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All Journal Scientific Journal of Informatics Bulletin of Information Technology (BIT)
Satrianansyah
Universitas Bina Insan
Computer Science & IT

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Klasifikasi Penyakit pada Buah Jeruk Berdasarkan Citra dengan Pendekatan Transfer Learning Menggunakan Arsitektur Densenet-121 Sheli Agustina; Asep Toyib Hidayat; Satrianansyah; Rudi Kurniawan
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.42-47

Abstract

This study aims to develop a classification system for citrus fruit diseases based on digital images using a machine learning approach. The primary challenge in citrus cultivation is disease attacks that affect both the quality and quantity of production. In this research, image processing techniques were applied to extract color, shape, and texture features from citrus fruit images, which were then used as input for classification algorithms. This study uses the DenseNet-121 architecture for orange fruit image classification. The dataset used consisted of images of healthy citrus fruits and those affected by various diseases, such as blackspot, canker, and greening. The testing results showed that the DenseNet-121 architecture achieved the highest accuracy in classifying citrus diseases, with an accuracy rate of up to 99%. This system is expected to assist farmers and relevant stakeholders in early disease detection and in taking appropriate control measures.
Klasifikasi Jenis Ayam Menggunakan Ensemble Voting CNN (Mobilenetv2 Dan Efficientnet-B0) Berbasis Citra Sudanto; Joni Karman; Satrianansyah; Budi Santoso
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3067

Abstract

Digital image-based chicken species classification plays a crucial role in supporting the identification process in the livestock sector. However, the similarity in visual characteristics between chicken species makes the manual classification process inefficient and error-prone. This study aims to develop a chicken species classification model using the Ensemble Soft Voting method by combining two Convolutional Neural Network (CNN) architectures, namely MobileNetV2 and EfficientNet-B0. The dataset used consists of 1,500 images from five chicken classes: Bangkok Chicken, Broiler Chicken, Free-Range Chicken, Bantam Chicken, and Laying Hen. The research stages include preprocessing, data augmentation, transfer learning, model training, and combining prediction probabilities using the Soft Voting method. Evaluation is carried out using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix metrics. The results showed that MobileNetV2 achieved an accuracy of 97.18%, EfficientNet-B0 achieved 96.48%, while the Ensemble Soft Voting method produced the best performance with an accuracy of 97.89%, a precision of 97.96%, a recall of 97.89%, and an F1-score of 97.88%. These results indicate that the Ensemble Soft Voting method is able to improve classification accuracy and stability compared to using a single model, so it has the potential to be applied as a solution for digital image-based chicken species identification.
Co-Authors Asep Toyib Hidayat Budi Santoso joni karman Rudi Kurniawan Sheli Agustina Sudanto