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Pasaribu, Hendra Handoko Syahputra
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Detection of Chicken Egg Quality with Digital Image using EfficientNet-B7 Vincent; Pasaribu, Hendra Handoko Syahputra; Audrey, Wilbert; Jefanya Alexander Meidi Bangun; Deryck Ethan Hong
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15233

Abstract

Chicken eggs are one of the staple food ingredients in Indonesia, playing a vital role in fulfilling the nutritional needs of the community. Therefore, an efficient, accurate, and reliable method for assessing egg quality is essential, especially to support the distribution process in the food industry. This study aims to develop a digital image-based classification system for assessing the quality of chicken eggs using deep learning methods with the EfficientNet-B7 architecture. EfficientNet-B7 was selected for its proven high accuracy in image classification tasks through the application of compound scaling, which simultaneously optimizes depth, width, and resolution. The dataset used in this study combines images collected from public sources and primary documentation, representing various conditions commonly found in chicken eggs. The preprocessing stage involved trimming techniques to focus on the egg object, followed by data augmentation using ImageDataGenerator, including rotation, shifting, zooming, and flipping to enhance dataset diversity. Model training was carried out with the early stopping technique to prevent overfitting. The experimental results showed that the model achieved an accuracy of 98.08% in classifying egg quality based on shell condition and other visual indicators. These findings demonstrate that the implementation of the EfficientNet-B7 model has great potential to support the automation of chicken egg quality assessment processes in a faster and more consistent manner. Thus, this research is expected to contribute to improving the efficiency of the food industry, particularly in the distribution process of chicken eggs in Indonesia.
Implementasi Algoritma YOLOv11 Dan Roboflow Untuk Deteksi Tingkat Kematangan Anggur Berbasis Web Nofita Sary; Pasaribu, Hendra Handoko Syahputra; Situmeang, Rahel Juliana; Darus, Rizky Darmawan
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/xeze6v30

Abstract

Manual detection of grape ripeness is inefficient and prone to subjective errors. This study developed a web-based automatic classification system using the YOLOv11 algorithm with the YOLO11s.pt model and the Roboflow platform. A deep learning approach was applied to automate the classification of grapes into four ripeness categories: unripe, semi-ripe, ripe, and rotten. The dataset used consisted of 897 images obtained directly from the vineyard, then expanded to 6,135 images through preprocessing and augmentation. The labeling process was carried out using Roboflow, and model training was carried out on Google Colab for 200 epochs. The training results showed high performance, with a recall value of 0.95, a precision of 0.98, and a mean Average Precision (mAP) of 0.84. The system was able to distinguish multi-class objects with an average detection time of 1,02 seconds per image, thus supporting semi real-time operations. However, the accuracy of the semi-ripe class classification is still a challenge due to visual similarities with other classes. This system has been integrated into a web application that displays classification results in semi real-time, and has the potential to be applied in a digital agricultural system. For further research, it is recommended to optimize the dataset, especially by adding the amount of training data on the rotten and half-ripe grape classes. In addition, the development of the application into a mobile application is recommended to increase accessibility and flexibility of use.
Co-Authors Audrey, Wilbert Darus, Rizky Darmawan Deryck Ethan Hong Jefanya Alexander Meidi Bangun Nofita Sary Situmeang, Rahel Juliana Vincent