Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
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This Author published in this journals
All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of Informatics and Communication Technology (JICT) Journal of Informatics and Communication Technology (JICT)
Yerezqy Bagus
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics

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Web-Based Library Information System Yerezqy Bagus; Prasetyo, Hendy
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 2 (2024)
Publisher : PPM Telkom University

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Abstract

Muh 4 Yogyakarta Middle School is still record the borrowing and returning of books in its library manually. This school haven’t yet store book data in a system causes many errors in data collection, one of which is data redundancy. Recording of borrowing and returning each book is still done manually, so the librarian must always check repeatedly. The aim of this research is to design and build a web-based library information system to overcome existing problems. This research uses the Unified Modeling Language (UML) development method. In design process this system uses class diagrams, use case diagrams and activity diagrams as well as applying programs using the CodeIgniter Framework and database storage in MySQL. The result of this research is a web-based library information system that can overcome problems in borrowing and returning books. System testing was carried out twice, namely using alpha and beta tests. Test results show that all features can be built and work well. Beta testing of 80% shows users strongly agree that this system is worth using
Web-Based Library Information System Yerezqy Bagus; Prasetyo, Hendy
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 2 (2024)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Muh 4 Yogyakarta Middle School is still record the borrowing and returning of books in its library manually. This school haven’t yet store book data in a system causes many errors in data collection, one of which is data redundancy. Recording of borrowing and returning each book is still done manually, so the librarian must always check repeatedly. The aim of this research is to design and build a web-based library information system to overcome existing problems. This research uses the Unified Modeling Language (UML) development method. In design process this system uses class diagrams, use case diagrams and activity diagrams as well as applying programs using the CodeIgniter Framework and database storage in MySQL. The result of this research is a web-based library information system that can overcome problems in borrowing and returning books. System testing was carried out twice, namely using alpha and beta tests. Test results show that all features can be built and work well. Beta testing of 80% shows users strongly agree that this system is worth using
Classification of Eye Diseases Using the AlexNet Convolutional Neural Network Model Algorithm Pratama, Moch Deny; Sultoni, Royal Fajar; Wardhani, Adil Sandy; Sechuti, Maulana Hassan; Yerezqy Bagus; Dina Zatusiva Haq; Yoga Ari Tofan
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.160

Abstract

This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.
Co-Authors Dina Zatusiva Haq Prasetyo, Hendy Pratama, Moch Deny Sechuti, Maulana Hassan Sultoni, Royal Fajar Wardhani, Adil Sandy Yoga Ari Tofan