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All Journal Journal of Information Systems and Informatics bit-Tech Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka INOVTEK Polbeng - Seri Informatika
Noor Latifah
Muria Kudus University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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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.
Time-Series Monitoring of Sentiment Dynamics in Reviews of Four Indonesian E-Wallet Applications Using a Hybrid TF-IDF and Bi-LSTM Framework Noor Latifah; Dias Henandra Eka Putra; Fajar Nugraha
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1488

Abstract

This study proposes a hybrid sentiment analysis framework to examine user perceptions of four Indonesian e-wallet applications using Google Play Store reviews. The framework combines TF-IDF features reduced through Truncated SVD with a Bidirectional Long Short-Term Memory (Bi-LSTM) model within a two-stage evaluation design consisting of holdout classification and external temporal inference. For supervised classification, 20,000 raw reviews were filtered and labeled using a rating-based strategy, resulting in 13,823 labeled reviews. Reviews with ratings of 4–5 stars were assigned to the positive class and 1–2 stars to the negative class; these labels should be interpreted as sentiment proxies rather than fully human-validated ground truth. A second dataset of 24,000 reviews was constructed for balanced cross-application temporal comparison across 2024–2026. On the holdout test set, the proposed model achieved an accuracy of 0.881, with macro-F1 and weighted-F1 scores of 0.881. Under the external temporal setting, DANA remained relatively stable, GoPay improved markedly in 2025 and remained high in 2026, ShopeePay showed a gradual decline, and OVO exhibited the strongest negative trend. These results indicate that the proposed framework is useful not only for supervised sentiment classification but also for structured temporal monitoring across e-wallet platforms.
LoRA Enhanced Sentiment Aware Topic Modeling for Indonesian Generative AI Perception Wisnu Ginanjar Saputra; Noor Latifah; Fajar Nugraha
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/4m8w7t49

Abstract

Public understanding of generative AI in low-resource language contexts remains underexplored, particularly in relation to how sentiment aligns with thematic discussions on social media. In Indonesia, empirical studies examining this interaction at scale are still limited. This study introduces a sentiment-aware topic modeling framework that integrates parameter-efficient fine-tuning of IndoBERT using low-rank adaptation with topic discovery via BERTopic. The approach enables large-scale analysis of Indonesian social media data under constrained computational settings. Analysis of Indonesian Twitter discourse shows that general discussions of generative AI are largely neutral and cautious, contrasting with more optimistic trends reported in Western contexts. In comparison, enthusiast communities exhibit predominantly positive sentiment, while ethics-related discussions display balanced polarization. These results highlight the contextual nature of public perception across different discussion domains. The findings demonstrate the applicability of parameter-efficient NLP methods for sentiment and topic analysis in under-resourced languages and provide insights relevant to technology development and policy formulation.
PENINGKATAN KUALITAS PELAYANAN PELANGGAN MELALUI SISTEM ADUAN BERBASIS WEB Nova Briyan Haidar; Noor Latifah
Jurnal Pengabdian Masyarakat Sabangka Vol 5 No 01 (2026): Jurnal Pengabdian Masyarakat Sabangka
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/sabangka.v5i01.2079

Abstract

: Customer service quality is a crucial factor for the sustainability of service-based companies, including CV. Selaras Abadi Network. One of the main challenges faced by the partner was the manual handling of customer complaints, which resulted in unstructured records, slow response times, and limited monitoring capabilities. This community service activity aimed to improve customer service quality through the implementation of a web-based complaint system. The implementation method consisted of partner needs analysis, system design, web-based system development, functional testing, as well as training and assistance for system users. The results of this activity indicate that the web-based complaint system was able to streamline the complaint recording and handling process, reduce administrative errors, and enhance service transparency and efficiency. Furthermore, the training activities helped partners operate the system independently. Overall, the implementation of a web-based complaint system proved to be an effective solution for supporting improved customer service quality at CV. Selaras Abadi Network.
Co-Authors Dias Henandra Eka Putra Eva Putri Rosanti Fajar Nugraha Fariz Dermawan Nova Briyan Haidar Wisnu Ginanjar Saputra