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"cells": [
{
"cell_type": "markdown",
"id": "faad7818",
"metadata": {},
"source": [
"# Synthetic Dummy Data Generator (UMKM)\n",
"Notebook ini membuat data sintetis yang meniru pola data bisnis riil UMKM untuk analisis operasional, NLP, dan klasifikasi bisnis.\n",
"\n",
"Fitur utama yang dihasilkan:\n",
"\n",
"- Monthly_Revenue (IDR)\n",
"- Net_Profit_Margin (%)\n",
"- Burn_Rate_Ratio\n",
"- Transaction_Count\n",
"- Avg_Historical_Rating\n",
"- Review_Text\n",
"- Review_Volatility\n",
"- Business_Tenure_Months\n",
"- Repeat_Order_Rate (%)\n",
"- Digital_Adoption_Score\n",
"- Peak_Hour_Latency\n",
"- Location_Competitiveness\n",
"- Sentiment_Score (-1.0 s/d 1.0)\n",
"- Class (Elite, Growth, Struggling, Critical)\n",
"\n",
"Karakteristik realisme yang dimodelkan:\n",
"\n",
"- Korelasi antar fitur operasional (contoh: adopsi digital cenderung meningkatkan retensi dan rating)\n",
"- Trade-off bisnis (kompetisi tinggi dan latency tinggi menekan profitabilitas)\n",
"- Distribusi finansial tidak simetris (AOV lognormal)\n",
"- Noise terkontrol agar data tidak terlalu \"rapi\"\n",
"- Review teks konsisten dengan sinyal kualitas (rating, volatility, latency)\n",
"- Sentiment score diekstrak dari `Review_Text`\n",
"\n",
"Catatan target:\n",
"\n",
"- Variabel target adalah `Class` (kolom paling kanan)\n",
"- Klasifikasi menggunakan threshold berbasis persentil agar distribusi kelas lebih seimbang\n",
"- Urutan kelas: `Elite`, `Growth`, `Struggling`, `Critical`"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "af962614",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated 150000 rows -> synthetic_umkm_data.csv\n",
"\n",
"Preview:\n"
]
},
{
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{
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"rawType": "float64",
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{
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{
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{
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{
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[
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" <th>Monthly_Revenue</th>\n",
" <th>Net_Profit_Margin (%)</th>\n",
" <th>Burn_Rate_Ratio</th>\n",
" <th>Transaction_Count</th>\n",
" <th>Avg_Historical_Rating</th>\n",
" <th>Review_Text</th>\n",
" <th>Review_Volatility</th>\n",
" <th>Business_Tenure_Months</th>\n",
" <th>Repeat_Order_Rate (%)</th>\n",
" <th>Digital_Adoption_Score</th>\n",
" <th>Peak_Hour_Latency</th>\n",
" <th>Location_Competitiveness</th>\n",
" <th>Sentiment_Score</th>\n",
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" <td>1</td>\n",
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" <td>161</td>\n",
" <td>4.75</td>\n",
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" <td>0.313</td>\n",
" <td>105</td>\n",
" <td>19.40</td>\n",
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" <td>9</td>\n",
" <td>-0.25</td>\n",
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" <th>1</th>\n",
" <td>2</td>\n",
" <td>5819101</td>\n",
" <td>4.46</td>\n",
" <td>0.968</td>\n",
" <td>104</td>\n",
" <td>4.21</td>\n",
" <td>Harga dan kualitas seimbang, pengalaman biasa ...</td>\n",
