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All Journal The IJICS (International Journal of Informatics and Computer Science)
liska sipayung
STMIK Pelita Nusantara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Book Tracking Methods In Libraries Using Online Public Access Catalog liska sipayung; 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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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.
Deep Learning-Based Sentiment Analysis on Social Media Text Using Long Short-Term Memory (LSTM) liska sipayung; Megaria Purba; Bayu Pratama; Sri Yessi Saragih Sumbayak
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

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The large volume and informal nature of Indonesian social media text make manual sentiment analysis slow, inconsistent, and difficult to scale. This study evaluates a Long Short-Term Memory (LSTM) model for classifying public comments from X/Twitter into positive, neutral, and negative sentiment. A balanced dataset of 3,000 public Indonesian-language posts collected from January to March 2026 was manually labeled into three equal classes. Duplicate, irrelevant, advertising, and empty posts were removed; the remaining text underwent case folding, noise removal, tokenization, and padding. The data were stratified into 2,400 training and 600 testing instances. The model used a 10,000-word vocabulary, 100-token sequences, a 128-dimensional embedding, 128 LSTM units, dropout of 0.5, and a softmax output layer. On the held-out test set, the model obtained 87.00% accuracy, 86.80% precision, 86.50% recall, and 86.60% F1-score. Positive sentiment produced the strongest class-level performance, whereas neutral comments were more difficult because factual, ambiguous, and mixed expressions provide weaker affective cues. The findings show that LSTM provides a useful baseline for three-class Indonesian social media sentiment classification. However, generalization remains limited by the single-platform, topic-dependent dataset and the absence of repeated or cross-domain evaluation.
Co-Authors Abiomega Maria Manalu Bayu Pratama Megaria Purba Puji Nirwana Sri Yessi Saragih Sumbayak