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All Journal Indonesian Journal of Electronics and Instrumentation Systems Jurnal Ilmu-Ilmu Peternakan (Indonesian Journal of Animal Science) Jurnal EECCIS Jurnal Ternak Tropika Jurnal Dinamika Penelitian Industri Briliant: Jurnal Riset dan Konseptual Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JURNAL BUDIDAYA PERTANIAN Jurnal ABDINUS : Jurnal Pengabdian Nusantara Indonesian Journal of Electrical Engineering and Computer Science INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Jurnal Pengabdian Masyarakat Asia Bioscientist : Jurnal Ilmiah Biologi Jurnal Jaringan Telekomunikasi JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Jurnal Abdi Masyarakat Indonesia Jurnal Multidisiplin West Science West Science Interdisciplinary Studies West Science Interdisciplinary Studies Jurnal Informatika Polinema (JIP) Jurnal ELTEK
Mochammad Junus
Fakultas Peternakan Universitas Brawijaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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Technology-Driven Community Waste Management Model: Transforming Organic Waste into Renewable Energy Junus, Mochammad; Mustain, Asalil; Putra, Indra Lukmana; Afrizal, Daffa; Bintang, Zahril; Rizky, M Aldo; Akbar, Fillah; Herdiana
Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v6i2.2834

Abstract

Purpose: This study addresses the challenge of ineffective organic waste management in Indonesia by developing a technology-driven, community-based model. Method: An integrated system was implemented at an Integrated Waste Processing Facility (TPST), featuring an automatic sorter using color, infrared, and weight sensors, combined with an anaerobic bioreactor for biogas production. The process was monitored through an IoT platform for real-time control. Practical Applications: The system improved sorting efficiency and reduced processing time, while community training increased household waste segregation participation from 15% to 48%. Conclusion: The model achieved 92% sorting accuracy and produced an average of 22 m³ of biogas per ton of waste, demonstrating that combining automation with social empowerment creates an effective and replicable solution for sustainable waste management and renewable energy transition.
K-Nearest Neighbor Algorithm for Intelligent Monitoring and Control System Integration in Renewable Energy Applications Junus, Mochammad; Fa‘izah, Laily Nur; Noor, Mohd; Putra, Indra Lukmana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1565

Abstract

A real-time biogas monitoring and control system was developed by integrating the K-Nearest Neighbor (KNN) algorithm into an IoT-based framework for methane pressure prediction and automated control. The system uses an ESP32 microcontroller connected to temperature, gas, and pressure sensors (DHT22, MQ-4, MPX5700) to continuously collect data, with cloud connectivity provided through Firebase and Blynk platforms. The predictive model operates within a live feedback loop, allowing immediate actuation based on forecasted methane conditions. With an optimal parameter of k=4, the KNN model achieved 93.33% accuracy, supported by a mean absolute error (MAE) of 0.18 and a root mean square error (RMSE) of 0.21. A comparative evaluation with Random Forest and Gradient Boosting algorithms showed that, although these models yielded slightly higher accuracy, KNN provided superior computational effi-ciency for embedded deployment. The system maintained stable operation during tests involving sensor anomalies, network interruptions, and data noise. However, redundancy mechanisms and improved vali-dation strategies are recommended to enhance robustness. The findings demonstrate that methane pro-duction can be effectively predicted using temperature and pressure data, with further accuracy im-provements possible through additional process variables such as pH and fermentation age.
Monitoring and Controlling System for Ammonia and Methane Gas in Broiler Chicken Farms Using Fuzzy Mamdani-Based Hybrid Junus, Mochammad; Saptono, Rachmad; Putri Nabila, Anggraeni
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1805

Abstract

The broiler poultry industry significantly contributes to food security by supplying animal protein; however, it also generates harmful gases such as ammonia (NH?) and methane (CH?) from accumulated waste. These gases not only endanger poultry health but also contribute to environmental pollution and climate change. This research proposes the development of an Internet of Things (IoT)-based monitoring and control system for ammonia and methane gas levels in broiler chicken farms. The system employs MEMS NH? and MQ4 gas sensors integrated with an ESP32 microcontroller, and applies the Mamdani fuzzy logic method to classify gas levels into safe, unhealthy, or dangerous categories. Based on the fuzzy output, a water pump powered by a hybrid solar energy system is activated automatically to reduce gas concentrations. Data is transmitted in real-time to a Firebase database and can be accessed via an Android application supporting both manual and automatic control modes. Experimental results demonstrate the system's effectiveness in detecting gas levels accurately and responding efficiently to maintain a healthy farm environment while utilizing renewable energy sources.
Smart Biogas Control for Communities Using Gaussian Naïve Bayes Junus, Mochammad; Koesmarjanto; Ria Amanda Salsabella
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1808

Abstract

The design and implementation of an intelligent biogas quality monitoring and control system that combines machine learning, actuator automation, and Internet of Things (IoT) technology is presented in this research. The system uses a thermocouple type K, MPX5700, MQ-4, and MQ-135, among other environmental sensors, to measure temperature, pressure, CO?, and CH? in real time. An ESP32 microcontroller processes sensor data using the Gaussian Naïve Bayes algorithm to categorize biogas quality into three classification, namely Good, Moderate, and Poor. A servo motor is utilized to control a valve that either permits or prohibits the flow of biogas to a generator based on the classification output. Through the Blynk IoT platform, the system has the capacity to be remotely monitored. Results from experiments with 40 biogas data demonstrated that the system had good precision and recall in each category and an overall accuracy of 92.5%. The approach exhibits dependability, affordability, and suitability for community-based biogas management in rural and semi-urban evironments.
Biogas Digester Monitoring System Using Machine Learning Classification Junus, Mochammad; Nuraini Putri Utami, Muslimah; Bin Abdullah, Mohd Noor
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1832

