Article Per Year (5 Year)
p-Index From 2020 - 2025
4.495
P-Index
This Author published in this journals
All Journal Humanis : Jurnal Pengabdian Kepada Masyarakat Jurnal Buana Informatika Pattingalloang : Jurnal Pemikiran Pendidikan dan Penelitian Kesejarahan Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Jambura History and Culture Journal Jurnal Tekno Kompak Jurnal Perspektif: Jurnal Kajian Sosiologi dan Pendidikan Jurnal Perangkat Lunak SELODANG MAYANG Journal of Applied Data Sciences Jurnal Karya Abdi Sipakainge: Inovasi Penelitian, Karya Ilmiah, dan Pengembangan Teknofile, Jurnal Sistem Informasi
Muh. Rasyid Ridha
Universitas Islam Indragiri
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
Improving Classification Accuracy of Local Coconut Fruits with Image Augmentation and Deep Learning Algorithm Convolutional Neural Networks (CNN) Usman, Usman; Yunita, Fitri; Ridha, Muh. Rasyid
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.389

Abstract

Local coconut varieties must be classified to maintain the quality and genetic diversity of coconuts as the main commodity in Indonesia's largest coconut-producing region. This study introduces a deep learning module for improved classification of coconuts, using color jitter as part of a data augmentation strategy to supplement the existing dataset and utilizing well-known CNN-based models like VGG16 for image analysis, with a focus on the needs of future research. The goal is to improve the classification accuracy of local coconut varieties through deep learning. We investigate both data augmentations and EDA, and we use VGG-16-based CNN models to enhance the classification performance. We used a confusion matrix for the model evaluation, containing metrics like accuracy, precision, recall, and f1-score. Results reveal that a color jitter augmentation model attained a training accuracy of 99.12%, testing accuracy of 97.33%, and validation accuracy of 97.33%. Model exploration using VGG16, on the other hand, improved all three: training accuracy—99.87%, testing accuracy—98.77%, and validation accuracy—98.97% average F1-score: 99%. Our research contributes massively to providing the best automatic classification method that will benefit and help farmers shorten their jobs while promoting economic growth in trading effectively across Indonesian regions. Its novelty is in combining image augmentation and CNNs, concerning the VGG16 model, showing better.
Co-Authors Adha R., Fiendra Aditia, Febri Alfiani, Jumriah Amelia Amirullah Amirullah -, Amirullah Arif , Muh. Al Awwal Ilham Wal Bayu Rianto Disnasari, Rita Dwi Yuli Prasetyo Firman Fitra Widya Wati Fitri Yunita Habib, Rois Indrawan Alsa Irwandi Rizki Putra Jamri Kabubu, Rusmala Dewi Khathab, Muhammad Kurniawan, Kharisma Mahdalina Malihu, La Muchlis Adnan, Indra Muhammad Rahir, Galing Najamuddin Najamuddin, Najamuddin Nurlianti Nurlianti, Nurlianti P, Stefanny Datu Reski Handayani Rifal, Rifal Rosliana, Rosliana Rozi, M. Sahrul Sanur, Ilham Samudra Sarna Pasawin Sawitri, Nina Shaleh, Muhammad Siti Wardah, Siti Sri Rahinda, Ayu Sumardin, Oschar Tarmizi, Dimar Usman Usman Usman Usman, Usman Wahyuddin wandi