = merged_rep_creator['findings_count'] / merged_rep_creator['active_months']
-        merged_rep_creator['avg_monthly_rate'] = merged_rep_creator['avg_monthly_rate'].replace([np.inf, -np.inf], np.nan)
-
-        # Filter hasil akhir untuk menghindari NaN
-        avg_rate_per_creator = merged_rep_creator.dropna(subset=['avg_monthly_rate'])
-        if not avg_rate_per_creator.empty:
-            # Tambahkan kolom untuk warna KE DATAFRAME
-            # Urutkan untuk menentukan 5 teratas
-            avg_rate_per_creator_sorted = avg_rate_per_creator.sort_values('avg_monthly_rate', ascending=True)
-            top_5_indices = avg_rate_per_creator_sorted.tail(5).index
-            # Buat warna default, lalu ubah untuk top 5
-            avg_rate_per_creator_sorted['color'] = '#1f77b4' # Warna default plotly
-            avg_rate_per_creator_sorted.loc[avg_rate_per_creator_sorted.index.isin(top_5_indices), 'color'] = '#4CAF50' # Warna hijau untuk top 5
-
-            # Pilihan sorting
-            sort_option_3c = st.selectbox("Sort 3c by:", ["Lowest First", "Highest First"], key='sort_3c')
-            if sort_option_3c == "Highest First":
-                avg_rate_per_creator_sorted = avg_rate_per_creator_sorted.sort_values('avg_monthly_rate', ascending=False)
-            # Jika "Lowest First", sudah diurutkan ascending di atas
-
-            # Ambil top 10 untuk visualisasi
-            top10_creators = avg_rate_per_creator_sorted.tail(1000) # Ambil 10 terakhir setelah sorting
-            fig_rep_creator = px.bar(
-                top10_creators,
-                x='avg_monthly_rate',
-                y='creator_name',
-                orientation='h',
-                title='Avg Monthly Finding by Creator Name',
-                labels={'avg_monthly_rate': 'Avg Monthly Finding Rate', 'creator_name': 'Creator Name'},
-                color='color', # Gunakan nama kolom yang ditambahkan
-                color_discrete_map={c: c for c in top10_creators['color'].unique()}, # Peta warna
-                text=top10_creators['avg_monthly_rate'].apply(lambda x: f'{x:.2f}') # Format 2 angka desimal
-            )
-            # Hapus legend untuk warna karena tidak informatif
-            fig_rep_creator.update_layout(yaxis={'categoryorder': 'total ascending'}, height=500, showlegend=False)
-            fig_rep_creator.update_traces(textposition='auto') # Posisi teks otomatis
-            st.plotly_chart(fig_rep_creator, use_container_width=True)
-
-            # AI Insight for 3c
-            top_creator = avg_rate_per_creator_sorted.iloc[-1] if not avg_rate_per_creator_sorted.empty else None
-            low_creator = avg_rate_per_creator_sorted.iloc[0] if not avg_rate_per_creator_sorted.empty else None
-            if top_creator is not None and low_creator is not None:
-                st.markdown("### Insight")
-                insight_text = (
-                    f"<div class='ai-insight'>"
-                    f"The reporter <strong>{top_creator['creator_name']}</strong> has the highest average monthly finding rate "
-                    f"(<strong>{top_creator['avg_monthly_rate']:.2f}</strong>), indicating active engagement. "
-                    f"<strong>{low_creator['creator_name']}</strong> has the lowest rate "
-                    f"(<strong>{low_creator['avg_monthly_rate']:.2f}</strong>), which might indicate lower activity or under-reporting. "
-                    f"Recognize high performers and investigate low performers."
-                    f"</div>"
-                )
-                st.markdown(insight_text, unsafe_allow_html=True)
+    if avg_rate_per_creator.empty:
+        st.warning("No data for reporter analysis by creator_name.")
+    else:
+        sort_option_3c = st.selectbox("Show 3c:", ["Top 10", "Bottom 10"], key='sort_3c')
+        
+        # Urutkan data dari tertinggi ke terendah
+        sorted_data_all = avg_rate_per_creator.sort_values('avg_monthly_rate', ascending=False)
+        
+        # Ambil Top 10 atau Bottom 10
+        if sort_option_3c == "Top 10":
+            sorted_data = sorted_data_all.head(10)
         else:
-            st.warning("No data or all rates are NaN for reporter analysis by creator_name.")
