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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Petir TIN: TERAPAN INFORMATIKA NUSANTARA Computer Science (CO-SCIENCE) Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Prioritas : Jurnal Pengabdian Kepada Masyarakat MDP Student Conference Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal Teknik Indonesia (JU-TI) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Daniel Udjulawa
Universitas Multi Data Palembang
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Klasifikasi Penyakit Daun Kentang Berbasis CNN MobileNetV2 dengan Optimasi Randomize Search Lie, Jeason; Rahman, Abdul; Udjulawa, Daniel
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9108

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.
Co-Authors Abdul Rahman Andreyas, Andreyas Anggoro Aryo P. Dafid Dafid Dafid Dafid Dandy, Dandy Darmawan, Dean Famerdi, Farhan Agung Fatchurrachman, Ahmad Fawzan, Muhammad Febbiola Febbiola Febbiola, Febbiola Ja'far, Ja'far Kadafi, Aldo Kasanova, Sinyo Krisvyanti, Fanny Kurniawan, Marthin Hokita Lie, Jeason M Fajri Septian Dwi Cahyo Martin, Nicolas Marzali, Azizurrahman Muhammad Haviz Irfani Mulyati Mulyati Mulyati Mulyati Novan Wijaya Nyimas Artina Oktarina, Serly Pramudiya, Relin Ramadhan, Ahmad Wildan Rizky Regiza Giovanno Rivaldo, Mario Robert, Michael Roda Robert, Victoria Valensita Setiawan, Jerry Sewithto, Muhyi Haadi Shela, Shela Simanjuntak, Andreas Jeremy Obet Sutanto, Steven Yesua Timoteus Ivan Sariyo William William Wiwik Handayani Yoannita, Yoannita Yohannes, Yohannes Zulnardi, Andre