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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Alfani Septiani Selan
Stikom Uyelindo Kupang
Computer Science & IT

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KLASIFIKASI PENYAKIT JAMUR PADA TANAMAN BAWANG MERAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) BERBASIS CITRA DIGITAL Alfani Septiani Selan; Franky Franky Y. Basilin; Tika Skolastika S. Igon
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5761

Abstract

Shallots are a type of bulb plant widely consumed by Indonesians, both as a cooking spice and herbal medicine. Shallot production in Kupang City has experienced a significant decline, with production dropping from 292.15 quantiles to 255.01 quantiles in 2024. This is a serious concern due to disease attacks on shallot plants that cause economic losses due to crop failure for farmers. Lack of understanding and knowledge about shallot diseases is a major obstacle in overcoming this problem. Therefore, an automated system based on digital image technology is needed that is capable of classifying diseases quickly and accurately. This study aims to implement a Convolutional Neural Network (CNN) in the process of classifying fungal diseases in shallot plants based on digital images. CNN is a deep learning method that has the ability to extract visual features through convolutional, pooling, and fully connected layers. The use of CNN in this study is expected to provide accurate results in classifying fungal diseases in shallot plants based on digital images, thereby reducing the potential for crop failure and increasing production yields. The test results using K-Fold Cross Validation showed that the best model was obtained in fold 5 with an accuracy of 90.00%, a sensitivity of 90.02%, and a specificity of 96.75%. In addition, the system obtained an average accuracy of 86.91%, a sensitivity of 87.61%, and a specificity of 95.61%. Based on these results, the system is able to classify fungal diseases in shallot plants with good performance.
Co-Authors Franky Franky Y. Basilin Tika Skolastika S. Igon