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All Journal Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Hariansyah, Oke
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Penyakit Daun Pada Tanaman Terong dengan Metode K-Nearest Neighbors Hariansyah, Oke; Saprudin, Saprudin; Cahyono, Yono; Rosyani, Perani
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7016

Abstract

Eggplant (Solanum melongena) is an important agricultural commodity with high economic value. However, various leaf diseases can hinder its growth and reduce crop yields. Therefore, rapid and accurate identification and classification of leaf diseases are crucial for improving agricultural productivity. This study proposes the use of the K-Nearest Neighbors (KNN) method for classifying eggplant leaf diseases based on image analysis. The model is developed using color histogram features extracted from leaf images as the basis for classification. This research involves collecting a dataset of eggplant leaf images with various disease categories, extracting color features using RGB and HSV color models, and implementing a KNN model with k=3k=3k=3. The model's performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the KNN model achieves an accuracy of approximately 87%, but challenges remain, such as dataset imbalance and misclassification of disease classes with similar color patterns. To improve accuracy, this study explores data augmentation techniques and optimizes the KNN model parameters. This research aims to enhance the effectiveness of KNN in detecting and classifying eggplant leaf diseases, ultimately assisting farmers in managing their crops more efficiently and effectively.
Klasifikasi Batu Permata Berbasis Citra Menggunakan Convolutional Neural Network Rosyani, Perani; Hariansyah, Oke; Permadi, Yuda; Rosdiana, Muhamad; Nanang, Nanang
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9101

Abstract

Manual gemstone identification still faces several limitations, such as subjective assessment and strong dependence on expert experience, which may lead to misclassification, particularly for gemstones with similar visual characteristics. This study aims to apply a Convolutional Neural Network (CNN) for automatic visual-based gemstone image classification using a limited dataset. The dataset consists of three gemstone classes, namely Alexandrite, Almandine, and Amazonite, with a balanced class distribution. Image preprocessing includes image resizing, pixel value normalization, and data augmentation to increase data variability. The proposed CNN model is a custom architecture composed of three convolutional layers with ReLU activation, followed by max pooling, a fully connected layer with dropout, and a Softmax output layer. Model performance is evaluated using a confusion matrix and classification metrics, including accuracy, precision, recall, and F1-score. Experimental results show that the CNN model achieves a testing accuracy of 93.33% on the limited test dataset with relatively balanced performance across classes. However, analysis of the training and validation curves indicates the presence of overfitting, suggesting that the model’s generalization capability to unseen data remains limited. These findings highlight that the achieved accuracy is conditional on the specific and constrained dataset used. Therefore, future work is recommended to expand dataset size and diversity, apply more comprehensive data augmentation strategies, and explore transfer learning approaches to improve model stability and generalization performance.
Co-Authors Cahyono, Yono nanang nanang Perani Rosyani Rosdiana, Muhamad Saprudin Saprudin, Saprudin Yuda Permadi, Yuda