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All Journal Epsilon: Jurnal Matematika Murni dan Terapan Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Jurnal Pengabdian Sumber Daya Manusia RAGAM: Journal of Statistics and Its Application
Dewi Anggraini
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Universitas Lambung Mangkurat, Indonesia
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Application of Categorical Boosting Model in Classifying Diseases of Tomato Leaves Fitria Rahmah; Selvi Annisa; Dewi Anggraini
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.38869

Abstract

Tomatoes are a strategic horticultural commodity whose productivity is often hampered by leaf diseases, particularly early blight and late blight. Manual identification through visual inspection is often inaccurate due to the similarity of symptoms between diseases. This study aims to improve the performance of tomato leaf disease classification using machine learning by overcoming the limitations of previous research by Ningsih et al., which focused solely on disease classes and did not include healthy leaf samples, thereby risking the model failing to recognize normal plant conditions. The proposed methodology integrates the VGG16 architecture as a feature extractor with the Categorical Boosting (CatBoost) algorithm as a classifier. The dataset sourced from Kaggle was cleaned and resized to 224x224 pixels, resulting in 3,285 images. The experimental results show that integrating VGG16 with CatBoost achieves good performance. The accuracy score achieved is 93.1%, while the F1 scores achieved are 90.2% (healthy leaves), 90.3% (early blight), and 98.6% (late blight). Compared to the research by Ningsih et al., this approach not only expands the scope of classification by including the healthy leaf class, but also shows better accuracy in identifying the health conditions of tomato plants.
Co-Authors Akhriyandi Wijanarta Aldianur Khairani Aprida Siska Lestia Azkia Azkia Dewi Sri Susanti Dhika Nugraha Dini Hidayati Dwidjo Susilo Faisal Faisal Fitria Rahmah Fuad Muhajirin Farid Lestia, Aprida Siska Muhammad Shiddieq Adjie Yudhistira Nabila Septiani Nur Salam Sekar Wahyuningtias Selvi Annisa Siti Jubaidah Syifa Ajeria Thaibatun Nissa Winda Adinda Tanjung Yeni Rahkmawati Yuana Sukmawaty Zakiah Zakiah