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Journal of Intelligent Systems and Information Technology
Published by Apik Cahaya Ilmu
ISSN : -     EISSN : 30465001     DOI : https://doi.org/10.61971/jisit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal of Intelligent Systems and Information Technology (JISIT) focuses on providing scientific articles related to Intelligent Systems and Information Technology, which are developed by publishing articles, research reports and reviews. Journal of Intelligent Systems and Information Technology (JISIT) accepts scientific articles in the field of research: Artificial Intelligence, Data Mining, Text Mining, Web Mining, Machine Learning, Deep Learning, Natural Language Processing (NLP), Social Network Analysis, Expert system, Decision Support System, Computer Network Security related AI, Image processing, Computer Vision, Big Data, and related fields
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 23 Documents
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Implementation of Machine Learning Algorithm for Credit Scoring Prediction in Islamic Microfinance Siregar, Kiki Hardiansyah; Ruslan, Dede; Faried, Annisa Ilmi; Sembiring, Rahmad
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.156

Abstract

Islamic microfinance institutions face complex challenges in data management and customer behavior prediction in the digital era. This study aims to optimize the Gradient Boosting algorithm with pruning techniques to predict customer collectibility. The analysis was conducted on data from 57 customers with 7 attributes from 2022 to 2024. The research methodology includes four stages: data collection, pre-processing, modeling, and evaluation. Pre-processing involves handling missing data, normalization, encoding, and feature selection. Modeling using XGBoost with and without pruning, followed by evaluation using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve metrics. The results show an increase in model performance with pruning: accuracy increased by 0.70%, precision 0.60%, recall 0.80%, and F1-score 0.70%. This technique is effective in reducing overfitting and increasing model generalization. This research provides significant contribution in developing more accurate credit scoring system for Islamic microfinance institutions, improving credit risk management and customer service in Islamic microfinance sector. The findings help Islamic microfinance institutions optimize credit decision-making process and reduce risk in the digital era.
Design of Forensic Analysis Framework for Single-Page Web Applications Faizal, Arif
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.163

Abstract

The Single-Page Application (SPA) architecture based on React, Vue, or Angular has revolutionized browser development with a dynamic user experience. However, its complexity poses challenges for forensic analysis that have not yet been maximally addressed by conventional frameworks. This research designs an open-source forensic testing framework specifically for SPAs, integrating volatile memory acquisition, client-side artifact analysis, and log correlation on APIs. This hybrid method combines live memory forensics with API transaction tracing to reconstruct user activity longitudinally. Evaluation of 15 SPAs showed that the framework successfully identified 92% of XSS and injection traces missed by traditional forensic tools, with a 40% improvement in user session reconstruction. The novelty of the research lies in the SPA-specific modules for virtual DOM extraction and state management, temporal integration using Plaso for synchronizing client-side events and server-side logs, and the adaptation of open-source forensic tools for the SPA environment. In this study, blind spots in forensic activities such as the dynamism of client-side rendering and encrypted API calls can be addressed. Case studies illustrate the effectiveness in uncovering credential hijacking in Vue.js applications. This framework provides a foundation with standards for SPA investigations that critically combine aspects of cloud, client, and API forensics.
Comparative Analysis of Machine Learning Algorithms in Detecting DDoS Attacks on CICIDS2017 Dataset Putra, Dika Kurnia; Pradana, Chandra Ari; Gilardin, Muhammad Hilal; Riyandi, Albert
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.182

Abstract

The rapid escalation of Distributed Denial of Service (DDoS) attacks has posed significant threats to global cybersecurity. This research presents a comparative analysis of three supervised machine learning algorithms—K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF)—in detecting DDoS attacks using the CICIDS2017 dataset. While many studies focus on broader intrusion detection, this study concentrates specifically on binary classification between benign and DDoS traffic. The CICIDS2017 dataset was chosen for its comprehensive and realistic representation of modern network traffic. The methodology involved preprocessing, training, and evaluating the models in Orange Data Mining using 10-fold cross-validation. Evaluation metrics included Accuracy, Precision, Recall, F1-Score, AUC, and Matthews Correlation Coefficient (MCC). Empirical results show that the Random Forest algorithm outperformed both KNN and Decision Tree, achieving perfect scores across all metrics (1.000). These findings highlight the robustness of ensemble learning in intrusion detection. The results have practical implications for the development of more reliable, efficient, and automated Intrusion Detection Systems (IDS), especially in real-world scenarios prone to volumetric DDoS attacks. Future work should explore multiclass classification and real-time implementation.

Page 3 of 3 | Total Record : 23

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