PREDIKSI HARGA PANGAN JAYAPURA MENGGUNAKAN ELM, LSTM, LIGHTGBM, DAN GB
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Abstract
Ketahanan pangan di wilayah Indonesia Timur menghadapi tantangan dari aspek geografis, fluktuasi harga, dan keterbatasan pasokan. Penelitian ini bertujuan untuk membandingkan empat algoritma pembelajaran mesin, yaitu Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), LightGBM, dan Gradient Boosting dalam memprediksi harga komoditas pangan strategis di Jayapura. Dataset yang digunakan berupa data deret waktu harga harian enam komoditas pangan yang dikumpulkan dari Januari 2018 hingga April 2025. Tahapan penelitian meliputi pra-pemrosesan data, analisis eksploratif (EDA), pelatihan model, dan evaluasi performa menggunakan metrik MAE, MAPE, RMSE, R², dan waktu pelatihan. Hasil percobaan menunjukkan bahwa ELM merupakan model dengan performa terbaik secara keseluruhan dengan nilai MAE 0.21, MAPE 0.76%, RMSE 0.36, R² 0.87, serta waktu pelatihan rata-rata 4.65 detik. Model LSTM menunjukkan akurasi yang baik namun memiliki waktu pelatihan yang jauh lebih tinggi. LightGBM dan Gradient Boosting memiliki performa keseluruhan yang kurang optimal. Dengan demikian, ELM direkomendasikan sebagai model utama untuk sistem prediksi harga komoditas pangan di Jayapura yang membutuhkan kombinasi antara akurasi dan efisiensi.
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