Automatic Chicken Feeding Machine Using Fuzzy Inference System

Mufdatul Islamiyah; Maisaroh Maisaroh

doi:10.21771/jrtppi.2025.v16.no1.p61-69

Authors

Mufdatul Islamiyah Institute of Technology and Business, Asia Malang
Maisaroh Maisaroh Institute of Technology and Business, Asia Malang

DOI:

https://doi.org/10.21771/jrtppi.2025.v16.no1.p61-69

Keywords:

Automatic Chicken Feed, Fuzzy Inference System, Livestock, Efficiency, Intelligent Control

Abstract

Efficient feed management is a crucial factor in increasing chicken farm productivity. However, manual feeding systems are often inconsistent and labor-intensive. To address these issues, this study designed and implemented an automatic chicken feeder equipped with a fuzzy inference system, where the fuzzy system plays a role in processing sensor data. The sensors used in this research are temperature sensors, ultrasonic, RTC, electric motors and microcontrollers. This system can automatically determine the timing and amount of feed given based on parameters such as time, number of chickens, and the level of remaining feed in the container. The fuzzy method is used because it can handle uncertain and variable input data and allows for more flexible decision-making. System testing was conducted using MATLAB simulations to test the fuzzy logic response to feed quantity and input conditions. In addition to simulation testing, hardware testing was also conducted to ensure that all physical components of the automatic chicken feeder functioned properly and directly according to the feed design. The simulation results showed that the defuzzifier value was 10, thus concluding that the motor movement of the device was categorized as moderate and also demonstrated that the system can provide feed in a timely and appropriate manner, while reducing feed waste. Thus, this tool has the potential to help farmers save time, operational costs, and improve feed efficiency.

References

Barzegar, Yas, Atrin Barzegar, Francesco Bellini, Stefano Marrone, and Laura Verde. 2024. “Fuzzy Inference System for Risk Assessment of Wheat Flour Product Manufacturing Systems.” Procedia Computer Science 246(C):4431–40. doi: 10.1016/j.procs.2024.09.293.

Birhanu, Mulugeta Y., Richard Osei-Amponsah, Frederick Yeboah Obese, and Tadelle Dessie. 2023. “Smallholder Poultry Production in the Context of Increasing Global Food Prices: Roles in Poverty Reduction and Food Security.” Animal Frontiers 13(1):17–25. doi: 10.1093/af/vfac069.

Dayat, Anuraga Jayanegara, and dan Heri Ahmad Sukria. 2023. “49868-194788-2-Pb.” EVALUASI KUALITAS PAKAN AYAM RAS PEDAGING(BROILER)YANG BEREDAR TERHADAP PENERAPAN STANDARNASIONALINDONESIA 5(September):103–14.

Fais, Muhammad. 2025. “Sistem Deteksi Pakan Otomatis Berbasis Sensor RGB Yang Terintegrasi Dengan Smartphone ABSTRAK :” 2(1):20–31.

FebryRahmadhaniHasibuan, PutriAgustinaAnggrainiArwira, RainiDahrianaPulungan, Adyla Syukhraini Marwi4, Aziz Husein Nasution. 2023. “View of Pengaruh Pola Pemberian Nutrisi Terhadap Pertumbuhan Anak Ayam Broiler Di Kecamatan Tembung, Kota Medan.Pdf.”

Hendrawati, Dwiana, Kurnianingsih, Brainvendra Widi Dionova, Muhammad Irsyad Abdullah, and Dita Anies Munawwaroh. 2025.

“Thermal Comfort Quality Monitoring and Controlling Using Fuzzy Inference System Based on IoT Technology.” International Journal on Advanced Science, Engineering and Information Technology 15(1):96–102. doi: 10.18517/ijaseit.15.1.20334.

Jalalifar, Rasoul, Mahmoud Reza Delavar, and Seyed Farid Ghaderi. 2025. “A Novel Approach for Photovoltaic Plant Site Selection in Megacities Utilizing Power Load Forecasting and Fuzzy Inference System.” Renewable Energy 243(January). doi: 10.1016/j.renene.2025.122527.

Julfikar Diawan,Mustamin Hamid, Gamaria Mandar. 2023. “Sistem Pemberian Pakan Cerdas Berbasis IOT Pada Peternakan Ayam.”

Kamga, Guy A. Fots., Yacine Bouroubi, Mickaël Germain, Georges Martin, and Laurent Bitjoka. 2025. “Beekeeping Suitability Prediction Based on an Adaptive Neuro-Fuzzy Inference System and Apiary Level Data.” Ecological Informatics 86(October 2024). doi: 10.1016/j.ecoinf.2025.103015.

Khan, Junaid, Muhammad Fayaz, Umar Zaman, Eunkyu Lee, Awatef Salim Balobaid, Muhammad Bilal, and Kyungsup Kim. 2025. “Optimizing Alpha–Beta Filter for Enhanced Predictions Accuracy in Industrial Applications Using Mamdani Fuzzy Inference System.” Alexandria Engineering Journal 119(March 2024):598–608. doi: 10.1016/j.aej.2025.01.116.