+            sorted_data = sorted_data_all.tail(10)
+        
+        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
+        sorted_data = sorted_data.sort_values('avg_monthly_rate', ascending=False).reset_index(drop=True)
+        sorted_data = sorted_data.iloc[::-1]  # ← Balik posisi data
+
+        
+        # Tambahkan warna untuk top 5 dari data yang ditampilkan
+        sorted_data['color'] = '#1f77b4'
+        top_5_indices = sorted_data.head(5).index
+        sorted_data.loc[top_5_indices, 'color'] = '#4CAF50'
+        
+        fig_rep_creator = px.bar(
+            sorted_data,
+            x='avg_monthly_rate',
+            y='creator_name',
+            orientation='h',
+            title='Avg Monthly Finding by Creator Name',
+            labels={'avg_monthly_rate': 'Avg Monthly Finding Rate', 'creator_name': 'Creator Name'},
+            color='color',
+            color_discrete_map={c: c for c in sorted_data['color'].unique()},
+            text=sorted_data['avg_monthly_rate'].apply(lambda x: f'{x:.2f}')
+        )
+        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
+        fig_rep_creator.update_layout(
+            yaxis={
+                'categoryorder': 'array',
+                'categoryarray': sorted_data['creator_name'].tolist()
+            },
+            height=500,
+            showlegend=False
+        )
+        fig_rep_creator.update_traces(textposition='auto')
+        st.plotly_chart(fig_rep_creator, use_container_width=True)
+        
+        # Insight
+        top = sorted_data.iloc[0] if sort_option_3c == "Top 10" else sorted_data_all.iloc[-1]
+        low = sorted_data.iloc[-1] if sort_option_3c == "Top 10" else sorted_data_all.iloc[0]
+        insight_text = (
+            f"<div class='ai-insight'>"
+            f"The reporter <strong>{top['creator_name']}</strong> has the highest average monthly finding rate "
+            f"(<strong>{top['avg_monthly_rate']:.2f}</strong>). "
+            f"<strong>{low['creator_name']}</strong> has the lowest rate "
+            f"(<strong>{low['avg_monthly_rate']:.2f}</strong>). "
+            f"Recognize high performers and investigate low performers."
+            f"</div>"
+        )
+        st.markdown(insight_text, unsafe_allow_html=True)
+
+
+# Baris 2: 3b & 3d
+col_3b, col_3d = st.columns(2)
+
+with col_3b:
+    st.markdown("<h5>3b. Average Lead Time by Division (Executor)</h5>", unsafe_allow_html=True)
+    if avg_leadtime_nama.empty:
+        st.warning("No data for executor analysis by division.")
     else:
-        st.warning("Column 'creator_name' not available for reporter analysis (3c).")
+        sort_option_3b = st.selectbox("Show 3b:", ["Top 10", "Bottom 10"], key='sort_3b')
+        
+        # Urutkan data dari tertinggi ke terendah
+        sorted_data_all = avg_leadtime_nama.sort_values('avg_monthly_leadtime', ascending=False)
+        
+        # Ambil Top 10 atau Bottom 10
+        if sort_option_3b == "Top 10":
+            sorted_data = sorted_data_all.head(10)
+        else:
+            sorted_data = sorted_data_all.tail(10)
+        
+        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
+        sorted_data = sorted_data.sort_values('avg_monthly_leadtime', ascending=False).reset_index(drop=True)
+        
+        # Tambahkan warna untuk top 5 dari data yang ditampilkan
+        sorted_data['color'] = '#1f77b4'
+        top_5_indices = sorted_data.head(5).index
+        sorted_data.loc[top_5_indices, 'color'] = '#D32F2F'
+        
+        fig_exec_nama = px.bar(
+            sorted_data,
+            x='avg_monthly_leadtime',
+            y='nama',
+            orientation='h',
+            title='Avg Monthly Lead Time by Division',
+            labels={'avg_monthly_leadtime': 'Avg Lead Time (Days)', 'nama': 'Division'},
+            color='color',
+            color_discrete_map={c: c for c in sorted_data['color'].unique()},
+            text=sorted_data['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}')
+        )
+        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
+        fig_exec_nama.update_layout(
+            yaxis={
+                'categoryorder': 'array',
+                'categoryarray': sorted_data['nama'].tolist()
+            },
+            height=500,
+            showlegend=False
+        )
+        fig_exec_nama.update_traces(textposition='auto')
+        st.plotly_chart(fig_exec_nama, use_container_width=True)
+        
+        # Insight
+        top = sorted_data.iloc[0] if sort_option_3b == "Top 10" else sorted_data_all.iloc[-1]
+        low = sorted_data.iloc[-1] if sort_option_3b == "Top 10" else sorted_data_all.iloc[0]
+        insight_text = (
+            f"<div class='ai-insight'>"
+            f"The division <strong>{top['nama']}</strong> has the highest average lead time "
+            f"(<strong>{top['avg_monthly_leadtime']:.2f} days</strong>). "
+            f"<strong>{low['nama']}</strong> has the fastest average resolution "
+            f"(<strong>{low['avg_monthly_leadtime']:.2f} days</strong>). "
+            f"Focus on improving SLA compliance in divisions with longer lead times."
