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All Journal Sisfo: Jurnal Ilmiah Sistem Informasi
Almuna Ramadhani
Universitas Malikussaleh
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

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Model Prediksi Produksi Pertanian Berbasis Machine Learning dan Data Lapangan Khaidir Khaidir; Fadhliani Fadhliani; Zurrahmi Wirda; Almuna Ramadhani
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/.v9i2.26015

Abstract

Ketidakpastian produksi pertanian merupakan tantangan krusial yang memengaruhi ketahanan pangan dan kesejahteraan petani di Indonesia. Penelitian ini bertujuan mengembangkan model prediksi produksi pertanian berbasis machine learning menggunakan data lapangan yang komprehensif. Data dikumpulkan dari lahan pertanian di Kota Lhokseumawe dan Kabupaten Aceh Utara selama tiga musim tanam, mencakup parameter tanah, iklim mikro, praktik budidaya, dan hasil panen aktual, dengan total 432 observasi. Empat algoritma machine learning dibandingkan, yaitu Random Forest, Support Vector Regression, XGBoost, dan Artificial Neural Network. Hasil penelitian menunjukkan bahwa XGBoost memberikan performa terbaik dengan nilai R² sebesar 0,89 dan RMSE 0,52 ton/ha pada dataset pengujian. Validasi lapangan pada musim tanam berikutnya mengonfirmasi kemampuan generalisasi model dengan RMSE 0,61 ton/ha. Analisis interpretabilitas model mengidentifikasi dosis pupuk nitrogen, kandungan C-organik tanah, dan curah hujan sebagai faktor paling berpengaruh terhadap produksi, dengan hubungan non-linear yang menunjukkan ambang optimal curah hujan pada kisaran 1.800–2.200 mm per musim tanam. Hasil penelitian ini menunjukkan bahwa integrasi machine learning dan data lapangan mampu menghasilkan prediksi produksi yang akurat dan relevan untuk mendukung pengambilan keputusan dalam sistem pertanian Indonesia.
Analysis of Machine Learning-Based Classification Models for Determining Fertilizer Types for Rice Crop Growth: Machine Learning Approach for Optimizing Fertilizer Selection in Rice Cultivation Mira Humaira; Almuna Ramadhani; Uchti Nuzul Qhinanti Lubis; Fadhliani Fadhliani; Septiarini Zuliati; Usnawiyah Usnawiyah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27283

Abstract

Determining the appropriate fertilizer type is essential for supporting rice plant growth and optimizing agricultural productivity. However, conventional fertilization practices still rely heavily on empirical judgment and often neglect dynamic soil and plant growth characteristics. This study aims to analyze and compare the performance of several machine learning classification models for fertilizer type determination in rice cultivation. The study employed a computational experimental approach adapted from the CRISP-DM framework using a dataset of 480 records consisting of soil and rice growth parameters, including Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, moisture, and plant height. Five classification algorithms were evaluated, namely Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM), and Random Forest. Model performance was assessed using accuracy, precision, recall, and F1-score, combined with Stratified k-Fold Cross Validation. The results showed that Random Forest achieved the best performance with an accuracy of 95.83%, precision of 95.54%, recall of 95.12%, and F1-score of 95.33%. These findings indicate that ensemble learning methods are more effective in handling heterogeneous and multivariable agricultural data than conventional classification approaches. This study contributes to the development of machine learning-based classification analysis for more accurate and data-driven fertilizer determination in rice cultivation.
Co-Authors Fadhliani, Fadhliani Khaidir Khaidir Mira Humaira Septiarini Zuliati Uchti Nuzul Qhinanti Lubis Usnawiyah Zurrahmi Wirda