Kusumadewi, Sri, Linda Rosita, Elyza Gustri Wahyuni, Sri Mulyati, and Aridhanyati Arifin. 2025. “Implementation of Decision Tree and Mamdani Fuzzy Inference System for Erythropoietin Resistance Prediction.” Biomedical Signal Processing and Control 104(December 2024). doi: 10.1016/j.bspc.2025.107496.

Mohammed, Nayeemuddin, Puganeshwary Palaniandy, Feroz Shaik, Hiren Mewada, and Deepanraj Balakrishnan. 2023. “Comparative Studies of RSM Box-Behnken and ANN-Anfis Fuzzy Statistical Analysis for Seawater Biodegradability Using TiO2 Photocatalyst.” Chemosphere 314(December 2022). doi: 10.1016/j.chemosphere.2022.137665.

Okello, Job Lazarus, Ahmed M. R. Fath El-Bab, Masahiko Yoshino, and Hassan A. El-Hofy. 2025. “Investigation of Kerf Width in CO2 Laser Fabrication of Microchannels on Aluminium-Coated Polymethyl Methacrylate (PMMA) Using Adaptive Neuro-Fuzzy Inference System (ANFIS).” Journal of Manufacturing Processes 141(March):1235–44. doi: 10.1016/j.jmapro.2025.02.086.

Otomatis, Ayam. 2024. “Implementasi Fuzzy Logic Control Pada Alat Pemberi Pakan Ayam Otomatis.” Jurnal Multidisiplin Saintek 02(03):80–89.

Rajaprakash, S., C. Bagath Basha, C. Sunitha Ram, I. Ameethbasha, V. Subapriya, and R. Sofia. 2025. “Using Convolutional Network in Graphical Model Detection of Autism Disorders with Fuzzy Inference Systems.” Intelligence-Based Medicine 11(October 2024). doi: 10.1016/j.ibmed.2025.100213.

Rasyaf, Muhammad. 2012. “Panduan Beternak Ayam Pedaging.” P. 25 in.

Salameh, Tareq, Mena Maurice Farag, Abdul Kadir Hamid, and Mousa Hussein. 2025. “Adaptive Neuro-Fuzzy Inference System for Accurate Power Forecasting for on-Grid Photovoltaic Systems: A Case Study in Sharjah, UAE.” Energy Conversion and Management: X 26(March). doi: 10.1016/j.ecmx.2025.100958.

Samanta, Sudipta, Muthukaruppan Swaminathan, Jianing Hu, Khai Tuck Lee, Ajitha Sundaresan, Chuan Keng Goh, Chor Hiang Siow, Kwok Seng Loh, Soh Ha Chan, Joshua K. Tay, and Ian Cheong. 2022. “Deep Learning Fuzzy Inference: An Interpretable Model for Detecting Indirect Immunofluorescence Patterns Associated with Nasopharyngeal Cancer.” American Journal of Pathology 192(9):1295–1304. doi: 10.1016/j.ajpath.2022.05.010.

Seidi, Masoud, Saeed Yaghoubi, and Ali Shirazi. 2025. “Employment of Fuzzy Inference System in Prediction of Optimal Solution Area for Thermal Stress Generated in the Friction Stir Welding Process.” Expert Systems with Applications 278(June 2024). doi: 10.1016/j.eswa.2025.127342.

Setiawan, Agung, Desriyanti, and Rhesma Intan Vidyastari. 2023. “Perancangan Alat Pemberian Pakan Dan Minum Ayam Broiler Secara Otomatis Menggunakan Notifikasi Blynk.” Digital Transformation Technology 3(1):185–91.

Sudarmawan, Putu adi, Agung Panji Sasmito, and Renaldi Primaswara. 2021. “PENERAPAN LOGIKA FUZZY PADA SISTEM MONITORING DAN KONTROL KANDANG AYAM OTOMATIS BERBASIS IoT.” JATI (Jurnal Mahasiswa Teknik Informatika) 5(1):315–20. doi: 10.36040/jati.v5i1.3265.

Trinaldi, Audia Faris. 2022. “Sistem Kontrol Dan Monitoring Suhu Kelembaban Kandang Pada Peternakan Ayam Broiler Dengan Metode Logika Fuzzy Mamdani Berbasis Internet of Things.” Prosiding Sains Nasional Dan Teknologi 12(1):349. doi: 10.36499/psnst.v12i1.7046.

Vinayagam, Ramesh, Adyasha Kar, Gokulakrishnan Murugesan, Thivaharan Varadavenkatesan, Louella Concepta Goveas, Adithya Samanth, Mohammad Boshir Ahmed, and Raja Selvaraj. 2023. “Low Temperature Carbonized Mesoporous Graphitic Carbon for Tetracycline Adsorption: Mechanistic Insight and Adaptive Neuro-Fuzzy Inference System Modeling.” Bioresource Technology Reports 22(May). doi: 10.1016/j.biteb.2023.101468.

Widodo, W. & Kurniawan, B. 2017. “Manajemen Pemeliharaan Ayam Broiler.” malang.

Widodo, S. A., R. C. I. Prahmana, A. S. Purnami, and Turmudi. 2018. “Teaching Materials of Algebraic Equation.” Journal of Physics: Conference Series 943(1). doi: 10.1088/1742-6596/943/1/012017.