Implementasi Algoritma Machine Learning untuk Prediksi Curah Hujan di Indonesia
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Curah hujan merupakan salah satu parameter penting dalam bidang meteorologi, pertanian, mitigasi bencana, dan pengelolaan sumber daya air. Ketidakpastian pola cuaca akibat perubahan iklim global menyebabkan kebutuhan terhadap sistem prediksi curah hujan yang lebih akurat semakin meningkat. Penelitian ini bertujuan untuk mengimplementasikan algoritma machine learning dalam memprediksi curah hujan berdasarkan data meteorologi. Metode penelitian menggunakan pendekatan kuantitatif dengan pemanfaatan algoritma Random Forest, Decision Tree, dan Support Vector Machine (SVM). Data penelitian diperoleh dari data historis cuaca yang meliputi suhu udara, kelembapan, tekanan udara, kecepatan angin, dan intensitas penyinaran matahari. Tahapan penelitian meliputi preprocessing data, pelatihan model, pengujian model, dan evaluasi menggunakan metrik akurasi, precision, recall, dan RMSE. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki performa terbaik dibandingkan algoritma lainnya dengan tingkat akurasi sebesar 92,4%. Implementasi machine learning terbukti mampu meningkatkan efektivitas prediksi curah hujan dan dapat digunakan sebagai pendukung pengambilan keputusan pada sektor pertanian, mitigasi banjir, dan pengelolaan lingkungan. Penelitian ini memberikan kontribusi dalam pengembangan sistem prediksi cuaca berbasis kecerdasan buatan di Indonesia.
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