feat: initial commit with seeded database and premium ui changes
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Dodo
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import math
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import numpy as np
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import skfuzzy as fuzz
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import skfuzzy.control as ctrl
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import pandas as pd
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PENGHASILAN_MAX = 5_000_000.0
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SKALA_MAX = 10.0
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# Human-readable labels for linguistic terms, used in the factor breakdown.
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TERM_LABELS = {
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"sangat_rendah": "Sangat Rendah",
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"rendah": "Rendah",
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"sedang": "Sedang",
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"tinggi": "Tinggi",
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"sedikit": "Sedikit",
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"banyak": "Banyak",
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"buruk": "Buruk",
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"cukup": "Cukup",
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"layak": "Layak",
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"tidak_punya": "Tidak Punya",
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}
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def build_fuzzy_system() -> ctrl.ControlSystem:
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"""Build and return the Mamdani fuzzy control system (rule base only).
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Simulations are created per computation — ControlSystemSimulation holds
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mutable input/output state and must not be shared across threads.
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"""
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penghasilan = ctrl.Antecedent(np.arange(0, PENGHASILAN_MAX + 1, 10_000), 'penghasilan')
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tanggungan = ctrl.Antecedent(np.arange(0, SKALA_MAX + 0.1, 0.1), 'tanggungan')
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kondisi_rumah = ctrl.Antecedent(np.arange(0, SKALA_MAX + 0.1, 0.1), 'kondisi_rumah')
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kepemilikan_aset = ctrl.Antecedent(np.arange(0, SKALA_MAX + 0.1, 0.1), 'kepemilikan_aset')
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prioritas = ctrl.Consequent(np.arange(0, 100.1, 0.1), 'prioritas')
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prioritas.defuzzify_method = 'centroid'
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# Edge sets use trapezoids saturating at the universe boundary, so clamped
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# extreme inputs (income 5M, tanggungan 10, aset 10) keep full membership
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# instead of dropping to 0 and silently deactivating every rule.
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penghasilan['sangat_rendah'] = fuzz.trimf(penghasilan.universe, [0, 0, 1_500_000])
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penghasilan['rendah'] = fuzz.trimf(penghasilan.universe, [500_000, 1_500_000, 2_500_000])
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penghasilan['sedang'] = fuzz.trimf(penghasilan.universe, [1_500_000, 2_500_000, 3_500_000])
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penghasilan['tinggi'] = fuzz.trapmf(penghasilan.universe, [2_500_000, 4_000_000, 5_000_000, 5_000_000])
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tanggungan['sedikit'] = fuzz.trimf(tanggungan.universe, [0, 0, 3])
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tanggungan['sedang'] = fuzz.trimf(tanggungan.universe, [2, 4, 6])
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tanggungan['banyak'] = fuzz.trapmf(tanggungan.universe, [5, 7, 10, 10])
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kondisi_rumah['buruk'] = fuzz.trimf(kondisi_rumah.universe, [0, 0, 4])
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kondisi_rumah['cukup'] = fuzz.trimf(kondisi_rumah.universe, [3, 5, 7])
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kondisi_rumah['layak'] = fuzz.trapmf(kondisi_rumah.universe, [6, 8, 10, 10])
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kepemilikan_aset['tidak_punya'] = fuzz.trimf(kepemilikan_aset.universe, [0, 0, 3])
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kepemilikan_aset['sedikit'] = fuzz.trimf(kepemilikan_aset.universe, [2, 4, 6])
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kepemilikan_aset['cukup'] = fuzz.trapmf(kepemilikan_aset.universe, [5, 7, 10, 10])
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# Output sets are evenly spaced so each pure category defuzzifies to a
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# distinct centroid (~9, 30, 50, 70, ~91) that score_to_label can separate.
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prioritas['tidak_prioritas'] = fuzz.trapmf(prioritas.universe, [0, 0, 10, 25])
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prioritas['rendah'] = fuzz.trimf(prioritas.universe, [15, 30, 45])
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prioritas['sedang'] = fuzz.trimf(prioritas.universe, [35, 50, 65])
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prioritas['tinggi'] = fuzz.trimf(prioritas.universe, [55, 70, 85])
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prioritas['sangat_tinggi'] = fuzz.trapmf(prioritas.universe, [75, 90, 100, 100])
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# Aggravating factors raise priority, mitigating factors lower it.
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memberatkan = (tanggungan['banyak']
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| kondisi_rumah['buruk']
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| kepemilikan_aset['tidak_punya'])
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# "Stable" baseline: explicitly no aggravating factor on any dimension.
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# Expressed with positive terms so the middle categories (tanggungan
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# sedang, rumah cukup, aset sedikit) actually participate in the decision.
