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All Journal Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Jurnal Rekayasa Teknologi Nusa Putra
Zalfa Dewi Zahrani
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Keamanan Kernel Linux : Pendekatan Hardening dan Perlindungan terhadap Serangan Eksploitasi Zalfa Dewi Zahrani; Novianto Andi Hardiansyah; Elkin Rilvani
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 1 (2025): Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i1.620

Abstract

Linux kernel security is a critical aspect of ensuring the integrity and stability of operating systems. Vulnerabilities like Dirty COW (CVE-2016-5195) illustrate how exploitative threats can severely impact systems, particularly those that are not regularly updated. This study analyzes the working mechanism of Dirty COW, its impact, and mitigation strategies based on Linux kernel hardening techniques, including the use of security modules like SELinux and AppArmor, as well as the Address Space Layout Randomization (ASLR) technique. Through attack simulations and mitigation evaluations, the findings emphasize the importance of regularly applying kernel patches to maintain system security. This study aims to provide practical guidance for enhancing Linux kernel resilience against exploitation attacks.
Prediksi Tingkat Keterlambatan Pengumpulan Tugas Mahasiswa Berdasarkan Aktivitas Perkuliahan Menggunakan Algoritma K-Nearest Neighbor (KNN) Hardiansyah, Andi; Zalfa Dewi Zahrani; Elkin Rilvani
Jurnal Rekayasa Teknologi Nusa Putra Vol 11 No 2 (2025): Agustus 2025
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/rekayasa.v11i2.653

Abstract

The delay in task submission is one of the indicators of low discipline and student engagement in the learning process. This study aims to predict the level of task submission delay among students based on academic activity using the K-Nearest Neighbor (KNN) algorithm. The dataset used includes five predictor variables: attendance rate (%), frequency of LMS login, forum participation, average quiz scores (%), and LMS access time, with one target variable being task delay categorized into three classes: On Time, Moderate Delay, and Severe Delay. The KNN method with k = 3 was applied to the normalized dataset using Min-Max Scaling. The test results showed that the model successfully classified all test data with an accuracy of 100%. Evaluation using the confusion matrix, precision, recall, and f1-score confirmed optimal performance across all delay categories. The study concludes that academic activity significantly influences task punctuality, and the KNN model can serve as a foundation for developing a data-driven early warning system to detect students at risk of delay. However, further research with larger datasets is needed to validate the generalizability of this model.
Co-Authors Elkin Rilvani Hardiansyah, Andi Novianto Andi Hardiansyah