ANALISIS PERAN FEATURE ENGINEERING PADA KINERJA MODEL MACHINE LEARNING UNTUK KLASIFIKASI POTENSI TSUNAMI
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https://doi.org/10.23960/jitet.v14i1.8303Abstract Views: 35 File Views: 25
Keywords:
Tsunami, Machine Learning, Feature Engineering, KlasifikasiAbstract
Indonesia memiliki risiko bencana tsunami yang tinggi akibat lokasinya di Cincin Api Pasifik, sehingga menuntut adanya sistem peringatan dini yang akurat. Penelitian ini mengusulkan model klasifikasi potensi tsunami yang valid secara metodologis. Berbeda dengan penelitian sebelumnya yang sering menggunakan data "bocor" (leaked features), penelitian ini menerapkan strategi feature engineering mendalam yang menggabungkan fitur fisis (seperti is_shallow) dan fitur kualitas data (gap, nst) pada dataset Cincin Api Pasifik. Lima model machine learning (Naive Bayes, Logistic Regression, SVM, Random Forest, XGBoost) dievaluasi menggunakan 10-Fold Cross-Validation. Hasil penelitian menunjukkan feature engineering adalah langkah paling krusial, meningkatkan F1-Score dari ~0.38 menjadi 0.86. Random Forest teridentifikasi sebagai model paling seimbang (F1-Score 0.8636), sementara Naive Bayes menunjukkan performa paling "aman" (Recall 0.9808). Penelitian ini membuktikan bahwa feature engineering yang tepat lebih berdampak daripada pemilihan model, menghasilkan model prediksi yang valid dan robust.
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