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All Journal Elkom: Jurnal Elektronika dan Komputer
I Made Bayu Artha Pratama
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Prediksi Stok Tanaman Hidroponik dengan Artificial Intelligence: Ensemble Learning dengan Optimasi Evolusioner Putu Bagus Adidyana Anugrah Putra; Septian Geges; Oktaviani Enjela Putri; I Made Bayu Artha Pratama
Elkom: Jurnal Elektronika dan Komputer Vol. 17 No. 2 (2024): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i2.2197

Abstract

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.
Co-Authors Oktaviani Enjela Putri Putra, Putu Bagus Adidyana Anugrah Septian Geges