" <td>0.632</td>\n",
" <td>95</td>\n",
" <td>14.87</td>\n",
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" <td>10</td>\n",
" <td>0.00</td>\n",
" <td>Growth</td>\n",
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" <th>2</th>\n",
" <td>3</td>\n",
" <td>5236404</td>\n",
" <td>-10.12</td>\n",
" <td>1.047</td>\n",
" <td>102</td>\n",
" <td>3.51</td>\n",
" <td>Pelayanan standar, masih bisa ditingkatkan.</td>\n",
" <td>0.470</td>\n",
" <td>17</td>\n",
" <td>21.00</td>\n",
" <td>3.37</td>\n",
" <td>Med</td>\n",
" <td>8</td>\n",
" <td>0.00</td>\n",
" <td>Struggling</td>\n",
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" <th>3</th>\n",
" <td>4</td>\n",
" <td>8043552</td>\n",
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" <td>99</td>\n",
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" <td>0.206</td>\n",
" <td>109</td>\n",
" <td>30.62</td>\n",
" <td>5.41</td>\n",
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" <td>13</td>\n",
" <td>-0.25</td>\n",
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" <th>4</th>\n",
" <td>5</td>\n",
" <td>6071256</td>\n",
" <td>4.22</td>\n",
" <td>0.954</td>\n",
" <td>115</td>\n",
" <td>4.34</td>\n",
" <td>Selalu repeat order karena kualitasnya terjaga...</td>\n",
" <td>0.232</td>\n",
" <td>74</td>\n",
" <td>20.87</td>\n",
" <td>2.67</td>\n",
" <td>Low</td>\n",
" <td>7</td>\n",
" <td>0.25</td>\n",
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" <th>5</th>\n",
" <td>6</td>\n",
" <td>6683141</td>\n",
" <td>29.68</td>\n",
" <td>0.727</td>\n",
" <td>108</td>\n",
" <td>4.54</td>\n",
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" <td>0.185</td>\n",
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" <td>16</td>\n",
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" <th>6</th>\n",
" <td>7</td>\n",
" <td>14123932</td>\n",
" <td>15.28</td>\n",
" <td>0.860</td>\n",
" <td>167</td>\n",
" <td>4.54</td>\n",
" <td>Produk cukup baik, kadang waktu tunggu agak la...</td>\n",
" <td>0.434</td>\n",
" <td>105</td>\n",
" <td>22.15</td>\n",
" <td>3.95</td>\n",
" <td>Med</td>\n",
" <td>6</td>\n",
" <td>0.00</td>\n",
" <td>Growth</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>8483571</td>\n",
" <td>8.51</td>\n",
" <td>0.862</td>\n",
" <td>180</td>\n",
" <td>4.83</td>\n",
" <td>Selalu repeat order karena kualitasnya terjaga.</td>\n",
" <td>0.346</td>\n",
" <td>124</td>\n",
" <td>23.17</td>\n",
" <td>7.59</td>\n",
" <td>Low</td>\n",
" <td>10</td>\n",
" <td>0.25</td>\n",
" <td>Growth</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>14900709</td>\n",
" <td>6.00</td>\n",
" <td>0.908</td>\n",
" <td>135</td>\n",
" <td>4.86</td>\n",
" <td>Selalu repeat order karena kualitasnya terjaga...</td>\n",
" <td>0.285</td>\n",
" <td>77</td>\n",
" <td>15.85</td>\n",
" <td>6.56</td>\n",
" <td>Low</td>\n",
" <td>7</td>\n",
" <td>0.25</td>\n",
" <td>Growth</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>9232562</td>\n",
" <td>-13.64</td>\n",
" <td>1.085</td>\n",
" <td>89</td>\n",
" <td>4.39</td>\n",
" <td>Transaksi digital lancar, proses checkout tida...</td>\n",
" <td>0.182</td>\n",
" <td>90</td>\n",
" <td>17.30</td>\n",
" <td>3.22</td>\n",
" <td>Low</td>\n",
" <td>9</td>\n",
