Article Per Year (5 Year)
p-Index From 2019 - 2024
0.659
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Indra Riyana Rahadjeng
Universitas Bina Sarana Informatika, Jakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Pemanfaatan Sistem Keputusan Dalam Mengevaluasi Penentuan Aplikasi Chatting Terbaik Dengan Multi Factor Evaluation Process Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar; Agus Perdana Windarto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.4021

Abstract

The rapid development of chat application features shows that information technology is increasingly global. This study aims to help smartphone users, especially beginners, to be more selective in choosing the chat application that suits their needs. The method used in this study is a Decision Support System with a Multi-Factor Evaluation Process (MFEP) as a solution for solving cases. The dataset used in this study was obtained by distributing questionnaires to respondents at random, both directly and virtually by using the google form to provide an assessment of the questionnaire to some active users of the Chat application. The alternatives used are Messager, Line, Instagram, Whatsapp, and Telegram and the criteria used are storage media, security, display (interface), application features, and network usage. The results obtained indicate that the Whatsapp alternative is the first recommendation as to the best chat application with a final score of 8.l5. Alternative Instagram became the second recommendation with a final score of 7.21 and Messager became the third recommendation with a final score of 6.18.
Deep Learning to Extract Animal Images With the U-Net Model on the Use of Pet Images Agus Perdana Windarto; Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar; Putrama Alkhairi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7199

Abstract

This article explores the innovative application of deep learning techniques, specifically the U-Net model, in the realm of computer vision, focusing on the extraction of animal images from diverse pet datasets. As the digital landscape becomes increasingly saturated with pet imagery, the need for precise and efficient image extraction methods becomes paramount. The study delves into the challenges posed by varying animal poses and backgrounds, presenting a comprehensive analysis of the U-Net model's adaptability in handling these complexities. Through rigorous experimentation, this research refines existing methodologies, enhancing the accuracy of animal image extraction. The findings not only contribute to advancing the field of computer vision but also hold significant implications for wildlife monitoring, veterinary diagnostics, and the broader domain of image processing.
Evaluasi Perbandingan Kinerja Convolutional Neural Networks untuk Klasifikasi Kualitas Biji Kakao Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar; Agus Perdana Windarto, M.Kom
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6533

Abstract

The assessment of cocoa bean quality plays a crucial role in the chocolate industry, and automated approaches utilizing image processing techniques and classifiers have become increasingly appealing. In this study, we implemented and compared the performance of image classifiers using Convolutional Neural Network (CNN) architectures for cocoa bean quality classification. By employing this approach, we developed a system capable of accurately and efficiently classifying cocoa bean images, reducing dependence on human evaluation. We compared several CNN architectures, including VGGNet, to evaluate their performance in cocoa bean image classification. Experimental results demonstrated that CNN-based classifiers can provide accurate assessments of cocoa bean quality, with significant success rates. This research contributes to the development of efficient and accurate image classification systems for cocoa beans, which can enhance efficiency in the chocolate industry and ensure product quality. Additionally, our testing results indicate that the model with a batch size of 64 achieved the highest accuracy of 98.44%, outperforming the other three tested batch sizes in cocoa bean classification performance.
Co-Authors Agus Perdana Windarto Muhammad Noor Hasan Siregar Putrama Alkhairi