Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) DIGITAL BUSINESS INSIGHTS JOURNAL
Ferdiansyah
Universitas Islam Indragiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
SISTEM MANAJEMEN STOK BARANG PADA TOKO SEMBAKO IBUK ISUM BERBASIS CLOUD Muhammad Efendi; Khairil Azhar; Ferdiansyah; Novrizal Nur
Digital Business Insights Journal Vol 1 No 2 (2025): DIGITAL BUSINESS INSIGHTS JOURNAL
Publisher : Fakultas Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/bidi.v1i2.4467

Abstract

Efficient stock management is a primary need in running a grocery store, especially to avoid shortages or excess inventory. Mrs. Isum's Grocery Store located in Teluk Kiambang Village still uses a manual recording method that is prone to errors, data loss, and irregularity in stock monitoring. This study aims to design a more structured and easy-to-use stock management system to support the smooth operation of the store. This system is designed with a simple but effective approach, containing features for recording incoming and outgoing goods, current stock information, and monthly reports that can be accessed quickly. By implementing this system, shop owners can control stock more accurately and efficiently. The implementation results show an increase in data accuracy and ease in the decision-making process related to procurement of goods.
Application for Local Coconut Quality Classification Using the MobileNetV2 Convolutional Neural Network (CNN) Algorithm Ferdiansyah; Muh Rasyid Ridha; Dwi Yuli Prasetyo
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5773

Abstract

Quality classification of local fibrous coconuts is still largely performed manually, a process that requires precision and significant time, while also potentially leading to inconsistencies in quality assessment. This study aims to develop an Android-based quality classification application for local fibrous coconuts using the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture. The research involved several stages: dataset collection (coconut images), preprocessing, data augmentation, model training, model testing, and implementation of the model into an Android application using TensorFlow Lite. The model was developed to classify coconut quality into three categories: immature, mature, and reject. The results demonstrate that the MobileNetV2 model effectively learned the visual characteristics of local fibrous coconuts, yielding optimal classification performance based on evaluations using accuracy, loss, precision, recall, F1-score, and a confusion matrix. The developed model was successfully implemented in an Android application, enabling automated classification using images captured via the device's camera or selected from its gallery. The findings indicate that the developed application offers a practical and efficient solution for the rapid and consistent quality identification of local fibrous coconuts. According to the research provided, the dataset consisted of 3,000 images of local fibrous coconuts, comprising 1,000 images each for the immature (semi-ripe), mature (ripe), and reject (damaged) classes. The dataset was split into 70% training data (2,100 images), 15% validation data (450 images), and 15% testing data (450 images). Following the training process using the CNN algorithm with the MobileNetV2 architecture, the model achieved a test accuracy of 96% and a test loss of 0.1098, demonstrating excellent classification performance in distinguishing between the three coconut quality classes.
Co-Authors Dwi Yuli Prasetyo Khairil Azhar Muh Rasyid Ridha Muhammad Efendi Novrizal Nur