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All Journal Elkom: Jurnal Elektronika dan Komputer Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) Jurnal Cendikia Jurnal Pengabdian kepada Masyarakat Nusantara ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat Jurnal Media Informatika Journal of Information Systems and Technology Research RISTEC : Research in Information Systems and Technology Journal of Informatics, Electrical and Electronics Engineering Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Pengabdian Masyarakat Jurnal Pengabdian UMKM JPEMAS: Jurnal Pengabdian Kepada Masyarakat Journal of Software Engineering And Technology PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Rosyana Fitria Purnomo
Universitas Mitra Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Analisis Performansi Pendekatan Machine Learning pada Deteksi Penyakit Daun Tanaman Kopi Yodhi Yuniarthe; Rosyana Fitria Purnomo; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/p2t2nm71

Abstract

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.  
Co-Authors Ahmad ikhwan ahmad ikhwan Alie, Maria S Anggun Sari, Ressy Arie Setya Putra Budi Hartanto Dirayati, Fadhilah Dwi Handoko Fadhilah Dirayati Fatimah Fahurian Fawaati, Teuku Muhammad Hartanto, M Budi Hendri, Romi Hendro Pramono, Doni Eko Herwanto, Riko Hilda Dwi Yunita ikhwan, Ahmad Ino Susanti Intan Oktaria Khozainuz Zuhri Luh Putu Ratna Sundari M Budi Hartanto Oktaria, Eka Travilta Putra, Billy Sistian Resy Anggun Sari Riko Herwanto Rini, Prima Sekarsari, Meita selipuri Sriyanto - Sukri, Hamdan Yodhi Yuniarthe Yodhi Yuniarthe Yudhinanto, Cahyo Nugroho