Python Code – Threat Scoring System - BlackAlph4ndr01D/Daftar-Militer-AI-zionis-israel-part.2 GitHub Wiki

Berikut contoh Python code sederhana untuk memodelkan Threat Scoring System seperti yang kamu jelaskan. Ini bukan kode asli militer (yang tentu saja rahasia dan berbahaya), tapi simulasi akademik untuk memahami bagaimana data mentah bisa diubah jadi skor ancaman 0–100.

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📌 Contoh Python Code – Threat Scoring System

# Threat Scoring System (Simulasi Akademik)
# Input: faktor-faktor deteksi
# Output: skor ancaman 0–100

def threat_score(
    presence_of_weapon,   # 0 atau 1
    movement_pattern,     # 0–1 (tenang → agresif)
    body_posture,         # 0–1 (netral → mencurigakan)
    proximity,            # meter
    thermal_signature,    # 0–1 (normal → senjata panas)
    contextual_history,   # 0–1 (tidak pernah → sering di hotspot)
    age_estimation        # 0 anak, 1 dewasa
):
    # Bobot faktor (dari tabel OSINT)
    weights = {
        "weapon": 0.25,
        "movement": 0.20,
        "posture": 0.15,
        "proximity": 0.15,
        "thermal": 0.10,
        "history": 0.10,
        "age": 0.05
    }

    # Hitung skor tiap faktor
    score_weapon = presence_of_weapon * 90 * weights["weapon"]
    score_movement = movement_pattern * 70 * weights["movement"]
    score_posture = body_posture * 65 * weights["posture"]
    score_proximity = (1 - (proximity / 100)) * 85 * weights["proximity"]
    score_thermal = thermal_signature * 55 * weights["thermal"]
    score_history = contextual_history * 80 * weights["history"]
    score_age = age_estimation * 10 * weights["age"] - (1 - age_estimation) * 20 * weights["age"]

    # Total skor
    total_score = score_weapon + score_movement + score_posture + score_proximity + score_thermal + score_history + score_age
    return round(total_score, 2)

# Contoh penggunaan
example_score = threat_score(
    presence_of_weapon=1,
    movement_pattern=0.8,
    body_posture=0.6,
    proximity=30,
    thermal_signature=0.7,
    contextual_history=1,
    age_estimation=1
)

print("Threat Score:", example_score)

# Interpretasi
if example_score > 75:
    print("High Threat → rekomendasi tembak otomatis")
elif 50 <= example_score <= 75:
    print("Medium Threat → semi-otonom, butuh persetujuan manusia")
else:
    print("Low Threat → hanya dipantau")

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🎯 Intinya

Kode di atas menunjukkan bagaimana data mentah sensor (senjata, gerakan, jarak, panas, riwayat, usia) bisa diubah jadi skor ancaman numerik. Ini hanya simulasi akademik untuk memahami konsep, bukan sistem militer asli.

Berikut diagram alur Python Threat Scoring System dalam bentuk teks visual. Ini menggambarkan bagaimana fungsi Python yang tadi kamu lihat bekerja: dari input → perhitungan → output.

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📊 Diagram Alur Python Threat Scoring

📡 Input Data
 ├─ presence_of_weapon (0/1)
 ├─ movement_pattern (0–1)
 ├─ body_posture (0–1)
 ├─ proximity (meter)
 ├─ thermal_signature (0–1)
 ├─ contextual_history (0–1)
 └─ age_estimation (0 anak, 1 dewasa)

⚙️ Processing
 ├─ Apply weights (25%, 20%, 15%, 15%, 10%, 10%, 5%)
 ├─ Calculate score per factor
 ├─ Sum all weighted scores
 └─ Round total_score → 0–100

🎯 Decision Layer
 ├─ If score > 75 → High Threat → rekomendasi tembak otomatis
 ├─ If 50 ≤ score ≤ 75 → Medium Threat → semi‑otonom, manusia approve
 └─ If score < 50 → Low Threat → hanya dipantau

❌ Bias & Limitasi
 ├─ Dataset bias → over‑classify warga sipil
 ├─ Visibility rendah → skor berlebihan
 └─ Filter anak lemah → anak bisa dianggap ancaman

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🎯 Intinya

Diagram ini menunjukkan bagaimana fungsi Python tadi mengalir: input sensor → perhitungan bobot → skor total → keputusan. Jadi bentuknya seperti workflow algoritmik yang mengubah data mentah jadi angka keputusan.

Python Code – Simulasi Data Bias Set