DOI: https://doi.org/10.14710/jati.21.3.246-256

PERAMALAN PRODUKSI HERBISIDA PADA INDUSTRI AGROKIMIA MENGGUNAKAN LSTM BERBASIS BAYESIAN OPTIMIZATION

*Louis Putra Purnama  -  Universitas Trisakti, Indonesia
Dedy Sugiarto  -  Universitas Trisakti, Indonesia
Heri Santosa  -  Universitas Trisakti, Indonesia

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Abstract

Peramalan produksi herbisida di industri agrokimia menghadapi tantangan besar akibat fluktuasi permintaan musiman dan dampak perubahan iklim. Metode konvensional sering kali gagal menangkap pola non-linier yang kompleks, menghasilkan akurasi prediksi yang rendah. Untuk mengatasi keterbatasan ini, penelitian ini mengembangkan model Long Short-Term Memory (LSTM) yang dioptimalkan menggunakan Bayesian Optimization (BO) untuk peramalan produksi herbisida bulanan. Penelitian ini membandingkan kinerja model LSTM konvensional dengan model BO-LSTM. Data historis produksi dari Januari 2019 hingga Desember 2023 digunakan, setelah melalui praproses data dan normalisasi dengan Min-Max Scaler. Hasil terbaik model LSTM konvensional menunjukkan MAPE 11,33%, tetapi pendekatan konvensional memerlukan banyak percobaan manual dan tidak efisien. Sebaliknya, pendekatan BO memungkinkan pengoptimalan hyperparameter LSTM yang terintegrasi, menghasilkan model BO-LSTM dengan performa yang lebih baik dengan MAE 37.588,61, RMSE 52.407,12, dan MAPE 7,81%. Peningkatan akurasi ini signifikan dibandingkan LSTM konvensional. Ketika model digunakan untuk peramalan tiga bulan ke depan, model menunjukkan pola yang konsisten dengan tren musiman historis. Hasil penelitian ini menegaskan bahwa integrasi BO-LSTM secara efektif meningkatkan akurasi peramalan, memberikan dasar yang lebih tepat untuk perencanaan produksi dan manajemen stok di industri agrokimia.

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Subject Agrochemical; Bayesian Optimization; BO-LSTM; Herbicide; LSTM, Time Series Forecasting
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Keywords: agrokimia; Bayesian Optimization; BO-LSTM; herbisida; LSTM; peramalan deret waktu
Funding: Universitas Trisakti

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