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All Journal Jurnal Civics: Media Kajian Kewarganegaraan INFORMASI Jurnal Natapraja : Kajian Ilmu Administrasi Negara Format : Jurnal Imiah Teknik Informatika JURNAL TEKNIK JIKA (Jurnal Informatika) Bulletin of Computer Science Research Insearch: Information System Research Journal Journal of Indonesian Management J-Intech (Journal of Information and Technology) Scientica: Jurnal Ilmiah Sains dan Teknologi Causa: Jurnal Hukum dan Kewarganegaraan JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Yanuardi Yanuardi
Universitas Ibn Khaldun Bogor
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Journal : Bulletin of Computer Science Research

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Klasifikasi Citra Penyakit Daun Anggur Menggunakan Radial Basis Function Neural Networks Moh. Erkamim; Yanuardi, Yanuardi; Mohammad Imam Shalahudin; Arisantoso, Arisantoso
Bulletin of Computer Science Research Vol. 4 No. 5 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i5.324

Abstract

Grapes are one of the important horticultural commodities in Indonesia, but their productivity is often disrupted by leaf diseases that affect both the quality and quantity of the harvest. Manual disease identification remains time-consuming, requires specialized expertise, and often results in inconsistent diagnoses. Challenges such as fatigue, varying levels of experience, and visual differences in leaf diseases hinder the ability to perform fast and accurate diagnosis. Therefore, this study focuses on developing an automatic grape leaf disease classification model using the Radial Basis Function Neural Networks (RBFNN) algorithm. The model utilizes color feature extraction through Mean Color to detect changes in the color distribution of infected leaves, as well as GLCM (Gray Level Co-occurrence Matrix) to analyze texture patterns that serve as disease indicators. The classification process is conducted by the RBFNN algorithm, which calculates the distance between inputs and neuron centers using radial basis functions in the hidden layer. The results of this study show that the model is capable of classifying grape leaf diseases with an overall accuracy of 92.5%, indicating that the model is highly effective in detecting and classifying both healthy and infected leaves with minimal errors.
Co-Authors Abdul Madjid Agung Budi Muljono Agung Wibowo Agus Supriyadi Ahmad Qaulan Syadid Ahmad Soleh Aksani, Muhammad Luthfi Alexander, Allan Desi Alfry Aristo Jansen Sinlae Ali Firdaus Anggalini, Titis Dewi Arisantoso Arisantoso Azhari, Lukman Damarsiwi, Eska Prima Monique Destriana, Rachmat Eko Wiyono Fajar, Kurnia Nur Faridi Faridi Fauyhi Eko Nugroho, Fauyhi Eko Fauzi, Hafiz Firdiansyah Firdaus Basri FITRIA NURAINI Fitriana, Kurnia Nur Handayani, Nurdiana hardiansyah, alim Harjian, Muhammad Rivaldi Hartaya, Kendra Herawati Herawati Heriyani, Nofitri Herlina, Rina Huasin, Syepry Maulana Karyadi, Bambang Layina, Andiny Lena Satlita Marita Ahdiyana Moh. Erkamim Mohammad Imam Shalahudin Mulyadi Mulyadi Nugroho, Aryanto Priyanggodo, Dyas Yudi Rohmat Taufiq Rusdianto, Hengki Sigit Wibowo Ulpiani, Ria Utami Dewi ZF Badoh, Ibrahim