Optimasi Prediksi Ketinggian Muka Air Sungai Ciliwung di Pintu Air Pos Depok Menggunakan Ensemble Learning
Abstract
Prediksi ketinggian muka air menjadi aspek penting dalam sistem peringatan dini banjir, terutama di wilayah hilir Sungai Ciliwung. Penelitian ini dilakukan pada Pintu Air Depok, titik pengamatan strategis aliran air dari hulu menuju DKI Jakarta, dengan tujuan membandingkan dan mengoptimalkan prediksi menggunakan ensemble machine learning. Dataset berupa data deret waktu resolusi 1 jam mencakup ketinggian muka air sebagai target, serta curah hujan, suhu, dan ketinggian muka air sebelumnya sebagai variabel prediktor, diperoleh dari BPBD DKI Jakarta dan ERA5 ECMWF. Metode yang digunakan meliputi XGBoost, Random Forest, dan Stacked Learning.Hasil menunjukkan seluruh model mampu memprediksi ketinggian muka air dengan baik, ditunjukkan oleh kombinasi R² > 0,74, MAE rendah, RMSE terkendali, dan MAPE kecil pada data pengujian. Stacked Learning memberikan keseimbangan terbaik antara akurasi dan generalisasi dengan error terendah, XGBoost unggul pada data training namun cenderung overfit ringan, sedangkan Random Forest menunjukkan performa yang stabil. Temuan ini menegaskan bahwa pendekatan ensemble mampu menghasilkan prediksi yang akurat, stabil, dan dapat mendukung sistem prediksi real-time untuk mitigasi banjir.
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