📅 14 January 2026
DOI: 10.51903/elkom.v18i2.3302
Analisis Performansi Pendekatan Machine Learning Pada Deteksi Penyakit Daun Tanaman Kopi
Jurnal Elektronika dan Komputer
Universitas Sains dan Teknologi Komputer
📄 Abstract
Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.
🔖 Keywords
#Coffee Classification; Image Processing; Machine Learning; Plant Disease Detection
ℹ️ Informasi Publikasi
Tanggal Publikasi
14 January 2026
Volume / Nomor / Tahun
Volume 18, Nomor 2, Tahun 2026
📝 HOW TO CITE
Purnomo, Rosyana Fitria; Purnomo, Rosyana Fitria; Yodhi Yuniarthe; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan, "Analisis Performansi Pendekatan Machine Learning Pada Deteksi Penyakit Daun Tanaman Kopi," Jurnal Elektronika dan Komputer, vol. 18, no. 2, Jan. 2026.
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