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All Journal Jurnal Teknologi Informasi Mura
Reza Novriansah
Universitas Bina Insan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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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 (JTI) Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura
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 Asep Toyib Hidayat Harma Oktavia LW3