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All Journal ILKOM Jurnal Ilmiah Jurnal Mantik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Indonesia Berdaya Jurnal Ilmiah Informatika dan Ilmu Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Pusat Kolaborasi Pengabdian Kepada Masyarakat Journal of Computer Science Advancements Journal of Information Technology Journal of Vocational, Informatics and Computer Education
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.
WIRELESS COMMUNICATION TECHNOLOGIES ENABLING RELIABLE INTERNET OF THINGS SMART FARMING APPLICATIONS Hamid Wijaya; Miku Fujita; Daiki Nishida
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3399

Abstract

The rapid expansion of smart farming systems has intensified the need for reliable wireless communication infrastructures capable of supporting Internet of Things (IoT) applications in heterogeneous agricultural environments. Ensuring stable connectivity in rural areas characterized by large coverage demands, energy constraints, and environmental interference remains a critical challenge. This study aims to evaluate wireless communication technologies and identify optimal configurations that enable reliable IoT-based smart farming operations. A mixed-method research design integrating large-scale field experiments and simulation-based scalability analysis was employed to assess LoRaWAN, NB-IoT, Zigbee, Wi-Fi, and 5G IoT modules. Reliability was measured using packet delivery ratio, latency, coverage range, scalability, and energy consumption indicators. Results indicate that no single technology achieves optimal performance across all reliability dimensions. LPWAN technologies demonstrated superior energy efficiency and wide-area coverage, while 5G achieved the lowest latency and highest throughput. Hybrid communication architectures consistently outperformed single-technology deployments, improving packet delivery ratio and operational resilience under varying environmental conditions. The study concludes that context-aware integration of complementary wireless technologies provides the most reliable and sustainable solution for smart farming IoT ecosystems.
Forensic Analysis for Detecting Deep-Fake Images Using A Convolutional Neural Network (CNN) and The National Institute of Standards and Technology (NIST) Methods Muhammad Na'im Al Jum'ah; Hamid Wijaya; Muh. Hajar Akbar; Suwito Pomalingo
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3178.281-291

Abstract

The development of Artificial Intelligence (AI) has significantly influenced audio, video, and image manipulation techniques, commonly known as deepfakes. Image forensics faces an urgent challenge in identifying and mitigating the impact of deepfake content to maintain the integrity and credibility of digital information. This research aims to perform forensic analysis in accordance with NIST standards and to implement Convolutional Neural Network (CNN) methods to detect deepfake images. Based on the test results, the Convolutional Neural Network (CNN) method can be effectively applied to deepfake image detection. The CNN architecture used can identify the distinct visual characteristics of deepfake images with high performance. The model demonstrates the ability to learn and minimize prediction errors on training data. Accuracy graphs indicate that the model has successfully learned data patterns, as evidenced by consistent improvements in both training and validation data as the number of epochs increases. Furthermore, the loss graph shows a downward trend, signifying a continuous reduction in model error. The precision graph demonstrates the model's effectiveness in reducing false positives, thereby minimizing errors in detecting the original data. The recall graph also indicates improved detection performance on the training data. The ROC curve suggests that the model possesses superior classification capabilities compared to random guessing. Additionally, the Area Under the Curve (AUC) of 0.6544 serves as a quantitative indicator of performance, indicating that the model has moderate capability for class differentiation. Detection results from the CNN model on a dataset of real and deepfake images show that the Confidence and Raw Score values can distinguish between the two; however, the confidence levels still fluctuate around the classification threshold. Low confidence values in certain images suggest that the extracted features are not yet optimal at distinguishing between real faces and manipulated images. Moreover, the application of the National Institute of Standards and Technology (NIST) standards (Collection, Examination, Analysis, and Reporting) for forensic analysis ensures that the evidence gathered is legally accountable in court. Thus, these standards can serve as a scientific reference to ensure a more structured and standardized investigation process for deepfake images.
Co-Authors Ahmad Fatih Zahir Al Jum'ah, Muhammad Na'im Anita Ninasari Anyan Anyan Arief Herdiansah Arisantoso Arisantoso Arisman Arisman Daiki Nishida Dedy Alamsyah Derlini Dirman Edy Winarno Elisabeth Nugraheni Eko Wardani 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 Miku Fujita Mochamad Sanwasih Muchamad Sobri Sungkar Muh. Hajar Akbar Muh. Na’im Al Jum’ah Muliyadi Mutmainnah Muchtar Rahmat Karim Rhaishudin Jafar Rumandan Rima Ruktiari Rizal Fani Sardjana Orba Manullang Sarimuddin, Sarimuddin Sunyanti Suwito Pomalingo Syaban, Kharis Syah, Setiawan Putra Warkianto Widjaja