Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
St Hajrah Mansyur
Unknown Affiliation
Computer Science & IT Engineering

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

Found 1 Documents
Search

 1 
PENERAPAN METODE CNN RESNET152 PADA PENGEMBANGAN APLIKASI VANILLATECH BERBASIS MOBILE UNTUK IDENTIFIKASI PENYAKIT TANAMAN VANILI Mush'ab Al Mubarak; Lilis Nur Hayati; St Hajrah Mansyur
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7285

Abstract

Vanilla is a high-value plantation commodity whose productivity is significantly affected by plant diseases that are difficult to identify accurately using conventional methods. This study aims to develop a mobile-based vanilla plant disease identification system using a Convolutional Neural Network (CNN) with the ResNet152 architecture. The dataset consists of primary field-acquired images, which were augmented to produce a total of 1,616 images across five disease classes. The model was trained using a transfer learning scheme with parameter adjustments designed to handle variations in field lighting conditions, image angles, and real-world visual characteristics. Experimental results demonstrate that the proposed ResNet152 model achieves high and stable classification accuracy. The integration of the trained model into a mobile application enables fast and practical disease diagnosis in real plantation environments. The novelty of this study lies in the field-oriented optimization of the ResNet152 model and its direct deployment in a mobile diagnostic system tailored for vanilla plant disease identification.
Co-Authors Lilis Nur Hayati Mush'ab Al Mubarak