Optimisasi LSTM untuk Prediksi Konsentrasi Kualitas Udara Jakarta menggunakan Cauchy PSO
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https://doi.org/10.32409/jikstik.24.3.3869
Abstract
Polusi udara merupakan masalah serius di kota-kota besar, khususnya di Jakarta yang kerap mengalami tingginya konsentrasi partikel debu (PM10). Kadar PM10 yang tinggi dapat berdampak buruk terhadap kesehatan masyarakat serta kualitas lingkungan secara keseluruhan. Penelitian ini mengusulkan model prediksi deret waktu berbasis Long Short-Term Memory (LSTM) yang dioptimasi menggunakan algoritma Cauchy Particle Swarm Optimization (CPSO). Algoritma CPSO digunakan untuk mencari kombinasi hyperparameter terbaik pada model LSTM, sehingga diharapkan dapat meningkatkan akurasi prediksi. Model menggunakan variable PM10, suhu, kelembapan, curah hujan, dan kecepatan angin dari beberapa stasiun pemantauan udara di Jakarta untuk memprediksi nilai PM10 pada 1, 3, dan 7 hari ke depan. Kinerja model dievaluasi berdasarkan Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan Correlation Coefficient (CC). Hasil eksperimen menunjukkan bahwa model CPSO-LSTM mampu menghasilkan error yang lebih kecil dibandingkan LSTM.
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