|
| 1 | +import html |
| 2 | + |
| 3 | +import folium |
| 4 | +import pandas as pd |
| 5 | +import streamlit as st |
| 6 | +from streamlit_folium import st_folium |
| 7 | + |
| 8 | +st.set_page_config(page_title="Cell Dump Analyzer", layout="wide") |
| 9 | + |
| 10 | +st.title("📱 Cell Dump & CDR Analysis Tool") |
| 11 | +st.markdown( |
| 12 | + "Unggah data *cell dump* (format CSV/Excel) untuk melakukan analisis lokasi, " |
| 13 | + "target, dan pola komunikasi." |
| 14 | +) |
| 15 | + |
| 16 | +uploaded_file = st.file_uploader( |
| 17 | + "Unggah File Cell Dump (CSV atau Excel)", type=["csv", "xlsx"] |
| 18 | +) |
| 19 | + |
| 20 | +if uploaded_file is None: |
| 21 | + st.info("Silakan unggah file *cell dump* terlebih dahulu melalui panel di atas.") |
| 22 | +else: |
| 23 | + try: |
| 24 | + if uploaded_file.name.lower().endswith(".csv"): |
| 25 | + df = pd.read_csv(uploaded_file) |
| 26 | + else: |
| 27 | + df = pd.read_excel(uploaded_file) |
| 28 | + except (pd.errors.ParserError, ValueError, OSError) as error: |
| 29 | + st.error(f"File tidak dapat dibaca: {error}") |
| 30 | + st.stop() |
| 31 | + |
| 32 | + if df.empty or len(df.columns) == 0: |
| 33 | + st.warning("File tidak berisi data atau kolom yang dapat dianalisis.") |
| 34 | + st.stop() |
| 35 | + |
| 36 | + st.success(f"Berhasil memuat {len(df)} baris data.") |
| 37 | + |
| 38 | + with st.expander("🔍 Lihat Preview Data Mentah"): |
| 39 | + st.dataframe(df.head()) |
| 40 | + |
| 41 | + st.sidebar.header("Filter Analisis") |
| 42 | + columns = df.columns.tolist() |
| 43 | + |
| 44 | + msisdn_default = columns.index("MSISDN") if "MSISDN" in columns else 0 |
| 45 | + imei_default = columns.index("IMEI") if "IMEI" in columns else min(1, len(columns) - 1) |
| 46 | + msisdn_col = st.sidebar.selectbox( |
| 47 | + "Pilih Kolom Nomor (MSISDN)", columns, index=msisdn_default |
| 48 | + ) |
| 49 | + imei_col = st.sidebar.selectbox("Pilih Kolom IMEI", columns, index=imei_default) |
| 50 | + target_number = st.sidebar.text_input("Cari Nomor Target (MSISDN / IMEI):", "") |
| 51 | + |
| 52 | + filtered_df = df.copy() |
| 53 | + if target_number: |
| 54 | + matching_number = ( |
| 55 | + filtered_df[msisdn_col].astype("string").str.contains( |
| 56 | + target_number, case=False, na=False, regex=False |
| 57 | + ) |
| 58 | + | filtered_df[imei_col].astype("string").str.contains( |
| 59 | + target_number, case=False, na=False, regex=False |
| 60 | + ) |
| 61 | + ) |
| 62 | + filtered_df = filtered_df[matching_number] |
| 63 | + |
| 64 | + col1, col2, col3 = st.columns(3) |
| 65 | + col1.metric("Total Record", len(filtered_df)) |
| 66 | + col2.metric("Target Unik (MSISDN)", filtered_df[msisdn_col].nunique()) |
| 67 | + col3.metric( |
| 68 | + "Menara BTS Unik", |
| 69 | + filtered_df["Tower_ID"].nunique() if "Tower_ID" in filtered_df else 0, |
| 70 | + ) |
| 71 | + |
| 72 | + tab1, tab2, tab3 = st.tabs( |
| 73 | + ["🗺️ Peta Lokasi (Mapping)", "📊 Analisis Frekuensi", "📋 Data Terfilter"] |
| 74 | + ) |
| 75 | + |
| 76 | + with tab1: |
| 77 | + st.subheader("Visualisasi Sebaran Lokasi BTS") |
| 78 | + if {"Lat", "Long"}.issubset(filtered_df.columns): |
| 79 | + locations = filtered_df.copy() |
| 80 | + locations["Lat"] = pd.to_numeric(locations["Lat"], errors="coerce") |
| 81 | + locations["Long"] = pd.to_numeric(locations["Long"], errors="coerce") |
| 82 | + locations = locations.dropna(subset=["Lat", "Long"]) |
| 83 | + locations = locations[ |
| 84 | + locations["Lat"].between(-90, 90) |
| 85 | + & locations["Long"].between(-180, 180) |
| 86 | + ] |
| 87 | + |
| 88 | + if locations.empty: |
| 89 | + st.warning("Tidak ada koordinat valid untuk ditampilkan pada peta.") |
| 90 | + else: |
| 91 | + map_lat = locations["Lat"].mean() |
| 92 | + map_lon = locations["Long"].mean() |
| 93 | + map_view = folium.Map(location=[map_lat, map_lon], zoom_start=13) |
| 94 | + |
| 95 | + for _, row in locations.iterrows(): |
| 96 | + msisdn = html.escape(str(row.get(msisdn_col, "-"))) |
| 97 | + timestamp = html.escape(str(row.get("Timestamp", "-"))) |
| 98 | + tower_id = html.escape(str(row.get("Tower_ID", "-"))) |
| 99 | + popup_text = ( |
| 100 | + f"MSISDN: {msisdn}<br>Waktu: {timestamp}<br>BTS: {tower_id}" |
| 101 | + ) |
| 102 | + folium.CircleMarker( |
| 103 | + location=[row["Lat"], row["Long"]], |
| 104 | + radius=5, |
| 105 | + color="red", |
| 106 | + fill=True, |
| 107 | + fill_color="red", |
| 108 | + popup=folium.Popup(popup_text, max_width=300), |
| 109 | + ).add_to(map_view) |
| 110 | + |
| 111 | + st_folium(map_view, width=1000, height=500) |
| 112 | + else: |
| 113 | + st.warning( |
| 114 | + "Kolom 'Lat' dan 'Long' tidak ditemukan dalam dataset untuk membuat peta." |
| 115 | + ) |
| 116 | + |
| 117 | + with tab2: |
| 118 | + st.subheader("Statistik Komunikasi & Aktivitas") |
| 119 | + top_numbers = filtered_df[msisdn_col].value_counts().head(10) |
| 120 | + st.write("**Nomor Paling Sering Muncul (Top 10):**") |
| 121 | + st.bar_chart(top_numbers) |
| 122 | + |
| 123 | + if "Tower_ID" in filtered_df.columns: |
| 124 | + top_towers = filtered_df["Tower_ID"].value_counts().head(10) |
| 125 | + st.write("**BTS Paling Sering Terhubung (Top 10):**") |
| 126 | + st.bar_chart(top_towers) |
| 127 | + |
| 128 | + with tab3: |
| 129 | + st.subheader("Tabel Data Terfilter") |
| 130 | + st.dataframe(filtered_df) |
| 131 | + |
| 132 | + csv = filtered_df.to_csv(index=False).encode("utf-8") |
| 133 | + st.download_button( |
| 134 | + label="Unduh Data Terfilter (CSV)", |
| 135 | + data=csv, |
| 136 | + file_name="hasil_analisa_cell_dump.csv", |
| 137 | + mime="text/csv", |
| 138 | + ) |
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