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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA Jurnal Teknologi Informasi Mura
Asep Toyib Hidayat
Universitas Bina Insan Lubuklinggau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Pengembangan Model Hybrid DenseNet-SVM Untuk Klasifikasi Penyakit Buah Jambu Berdasarkan Citra Digital Reza Novriansah; Asep Toyib Hidayat; Harma Oktavia LW3
Jurnal Teknologi Informasi Mura Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura MARET
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2958

Abstract

Abstract Diseases in guava fruit such as Anthracnose and fruit fly attacks can drastically reduce crop quality. Manual identification is often subjective and slow. This research proposes a hybrid model combining Deep Learning architecture DenseNet121 as a feature extractor and Support Vector Machine (SVM) as a classifier. The dataset used is the "Guava Disease Dataset" which has been augmented into 3,784 images. The results showed that the hybrid DenseNet-SVM model with a linear kernel achieved the highest testing accuracy of 99.62%. This proves that combining deep feature extraction with an optimal margin classifier is highly effective for plant disease detection. Keywords— guava, disease classification, DenseNet121, SVM, digital image
KLASIFIKASI PENYAKIT DAUN KEDELAI BERBASIS DEEP LEARNING DENGAN ARSITEKTUR MOBILENET DAN RESNET-50 wahyu raihan suryatman; Muhammad Nur Alamsyah; Asep Toyib Hidayat; Ahmad Sobri
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 11 No. 3 (2026): JUTIM (Jurnal Teknik Informatika Musirawas)
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v11i3.3463

Abstract

This study aims to compare the performance of two deep learning architectures, namely MobileNetV2 and ResNet50, in classifying soybean leaf diseases based on digital images. The classification task involves four classes: Bacterial Pustule, Healthy, Sudden Death Syndrome, and Yellow Mosaic. Model performance was evaluated using a confusion matrix and classification report, including accuracy, precision, recall, and f1-score metrics. The experimental results indicate that both architectures demonstrate excellent classification performance. MobileNetV2 achieved an accuracy of 95.45%, with very few misclassifications, namely one error in the Bacterial Pustule class and one in the Yellow Mosaic class. The macro average and weighted average values of 0.9545 indicate that MobileNetV2 provides stable and balanced performance, making it an effective and efficient model, particularly for systems with limited computational resources. On the other hand, ResNet50 demonstrated superior performance by achieving perfect classification results. All test data were correctly classified, as reflected by a confusion matrix entirely along the main diagonal and precision, recall, f1-score, and accuracy values of 1.000 (100%). This result indicates that ResNet50 has very strong feature extraction and generalization capabilities in recognizing each disease class. In conclusion, ResNet50 outperforms MobileNetV2 in terms of classification accuracy and prediction precision. However, MobileNetV2 remains a highly viable alternative due to its lightweight architecture and computational efficiency while still delivering near-equivalent performance. Therefore, the selection of the model can be adapted to system requirements, where ResNet50 is suitable for maximum accuracy, while MobileNetV2 is more appropriate for deployment on resource-constrained devices.
Pengembangan Model Hybrid DenseNet-SVM Untuk Klasifikasi Penyakit Buah Jambu Berdasarkan Citra Digital Reza Novriansah; Asep Toyib Hidayat; Harma Oktavia LW3
Jurnal Teknologi Informasi Mura Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura MARET
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2958

Abstract

Abstract Diseases in guava fruit such as Anthracnose and fruit fly attacks can drastically reduce crop quality. Manual identification is often subjective and slow. This research proposes a hybrid model combining Deep Learning architecture DenseNet121 as a feature extractor and Support Vector Machine (SVM) as a classifier. The dataset used is the "Guava Disease Dataset" which has been augmented into 3,784 images. The results showed that the hybrid DenseNet-SVM model with a linear kernel achieved the highest testing accuracy of 99.62%. This proves that combining deep feature extraction with an optimal margin classifier is highly effective for plant disease detection. Keywords— guava, disease classification, DenseNet121, SVM, digital image
Co-Authors Ahmad Sobri Harma Oktavia LW3 Muhammad Nur Alamsyah Reza Novriansah wahyu raihan suryatman