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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor JAIS (Journal of Applied Intelligent System) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informasi Cyberku
Ruri Suko Basuki
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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DEEP LEARNING JARINGAN SARAF TIRUAN UNTUK PEMECAHAN MASALAH DETEKSI PENYAKIT DAUN APEL Sutriawan, Sutriawan; Fanani, Ahmad Zainul; Alzami, Farrikh; Basuki, Ruri Suko
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 11, No 1 (2023): Jurnal TIKomSiN, Vol. 11, No. 1, April 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v11i1.729

Abstract

Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many methods, such as data preparation and a preset VGG-16 artificial neural network (CNN) architecture, are recommended for use in the deep artificial neural network processing method. The most precise outcomes occurred when the beta parameter value was set to 2 = 0.999 at Ephoch to 85/100 with an accuracy of 0.7582, and when the epsilon parameter value was set to 1e-07 at Ephoch to 32/100 with an accuracy of 0.7582 with the best accuracy.
Co-Authors Adhitya Nugraha Agung Nugroho Ahmad Zainul Fanani Ahmad Zaniul Fanani Akbar Aji Nugroho Alzami, Farrikh Astrid Novita Putri, Astrid De Rosal Ignatius Moses Setiadi Edi Faisal Eko Hari Rachmawanto Fikri Budiman Heru Agus Santoso Jehad A.H Hammad Latius Hermawan Linda Marlinda Mauridhi Hery Purnomo Mochamad Hariadi Pulung Nurtantio Andono Setiawan, Dicky Suharnawi Suharnawi Suharnawi Sutriawan Sutriawan Tengku Riza Zarzani N Wicaksono, Duta Firdaus