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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal E-Komtek Jurnal Masyarakat Madani Indonesia
Tegar Prasetyo
Politeknik Negeri Sriwijaya
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Public Health Social Sciences

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Sosialisasi Dan Pelatihan Pengomposan Sampah Organik Secara Anaerob di Quranic Farm Kalidoni Palembang Gemala Cahya; Nancy Eka Putri Manurung; Abi Burhan; Fernando Africano; Billy Dewantara; Dyah Utari Yusa Wardhani; Tegar Prasetyo; Septi Hermialingga; Ebtaria Nadeak; Yoga Aji Nugraha
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 3 (2026): Agustus
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/m8109m77

Abstract

Pengelolaan sampah organik yang tidak tepat di tingkat rumah tangga merupakan tantangan lingkungan yang serius di Indonesia, berkontribusi pada pencemaran dan peningkatan emisi gas metana di tempat pembuangan akhir (TPA). Pengabdian masyarakat ini bertujuan untuk meningkatkan pengetahuan dan kesadaran anak-anak sekolah kelas 6 hingga 8 mengenai pentingnya pemilahan dan pengelolaan sampah organik melalui metode ceramah interaktif. Hasil kegiatan menunjukkan peningkatan pemahaman peserta yang signifikan mengenai klasifikasi sampah (organik, anorganik, berbahaya, dan residu) serta teknik pengomposan sebagai solusi berkelanjutan. Peserta juga menunjukkan perubahan sikap positif, dari kebiasaan membuang sampah organik menjadi termotivasi untuk memanfaatkannya. Kegiatan ini menegaskan pentingnya edukasi dini dalam membangun perilaku ramah lingkungan dan mendorong pengelolaan sampah mandiri yang mendukung keberlanjutan.
Design and Development of a Web-Monitored Integrated Electricity KWh Meter Using an IoT-Based Microcontroller Khoirul Ahmat Efendi; Asni Tafrikhatin; Jati Sumarah; Juri Benedi; El Vionna Laellyn Nurul Fatich; Tegar Prasetyo
Jurnal E-Komtek Vol 10 No 1 (2026)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v10i1.3353

Abstract

This study aims to develop an Internet of Things (IoT)-based electrical energy (kWh) monitoring system using an ESP32 microcontroller integrated with a web monitoring platform. The system employs ACS712 current and ZMPT101B voltage sensors to measure voltage, current, power, and electrical energy in real time. The research method used is Research and Development (R&D) with the 4D model, consisting of define, design, development, and dissemination stages. The test results indicate that the ZMPT101B sensor has an error rate of 0.49%–0.81%, while the ACS712 sensor has an error rate of 4.0%–14.2%. Measurement data were successfully transmitted and displayed on the monitoring website through an internet connection. The developed system is capable of providing real-time electrical energy monitoring with adequate accuracy and easy accessibility for users.
YOLOv9-Assisted Vision System for Health Assessment in Poultry Using Deep Neural Networks Pola Risma; Tegar Prasetyo; Yahya Muhammad Amri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2414

Abstract

Poultry farming represents one of the fastest-growing sectors in global food production, yet disease outbreaks, high mortality, and labor shortages continue to threaten its sustainability. Conventional health monitoring methods based on visual inspection are time-consuming, subjective, and inadequate for early anomaly detection. In response, computer vision and deep learning have emerged as transformative tools for livestock management. While prior implementations of the YOLO object detection family, such as YOLOv5 and YOLOv8, have achieved notable success, their performance often deteriorates in dense flocks, low-light conditions, and occlusion-prone environments. This study introduces a YOLOv9-assisted vision framework tailored for poultry health assessment in commercial farm settings. The system integrates smart cameras with edge computing to enable real-time detection of behavioral and physiological anomalies without dependence on high-bandwidth or cloud-based resources. A dataset of 903 annotated poultry images, categorized into healthy and sick classes, was employed for model development. The trained model achieved 88.7% precision, 97% recall, an F1-score of 0.82, and a mAP@0.5 of 0.88, demonstrating robustness under variable illumination, bird occlusion, and high-density environments. Comparative evaluation confirmed that YOLOv9 provides a superior balance of accuracy, generalization, and computational efficiency relative to YOLOv8–YOLOv11, supporting practical deployment on edge devices. Limitations include the binary scope of health classification and reliance on a single dataset. Future directions involve extending the framework to multi-class disease recognition, cross-dataset validation, behavior-based temporal modeling, and multimodal fusion, thereby advancing predictive analytics and welfare-oriented poultry farming.
Co-Authors Abi Burhan Africano, Fernando Asni Tafrikhatin BillY Dewantara Dyah Utari Yusa Wardhani Ebtaria Nadeak El Vionna Laellyn Nurul Fatich Gemala Cahya Jati Sumarah Juri Benedi Khoirul Ahmat Efendi Nancy Eka Putri Manurung Pola Risma Septi Hermialingga Yahya Muhammad Amri Yoga Aji Nugraha