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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Muhammad Al Adib
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Evaluasi Kinerja CNN, LSTM, dan DNN untuk Deteksi Serangan DDoS Berbasis Flow features pada Dataset CSE-CIC-IDS2018 Muhammad Al Adib; Pebruarianto Hutabarat; Heru Fredi; Bill Raj; Prasetyo; Empiter Gea
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.727

Abstract

Deep learning approaches have been proven effective in detecting Distributed Denial of Service (DDoS) attacks on networks, particularly through the analysis of flow features. This study aims to evaluate CNN, LSTM, and DNN in detecting DDoS attacks using flow features on the CSE-CIC-IDS2018 dataset. Each model is systematically compared with baseline algorithms to assess accuracy, precision, recall, and F1-score, in order to determine the most optimal model for a Network Intrusion Detection System (NIDS). All models demonstrated very high accuracy above 99%, with CNN standing out as the best-performing deep learning model for detecting DDoS patterns, while XGBoost emerged as the most effective baseline. These results emphasize that the choice of detection model should consider data characteristics, the complexity of flow features, and the diversity of attack types to achieve optimal performance in a NIDS. The study shows that both CNN, DNN, and LSTM, as well as baseline models such as XGBoost, can detect DDoS attacks based on flow features with accuracy above 99%, confirming the effectiveness of this approach and the importance of selecting models according to data characteristics.
Classification and Interpretability of Employee Burnout Using Linear Discriminant Analysis Rochmawati, Dwi Robiul; Muhammad Al Adib; Diyo Mollana Fazri; Bill Raj; Romi Antoni; Rahmad Santoso; Wahyu Saptha Negoro
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.811

Abstract

Employee burnout has become a critical challenge in modern organizations due to its negative impact on employees’ mental well-being, work performance, and organizational sustainability. In many workplaces, burnout identification still relies on subjective assessments and retrospective surveys, limiting the effectiveness of early intervention strategies. This study aims to develop an employee burnout risk classification model that achieves high predictive performance while maintaining strong interpretability. Linear Discriminant Analysis (LDA) is employed as the primary method because of its ability to separate classes optimally and provide explicit discriminant coefficients for explanatory analysis. The study utilizes a secondary dataset from the Mental Health in Workplace Survey, consisting of 3,000 employee records and 15 variables related to job characteristics, psychosocial factors, and individual conditions. The dataset is divided into training and testing sets with an 80:20 ratio. Experimental results show that the LDA model achieves an accuracy of 96.17%, with a precision of 89.50%, recall of 100%, F1-score of 94.46%, and an AUC value of 0.9988, indicating excellent classification capability. Further analysis of discriminant coefficients reveals that individual burnout indicators, job roles, work–life balance, and career growth opportunities are the most influential factors in determining burnout risk. These findings demonstrate that LDA offers an effective and interpretable approach for early burnout detection and supports evidence-based decision-making for human resource management.
Co-Authors Bill Raj Diyo Mollana Fazri Dwi Robiul Rochmawati Empiter Gea Heru Fredi Pebruarianto Hutabarat Prasetyo Rahmad Santoso Romi Antoni Wahyu Saptha Negoro