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Manullang, Maribeth Adventina
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Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Design of a Mamdani Fuzzy Logic Prototype System for Free-Range Chicken Egg Incubators Manullang, Maribeth Adventina; Arihta, Michael; Rahmadewi, Reni
Journal of Energy and Electrical Engineering Vol 7, No 1: October 2025
Publisher : Teknik Elektro Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jeee.v7i1.15806

Abstract

This research aims to design a temperature and humidity control system for chicken egg incubators using the Mamdani Fuzzy logic method. The main problem addressed is the fluctuation of temperature and humidity in conventional systems that still rely on on/off control. The developed system utilizes DHT sensors as temperature and humidity gauges, Arduino as a microcontroller, and regulates the light intensity of incandescent lamps using an AC dimmer. Mamdani Fuzzy Logic is used to process two inputs, namely temperature and humidity and produce an output in the form of the brightness level of the incandescent lamp. The system relies on nine Fuzzy rules in making decisions based on variations in input conditions. The test results show that the system is able to provide appropriate responses to changes in temperature and humidity, and produce outputs in accordance with the designed rules, such as Rule R1, R5, and R9. Thus, this system succeeds in maintaining the stability of the temperature in the incubator, so as to increase the efficiency and success of the egg hatching process. Overall, the application of Mamdani Fuzzy Logic proved effective in designing an intelligent control system for the incubator.
Implementation of YOLOv8 for Classifying Fertile and Infertile Eggs in the Chicken Hatching Process Arihta, Michael; Manullang, Maribeth Adventina; Hanafi, Zikri; Rahmadewi, Reni
Journal of Energy and Electrical Engineering Vol 7, No 1: October 2025
Publisher : Teknik Elektro Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jeee.v7i1.15774

Abstract

This study aims to develop an embryo detection system in chicken eggs using the YOLOv8 algorithm based on computer vision. This approach is proposed as a solution to the manual candling method which is often inaccurate and time consuming. The dataset used amounted to 4,396 chicken egg images, consisting of fertile and infertile categories. The model was trained using Google Collaboratory with GPU support, where the model was trained for 100 epochs to maximize accuracy. The evaluation results show that the YOLOv8 model is able to detect embryos with a high level of accuracy, indicated by a precision value of 93.2%, mean average precision (mAP) of 98.5%, and recall of 87.2%. The fertile category was successfully detected with a precision of 100% and a recall of 94.2%, while the infertile category had a precision percentage of 86.4% and a recall of 100%. These findings prove that the YOLOv8 algorithm can be effectively implemented to automate the selection process of fertile and infertile eggs, thereby improving efficiency and accuracy in the livestock production process.
Co-Authors Arihta, Michael Hanafi, Zikri Reni Rahmadewi, Reni