Optimasi Hiperparameter Gradient Boosting Regressor Untuk Prediksi Curah Hujan Berdasarkan Data Cuaca Harian BMKG
-
Mohamad Arif Abdul Syukur
*
UIN Maulana Malik Ibrahim, Malang,
Indonesia
- Suhartono Suhartono UIN Maulana Malik Ibrahim, Malang, Indonesia
- M. Imamudin UIN Maulana Malik Ibrahim, Malang, Indonesia (*) Corresponding Author
Abstract
Abstrak−Prediksi curah hujan harian memiliki peran krusial dalam mitigasi bencana hidrometeorologi dan perencanaan sektor pertanian. Namun, karakteristik data curah hujan yang bersifat zero-inflated dan non-linear menjadi tantangan utama dalam menghasilkan prediksi yang akurat. Penelitian ini bertujuan untuk membangun model prediksi curah hujan menggunakan algoritma Gradient Boosting Regressor dengan membandingkan dua skenario: model default dan model yang telah dioptimasi. Dataset yang digunakan adalah data cuaca harian dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) sebanyak 942 observasi, yang mencakup variabel suhu minimum (TN), suhu maksimum (TX), suhu rata-rata (TAVG), kelembaban rata-rata (RH_AVG), durasi penyinaran matahari (SS), serta kecepatan angin rata-rata (FF_AVG). Proses pra-pemrosesan data dilakukan melalui penanganan nilai kosong menggunakan K-Nearest Neighbors Imputer dan standarisasi fitur dengan StandardScaler. Optimasi hyperparameter dilakukan menggunakan RandomizedSearchCV dengan validasi silang deret waktu (TimeSeriesSplit). Hasil penelitian menunjukkan bahwa model teroptimasi mengungguli model default dengan mencapai nilai Root Mean Squared Error (RMSE) sebesar 12,207 dan Mean Absolute Error (MAE) sebesar 7,704. Temuan ini membuktikan bahwa integrasi pra-pemrosesan yang sistematis dan optimasi parameter secara empiris secara signifikan meningkatkan kemampuan model dalam mempelajari pola variabilitas curah hujan harian.
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