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All Journal Computer Science and Information Technologies
Tri Wahyuningsih
Satya Wacana Christian University
Computer Science & IT Engineering

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Journal : Computer Science and Information Technologies

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Exploring network security threats through text mining techniques: a comprehensive analysis Tri Wahyuningsih; Irwan Sembiring; Adi Setiawan; Iwan Setyawan
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p258-267

Abstract

In response to the escalating cybersecurity threats, this research focuses on leveraging text mining techniques to analyze network security data effectively. The study utilizes user-generated reports detailing attacks on server networks. Employing clustering algorithms, these reports are grouped based on threat levels. Additionally, a classification algorithm discerns whether network activities pose security risks. The research achieves a noteworthy 93% accuracy in text classification, showcasing the efficacy of these techniques. The novelty lies in classifying security threat report logs according to their threat levels. Prioritizing high-risk threats, this approach aids network management in strategic focus. By enabling swift identification and categorization of network security threats, this research equips organizations to take prompt, targeted actions, enhancing overall network security.
Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting Tri Wahyuningsih; Ade Iriani; Hindriyanto Dwi Purnomo; Irwan Sembiring
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p29-37

Abstract

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students. 
Co-Authors Ade Iriani Adi Setiawan Hindriyanto Dwi Purnomo Irwan Sembiring Iwan Setyawan