KLASIFIKASI JENIS PENYAKIT BUAH MANGGA BERBASIS DEEP LEARNING MENGGUNAKAN ARSITEKTUR RESNET DAN MOBILENET

Nanda Cornelis Rasyid; Joni Karman; Asep Toyib Hidayat; Harma Oktavia Lingga Wijaya

doi:10.64626/jukomtek.v5i1.570

Penulis

Nanda Cornelis Rasyid Universitas Bina Insan Lubuklinggau
Joni Karman Universitas Bina Insan
Asep Toyib Hidayat Universitas Bina Insan
Harma Oktavia Lingga Wijaya Universitas Bina Insan

DOI:

https://doi.org/10.64626/jukomtek.v5i1.570

Kata Kunci:

Mango disease, deep learning, ResNet, MobileNet, image classification

Abstrak

Mango plantations in Indonesia face serious challenges due to pest and disease attacks that cause decreased productivity and economic losses for farmers. Manual disease identification requires specialized expertise and is often time-consuming. This study uses a qualitative approach with stages according to the AI Project Life Cycle framework using the Confusion Matrix Method, aiming to develop a deep learning-based classification system for mango plant diseases using the ResNet and MobileNet architectures. The dataset consists of 1,600 images divided into 5 classes. The model was trained using transfer learning with ImageNet weights and evaluated using accuracy, precision, recall, F1-score, and ROC curves. The results show that ResNet achieved an accuracy of 87.56%, while MobileNet achieved 84.55%. Both models excelled in the Healthy and Stem-end Rot classes, but struggled with Black Mold Rot due to its visual similarity to other diseases. MobileNet offers better computational efficiency, suitable for mobile applications, while ResNet provides higher accuracy in environments with adequate resources

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