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All Journal International Journal of Artificial Intelligence Research Indonesian Journal on Software Engineering (IJSE) JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Kridatama Sains dan Teknologi Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Taroa : Jurnal Pengabdian Masyarakat Jurnal Pendidikan Terapan Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Journal of Excellence Humanities and Religiosity Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat Jurnal Pengabdian kepada Masyarakat (PEMAS) Scientific: Journal of Computer Science and Informatics Journix: Journal of Informatics and Computing Indonesian Applied Research Computing and Informatics Journal of Digital Technology and Computer Science
Siti Mutmainah
Universitas Muhammadiyah Bima
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : journal of digital technology and computer science

 1 
Development of an IoT-based Smart Farming System using ESP32 for Livestock Monitoring Rizki Fikriansyah; Siti Mutmainah; Dahlan
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i2.607

Abstract

Purpose – Livestock farming is a vital sector of the Indonesian economy, yet the common practice of allowing livestock to roam freely renders manual monitoring inefficient and exposes farmers to risks of loss, accidents, and theft. This study presents an IoT-based livestock monitoring prototype designed to enable real-time location tracking and automated boundary violation alerts, addressing the lack of affordable and practical smart monitoring solutions for smallholder farmers. Methods – The system was developed using an ESP32 microcontroller integrated with a Neo-6M GPS module and a Telegram bot for automatic notifications. A geofencing boundary of 50 meters was configured from a fixed reference point. Twelve trials were conducted across morning, afternoon, and evening sessions to evaluate system performance under varying conditions. Findings – The system delivered location alerts every ten minutes with Google Maps links and coordinates. Under normal conditions, livestock positions were detected within 5.0–12.7 meters of the reference point. Boundary violations exceeding 50 meters triggered immediate alerts, with notification latency ranging from 3 to 8 seconds under stable network conditions. GPS baseline error was approximately 5.0–5.5 meters, with an accuracy variation of ±2–3 meters. Research Implications – System performance is constrained by Wi-Fi network stability and environmental factors affecting GPS accuracy, limiting its generalizability to areas with reliable connectivity. Further field testing is required before broader implementation. Originality – This study contributes a low-cost, ESP32-based geofencing solution integrated with Telegram, offering a practical and scalable approach to smart livestock monitoring in developing agricultural contexts.
YOLOv9-Based Classification of Ganyong Plant Health for Early Detection of Leaf Spot Disease M Fikram; Siti Mutmainah; Dahlan
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i2.551

Abstract

Purpose – This study implements the YOLOv9 architecture to automatically classify the health condition of ganyong leaves (Canna edulis Kerr.) as an early-detection tool for leaf spot disease. The study addresses the limitations of subjective manual identification and supports farmers in Bumipajo Village, Bima Regency, in reducing potential crop failure. Methods – A primary field dataset consisting of 1,383 image objects was collected and divided into training, validation, and testing sets using a 70:20:10 ratio. YOLOv9 was implemented by integrating Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). Model training was conducted in Google Colab using GPU acceleration, a batch size of 4, and 50 epochs. Findings – Evaluation on independent test data showed strong detection performance, with mAP@50 of 99%, Precision of 99%, Recall of 100%, and an average inference speed of 58.4 ms per image. These results indicate that YOLOv9 can effectively preserve disease-related morphological features in visually complex biological objects. Research implications – The findings are limited to the environmental conditions of the data collection site and one disease type. The reported time efficiency also depends on GPU-based hardware and requires further validation on mobile devices. Originality – This study contributes a field-based primary dataset of ganyong leaves and validates YOLOv9 for a local agricultural commodity that remains underexplored.
Co-Authors Ade Yuliati Anas Satria Anis Yaturahmah Ansor Lorosae, Teguh Citra, Erin Eka Dahlan Dhomas Hatta Fudholi Dikan , Noval Erin Eka Citra Erin Eka Citra Fathir Fathir Fathir Fifi Andriani Fudholi, Dhomas Hatta Ghufran, Syed Muhammad Hayaty, Mardhiya Ilmiati Ilmiati Khairunnas Khairunnas Khairunnas Khairunnas Khairunnas, Khairunnas Khairunnisa Lisdiawati Lisdiawati Lukman Lukman LUKMAN, LUKMAN M Fikram M Ridwan M. Fikram Mechi Sakinatun Nufus Miftahul Jannah Muhammad Akbar Muhammad Amirul Mu'min Muhammad Amirul Mu'min Muhammad Hayun Nur Hunul Khatimah Nurfadila Nurfidianty Annafi Nurfidianty Annafi, Nurfidianty Patryansya , Yoga Purwoko, Agus Ramadhan, Sahrul Randitha Missouri Rizki Fikriansyah Rizki, Afsa Sabar Aritonang Rajagukguk Sahrul Fauzan Djiaulhaq Sahrul Ramadhan Saifullah Setiawan, Muhammad Iwan Sutriawan syafrudin _ Syarifuddin Syarifuddin Syarifuddin Syarifuddin Sya’Ban, Ahmad Taufiqurahman Taufiqurahman Taufiqurahman, Taufiqurahman Teguh Ansyor Lorosae Tri Stiyo Famuji Widayati, Ulfa Yuliyanti yuliyanti Zumhur Alamin Zumhur Alamin