" <td>-0.25</td>\n",
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" ID Monthly_Revenue Net_Profit_Margin (%) Burn_Rate_Ratio \\\n",
"0 1 6680716 22.72 0.811 \n",
"1 2 5819101 4.46 0.968 \n",
"2 3 5236404 -10.12 1.047 \n",
"3 4 8043552 0.04 0.969 \n",
"4 5 6071256 4.22 0.954 \n",
"5 6 6683141 29.68 0.727 \n",
"6 7 14123932 15.28 0.860 \n",
"7 8 8483571 8.51 0.862 \n",
"8 9 14900709 6.00 0.908 \n",
"9 10 9232562 -13.64 1.085 \n",
"\n",
" Transaction_Count Avg_Historical_Rating \\\n",
"0 161 4.75 \n",
"1 104 4.21 \n",
"2 102 3.51 \n",
"3 99 4.33 \n",
"4 115 4.34 \n",
"5 108 4.54 \n",
"6 167 4.54 \n",
"7 180 4.83 \n",
"8 135 4.86 \n",
"9 89 4.39 \n",
"\n",
" Review_Text Review_Volatility \\\n",
"0 Transaksi digital lancar, proses checkout tida... 0.313 \n",
"1 Harga dan kualitas seimbang, pengalaman biasa ... 0.632 \n",
"2 Pelayanan standar, masih bisa ditingkatkan. 0.470 \n",
"3 Transaksi digital lancar, proses checkout tida... 0.206 \n",
"4 Selalu repeat order karena kualitasnya terjaga... 0.232 \n",
"5 Pengiriman cepat, admin komunikatif. Culpa ver... 0.185 \n",
"6 Produk cukup baik, kadang waktu tunggu agak la... 0.434 \n",
"7 Selalu repeat order karena kualitasnya terjaga. 0.346 \n",
"8 Selalu repeat order karena kualitasnya terjaga... 0.285 \n",
"9 Transaksi digital lancar, proses checkout tida... 0.182 \n",
"\n",
" Business_Tenure_Months Repeat_Order_Rate (%) Digital_Adoption_Score \\\n",
"0 105 19.40 4.24 \n",
"1 95 14.87 1.27 \n",
"2 17 21.00 3.37 \n",
"3 109 30.62 5.41 \n",
"4 74 20.87 2.67 \n",
"5 23 26.35 5.59 \n",
"6 105 22.15 3.95 \n",
"7 124 23.17 7.59 \n",
"8 77 15.85 6.56 \n",
"9 90 17.30 3.22 \n",
"\n",
" Peak_Hour_Latency Location_Competitiveness Sentiment_Score Class \n",
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"1 Med 10 0.00 Growth \n",
"2 Med 8 0.00 Struggling \n",
"3 Low 13 -0.25 Growth \n",
"4 Low 7 0.25 Growth \n",
"5 Low 16 0.55 Elite \n",
"6 Med 6 0.00 Growth \n",
"7 Low 10 0.25 Growth \n",
"8 Low 7 0.25 Growth \n",
"9 Low 9 -0.25 Struggling "
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" count unique \\\n",
"ID 150000.0 NaN \n",
"Monthly_Revenue 150000.0 NaN \n",
"Net_Profit_Margin (%) 150000.0 NaN \n",
"Burn_Rate_Ratio 150000.0 NaN \n",
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"output_type": "stream",
"text": [
"\n",
"Class counts:\n",
"Class\n",
"Growth 85678\n",
"Struggling 41571\n",
"Critical 12561\n",
"Elite 10190\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"import random\n",
"from typing import List\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from faker import Faker\n",
"\n",
"# Reproducibility\n",
"SEED = 42\n",
"np.random.seed(SEED)\n",
"random.seed(SEED)\n",
"fake = Faker(\"id_ID\")\n",
"Faker.seed(SEED)\n",
"\n",
"# Config\n",
"N_SAMPLES = 150000\n",
"OUTPUT_CSV = \"synthetic_umkm_data.csv\"\n",
"\n",
"# Review templates aligned with sentiment\n",
"POSITIVE_REVIEWS = [\n",
" \"Pelayanan cepat dan ramah, pesanan selalu tepat.\",\n",
" \"Kualitas produk konsisten, harga masih masuk akal.\",\n",
" \"Aplikasi pemesanan mudah dipakai dan responsif.\",\n",
" \"Pengiriman cepat, admin komunikatif.\",\n",
" \"Selalu repeat order karena kualitasnya terjaga.\",\n",
