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All Journal Journal of Information Systems and Informatics bit-Tech
Noor Latifah
Muria Kudus University
Computer Science & IT

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Optimizing Book Genre Classification through AI on a Web Platform Fariz Dermawan; Noor Latifah
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.3001

Abstract

In the rapidly evolving digital era, the exponential growth of online book collections poses challenges in efficiently classifying literature according to genre. Manual classification methods are often time-consuming, subjective, and inconsistent, necessitating the adoption of advanced, automated approaches. This study aims to develop and implement an Artificial Intelligence (AI)-based genre classification system integrated into a web platform to enhance the accuracy, efficiency, and user experience in book discovery. Leveraging Machine Learning (ML) algorithms—particularly Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Deep Learning—alongside Natural Language Processing (NLP) techniques such as tokenization, stemming, and TF-IDF, the system analyzes book descriptions and synopses to determine the most appropriate genre. The research follows a qualitative and literature study approach, utilizing a dataset sourced from Kaggle, with preprocessing steps to remove noise and convert text into numerical representations. Experimental results demonstrate that the SVM model achieved the highest accuracy, precision, recall, and F1-score compared to other tested algorithms, effectively handling high-dimensional and non-linear data. The developed web application features an interactive dashboard, real-time classification, and a hybrid recommendation system. This work confirms the feasibility and advantages of AI-driven genre classification for large-scale digital libraries and online bookstores. While limitations such as data imbalance and overlapping genre semantics remain, the findings provide a strong foundation for future research employing larger, more diverse datasets and advanced deep learning architectures to further improve classification performance.
Application of the Key Performance Indicator Method in an Employee Information System Eva Putri Rosanti; Noor Latifah; Fajar Nugraha
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1439

Abstract

The rapid development of information technology has significantly encouraged the integration of information systems in human resource management to enhance efficiency, effectiveness, and objectivity. However, performance appraisal systems that lack standardized indicators can lead to subjectivity and inconsistency, impacting employee productivity and managerial decision-making. This study proposes a web-based Personnel Management Information System (PMIS) that integrates Key Performance Indicators (KPIs) to provide an objective and measurable performance evaluation system. The system design incorporates KPIs, weights, and targets, supported by a structured, transparent process for performance assessments. The system was implemented at PT Kebon Agung Trangkil, a sugar industry company, to improve employee performance evaluations and managerial decision-making. This research adopts the Waterfall system development method and includes a User Acceptance Test (UAT) with 15 respondents, achieving an 88% acceptance rate. The results indicate that the developed system improves assessment efficiency, reduces subjectivity, and supports more transparent decision-making. The study concludes with recommendations for expanding the system’s capabilities and improving KPI validation through formal methods.
Co-Authors Eva Putri Rosanti Fajar Nugraha Fariz Dermawan