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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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Journal : jurnal eeccis

 1 
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.
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 Suprianto Suprianto Takwim, R N Akhsanu Victor G Siahaya Wahyono Suprapto Waluyo Waluyo Widayani, Anna Windi Zamrudy Wulandari Saepuloh Zubaidi Zubaidi Zubaidi