" \"Transaksi digital lancar, proses checkout tidak ribet.\",\n",
"]\n",
"\n",
"NEUTRAL_REVIEWS = [\n",
" \"Produk cukup baik, kadang waktu tunggu agak lama.\",\n",
" \"Pelayanan standar, masih bisa ditingkatkan.\",\n",
" \"Harga dan kualitas seimbang, pengalaman biasa saja.\",\n",
" \"Kadang stok kosong saat jam ramai.\",\n",
" \"Secara umum oke, hanya respon chat kadang lambat.\",\n",
"]\n",
"\n",
"NEGATIVE_REVIEWS = [\n",
" \"Pesanan sering terlambat saat jam sibuk.\",\n",
" \"Kualitas tidak konsisten, kadang bagus kadang kurang.\",\n",
" \"Respons admin lambat dan informasi kurang jelas.\",\n",
" \"Proses pembayaran sering bermasalah.\",\n",
" \"Harga naik tapi layanan tidak membaik.\",\n",
" \"Sudah beberapa kali order, pengalaman makin menurun.\",\n",
"]\n",
"\n",
"\n",
"def clamp(x: np.ndarray, low: float, high: float) -> np.ndarray:\n",
" return np.clip(x, low, high)\n",
"\n",
"\n",
"def pick_review(rating: float, volatility: float, latency: str) -> str:\n",
" \"\"\"Generate review text coherent with quality signal.\"\"\"\n",
" base_pool: List[str]\n",
"\n",
" if rating >= 4.2 and volatility < 0.45 and latency == \"Low\":\n",
" base_pool = POSITIVE_REVIEWS\n",
" elif rating < 3.4 or latency == \"High\":\n",
" base_pool = NEGATIVE_REVIEWS\n",
" else:\n",
" base_pool = NEUTRAL_REVIEWS\n",
"\n",
" text = random.choice(base_pool)\n",
"\n",
" # Add slight random variation so reviews don't look templated\n",
" if random.random() < 0.3:\n",
" text += \" \" + fake.sentence(nb_words=6)\n",
" return text\n",
"\n",
"\n",
"def calculate_sentiment_score(review_text: str) -> float:\n",
" \"\"\"Convert review text into sentiment score in range [-1.0, 1.0].\"\"\"\n",
" review_lower = review_text.lower()\n",
"\n",
" positive_keywords = {\n",
" \"cepat\": 0.30,\n",
" \"ramah\": 0.30,\n",
" \"mudah\": 0.25,\n",
" \"responsif\": 0.30,\n",
" \"lancar\": 0.25,\n",
" \"komunikatif\": 0.25,\n",
" \"terjaga\": 0.25,\n",
" \"konsisten\": 0.20,\n",
" \"tepat\": 0.20,\n",
" }\n",
" negative_keywords = {\n",
" \"lambat\": -0.30,\n",
" \"tidak\": -0.20,\n",
" \"kurang\": -0.25,\n",
" \"bermasalah\": -0.35,\n",
" \"terlambat\": -0.35,\n",
" \"ribet\": -0.30,\n",
" \"buruk\": -0.40,\n",
" \"menurun\": -0.30,\n",
" }\n",
"\n",
" score = 0.0\n",
" for word, weight in positive_keywords.items():\n",
" if word in review_lower:\n",
" score += weight\n",
" for word, weight in negative_keywords.items():\n",
" if word in review_lower:\n",
" score += weight\n",
"\n",
" return float(clamp(np.array([score]), -1.0, 1.0)[0])\n",
"\n",
"\n",
"# 1) Business maturity and competitiveness\n",
"business_tenure = np.random.randint(3, 180, size=N_SAMPLES) # months\n",
"location_competitiveness = np.random.poisson(lam=8, size=N_SAMPLES) + 1\n",
"\n",
"# 2) Digital adoption (1-10), positively related with tenure (up to a limit)\n",
"base_digital = 3.3 + 0.02 * np.sqrt(business_tenure)\n",
"noise_digital = np.random.normal(0, 1.8, N_SAMPLES)\n",
"digital_adoption = clamp(base_digital + noise_digital, 1, 10)\n",
"\n",
"# 3) Transaction count depends on maturity, digital, and local competition\n",
"transaction_lambda = (\n",
" 50\n",
" + 0.65 * business_tenure\n",
" + 8.5 * digital_adoption\n",
" - 2.4 * location_competitiveness\n",
