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Hamid Wijaya
Universitas Sembilanbelas November Kolaka
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Optimization of Fertilizer and Pesticide Efficiency Based on Hybrid Artificial Intelligence to Increase Rice Production Hamid Wijaya; Rima Ruktiari; Rizal Fani
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.725

Abstract

Purpose – This study aims to develop a Hybrid Artificial Intelligence model integrating Artificial Neural Network (ANN) and Genetic Algorithm (GA) to optimize fertilizer and pesticide efficiency while improving rice production under diverse agricultural conditions. The research addresses the limitations of conventional agricultural input management, which often relies on generalized cultivation practices and leads to inefficient resource utilization and environmental degradation. Methods – The study employed a quantitative predictive and optimization-based experimental design using 1,080 rice cultivation records collected from Southeast Sulawesi Province, Indonesia. The dataset included agronomic and environmental variables such as soil pH, rainfall, pest infestation intensity, fertilizer dosage, pesticide dosage, and rice yield. ANN was utilized to predict rice production patterns, while GA was implemented to optimize fertilizer and pesticide dosage combinations. Model performance was evaluated using MAPE, MSE, RMSE, MAE, and coefficient of determination (R²). Findings – The ANN model demonstrated strong predictive capability with a MAPE value of 3.27%, RMSE of 0.22, and R² value of 0.53, indicating its ability to capture complex non-linear relationships among cultivation variables. Furthermore, the hybrid ANN-GA model successfully optimized agricultural input usage by reducing fertilizer dosage by 76.61% and pesticide dosage by 66.32%, while increasing predicted rice production from 4.28 tons/ha to 6.09 tons/ha. These results indicate that hybrid AI systems can improve agricultural efficiency and support sustainable rice production management. Research implications – This study contributes theoretically to the advancement of hybrid AI applications in precision agriculture by integrating predictive learning and adaptive optimization within a unified framework. Practically, the findings provide an intelligent decision-support model that may assist farmers and agricultural stakeholders in improving productivity, reducing excessive chemical input usage, and promoting environmentally sustainable farming practices. Originality – The originality of this study lies in its contribution to the advancement of hybrid AI applications in precision agriculture through the integration of predictive learning and adaptive optimization within a unified framework.
Co-Authors Ahmad Fatih Zahir Anita Ninasari Anyan Anyan Arief Herdiansah Arisantoso Arisantoso Arisman Arisman Dedy Alamsyah Derlini Dirman Edy Winarno Faiz Muqorrir Kaaffah Firman Aziz Friska Heriyanti Guna Yanti Kumala Sari Siregar Pahu Hendrik ES Samosir Hengki Rusdianto I Gede Iwan Sudipa Iim Abdurrohim Jentot Tugiyono Matius Stefanus Batu Mochamad Sanwasih Muchamad Sobri Sungkar Muh. Na’im Al Jum’ah Muhammad Na'im Al Jum'ah Muliyadi Mutmainnah Muchtar Nugraheni Eko Wardani Rahmat Karim Rhaishudin Jafar Rumandan Rima Ruktiari Rizal Fani Sardjana Orba Manullang Sarimuddin, Sarimuddin Sunyanti Syaban, Kharis Syah, Setiawan Putra Warkianto Widjaja