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All Journal Journal of Computer System and Informatics (JoSYC) Bulletin of Computer Science Research
Nurcahaya Simanjuntak
Universitas Amikom Yogyakarta, Sleman
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Utilizing Lightweight YOLOv8 Models for Accurate Determination of Ambarella Fruit Maturity Levels Simanjuntak, Nurchaya; Saragih, Raymond Erz; Pernando, Yonky
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5123

Abstract

In the agricultural sector, accurately determining fruit ripeness remains a crucial yet challenging task. Among intriguing Indonesian fruits, the Ambarella presents a particular difficulty. In Ambarella fruit, the peel changes from green to golden yellow as it ripens, serving as a visual indicator for optimal harvest time, thus determining the maturity is crucial for harvesting the Ambarella fruit. Traditionally, ripeness assessment relies on manual methods, which suffer from drawbacks like high labor costs, significant time investment, and inconsistency in results. This work explores the potential of employing YOLOv8, a cutting-edge deep learning model, to automate Ambarella fruit ripeness classification. This work focuses on the YOLOv8n, YOLOv8s, and YOLOv8m, lightweight models within the YOLOv8 family. Our results are promising: all three models achieved 100% accuracy on the training set, with YOLOv8s demonstrating the lowest loss at 0.00286. The web application was utilised to deploy the trained models, allowing users to upload images of Ambarella fruit and run the model for inference.
Analisis Perbandingan Algoritma SVM dan CNN dalam Mendeteksi Website Judi Online Berdasarkan Konten Teks Nurcahaya Simanjuntak; Alva Hendi Muhammad
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.586

Abstract

This study aims to compare the effectiveness of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms in detecting Indonesian-language online gambling websites. With the increasing number of online gambling players in Indonesia, it is essential to develop effective methods for identifying gambling content. The dataset used consists of 34,336 gambling websites and 36,529 non-gambling websites, collected through web scraping. The SVM model demonstrated an accuracy of 99%, with evaluation metrics including a precision of 1.00, recall of 0.99, and F1-score of 0.99. In contrast, the CNN model achieved perfect accuracy of 100%, with precision, recall, and F1-score all at 1.00. However, it is important to note that this perfect accuracy was achieved under certain conditions, including a relatively clean dataset and optimal training processes. Evaluation results using cross-validation techniques indicated that SVM maintained a consistent accuracy of approximately 99%, while CNN exhibited an average accuracy of 99.61% with a very low standard deviation. This research emphasizes the importance of data pre-processing in enhancing model accuracy and highlights the advantages of CNN in capturing complex patterns within text. These findings contribute significantly to the development of detection methods for online gambling websites in Indonesia and open avenues for further research in this field.
Co-Authors Alva Hendi Muhammad Pernando, Yonky Saragih, Raymond Erz