" + np.random.normal(0, 18, N_SAMPLES)\n",
")\n",
"transaction_lambda = clamp(transaction_lambda, 20, 900)\n",
"transaction_count = np.random.poisson(transaction_lambda).astype(int)\n",
"transaction_count = np.maximum(transaction_count, 5)\n",
"\n",
"# 4) Average order value (AOV) and monthly revenue\n",
"aov = np.random.lognormal(mean=np.log(65000), sigma=0.45, size=N_SAMPLES)\n",
"aov = clamp(aov, 12000, 450000)\n",
"\n",
"monthly_revenue = transaction_count * aov\n",
"seasonality_noise = np.random.normal(1.0, 0.08, N_SAMPLES)\n",
"monthly_revenue = monthly_revenue * seasonality_noise\n",
"monthly_revenue = clamp(monthly_revenue, 1_500_000, 850_000_000)\n",
"\n",
"# 5) Peak hour latency category influenced by transaction pressure and digital adoption\n",
"latency_score = (\n",
" 0.0045 * transaction_count\n",
" - 0.28 * digital_adoption\n",
" + 0.09 * location_competitiveness\n",
" + np.random.normal(0, 0.9, N_SAMPLES)\n",
")\n",
"\n",
"peak_hour_latency = np.where(\n",
" latency_score < 0.0,\n",
" \"Low\",\n",
" np.where(latency_score < 1.3, \"Med\", \"High\")\n",
")\n",
"\n",
"# 6) Burn rate ratio (expense/revenue)\n",
"latency_penalty = np.select(\n",
" [peak_hour_latency == \"Low\", peak_hour_latency == \"Med\", peak_hour_latency == \"High\"],\n",
" [0.0, 0.10, 0.22],\n",
" default=0.10,\n",
")\n",
"\n",
"burn_rate_ratio = (\n",
" 0.80\n",
" + 0.015 * location_competitiveness\n",
" - 0.014 * digital_adoption\n",
" + latency_penalty\n",
" + np.random.normal(0, 0.10, N_SAMPLES)\n",
")\n",
"burn_rate_ratio = clamp(burn_rate_ratio, 0.40, 1.55)\n",
"\n",
"# 7) Net profit margin (%), inverse relation with burn rate\n",
"net_profit_margin = (\n",
" (1 - burn_rate_ratio) * 100\n",
" + 0.55 * (digital_adoption - 5)\n",
" - 0.18 * np.log1p(location_competitiveness)\n",
" + np.random.normal(0, 3.2, N_SAMPLES)\n",
")\n",
"net_profit_margin = clamp(net_profit_margin, -35, 45)\n",
"\n",
"# 8) Repeat order rate (%), boosted by digital adoption and tenure\n",
"repeat_order_rate = (\n",
" 16\n",
" + 1.9 * digital_adoption\n",
" + 0.03 * business_tenure\n",
" - 0.6 * location_competitiveness\n",
" + np.random.normal(0, 7.0, N_SAMPLES)\n",
")\n",
"repeat_order_rate = clamp(repeat_order_rate, 2, 90)\n",
"\n",
"# 9) Review volatility\n",
"review_volatility = (\n",
" 0.24\n",
" + 0.18 * (peak_hour_latency == \"Med\").astype(float)\n",
" + 0.34 * (peak_hour_latency == \"High\").astype(float)\n",
" + 0.06 * (burn_rate_ratio > 1.0).astype(float)\n",
" + np.random.normal(0, 0.09, N_SAMPLES)\n",
")\n",
"review_volatility = clamp(review_volatility, 0.06, 1.30)\n",
"\n",
"# 10) Average historical rating (1-5)\n",
"avg_historical_rating = (\n",
" 3.95\n",
" + 0.08 * digital_adoption\n",
" + 0.016 * net_profit_margin\n",
" - 0.38 * review_volatility\n",
" - 0.12 * (peak_hour_latency == \"High\").astype(float)\n",
" + np.random.normal(0, 0.26, N_SAMPLES)\n",
")\n",
"avg_historical_rating = clamp(avg_historical_rating, 1.0, 5.0)\n",
"\n",
"# 11) Review text generation coherent with rating/volatility/latency\n",
"review_text = [\n",
" pick_review(rating=r, volatility=v, latency=l)\n",
" for r, v, l in zip(avg_historical_rating, review_volatility, peak_hour_latency)\n",
"]\n",
"\n",
