SISTEM SIMULASI KETAHANAN NASIONAL (SISKATANAS)
Berdasarkan sistem pengukuran ketahanan nasional Lemhannas RI, berikut rancangan software yang komprehensif:
## **SISTEM SIMULASI KETAHANAN NASIONAL (SISKATANAS)**
### **Arsitektur Sistem**
```
Frontend: React.js + OpenLayers/Leaflet (GIS)
Backend: Python (Django/FastAPI) + PostGIS
Database: PostgreSQL + PostGIS
Analytics: Python (Pandas, NumPy, SciPy)
Visualization: D3.js, Chart.js
AI/ML: TensorFlow/PyTorch (untuk prediksi)
```
### **Modul Utama**
#### **1. Modul Data Management**
```python
class DataManager:
def import_data_sources(self):
# CSV, Excel, API, Shapefiles
# 821 indikator dari 108 variabel
pass
def data_validation(self):
# Validasi kualitas data
pass
def spatial_data_processing(self):
# Processing data geospasial
pass
```
#### **2. Modul ASTA GATRA Analytics**
```python
class AstaGatraEngine:
GATRA_WEIGHTS = {
'geografi': 0.125,
'demografi': 0.125,
'sda': 0.125,
'ideologi': 0.125,
'politik': 0.125,
'ekonomi': 0.125,
'sosbud': 0.125,
'hankam': 0.125
}
def calculate_national_resilience(self, indicators):
# Hitung indeks ketahanan nasional
pass
def gatra_correlation_analysis(self):
# Analisis keterkaitan antar gatra
pass
```
#### **3. Modul Sistem Dinamik**
```python
class DynamicSystemModel:
def __init__(self):
self.stock_variables = []
self.flow_variables = []
def create_causal_loop_diagram(self, strategic_issues):
# Diagram sebab-akibat
pass
def build_stock_flow_model(self):
# Model matematika dinamik
pass
def policy_simulation(self, scenarios):
# Simulasi kebijakan
pass
```
#### **4. Modul GIS & Visualization**
```javascript
class GISVisualization {
constructor() {
this.map = null;
this.layers = {};
}
create_resilience_dashboard() {
// Dashboard interaktif
}
spatial_analysis_tools() {
// Analisis keruangan
}
policy_impact_mapping() {
// Pemetaan dampak kebijakan
}
}
```
### **Implementasi Kode Inti**
#### **Backend API (Python FastAPI)**
```python
from fastapi import FastAPI, UploadFile, File
from pydantic import BaseModel
import pandas as pd
import numpy as np
app = FastAPI(title="SISKATANAS API")
class ResilienceInput(BaseModel):
tahun: int
provinsi: str
indicators: dict
class PolicyScenario(BaseModel):
nama_scenario: str
kebijakan: dict
duration: int
@app.post("/calculate-resilience")
async def calculate_resilience_index(data: ResilienceInput):
"""Hitung indeks ketahanan nasional"""
# Normalisasi data
normalized_data = normalize_indicators(data.indicators)
# Hitung per gatra
gatra_scores = calculate_gatra_scores(normalized_data)
# Aggregasi ke indeks nasional
national_index = aggregate_to_national_index(gatra_scores)
return {
"tahun": data.tahun,
"provinsi": data.provinsi,
"indeks_nasional": national_index,
"gatra_scores": gatra_scores,
"kategori": categorize_resilience(national_index)
}
@app.post("/policy-simulation")
async def simulate_policy(scenario: PolicyScenario):
"""Simulasi dampak kebijakan"""
# Run model dinamik
simulation_result = run_dynamic_simulation(
scenario.kebijakan,
scenario.duration
)
return {
"scenario": scenario.nama_scenario,
"results": simulation_result,
"recommendations": generate_recommendations(simulation_result)
}
def normalize_indicators(indicators):
"""Normalisasi 821 indikator ke skala 1-5"""
normalized = {}
for key, value in indicators.items():
# Logika normalisasi berdasarkan benchmark
normalized[key] = min(5, max(1, value / benchmark[key] * 3))
return normalized
def categorize_resilience(score):
"""Kategorikan tingkat ketahanan"""
