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Journal : The IJICS (International Journal of Informatics and Computer Science)

Book Tracking Methods In Libraries Using Online Public Access Catalog sipayung, liska; Megaria Purba; Abiomega Maria Manalu; Puji Nirwana
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

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Abstract

Advances in information technology have encouraged libraries to transform from conventional service systems to digital-based services to improve the quality of information access for users. One problem still frequently encountered in libraries is the limited access for users to quickly and accurately search for books, especially in libraries with growing collections. This study examines the implementation of a book tracking method in libraries using the Online Public Access Catalog (OPAC) as the primary means of searching collections. The purpose of this study is to analyze the effectiveness of OPAC use in helping users find bibliographic information and book locations independently and efficiently. The research methods used included literature review, user needs analysis, digital catalog system design, and implementation of a web-based OPAC integrated with the library's collection database. The OPAC system is designed to support book searches based on various parameters, such as title, author, subject, and keywords, thus facilitating user access to relevant information. System testing was conducted through functional testing and usability evaluation to assess the accuracy of search results and user-friendliness of the interface. The results indicate that the implementation of OPAC can improve the speed and accuracy of the book tracking process, reduce search errors, and increase user satisfaction with library services. Furthermore, this system contributes to improving librarians' work efficiency and supporting more structured collection management. Therefore, the OPAC book tracking method can be a strategic solution to support the modernization of library services and the sustainable optimization of information access.
Analysis of Digital Image Forensics Authentication in Image Forgery Cases Dameria E Br Jabat; Megaria Purba; Mhd. Avin Winata; Sophia Widiana
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9440

Abstract

This document introduces a combined framework for validating digital images in forensic contexts by merging Error Level Analysis (ELA) with Convolutional Neural Networks (CNN). The innovation of this research resides in the direct integration of a conventional explainable forensic method alongside a datadriven deep learning approach to ensure both clarity and enhanced detection efficacy. ELA serves to identify JPEG compression irregularities as forensic indicators, whereas CNN is employed to extract significant hierarchical features for robust image categorization. Trials were performed on the CASIA v2.0 dataset, which comprises 10,002 authentic and altered images. The suggested two-stream architecture concurrently processes original images and ELA-generated maps, facilitating synergistic feature acquisition. The hybrid model secures an accuracy rate of 74.32%, illustrating a 7.2% enhancement over isolated ELA. Furthermore, the framework diminishes the false positive rate from 50.2% to 34.8% while maintaining high sensitivity (0.84) in identifying altered regions. From a machine learning angle, this research illustrates how manually crafted forensic attributes can boost CNN capabilities when merged at the input stage. From an image processing viewpoint, it confirms ELA as a potent preprocessing strategy for directing deep feature extraction. The proposed framework provides an equilibrium between precision and forensic transparency, making it ideal for real-world digital forensic practices, including application in environments with limited resources.