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All Journal ILKOM Jurnal Ilmiah Journal Of Artificial Intelligence And Software Engineering
Saputra, Guntur Eka
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Computer Science & IT

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Implementation of YOLOv8 Algorithm for Web-Based Detection of Coffee Fruit Ripeness Putra, Alfito Dwi; Saputra, Guntur Eka
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6730

Abstract

This research focuses on the application of computer vision technology in smart agriculture, particularly for detecting the ripeness level of coffee cherries. The YOLOv8 algorithm was utilized to build a detection model, which was then integrated into a web-based application developed using Streamlit framework. Python was used to implement YOLOv8 and support real-time object detection. The model development process followed the CRISP-DM approach, while the application development adopted a prototyping method. The dataset consisted of 100 primary images collected from Kebun Raya Bogor and 4547 secondary images from Roboflow, divided into 3253 training images, 930 validation images, and 464 testing images. The model achieved an overall mAP50 accuracy of 82.9%, with class-wise accuracy of 90.2% for dry, 76.2% for ripe, 80.9% for unripe, and 84.3% for half-ripe coffee cherries, exceeding the success criteria of 80%. The developed application provides features for detecting coffee cherry ripeness through image uploads and real-time detection using a camera. Usability testing conducted with 16 respondents using the System Usability Scale (SUS) resulted in an average score of 90, classified as "Excellent" with a grade of A. This indicates that the application is highly usable and effectively supports users in detecting coffee cherry ripeness.
Evaluation of Deep Learning Model for Detection of Banana Consumption Feasibility Using Yolov8 Method Saputra, Guntur Eka; Yanni, Revanza Raditya Putra
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2775.213-226

Abstract

This study aims to improve the accuracy of banana edibility detection using the YOLOv8 deep learning model. A total of 346 banana images were captured using a smartphone camera and split into training (303), validation (29), and testing (14) subsets. The research framework consisted of four main stages: data collection, preprocessing, model training, and performance evaluation. Preprocessing was conducted using the Roboflow platform and included several techniques such as image annotation, resizing, automatic orientation correction, contrast adjustment, and data augmentation through rotation, mosaic, and noise addition to enrich data variation and model robustness. The YOLOv8 model was trained for 60 epochs, achieving optimal convergence in 0.173 hours. Random search was utilized for hyperparameter optimization to achieve the best model configuration. The evaluation demonstrated remarkable results with a precision of 99.7%, recall of 100%, and mean Average Precision (mAP) of 99.5%. Visualization metrics, including the Precision-Confidence, Recall-Confidence, and F1-Confidence curves, each reached 100%, and the normalized confusion matrix demonstrated flawless classification performance. Testing on unseen data further confirmed the model’s ability to accurately detect and classify bananas into Good Quality and Bad Quality classes with high confidence scores. These findings highlight the capability of YOLOv8 as a robust and reliable model for automated fruit quality assessment. The implementation of this approach offers a non-destructive, fast, and consistent method for evaluating banana edibility, reducing dependency on manual inspection and human error. In addition, this study contributes to the advancement of smart agriculture and post-harvest management by demonstrating the potential of deep learning and computer vision to support real-time quality control and decision-making in the agricultural industry.
Co-Authors Armaini Akhirson Putra, Alfito Dwi Rehulina Apriyanti Riswanti, Sri Tua, Robert Managem Yanni, Revanza Raditya Putra