+            f"</div>"
+        )
+        st.markdown(insight_text, unsafe_allow_html=True)
 
 with col_3d:
     st.markdown("<h5>3d. Average Lead Time by Executor (Name)</h5>", unsafe_allow_html=True)
-    if 'nama_pic' in df_local.columns and 'days_to_close' in df_local.columns:
-        # Hitung rata-rata lead time per executor per bulan
-        leadtime_by_executor_month = df_local.groupby(['created_month', 'nama_pic'])['days_to_close'].mean().reset_index(name='avg_leadtime')
-        # Hitung jumlah bulan aktif per executor
-        active_months_by_executor = leadtime_by_executor_month.groupby('nama_pic')['created_month'].nunique().reset_index(name='active_months')
-        # Hitung total lead time per executor
-        total_leadtime_by_executor = leadtime_by_executor_month.groupby('nama_pic')['avg_leadtime'].sum().reset_index()
-        # Gabung semua
-        merged_exec_pic = total_leadtime_by_executor.merge(active_months_by_executor, on='nama_pic', how='outer')
-        # Isi NaN dengan 0
-        merged_exec_pic = merged_exec_pic.fillna({'avg_leadtime': 0, 'active_months': 0})
-        # Filter untuk menghindari pembagian dengan nol
-        merged_exec_pic = merged_exec_pic[merged_exec_pic['active_months'] > 0]
-        # Hitung rata-rata bulanan (ignore NaN)
-        merged_exec_pic['avg_monthly_leadtime'] = merged_exec_pic['avg_leadtime'] / merged_exec_pic['active_months']
-        merged_exec_pic['avg_monthly_leadtime'] = merged_exec_pic['avg_monthly_leadtime'].replace([np.inf, -np.inf], np.nan)
-
-        # Filter hasil akhir untuk menghindari NaN
-        avg_leadtime_per_executor = merged_exec_pic.dropna(subset=['avg_monthly_leadtime'])
-        if not avg_leadtime_per_executor.empty:
-            # Tambahkan kolom untuk warna KE DATAFRAME
-            # Urutkan untuk menentukan 5 teratas
-            avg_leadtime_per_executor_sorted = avg_leadtime_per_executor.sort_values('avg_monthly_leadtime', ascending=True)
-            top_5_indices = avg_leadtime_per_executor_sorted.tail(5).index
-            # Buat warna default, lalu ubah untuk top 5
-            avg_leadtime_per_executor_sorted['color'] = '#1f77b4' # Warna default plotly
-            avg_leadtime_per_executor_sorted.loc[avg_leadtime_per_executor_sorted.index.isin(top_5_indices), 'color'] = '#D32F2F' # Warna merah untuk top 5
-
-            # Pilihan sorting
-            sort_option_3d = st.selectbox("Sort 3d by:", ["Fastest First", "Slowest First"], key='sort_3d')
-            if sort_option_3d == "Slowest First":
-                avg_leadtime_per_executor_sorted = avg_leadtime_per_executor_sorted.sort_values('avg_monthly_leadtime', ascending=False)
-            # Jika "Fastest First", sudah diurutkan ascending di atas
-
-            # Ambil top 10 untuk visualisasi
-            top10_executors = avg_leadtime_per_executor_sorted.nlargest(1000, 'avg_monthly_leadtime') # Ambil 10 terlama
-            fig_exec_pic = px.bar(
-                top10_executors,
-                x='avg_monthly_leadtime',
-                y='nama_pic',
-                orientation='h',
-                title='Avg Monthly Lead Time by Executor (Name)',
-                labels={'avg_monthly_leadtime': 'Avg Monthly Lead Time (Days)', 'nama_pic': 'Executor Name'},
-                color='color', # Gunakan nama kolom yang ditambahkan
-                color_discrete_map={c: c for c in top10_executors['color'].unique()}, # Peta warna
-                text=top10_executors['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}') # Format 2 angka desimal
-            )
-            # Hapus legend untuk warna karena tidak informatif
-            fig_exec_pic.update_layout(yaxis={'categoryorder': 'total ascending'}, height=500, showlegend=False)
-            fig_exec_pic.update_traces(textposition='auto') # Posisi teks otomatis
-            st.plotly_chart(fig_exec_pic, use_container_width=True)
-
-            # AI Insight for 3d
-            top_executor = avg_leadtime_per_executor_sorted.iloc[-1] if not avg_leadtime_per_executor_sorted.empty else None
-            low_executor = avg_leadtime_per_executor_sorted.iloc[0] if not avg_leadtime_per_executor_sorted.empty else None
-            if top_executor is not None and low_executor is not None:
-                st.markdown("### Insight")
-                insight_text = (
-                    f"<div class='ai-insight'>"
-                    f"The executor <strong>{top_executor['nama_pic']}</strong> has the highest average monthly lead time "
-                    f"(<strong>{top_executor['avg_monthly_leadtime']:.2f} days</strong>), indicating slower resolution. "
-                    f"<strong>{low_executor['nama_pic']}</strong> resolves tasks fastest on average "
-                    f"(<strong>{low_executor['avg_monthly_leadtime']:.2f} days</strong>). "
-                    f"Focus on improving SLA compliance for executors with longer lead times."
