Vol. 1 No. 2 (2025): Regular Issue: June 2025

This regular June 2025 issue comprises five original research articles authored and co-authored by 11 contributors representing 4 countries: Indonesia, Malaysia, South Korea, and Japan.

DOI: https://doi.org/10.63913/jcl.v1i2

Published: 2025-06-03

Predicting Fraud Cases in ECommerce Transactions Using Random Forest Regression: A Data Mining Approach for Enhancing Cybersecurity and Transaction Integrity

👥 Yusuf Durachman, Abdul Wahab Bin Abdul Rahman

📈 Abstract: 0

🗎 PDF

📄 116-130

🗎 PDF: 0

📌 10.63913/jcl.v1i2.15

Detecting Threatening Content in Social Media: A Data Mining Approach Using Random Forest for Classification of Tweets in Cyberlaw Context

👥 Husni Teja Sukmana, Lee Kyung Oh

📈 Abstract: 0

🗎 PDF

📄 131-146

🗎 PDF: 0

📌 10.63913/jcl.v1i2.14

Predicting Cyber Attack Types Using XGBoost: A Data Mining Approach to Enhance Legal Frameworks for Cybersecurity

👥 I Gede Agus Krisna Warmayana, Yuichiro Yamashita, Nobuto Oka

📈 Abstract: 0

🗎 PDF

📄 147-160

🗎 PDF: 0

📌 10.63913/jcl.v1i2.13

Financial Loss Estimation in Cybersecurity Incidents: A Data Mining Approach Using Decision Tree and Linear Regression Models

👥 Ika Maulita, B Herawan Hayadi

📈 Abstract: 0

🗎 PDF

📄 161-174

🗎 PDF: 0

📌 10.63913/jcl.v1i2.12

Classifying Cybersecurity Threats in URLs Using Decision Tree and Naive Bayes Algorithms: A Data Mining Approach for Phishing, Defacement, and Benign Threat Detection

👥 Deshinta Arrova Dewi, Tri Basuki Kurniawan

📈 Abstract: 0

🗎 PDF

📄 175-189

🗎 PDF: 0

📌 10.63913/jcl.v1i2.11

Journal Metrics
Acceptance Rate	49%
Review Speed	45 days
Issues per Year	4
Number of Volumes	2
Number of Issues	5
Number of Articles	25
Number of Reviewers	5
Number of Contributors	57
Contributing Countries	6
WoS Citations	18
Scopus Citations	33
Google Scholar Citations	53
Abstract Views	2,422 views
PDF Downloads	1,016
Updated at 18 May 2026

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Articles

Predicting Fraud Cases in ECommerce Transactions Using Random Forest Regression: A Data Mining Approach for Enhancing Cybersecurity and Transaction Integrity

Detecting Threatening Content in Social Media: A Data Mining Approach Using Random Forest for Classification of Tweets in Cyberlaw Context

Predicting Cyber Attack Types Using XGBoost: A Data Mining Approach to Enhance Legal Frameworks for Cybersecurity

Financial Loss Estimation in Cybersecurity Incidents: A Data Mining Approach Using Decision Tree and Linear Regression Models

Classifying Cybersecurity Threats in URLs Using Decision Tree and Naive Bayes Algorithms: A Data Mining Approach for Phishing, Defacement, and Benign Threat Detection