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All Journal Journal of System and Computer Engineering Jurnal Pengabdian Mandiri Knowbase : International Journal of Knowledge in Database Jurnal Bahasa Rupa
Mursalim Mursalim
Universitas Teknologi Akba Makassar
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : journal of system and computer engineering

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Augmented Reality and Virtual Reality in English Learning: Bibliometric Analysis of Research Trends, Citation Patterns, and Future Directions Tamra Tamra; Wisda Wisda; Muhammad Rizal H; First Wanita; 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 khaidir rahman nasir; Tamra Tamra; Muhammad Rizal H; First Wanita; 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.
Explainable Machine Learning for Long-Term Monthly Hydroclimatic Forecasting and Extreme-Event Detection Arif Fadillah; Markani Pato; Nuraida Latif; Benny Leornard Encrico Panggabean; Muhammad Rizal; Mursalim Mursalim; Muhajirin Muhajirin
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2741

Abstract

Long-term hydroclimatic prediction in arid urban environments remains methodologically demanding because monthly records are often intermittent, highly seasonal, zero-inflated, and dominated by rare but consequential extreme events. Using a 121-year monthly hydroclimatic record for Makkah, Saudi Arabia, spanning January 1901 to December 2021, this study develops an explainable hybrid machine-learning framework for monthly forecasting, seasonal diagnostics, and extreme-event detection. The dataset contains 1,452 monthly observations with a mean value of 6.19, median of 3.00, standard deviation of 8.05, and maximum of 52.00, indicating a strongly skewed distribution. Exploratory analysis reveals pronounced seasonality: November, December, and January exhibit the highest hydroclimatic values, whereas June is consistently dry across the full record. A temporal feature set was constructed using lag variables, rolling statistics, annual seasonal memory, cyclical month encodings, and trend indicators. Several predictive models were evaluated, including Random Forest, Extra Trees, Histogram Gradient Boosting, XGBoost, and a hybrid SARIMA–Random Forest residual-correction model. Extra Trees achieved the best forecasting performance on the holdout period, with MAE = 2.997, RMSE = 5.603, sMAPE = 57.669%, and R² = 0.518. Extreme-event detection was performed using a 90th-percentile threshold of 17.68, identifying 146 extreme months over the full record. The best classification trade-off was obtained by Histogram Gradient Boosting, while Random Forest produced the highest ROC-AUC. SHAP-based interpretation demonstrates that seasonal phase variables and annual memory features dominate model behaviour, especially month_cos, month_sin, same_month_last_year, and lag_12. The findings show that interpretable ensemble learning can provide a more transparent and operationally relevant framework than accuracy-only forecasting for arid-region hydroclimatic risk assessment.
Co-Authors - STMIK AKBA Makassar, Wisda Ahmad Nur Ikhsan Amran Amiruddin Andi Maulidinnawati Abdul Kadir Parewe Arif Fadillah Arif Fadillah Asnimar Asnimar Benny Leornard Encrico Panggabean Butsiarah First Wanita Hermawati Hermawati Khaidir Rahman Nasir Latif, Nuraida Markani, Markani Muhajirin, Muhajirin Muhammad Rizal H Neneng Awaliah Nurnaningsih Nurnaningsih Ramlah P Syaharullah Disa, Syaharullah Tamra Tamra Titis Sari Putri