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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Journal of Computer Science and Engineering (JCSE) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Lentera Pengabdian
Isnaini, Khairunnisak Nur
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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EFFECTIVENESS HYPERPARAMETER TUNING ON RANDOM FOREST, LINEAR DISCRIMINANT ANALYSIS, LOGISTIC REGRESSION AND NAÏVE BAYES ALGORITHMS FOR DETECTING DOS NETWORK ATTACKS Saputri, Inka; Arsi, Primandani; Isnaini, Khairunnisak Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4175

Abstract

Denial of Service (DoS) attacks are a major threat to network security, characterized by overwhelming system resources with illegitimate requests. Such attacks can disrupt critical services and cause substantial financial losses. However, there is still a need for a more efficient model to detect DoS attack with high accuracy. The aim of this research is to determine the impact of hyperparameter tuning on the four algorithms to identify the best algorithm for detecting DoS network attacks. The research method involves data preprocessing, feature selection, encoding, balancing using SMOTE (Synthetic Minority Over-Sampling Techinuque) and evaluation using confusion matrix. This research use the NSL-KDD dataset because it is relevant for DoS attack detection and flexible for testing various classification algorithms and utilizing hyperparameter tuning. This study evaluates the effectiveness hyperparameter tuning on several machine learning alghorithms namely Random Forest, Linear Discriminant Analysis (LDA), Logistic Regression and Naïve Bayes in detecting DoS attacks. Results indicate that Random Forest achieves highest accuracy (99,97%) and robust performance across all metrics, demonstrating superior generalization and precision. LDA, Logistic Regression and Naïve Bayes also performed well but fell short of Random Forest in handling complex patterns in the dataset. The utilization of hyperparameter tuning can improve the accuracy, consistency and efficiency of the algorithm so as to optimize the combination of various parameters in detecting DoS attacks. The findings provide valuable insights into selecting suitable algorithms for future implementations in cybersecurity systems.
NETWORK PROGRAMMABILITY FOR NETWORK ISSUE USING PARAMIKO LIBRARY Mutiara, Dwi Ayu; Isnaini, Khairunnisak Nur; Suhartono, Didit
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.691

Abstract

In a company, information technology is needed, especially computer networks, to facilitate data communication. The management of a computer network, of course, requires good administration. The criteria for whether or not a network is good can be seen from the performance, reliability, and security indicators so that it will not cause network issues. Events such as server downs, data loss, lost connections, and undetected computers cause the organization's business performance to be disrupted. This study's purpose is to detect network issues with network programmability technology automatically. Paramiko library supports network automation systems and implements OSPF routing protocol in finding the shortest path to send network packets. This study uses the PPDIOO flow, namely prepare, plan, design, implement, operate, and optimize, because it is considered by the flow of making network detection tools. The results showed that the design and implementation of a small-scale network were successfully built by utilizing network programmability technology and the paramiko library, which helps detect network conditions at any time. This design has a dashboard, provisioning, assurance, and policy features that allow administrators to manage and monitor information on each network device. The network design is fitted with REST-API technology and security through a secure shell (ssh) from the Network Controller that can detect the device's connection conditions and the device's health and update the DNS configuration used. Network Issues that have been seen are devices being down, and the connection being lost. Future research can improve features for network troubleshooting when the connection is lost.
Co-Authors Afandi, Marcel Arsi, Primandani Aulia Hamdi Didit Suhartono Ikhsan, Ali Nur Imam Tahyudin Jamil, Muhammad Thoriq Khoirunnita, Anisaa Qothrunnada Kusuma, Bagus Adhi Mutiara, Dwi Ayu Primukti, Aditya Pungkas Subarkah Putra, Bernardus Septian Cahya Putri Kartika Sari Saputri, Inka Senduk, Fabianus Kevin Siti Alvi Solikhatin Waluyo, Retno