if score < 2.0: return "Rawan"
elif score < 2.5: return "Kurang Tangguh"
elif score < 3.0: return "Cukup Tangguh"
elif score < 4.0: return "Tangguh"
else: return "Sangat Tangguh"
```
#### **Frontend Dashboard (React.js)**
```jsx
import React, { useState, useEffect } from 'react';
import { MapContainer, TileLayer, GeoJSON } from 'react-leaflet';
import { BarChart, LineChart, PieChart } from 'recharts';
const ResilienceDashboard = () => {
const [resilienceData, setResilienceData] = useState(null);
const [selectedYear, setSelectedYear] = useState(2024);
const [simulationResults, setSimulationResults] = useState(null);
// Load data ketahanan
useEffect(() => {
fetchResilienceData(selectedYear);
}, [selectedYear]);
const runPolicySimulation = async (scenario) => {
const response = await fetch('/api/policy-simulation', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(scenario)
});
setSimulationResults(await response.json());
};
return (
<div className="dashboard">
{/* Peta Ketahanan Nasional */}
<div className="map-section">
<h3>Peta Indeks Ketahanan Nasional</h3>
<MapContainer center={[-2.5489, 118.0149]} zoom={5}>
<TileLayer url="https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png" />
<GeoJSON data={resilienceData?.spatial} />
</MapContainer>
</div>
{/* Chart ASTA GATRA */}
<div className="charts-section">
<h3>Analisis ASTA GATRA</h3>
<BarChart data={resilienceData?.gatra_scores}>
{/* Implementasi chart */}
</BarChart>
</div>
{/* Simulasi Kebijakan */}
<div className="simulation-section">
<h3>Simulasi Kebijakan</h3>
<PolicySimulationForm onRunSimulation={runPolicySimulation} />
{simulationResults && (
<SimulationResults data={simulationResults} />
)}
</div>
</div>
);
};
export default ResilienceDashboard;
```
#### **Model Sistem Dinamik**
```python
import numpy as np
from scipy.integrate import odeint
class NationalResilienceModel:
def __init__(self):
self.parameters = self.load_parameters()
def differential_equations(self, y, t, params):
"""Sistem persamaan diferensial untuk model ketahanan"""
geo, demo, sda, ideo, pol, eco, sos, ham = y
# Interaksi antar gatra (contoh sederhana)
d_geo_dt = params['alpha'] * eco - params['beta'] * geo
d_demo_dt = params['gamma'] * sos - params['delta'] * demo
# ... persamaan untuk 8 gatra
return [d_geo_dt, d_demo_dt, d_sda_dt, d_ideo_dt,
d_pol_dt, d_eco_dt, d_sos_dt, d_ham_dt]
def simulate(self, initial_conditions, policy_intervention, years=10):
"""Simulasi dampak kebijakan selama n tahun"""
t = np.linspace(0, years, years*12) # Monthly intervals
solution = odeint(
self.differential_equations,
initial_conditions,
t,
args=(self.parameters,)
)
return self.analyze_results(solution, policy_intervention)
```
### **Fitur Utama**
1. **Dashboard Real-time** monitoring indeks ketahanan
2. **Analisis Spasial** berbasis GIS untuk visualisasi regional
3. **Simulator Kebijakan** dengan multiple scenarios
4. **Prediksi Trend** menggunakan machine learning
5. **Early Warning System** untuk isu strategis
6. **Reporting Otomatis** dengan rekomendasi kebijakan
### **Teknologi Pendukung**
- **Containerization**: Docker + Kubernetes
- **CI/CD**: GitLab CI/CD
- **Monitoring**: Prometheus + Grafana
- **Security**: OAuth2 + JWT
- **Cache**: Redis
- **Message Queue**: RabbitMQ
Software ini dapat diimplementasikan secara bertahap, dimulai dari modul pengumpulan data dan analisis dasar, kemudian berkembang ke sistem dinamik dan simulasi kebijakan yang lebih kompleks.
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