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Contact Name
M. Miftach Fakhri
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fakhri.abcollab@gmail.com
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voice.abcollab@gmail.com
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INDONESIA
Journal of Vocational, Informatics and Computer Education
ISSN : 29884918     EISSN : 29886325     DOI : https://doi.org/10.66053/voice
Core Subject : Science, Education,
1. Informatics and Computing Research addressing the design, development, implementation, and evaluation of computing technologies relevant to educational, professional, and digital learning environments, including but not limited to: Artificial Intelligence and Machine Learning Deep Learning and Neural Networks Data Science, Big Data, and Data Analytics Software Engineering and Software Development Computer Networks and Internet Technologies Cloud Computing and Distributed Computing Systems Internet of Things (IoT) and Smart Systems Human–Computer Interaction (HCI) and User Experience (UX) Intelligent Systems and Decision Support Systems Natural Language Processing and Computational Applications Cybersecurity and Information Security Emerging Computing Technologies and Digital Systems 2. Information Technology in Education Studies focusing on the design, integration, implementation, and evaluation of digital technologies in teaching and learning environments, including: Computer Science Education and Programming Education Artificial Intelligence in Education (AIED) Educational Data Mining and Learning Analytics Intelligent Tutoring Systems and Adaptive Learning Systems Digital Learning Environments and Online Learning Systems Learning Management Systems (LMS) and E-learning Platforms Immersive Learning Technologies (Virtual Reality, Augmented Reality, Extended Reality) Mobile Learning and Ubiquitous Learning Environments Technology-Enhanced Learning (TEL) and Digital Pedagogy Educational Software and Learning System Development Digital Assessment and Technology-Based Evaluation Systems Computational Thinking, AI Literacy, and Digital Literacy in Education 3. Vocational Technology Education Research examining the integration of computing technologies and digital innovation in vocational, technical, and professional education, including: Curriculum Development in Informatics and Computing Education Competency-Based Training and Digital Skill Development Teaching Factory and Industry 4.0 Learning Environments Smart Learning Environments for Technical and Vocational Education Work-Process Knowledge and Workplace Learning Work-Based Learning and Apprenticeship Systems Industry–Education Collaboration in Computing and Technology Fields Workforce Preparation for Digital and Technology-Driven Industries Digital Literacy and Cybersecurity Education in Vocational Contexts Professional Skills Development for the Digital Economy 4. Innovative Digital Learning and Educational Innovation Research exploring innovative pedagogical approaches, emerging technologies, and new learning ecosystems in digital and technology-enhanced education, including: Innovative Digital Pedagogy and Instructional Design Gamification and Game-Based Learning in Computing and Technology Education Project-Based Learning and Problem-Based Learning Supported by Technology Learning Innovation Using Artificial Intelligence and Intelligent Systems Automation and Smart Learning Technologies in Education Digital Transformation in Education and Training Institutions Emerging Educational Technologies and Future Learning Environments Smart Education Ecosystems and Data-Driven Learning Systems Educational Innovation for Developing Digital Competencies and Future Skills
Articles 1 Documents
Search results for , issue "Vol 4, No 2 (2026): June 2026" : 1 Documents clear
Optimization of Fertilizer and Pesticide Efficiency Based on Hybrid Artificial Intelligence to Increase Rice Production Wijaya, Hamid; Ruktiari, Rima; Fani, Rizal
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.

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