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All Journal Jurnal Ilmu Komputer
Widya Eka Pranata
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Computer Science & IT

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APPLE FRUIT QUALITY DETECTION (GOOD AND ROTTEN) USING THE YOLOV5 METHOD Fathir Adisyar; Mohamad Ilyas Abas; Widya Eka Pranata; Rizal Lamusu; Syahrial Syahrial; Irawan Ibrahim
Jurnal Ilmu Komputer (JUIK) Vol 6, No 1 (2026): February 2026
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v6i1.5539

Abstract

Fruit quality is an important factor that affects nutritional value, consumption safety, and market value of agricultural products. Apples, as one of the most widely consumed fruits, are prone to quality degradation due to spoilage, which is often difficult to accurately identify through human visual observation. Manual sorting of apples is subjective, time-consuming, and prone to errors. Therefore, this study aims to develop an automatic apple quality detection and classification system using the You Only Look Once version 5 (YOLOv5) deep learning method. Apple quality is classified into two categories, namely fresh apples and rotten apples, based on digital images. The dataset used in this study consists of 4,035 images obtained from the Roboflow platform, comprising 2,925 training images, 707 validation images, and 403 testing images. All images were resized to 640 × 640 pixels without data augmentation. The model was trained for 50 epochs using GPU acceleration on Google Colab. Model performance was evaluated using a confusion matrix on the testing dataset. The experimental results show that the YOLOv5 model successfully classified all testing images correctly without any misclassification, indicating excellent detection and classification performance. These results demonstrate that YOLOv5 is an effective and reliable method for automatic apple quality detection and has strong potential for application in agriculture and the food industry to improve efficiency and accuracy in fruit quality inspection.
Avocado Ripeness Classification Using a Convolutional Neural Network (CNN) Nur'aini Mufaidhah Tangahu; Mohamad Ilyas Abas; Widya Eka Pranata; Rizal Lamusu; Syahrial Syahrial; Irawan Ibrahim
Jurnal Ilmu Komputer (JUIK) Vol 6, No 1 (2026): February 2026
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v6i1.5540

Abstract

The determination of avocado ripeness plays a crucial role in post-harvest handling and quality The determination of avocado ripeness plays a crucial role in post-harvest handling and quality control within the agricultural sector; however, conventional assessment methods based on visual inspection and human experience are often subjective and inconsistent, potentially leading to classification errors and economic losses. To address this issue, this study proposes an automated avocado ripeness classification system using a Convolutional Neural Network (CNN) based on digital image analysis. The model employs a transfer learning approach using the MobileNet architecture implemented through the Teachable Machine platform. The dataset utilized in this research was obtained from Mendeley Data and consists of avocado images categorized into four ripeness levels: underripe, breaking, ripe, and overripe. Prior to model training, the images underwent preprocessing and data augmentation to improve model robustness and generalization. Model evaluation was conducted using 1,200 test images, with 300 samples per class. Experimental results show that the proposed model achieved an overall accuracy of 91.42%, indicating strong and stable classification performance. Analysis using a confusion matrix reveals that most predictions were correctly classified, while misclassifications primarily occurred between ripeness stages with visually similar characteristics. Among all classes, the underripe category demonstrated the highest performance with minimal classification errors. These findings indicate that the proposed CNN-based approach is effective and reliable, and it has significant potential to be further developed as an automated system for avocado ripeness classification and post-harvest quality assessment.
Co-Authors Fathir Adisyar Ibrahim, Irawan Mohamad Ilyas Abas Nur'aini Mufaidhah Tangahu Rizal Lamusu Syahrial Syahrial