cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Sri Ngudi Wahyuni
Contact Email
ijcsr@subset.id
Phone
+6282138594141
Journal Mail Official
ijcsr@subset.id
Editorial Address
Jl. Gatotkaca, Janti Buana Asri 4 Nomor B7, Jurugentong, Banguntapan, Bantul, Yogyakarta, Indonesia
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
The Indonesian Journal of Computer Science Research
Published by Hemispheres Press
ISSN : -     EISSN : 29639174     DOI : https://doi.org/10.59095/ijcsr
Core Subject : Science,
Computer Science & IT
The Indonesian Journal of Computer Science Research (IJCSR) adalah jurnal yang memuat naskah ilmiah dari peneliti, akademisi, maupun praktisi, berupa hasil penelitian, tinjauan pustaka ( literature review ) dan/atau bentuk karya tulis ilmiah lainnya, yang khusus mengkaji bidang Ilmu Komputer antara lain sebagai berikut : Computational and algorithm Numerical Methods and Algorithms Autonomic Computing Big Data Computer and Network Architecture Cloud Computing Cluster Computing Workflow Design and Practice Data Mining Artificial Intelligence Web-Based Computing Scientific Visualization Computer Graphics Pattern Recognition Virtual Reality Augmented Reality Geometric Modeling Industry 4.0 Bioinformatics Digital Forensic
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 92 Documents
 6   7   8   9   10 
Implementation of NFtables and Fail2ban on Linux Server for Adaptive Defense Firewall Development Prasetyo purnomo; Juarisman; Irma Suwarning Widyastuty; Eko Tri Anggono
The Indonesian Journal of Computer Science Research Vol. 5 No. 2 (2026): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i2.288

Abstract

Linux server security management today faces the challenges of command-line configuration complexity and slow response to automated attacks. This research aims to develop a prototype of an Adaptive Defense Firewall web application that integrates firewalld, nftables, and fail2ban centrally. The importance of this research lies in the provision of visual management that makes it easier for administrators to mitigate network intrusions in real-time. The novelty offered is the Adaptive Defense Levelsfeature (Safe, Moderate, Aggressive, Under Attack profile), which is able to dynamically adjust fail2ban mitigation parameters and kernel-level blocking by nftables. The methodology uses a prototype approach that includes needs analysis, architectural design, React.js and Python implementation, and penetration testing. The test dataset was generated from a controlled attack simulation using the Hydra program for brute-force on SSH and ftp services, as well as Nmap for port scanning. The results of the study showed that the system effectively detected and blocked all the IP addresses of the attacker. The blocking response time was recorded at1,030 seconds for SSH, 0.301 seconds for FTP, and 0.492 seconds for port scanning, with an average increase in CPU resources of 2.8%–6.1% and RAM resources of 49.5%.
Feature Engineering for Virtual Currency Price Prediction in Online Games Using a Multiple Linear Regression Approach artherio chanifudin; Sri Ngudi Wahyuni
The Indonesian Journal of Computer Science Research Vol. 6 No. 1 (2027): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v6i1.287

Abstract

Changes in the price of Growtopia's virtual currency can influence players' decisions when buying, selling, or storing items, while price information circulating in the community is often inconsistent. This condition confirms the need for transparent, data-driven predictions. This study aims to predict the prices of World Lock (WL), Diamond Lock (DL), and Blue Gem Lock (BGL) using multiple linear regression. The novelty of the study lies in the application of three separate models with a combination of lag features and cyclic time features, accompanied by chronological data sharing to avoid information leakage. The initial dataset contained 925 daily observations from 2024–2026, while the modeling was limited to 194 data points from 2026 that had a consistent scale. After the formation of Lag_1, Lag_2, Lag_7, Day_Sin, and Day_Cos, 187 complete lines are available, divided into 150 training data and 37 test data. The models were evaluated using MSE, RMSE, MAPE, and R². All three models yield an R² of 0.9318, a MAPE of 2.3191%, as well as a MAPE-based accuracy of 97.6809%. The RMSE is 0.1400 for WL, 14.0017 for DL, and 1,400.1705 for BGL, respectively. As of July 13, 2026, the actual and predicted differences are 0.1743 WL, 17.4286 DL, and 1,742.8648 BGL, respectively. The 123-day recursive prediction yields a final value of 2.8357 WL, 283.5670 DL, and 28,356.6979 BGL. The results show the model follows a general pattern, but its response weakens when price changes occur suddenly. The findings can be used as companion information to transactions, rather than as price certainty or investment recommendations.

Page 10 of 10 | Total Record : 92

 6   7   8   9   10 

Filter by Year

2021 2026


Reset
Filter By Issues
All Issue Vol. 6 No. 1 (2027): Januari Vol. 5 No. 2 (2026): Juli Vol. 5 No. 1 (2026): Januari Vol. 4 No. 2 (2025): Juli Vol. 4 No. 1 (2025): Januari Vol. 3 No. 2 (2024): Juli Vol. 3 No. 1 (2024): Januari Vol. 2 No. 2 (2023): Juli Vol. 2 No. 2 (2023): July Vol. 2 No. 1 (2023): Januari Vol. 2 No. 1 (2023): January Vol. 1 No. 2 (2022): July Vol. 1 No. 2 (2022): Juli Vol. 1 No. 1 (2022): Januari Vol. 1 No. 1 (2021): January More Issue