"# 12) Sentiment score derived from review text\n",
"sentiment_scores = np.array([calculate_sentiment_score(text) for text in review_text])\n",
"\n",
"# Optional: post-adjustment for severe deficit businesses\n",
"deficit_mask = burn_rate_ratio > 1.25\n",
"avg_historical_rating[deficit_mask] = np.minimum(\n",
" avg_historical_rating[deficit_mask],\n",
" np.random.uniform(1.5, 3.5, deficit_mask.sum()),\n",
")\n",
"repeat_order_rate[deficit_mask] = np.minimum(\n",
" repeat_order_rate[deficit_mask],\n",
" np.random.uniform(3, 30, deficit_mask.sum()),\n",
")\n",
"\n",
"# 13) Target Class with percentile-based thresholds (balanced by design)\n",
"target_class = np.full(N_SAMPLES, \"Growth\", dtype=object)\n",
"\n",
"elite_mask = (\n",
" (net_profit_margin > np.percentile(net_profit_margin, 70))\n",
" & (burn_rate_ratio < np.percentile(burn_rate_ratio, 25))\n",
" & (repeat_order_rate > np.percentile(repeat_order_rate, 70))\n",
" & (avg_historical_rating > np.percentile(avg_historical_rating, 75))\n",
")\n",
"\n",
"critical_mask = (\n",
" (burn_rate_ratio > np.percentile(burn_rate_ratio, 92))\n",
" | ((business_tenure < 7) & (location_competitiveness >= 12))\n",
" | ((net_profit_margin < np.percentile(net_profit_margin, 5)) & (avg_historical_rating < 3.0))\n",
")\n",
"\n",
"struggling_mask = (\n",
" ((net_profit_margin < np.percentile(net_profit_margin, 35)) & (burn_rate_ratio > np.percentile(burn_rate_ratio, 60)))\n",
" | ((peak_hour_latency == \"High\") & (avg_historical_rating < np.percentile(avg_historical_rating, 40)))\n",
" | ((burn_rate_ratio > np.percentile(burn_rate_ratio, 75)) & (avg_historical_rating < np.percentile(avg_historical_rating, 65)))\n",
")\n",
"\n",
"target_class[elite_mask] = \"Elite\"\n",
"target_class[struggling_mask] = \"Struggling\"\n",
"target_class[critical_mask] = \"Critical\"\n",
"\n",
"# Final DataFrame (class at the rightmost position)\n",
"df = pd.DataFrame(\n",
" {\n",
" \"ID\": np.arange(1, N_SAMPLES + 1),\n",
" \"Monthly_Revenue\": np.round(monthly_revenue, 0).astype(int),\n",
" \"Net_Profit_Margin (%)\": np.round(net_profit_margin, 2),\n",
" \"Burn_Rate_Ratio\": np.round(burn_rate_ratio, 3),\n",
" \"Transaction_Count\": transaction_count.astype(int),\n",
" \"Avg_Historical_Rating\": np.round(avg_historical_rating, 2),\n",
" \"Review_Text\": review_text,\n",
" \"Review_Volatility\": np.round(review_volatility, 3),\n",
" \"Business_Tenure_Months\": business_tenure.astype(int),\n",
" \"Repeat_Order_Rate (%)\": np.round(repeat_order_rate, 2),\n",
" \"Digital_Adoption_Score\": np.round(digital_adoption, 2),\n",
" \"Peak_Hour_Latency\": peak_hour_latency,\n",
" \"Location_Competitiveness\": location_competitiveness.astype(int),\n",
" \"Sentiment_Score\": np.round(sentiment_scores, 3),\n",
" \"Class\": target_class,\n",
" }\n",
")\n",
"\n",
"# Save and preview\n",
"df.to_csv(OUTPUT_CSV, index=False)\n",
"\n",
"print(f\"Generated {len(df)} rows -> {OUTPUT_CSV}\")\n",
"print(\"\\nPreview:\")\n",
"display(df.head(10))\n",
"\n",
"print(\"\\nSummary stats:\")\n",
"display(df.describe(include=\"all\").transpose())\n",
"\n",
"print(\"\\nClass counts:\")\n",
"print(df[\"Class\"].value_counts())"
]
}
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