-                    f"</div>"
-                )
-                st.markdown(insight_text, unsafe_allow_html=True)
-        else:
-            st.warning("No data or all lead times are NaN for executor analysis by nama_pic.")
+    if avg_leadtime_per_executor.empty:
+        st.warning("No data for executor analysis by nama_pic.")
     else:
-        st.warning("Columns 'nama_pic' or 'days_to_close' not available for executor analysis (3d).")
-    ####OBJECTIVE 4
+        sort_option_3d = st.selectbox("Show 3d:", ["Top 10", "Bottom 10"], key='sort_3d')
+        
+        # Urutkan data dari tertinggi ke terendah
+        sorted_data_all = avg_leadtime_per_executor.sort_values('avg_monthly_leadtime', ascending=False)
+        
+        # Ambil Top 10 atau Bottom 10
+        if sort_option_3d == "Top 10":
+            sorted_data = sorted_data_all.head(10)
+        else:
+            sorted_data = sorted_data_all.tail(10)
+        
+        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
+        sorted_data = sorted_data.sort_values('avg_monthly_leadtime', ascending=False).reset_index(drop=True)
+        
+        # Tambahkan warna untuk top 5 dari data yang ditampilkan
+        sorted_data['color'] = '#1f77b4'
+        top_5_indices = sorted_data.head(5).index
+        sorted_data.loc[top_5_indices, 'color'] = '#D32F2F'
+        
+        fig_exec_pic = px.bar(
+            sorted_data,
+            x='avg_monthly_leadtime',
+            y='nama_pic',
+            orientation='h',
+            title='Avg Monthly Lead Time by Executor (Name)',
+            labels={'avg_monthly_leadtime': 'Avg Monthly Lead Time (Days)', 'nama_pic': 'Executor Name'},
+            color='color',
+            color_discrete_map={c: c for c in sorted_data['color'].unique()},
+            text=sorted_data['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}')
+        )
+        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
+        fig_exec_pic.update_layout(
+            yaxis={
+                'categoryorder': 'array',
+                'categoryarray': sorted_data['nama_pic'].tolist()
+            },
+            height=500,
+            showlegend=False
+        )
+        fig_exec_pic.update_traces(textposition='auto')
+        st.plotly_chart(fig_exec_pic, use_container_width=True)
+        
+        # Insight
+        top = sorted_data.iloc[0] if sort_option_3d == "Top 10" else sorted_data_all.iloc[-1]
+        low = sorted_data.iloc[-1] if sort_option_3d == "Top 10" else sorted_data_all.iloc[0]
+        insight_text = (
+            f"<div class='ai-insight'>"
+            f"The executor <strong>{top['nama_pic']}</strong> has the highest average monthly lead time "
+            f"(<strong>{top['avg_monthly_leadtime']:.2f} days</strong>). "
+            f"<strong>{low['nama_pic']}</strong> resolves tasks fastest on average "
+            f"(<strong>{low['avg_monthly_leadtime']:.2f} days</strong>). "
+            f"Focus on improving SLA compliance for executors with longer lead times."
+            f"</div>"
+        )
+        st.markdown(insight_text, unsafe_allow_html=True)
+
+
+####OBJECTIVE 4
 try:
     from wordcloud import WordCloud
     import matplotlib.pyplot as plt