Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Ilham Rahmana Syihad
Universitas Muhammadiyah Malang
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Identifikasi Penyakit Tanaman Pisang Melalui Citra Daun Pisang Menggunakan Metode CNN Dengan Model ResNet50 dan VGG-19 Ilham Rahmana Syihad; Muhammad Rizal; Zamah Sari; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5000

Abstract

Identify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana plants through leaf images. It employs the CNN method, incorporating ResNet50 because ResNet50 is one of the best models and a suitable model for the data set used, and the VGG-19 model is used because VGG-19 was one of the winning models of the 2014 ImageNet Challenge and is a model that also fits the data set used. The research objectives encompass data set processing, model architecture development, evaluation, and result reporting, all aimed at improving disease identification in banana plants. The ResNet50 model achieved impressive 94% accuracy, with 88% precision, 91% recall, and an F1 score of 89%, while the VGG-19 model demonstrated strong performance with 91% accuracy, surpassing previous research and highlighting the effectiveness of these models in identifying banana plant diseases through leaf images. In conclusion, the exceptional accuracy positions it as the preferred model for CNN-based disease identification in banana plants, offering significant advances and insights for agricultural practices. Future research opportunities include exploring alternative CNN models, architectural variations, and more extensive training datasets to improve disease identification accuracy.
Co-Authors Muhammad Rizal Yufis Azhar Zamah Sari