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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 733 Documents
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A Decision Support Model for Online Lending Creditworthiness Using Comparative Personality Indicators Iwan Purwanto; Syandra Sari
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.v8i1.1478

Abstract

The rise of online lending platforms has improved financial accessibility but also increased credit default risk due to information asymmetry and limited borrower profiling. Traditional creditworthiness models rely primarily on financial and demographic data, which often fail to capture behavioral characteristics. This study proposes a decision support model for creditworthiness prediction by integrating personality indicators from the Big Five Personality Traits and the California Psychological Inventory (CPI). The framework incorporates these personality-based features into a machine learning-driven system alongside traditional borrower data. Psychological indicators are quantified and assessed using multiple classification models to evaluate their impact on predictive performance. The model's effectiveness is measured using metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Empirical results show a significant improvement in prediction accuracy, with the AUC rising from 0.74 in the baseline model to 0.87 after including personality features. A comparative analysis reveals the relative contributions of each personality framework, demonstrating that personality indicators enhance predictive performance over traditional models. These findings emphasize the value of incorporating behavioral factors, supporting the development of more effective and sustainable credit risk assessment systems.
Usability Evaluation of a School Library OPAC Using Heuristic Evaluation and User Testing Faradina Faradina; Taqwa Hariguna; Fandy Setyo Utomo
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.v8i1.1528

Abstract

This study evaluates the usability of the Online Public Access Catalog (OPAC) at SMK Negeri 1 Purwokerto to address the persistent gap between traditional library information architectures and the modern search behaviors of vocational students within the Kurikulum Merdeka ecosystem. The research aims to solve the problem of "mental model dissonance" that hinders independent information literacy among digital native learners. A hybrid evaluation approach was employed, integrating a Heuristic Evaluation by three experts with empirical User Testing involving students. The study utilized the Think-Aloud protocol and the System Usability Scale (SUS) to capture both performance and perception data. Result: The expert inspection identified 18 significant usability violations, primarily in library technical jargon (H2) and error prevention (H5). Empirical testing revealed a low average Task Success Rate (TSR) of 49.3% and a mean SUS score of 55.0, placing the system in the "Unacceptable" category. These figures confirm that current cataloging logic significantly obstructs retrieval efficiency. The originality of this research lies in the identification of specific dissonance points between vocational students' mental models and bibliographic metadata. It provides a strategic framework for interface restructuring through semantic simplification and department-based navigation, offering a practical model for developing user-centric "smart" library services in vocational education.
URL-Based Phishing Detection Using a BERT-LSTM Model Hilman Singgih Wicaksana; Usman Ependi; Ari Muzakir
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.v8i1.1543

Abstract

The rising prevalence of phishing websites presents substantial cybersecurity threats by deceiving users into revealing sensitive information through malicious URLs. This study aims to enhance phishing URL detection by introducing a deep learning model that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM). In this framework, BERT is fine-tuned on a phishing URL dataset and utilized as a contextual embedding to represent URL tokens, while Bayesian Optimization is employed to identify optimal hyperparameter settings during model training. Experimental results demonstrate that the BERT-LSTM model achieves impressive detection performance, with a precision of 0.9299, recall of 0.9795, F1-score of 0.9540, accuracy of 0.9756, and ROC-AUC of 0.9962. The model consistently outperforms embedding-based methods such as Word2Vec, FastText, and GloVe, as well as a classical baseline model using Logistic Regression with TF-IDF features. These findings suggest that the contextual embeddings generated by BERT effectively capture structural patterns in URLs, leading to more accurate phishing detection and providing a promising approach for enhancing cybersecurity systems.

Page 74 of 74 | Total Record : 733

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