DOI: https://doi.org/10.14710/jati.21.3.%25p

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 3 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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