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All Journal Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Journal of System and Computer Engineering Jurnal Minfo Polgan (JMP) Ilmu Komputer untuk Masyarakat
First Wanita, First
Program Studi Teknik Informatika STMIK AKBA
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Mathematics Social Sciences Other

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Journal : Journal of System and Computer Engineering

 1 
Enhancing Intrusion Detection Using Random Forest and SMOTE on the NSL‑KDD Dataset Saputra, Febri Hidayat; Ilham, Ilham; Rizal, Muhammad; Wisda, Wisda; Wanita, First; Mursalim, Mursalim; Fadillah, Arif
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2056

Abstract

Intrusion Detection Systems (IDS) play a crucial role in identifying suspicious activities on computer networks. However, a major challenge in developing machine learning-based IDS is the issue of class imbalance, where attacks—being minority classes—are often overlooked by classification models. This study aims to construct an intrusion detection system based on the Random Forest algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this problem. The NSL-KDD dataset is used for evaluation, with the data split into 80% for training and 30% for testing. Experiments include Random Forest-based feature selection and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Random Forest–SMOTE combination achieves an accuracy of 99.78%, precision of 99.70%, recall of 99.88%, and an F1-score of 99.79%. The confusion matrix indicates a very low rate of false positives and false negatives. Additionally, selecting the most influential features such as src_bytes and dst_bytes improves model efficiency. Thus, the integration of Random Forest and SMOTE proves to be effective in enhancing detection sensitivity toward attacks without compromising model precision. This approach offers a significant contribution to the development of adaptive, accurate, and deployable IDS in real-world network environments.
Augmented Reality and Virtual Reality in English Learning: Bibliometric Analysis of Research Trends, Citation Patterns, and Future Directions Tamra, Tamra; Wisda, Wisda; H, Muhammad Rizal; Wanita, First; Mursalim, Mursalim
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2472

Abstract

This study conducts a comprehensive bibliometric analysis to map the development of research on Augmented Reality (AR) and Virtual Reality (VR) in English language learning (ELL) from 2010 to 2025. Using 386 Scopus-indexed documents, the analysis examines publication growth, citation performance, influential authors and countries, core sources, and the thematic evolution of immersive learning research. The findings show a sharp increase in scientific production after 2020, reflecting the global rise of digital and immersive technologies in education. China, Korea, and Malaysia emerge as dominant contributors, demonstrating Asia’s leading role in AR/VR-driven language innovation. Citation trends reveal the coexistence of foundational highly cited works and rapidly influential recent publications. Source impact analysis confirms the interdisciplinary character of the field, spanning educational technology, linguistics, psychology, and computer science. Trend-topic analysis indicates a shift from general pedagogical themes toward AI-enhanced AR applications, deep learning, virtual reality environments, and interactive vocabulary learning systems. Despite significant growth, gaps remain in long-term studies, cross-country collaboration, and research on advanced language competencies. Overall, the study provides a data-driven understanding of how AR and VR have evolved as transformative tools for English language learning and offers strategic insights for guiding future research agendas in immersive educational technologies.
Implementation of Fisher-Yates Shuffle Algorithm in Mobile-Based Vocabulary Learning Game for Children with Disabilities nasir, khaidir rahman; Tamra, Tamra; H, Muhammad Rizal; Wanita, First; Mursalim, Mursalim
Journal of System and Computer Engineering Vol 7 No 2 (2026): JSCE: April 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i2.2479

Abstract

Children with disabilities face significant challenges in vocabulary acquisition, necessitating the development of specialized educational technologies that accommodate their unique learning characteristics. This study aims to implement the Fisher-Yates shuffle algorithm in a mobile-based vocabulary learning game specifically designed for children with disabilities, ensuring unbiased randomization of educational content to promote authentic vocabulary comprehension. This research employed the Multimedia Development Life Cycle methodology, encompassing concept definition, design, material collection, assembly, testing, and distribution phases. The Fisher-Yates shuffle algorithm was implemented following the modern Durstenfeld variant, operating through backward iteration, generating random indices, and performing in-place element swapping. Algorithm validation was conducted through simulation calculations and chi-square goodness-of-fit statistical testing across ten thousand randomization trials. The application "Tebak Kosakata" successfully integrates the randomization algorithm with an accessible user interface, featuring multimodal content presentation, immediate positive feedback mechanisms, and cumulative scoring systems. Simulation calculations confirmed that each vocabulary item maintains an equal probability for occupying any position in the final sequence. Statistical validation yielded a chi-square value of 8.47 with nine degrees of freedom and a probability value of 0.487, confirming uniformly distributed randomization without detectable bias. The algorithm achieves optimal computational efficiency with linear time complexity and constant auxiliary space complexity. The randomization of question sequences and answer option positions effectively prevents pattern-based response strategies, encouraging authentic vocabulary learning rather than positional memorization. This study establishes that the Fisher-Yates shuffle algorithm constitutes an effective mechanism for implementing unbiased randomization in educational games for children with disabilities, bridging computational algorithm theory with special education pedagogy while providing a replicable methodological framework for future development.
Co-Authors Aman, Andryanto Arif Fadillah H, Muhammad Rizal Ilham Ilham Markani, Markani Mochammad Imron Awalludin Muhajirin, Muhajirin MUHAMMAD RIZAL Mursalim Mursalim nasir, khaidir rahman Pratma, Jordan Nusti Saputra, Febri Hidayat Sulehu, Marwa Tamra, Tamra Wisda, Wisda