Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal International Journal Software Engineering and Computer Science (IJSECS)
Muhammad Fazly Qusyairy
Universitas Esa Unggul
Computer Science & IT

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Data Mining for Predicting Creditworthiness in Credit Card Approval: A Systematic Literature Review Wahyu Purnama Magribi; Muhammad Fazly Qusyairy; Tino Saputra
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6618

Abstract

The growing volume of credit card applications has led financial institutions to seek faster and more reliable methods in the approval process. Manual evaluation is not only time-consuming but also susceptible to human error, which can result in poor credit decisions and measurable financial losses. This study conducts a Systematic Literature Review (SLR) to examine data mining techniques applied to creditworthiness prediction. Five research questions were formulated to identify: (1) commonly used data mining techniques, (2) frequently used datasets, (3) performance evaluation metrics, (4) algorithms with the strongest performance, and (5) recurring challenges and practical recommendations. A structured search across three academic databases — Scopus, Google Scholar, and GARUDA — yielded 8 relevant articles (7 primary experimental studies and 1 secondary study) published between 2021 and 2025. The findings show that Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors are the most widely applied methods. Tree-based algorithms such as Decision Tree and Random Forest consistently yield high accuracy, while K-Nearest Neighbors also delivers strong results in specific experimental settings. Naïve Bayes appears most frequently across studies, and its performance can be improved through metaheuristic approaches such as Particle Swarm Optimization (PSO). Standard evaluation metrics include accuracy, precision, recall, F1-score, and AUC-ROC. The review underscores the importance of data preprocessing, class imbalance handling, and hyperparameter tuning in building reliable prediction models — findings with direct implications for financial institutions seeking to reduce non-performing loan rates.
Integration of Static Knowledge and Telemetry Data for an IoT-Based Customer Support AI Agent Jauhar Maknun Adib; Wahyu Purnama Magribi; Muhammad Fazly Qusyairy; Eric Julianto; Khusnul Fajri Rhomadon
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.7875

Abstract

The operational effectiveness of artificial intelligence agents in customer support is frequently attributed to the generative sophistication of underlying language models, although answer reliability primarily depends on the structure and quality of the referenced knowledge base. In complex technical domains such as the Internet of Things (IoT), static documentation alone often fails to resolve customer inquiries that involve real-time device states, sensor readings, and connectivity logs. This study investigates how progressive tiers of knowledge integration affect the response quality of an AI agent within an IoT customer support context. Employing a mixed-method pilot design combining literature synthesis, comparative analysis, experimental testing, and conceptual exploration, thirty representative questions were classified into informational, status, and diagnostic categories. These queries were evaluated across four operational scenarios: without retrieval-augmented generation (RAG), article-based RAG, article plus device metadata records, and a comprehensive hybrid framework integrating static articles, device records, and telemetry streams. Answer quality was measured using Cosine Similarity, BERTScore (F1), and a randomized blind human evaluation assessing relevance, correctness, and usefulness. The results demonstrate that while curated static documentation adequately addresses procedural informational queries, resolving status and diagnostic issues necessitates real-time operational context. The hybrid integration delivered the highest overall performance, achieving a human-evaluation score of 4.40 compared with 2.53 for article-based RAG (p = 0.0039). These empirical findings confirm that robust IoT support agents require dynamic knowledge governance that bridges versioned documentation, synchronized asset metadata, and live telemetry data within a unified retrieval framework.
Co-Authors Eric Julianto Jauhar Maknun Adib Khusnul Fajri Rhomadon Tino Saputra Wahyu Purnama Magribi