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stabil = ((tanggungan['sedikit'] | tanggungan['sedang'])
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& (kondisi_rumah['cukup'] | kondisi_rumah['layak'])
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& (kepemilikan_aset['sedikit'] | kepemilikan_aset['cukup']))
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meringankan = kondisi_rumah['layak'] & kepemilikan_aset['cukup']
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rules = [
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# ── Penghasilan sangat rendah: baseline TINGGI ──
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ctrl.Rule(penghasilan['sangat_rendah'] & memberatkan,
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prioritas['sangat_tinggi']),
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ctrl.Rule(penghasilan['sangat_rendah'] & stabil,
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prioritas['tinggi']),
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ctrl.Rule(penghasilan['sangat_rendah'] & meringankan,
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prioritas['sedang']),
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# ── Penghasilan rendah: baseline SEDANG ──
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ctrl.Rule(penghasilan['rendah'] & tanggungan['banyak']
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& (kondisi_rumah['buruk'] | kepemilikan_aset['tidak_punya']),
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prioritas['sangat_tinggi']),
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ctrl.Rule(penghasilan['rendah'] & memberatkan,
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prioritas['tinggi']),
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ctrl.Rule(penghasilan['rendah'] & stabil,
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prioritas['sedang']),
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ctrl.Rule(penghasilan['rendah'] & tanggungan['sedikit'] & meringankan,
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prioritas['rendah']),
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# ── Penghasilan sedang: baseline RENDAH ──
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ctrl.Rule(penghasilan['sedang'] & tanggungan['banyak'] & kondisi_rumah['buruk'],
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prioritas['tinggi']),
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ctrl.Rule(penghasilan['sedang'] & memberatkan,
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prioritas['sedang']),
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ctrl.Rule(penghasilan['sedang'] & stabil,
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prioritas['rendah']),
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ctrl.Rule(penghasilan['sedang'] & meringankan,
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prioritas['tidak_prioritas']),
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# ── Penghasilan tinggi: baseline TIDAK PRIORITAS ──
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ctrl.Rule(penghasilan['tinggi'] & tanggungan['banyak']
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& kondisi_rumah['buruk'] & kepemilikan_aset['tidak_punya'],
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prioritas['sedang']),
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ctrl.Rule(penghasilan['tinggi'] & memberatkan,
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prioritas['rendah']),
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ctrl.Rule(penghasilan['tinggi'] & stabil,
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prioritas['tidak_prioritas']),
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]
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return ctrl.ControlSystem(rules)
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def _clamp_inputs(penghasilan: float,
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tanggungan: float,
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kondisi_rumah: float,
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kepemilikan_aset: float) -> dict:
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"""Clamp inputs to universe boundaries — values outside the range
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would zero all membership activations and kill every rule."""
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values = {
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'penghasilan': max(0.0, min(PENGHASILAN_MAX, float(penghasilan))),
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'tanggungan': max(0.0, min(SKALA_MAX, float(tanggungan))),
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'kondisi_rumah': max(0.0, min(SKALA_MAX, float(kondisi_rumah))),
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'kepemilikan_aset': max(0.0, min(SKALA_MAX, float(kepemilikan_aset))),
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}
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for name, v in values.items():
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if not math.isfinite(v):
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raise ValueError(f"Input '{name}' is not a finite number")
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return values
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def compute_priority(system: ctrl.ControlSystem,
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penghasilan: float,
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tanggungan: float,
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kondisi_rumah: float,
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kepemilikan_aset: float) -> float:
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"""Run simulation for one warga, return priority score 0-100."""
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values = _clamp_inputs(penghasilan, tanggungan, kondisi_rumah, kepemilikan_aset)
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sim = ctrl.ControlSystemSimulation(system)
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for name, v in values.items():
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sim.input[name] = v
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sim.compute()
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return float(np.clip(sim.output['prioritas'], 0.0, 100.0))
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def describe_inputs(system: ctrl.ControlSystem,
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penghasilan: float,
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tanggungan: float,
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kondisi_rumah: float,
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kepemilikan_aset: float) -> dict:
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"""Return the dominant linguistic category and membership degree per input,
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so the decision can be explained to the user."""
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values = _clamp_inputs(penghasilan, tanggungan, kondisi_rumah, kepemilikan_aset)
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detail = {}
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for var in system.antecedents:
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x = values[var.label]
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memberships = {
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name: float(fuzz.interp_membership(var.universe, term.mf, x))
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for name, term in var.terms.items()
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}
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dominant = max(memberships, key=memberships.get)
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detail[var.label] = {
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'kategori': TERM_LABELS.get(dominant, dominant),
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'derajat': round(memberships[dominant], 2),
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}
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return detail
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def score_to_label(score: float) -> str:
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"""Convert numeric score to Indonesian priority category label.
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Thresholds sit at the midpoints between the centroids of the output sets
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(~9, 30, 50, 70, ~91), so a pure category result maps back to its own label.
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"""
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if score >= 80:
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return "SANGAT TINGGI"
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elif score >= 60:
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return "TINGGI"
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elif score >= 40:
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return "SEDANG"
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elif score >= 20:
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return "RENDAH"
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else:
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return "TIDAK PRIORITAS"
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def run_batch(system: ctrl.ControlSystem,
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df: pd.DataFrame) -> pd.DataFrame:
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"""Run all rows in dataframe, return df with skor and prioritas columns added."""
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result = df.copy()
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scores = [
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compute_priority(system, row['penghasilan'], row['tanggungan'],
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row['kondisi_rumah'], row['kepemilikan_aset'])
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for _, row in result.iterrows()
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]
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result['skor'] = scores
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result['prioritas'] = [score_to_label(s) for s in scores]
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return result
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