Abstract

Abstract— The problem faced in the biogas fermentation process is the challenge of continuously monitoring environmental conditions such as temperature, humidity, methane gas (CH?) concentration, and pressure, which have a major effect on gas production efficiency. This research aims to design a biogas fermentation monitoring system that uses Internet of Things (IoT) technology so that it can automatically classify fermentation conditions with the help of the K-Means Clustering algorithm. The system utilizes ESP32 microcontroller connected with DHT22 and MQ-4 sensors to measure temperature, humidity, and CH? parameters, and sends the data directly to Blynk platform via WiFi connection. The data collection process was carried out every five hours for 15 days after the initial fermentation lasted for three weeks. The resulting data was then analyzed using the K-Means algorithm to classify fermentation conditions into three categories: early, transitional, and active. Evaluation results using the Elbow and Silhouette Score methods indicated that the ideal number of clusters was three (K=3), with most of the data belonging to the active cluster. The 3D representation and scatter diagram confirmed that each cluster had significantly different sensor characteristics. The system successfully facilitated the monitoring of the fermentation process and provided important classification information to support decision-making. This research shows that combining IoT and machine learning can improve the efficiency of biogas fermentation management.
AIoT-enabled automated waste classification and real-time capacity monitoring system for smart waste management Adi, Ginanjar Suwasono; Suprianto, Dodit; Novianti, Atik; Rohmah, Khurnia Fiddiana; Faizal, Elka; Junus, Mochammad; Media, Riona Ihsan
JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Vol. 6 No. 1: March 2026
Publisher : Jurusan Teknik Elektro, Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/jitel.v6.i1.2026.23-34

Abstract

Population growth and urbanization have increased waste generation and intensified global waste management challenges. In Indonesia, national waste generation reached 38.4 million tons in 2023, with 38.38% remaining inadequately managed. This study aims to develop an Artificial Intelligence of Things (AIoT)-based system for automatic waste classification and real-time bin capacity monitoring. The system integrates the YOLOv8n model to identify four waste categories (organic, inorganic, hazardous/B3, and others) with ultrasonic sensors for capacity measurement, coupled with a web platform for data visualization. Model evaluation yielded a Macro F1-Score of 63.9%, with the best performance in the organic class (91.33%), followed by inorganic (68.37%), and hazardous/B3 (31.92%). Ultrasonic sensors demonstrated a near-linear relationship between waste height and capacity percentage (4.5% per cm), validating their reliability for real-time monitoring. The developed system proves the feasibility of AIoT integration for automated waste sorting, although further optimization is required to improve classification accuracy for minority classes. This research contributes to the development of intelligent solutions supporting more efficient and sustainable urban waste management.
Co-Authors Adi, Ginanjar Suwasono Afrizal, Daffa Agung Sugeng Widodo Akbar, Fillah Akhsanu Takwim, R N Anang Lastriyanto Asalil Mustain ATIK NOVIANTI Atmadja, Martono Dwi Besari, Ratna Iffany F Bestari, Karina Bella Bin Abdullah, Mohd Noor Bintang, Zahril Daffa Afrizal Wijaya Dewi Masyithoh Dhea Rahman, Akbar Erwan Erwan Faizal, Elka Fa‘izah, Laily Nur Fiernaningsih, Nilawati Fikri Shodiq, Ridhofir Firman Jaya Guntur Yanuar Astono Habibi, Isaz Ilham Akbar Hadiwiyatno, Hadiwiyatno Harijanto, Priya Surya Herdiana Himmah, Mahmudatul Hudiono Hudiono, Hudiono Imam, Muhammad Kholisul INDRA LUKMANA PUTRA Ismanto Ismanto Ismanto Jati Batoro Karin Febri Absari Khristiana, Harrij Mukti Koesmarjanto Lamerkabel, J. S. A. Lilik Eka Radiati Maharani, Zahra habibah Miftakhul Huda Moh. Abdullah Anshori Mohamad Imam Zarkasi Muhammad Furqon Hija Mustafa, Lis Diana Noor, Mohd Nugroho Suharto Nur Cholis Nuraini Putri Utami, Muslimah Pinandita, Eggi Pur Purnamasari, Sinta Winda Putradi, David Fydo Putri Nabila, Anggraeni Rachmad Saptono Rama Akbaruddin Ria Amanda Salsabella Riona Ihsan Media Risdiana, Devi Mega RIZKY ARDIANSYAH Rizky, M Aldo Rizqiyatul Khoiriyah Rohmah, Khurnia Fiddiana Rozaq, Naufal Abdir Saptono, Rachmad Septriandi Wirayoga Simanjuntak, Aurora Ivana Br Siti Nurul Kamaliyah Siti Rachmah Soelistianto, Farida Arinie Soen, Jopie Meiske Sri Minarti Takwim, R N Akhsanu Victor G Siahaya Wahyono Suprapto Waluyo Waluyo Widayani, Anna Windi Zamrudy Wulandari Saepuloh Zubaidi Zubaidi Zubaidi