Klasifikasi Penyakit Daun Kentang Berbasis CNN MobileNetV2 dengan Optimasi Randomize Search
- Jeason Lie Universitas Multi Data Palembang, Palembang, Indonesia
- Abdul Rahman Universitas Multi Data Palembang, Palembang, Indonesia
-
Daniel Udjulawa
*
Universitas Multi Data Palembang, Palembang,
Indonesia
(*) Corresponding Author
Abstract
Potatoes are a vital food commodity in Indonesia, but their productivity often declines significantly due to attacks by various leaf diseases that inhibit growth. This study aims to build an efficient and accurate automatic classification model for potato leaf diseases using Deep Learning technology. The approach used in this study is the MobileNetV2 Convolutional Neural Network (CNN) architecture based on transfer learning, which is known to have high computational efficiency. To obtain the most optimal model performance, this study applies an automatic hyperparameter tuning strategy using the Randomize Search method and performs robust model validation using the K-Fold Cross Validation technique with 5 folds. In addition, the balanced class weight technique is also applied to overcome the problem of data imbalance in the eight disease classes tested. The experimental results show that the best model configuration is achieved at the 15th iteration of the 5th fold using a combination of RMSprop optimizer parameters, 35 epochs, and a learning rate of 0.001. The final evaluation on independent test data produces an accuracy of 78.45%, a precision of 84.24%, and a recall of 72.94%. The very small difference between the validation accuracy of 79.30% and the test accuracy indicates good generalization ability without overfitting. Although the model achieved excellent results in Alternaria classification, challenges remained in identifying the Fungi class, which has high visual similarity to Phytopthora and Pest. This study concludes that the integration of MobileNetV2 with hyperparameter optimization is capable of effectively classifying potato leaf diseases.
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References
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Copyright (c) 2026 Jeason Lie, Abdul Rahman, Daniel Udjulawa
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