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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Applied Data Sciences
Taqwa Hariguna
AMIKOM Purwokerto University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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IMPLEMENTASI SECURITY INFORMATION AND EVENT MANAGEMENT (SIEM) MENGGUNAKAN WAZUH UNTUK DETEKSI DAN ANALISIS INSIDEN KEAMANAN WEB SERVER: IMPLEMENTATION OF SECURITY INFORMATION AND EVENT MANAGEMENT (SIEM) USING WAZUH FOR DETECTION AND ANALYSIS OF WEB SERVER SECURITY INCIDENTS Rizky Adhytia; Taqwa Hariguna
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8054

Abstract

The rapid development of web server technology has increased the risk of cyber threats such as brute force and Distributed Denial of Service (DDoS) attacks. This study aims to implement a Security Information and Event Management (SIEM) system using Wazuh to detect and analyze security incidents on a web server in real time. The research method used is experimental, consisting of requirements analysis, system design, implementation, testing, and evaluation. The system is built using a Wazuh Server, a Wazuh Agent installed on an Ubuntu-based web server, and Telegram notification integration for automatic alerts to administrators. Testing was conducted through attack simulations using Hydra for SSH brute force, Slowloris, and DDoS-Ripper for DoS attacks. The results show that the system successfully detected various attacks with Rule ID 5712 and 5763 for SSH brute force, Rule ID 100502 for Slowloris HTTP flood, and Rule ID 100500 and 100501 for DDoS-Ripper. All attacks were successfully detected and reported to Telegram in real time. The Wazuh-based SIEM implementation improves monitoring, detection, and response capabilities for web server security incidents in a centralized manner and provides better security visibility for administrators in handling cyber threats.  
Health and Socio-Demographic Risk Factors of Childhood Stunting: Assessing the Role of Factor Interactions Through the Development of an AI Predictive Model Taqwa Hariguna; Sarmini Sarmini; Abdul Azis
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.612

Abstract

Stunting is a significant global health problem, especially in developing countries such as Indonesia. This study aims to develop and evaluate an artificial intelligence (AI)-based predictive model to identify the risk of stunting in children using the CatBoost algorithm which is a combination of Weighted Apriori and XGBoost. This model is designed to utilize the advantages of each algorithm in handling data with variable weights to improve prediction accuracy. Feature analysis shows that "Height (cm) Age (months)" are the main indicators in classifying children's nutritional status. Model evaluation shows high accuracy of 94.85%, precision of 95%, recall of 94.85%, and F1 Score of 94.84%. Kappa Coefficient and Matthews Correlation Coefficient (MCC) reached 93.13% and 93.19%, respectively, while ROC-AUC reached 99.70%. These findings indicate that the CatBoost model can provide highly accurate results in detecting the risk of stunting and offer in-depth insights into risk factors that can improve the effectiveness of health interventions. This study fills the gap in the literature by integrating the Weighted Apriori and XGBoost algorithms, providing a significant contribution to early detection of stunting and supporting government efforts to reduce the prevalence of stunting in Indonesia and other regions.
Co-Authors Abdul Azis Rizky Adhytia Sarmini Sarmini