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Anisya Anisya
Department Of Informatics Engineering, Institut Teknologi Padang, West Sumatra, Indonesia
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Journal : Jurnal Informatika: Jurnal Pengembangan IT

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Segmentasi Citra Daun Tomat untuk Klasifikasi Penyakit Tanaman Menggunakan Support Vector Machine (SVM) Azli, Puteri Amelia; Minarni, Minarni; Syahrani, Anna; Swara, Ganda Yoga; Anisya, Anisya
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9404

Abstract

Tomatoes are one of the most widely grown crops worldwide. In Indonesia, particularly in West Sumatra, tomato production has declined. This is due to extreme weather conditions and plant disease outbreaks. One solution to help with early identification of tomato plant diseases is through digital image-based classification. This process involves several important stages, starting from image acquisition, preprocessing, segmentation, feature extraction, and classification. However, the quality of classification is highly dependent on the effectiveness of segmentation in separating leaf objects from the background. This study proposes a method for segmenting tomato leaf images based on a combination of color thresholding techniques, morphological operations, contour filtering, and bitwise masking to ensure that only the leaf parts are processed further. After undergoing the segmentation process, images are extracted based on color characteristics in HSV space and GLCM texture, then further processed using an SVM algorithm with an RBF kernel. The dataset used consists of 4000 tomato leaf images with an 80% training and 20% testing data division scheme, accompanied by 5-fold cross validation. The model achieved an accuracy of 96.97% on the training data and 93.75% on the testing data. The results show that segmentation methods using color thresholding, morphology, contours, and bitwise masking can help improve the consistency of extracted features, thereby potentially supporting more stable classification performance.
Co-Authors Agung Haryono Ahmad Alfaishal Amrullah Alkadri Masnur Allazi, Ersab Anna, Anna Syahrani Anugrah, Septriyan Aria Wahyuni Azha, Rifo Dwipa Azli, Puteri Amelia Bayu Febriadi, Bayu Boby Irawan Busran Busran Devalio, Devalio Eko, Eko Kurniawanto Putra FAJAR NUGROHO Fajar Nugroho Fajrin, Fajrin Fitrianti, Wahyu Ganda Yoga Swara Ganda Yoga Swara Hamdi, Yori hendri, nofri Indra Warman Joni Ermon Lisa Rokhmani Mahendra Mahendra Masruro, Umi Minarni Minarni Nora Idiawati Ombai, Taufiq Ombai Pandu Pratama Putra, Pandu Pratama Prayitno, Dwi Imam Putra, Dede Wira Trise Putra, Eko Kurniawanto Putri Mandarani Putri, Putri Mandarani Ramadhan, Novri Ramadhani, Denisa Sri Ro'ufah Inayati Sartika, Aprilina Septio Rita Sumijan Sumijan Syahrul Munir Trise, Dede Wira Wahyu Hidayat Wahyu Hidayat Wira Trise Putra, Dede Yoga Swara, Ganda Yunita Wandyra