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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Penelitian Pendidikan IPA (JPPIPA)
Amarudin
Universitas Teknokrat indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Engineering Materials Science & Nanotechnology Physics

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Implementation of Cache Memory Technology in Improving the Performance of Modern Computing Systems M Sahyudi; Amarudin
Jurnal Penelitian Pendidikan IPA Vol 11 No 6 (2025): June
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i6.11545

Abstract

The gap between increased processor speed and access to the main memory wall is a significant obstacle in the optimization of modern computing systems, where today's applications require processing large data with real-time responses. This study aims to analyze the effectiveness of the implementation of cache memory technology in improving the performance of modern computing systems, focusing on: 1) identification of key parameters that affect the effectiveness of cache on various workloads, 2) evaluation of adaptive cache replacement algorithms, 3) analysis of performance trade-offs with energy efficiency and security, and 4) formulation of optimal cache architecture recommendations. The research method uses a qualitative approach through a comprehensive literature study of 2020-2024 publications from the academic databases of IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, and SINTA with thematic content analysis and comparative evaluation of various cache technology implementations. The results showed that: the multi-level caching architecture increased system throughput by an average of 37.5%; adaptive algorithms such as RRIP increased hit rate by 23.7% compared to conventional LRU; SRAM/STT-MRAM hybrid technology saves up to 44.3% energy with minimal performance overhead; and the proposed integrated framework resulted in a 34.8% performance increase with a 27.5% reduction in energy consumption. Further research is recommended to implement and experimentally test the proposed framework on various computing platforms, develop more adaptive machine learning-based cache replacement algorithms, and explore the integration of cache technology with neuromorphic computing architectures.
PERBANDINGAN KINERJA MODEL SUPPORT VECTOR MACHINE DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN SUPER APP POLRI Bagastian; Ryan Randy Suryono; Amarudin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7582

Abstract

Digital transformation of public services has driven the Indonesian National Police to develop the Polri Super App, yet faces user acceptance challenges reflected in diverse reviews. This study aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying user sentiment of the Polri Super App. The research utilized 3,997 reviews from Apple Store undergoing comprehensive preprocessing including normalization, tokenization, stopword removal, and Sastrawi stemming. Sentiment labeling employed InSet Lexicon, yielding 55.0% positive and 45.0% negative reviews. Feature extraction used TF-IDF method with 80:20 data split for training and testing. Evaluation results demonstrate SVM significantly outperforms Naïve Bayes with 91.5% versus 79.0% accuracy (12.5 percentage points difference). SVM maintains balanced F1-scores of 90.6% (negative) and 92.2% (positive), while Naïve Bayes exhibits imbalance with 87.2% recall (negative) but only 72.3% (positive). SVM's superiority stems from hyperplane optimization capability in handling high-dimensional text data without rigid feature independence assumptions. The study recommends SVM implementation for police digital service sentiment monitoring systems and exploration of ensemble algorithms and deep learning for future research.
PERSPEKTIF BIBLIOMETRIK TERHADAP INTEGRASI METAVERSE DALAM PENDIDIKAN: TREN PUBLIKASI, PENULIS, DAN ARAH PENELITIAN TERKINI Muhammad Fadli; Dian Sri Purwanti; Windia Hanifah; Valdi Mughni Budiman; Rifka Simbolon; Riyan Maruly; Amarudin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8047

Abstract

The Metaverse has emerged as a promising digital technology in education due to its ability to create immersive, interactive, and collaborative learning environments. This study aims to analyze the development of research on the integration of the Metaverse in education through a bibliometric approach. The research data were collected from the Dimensions database using the keywords “Metaverse” and “Education,” resulting in a total of 636 publications published between 2015 and 2025. Bibliometric analysis was conducted and visualized using VOSviewer software to identify publication trends, productive authors, leading contributing countries, and emerging research themes. The findings reveal a significant increase in the number of publications since 2021, with the highest publication output recorded in 2024. China, South Korea, and the United States were identified as the leading contributors to the field, while Hwang G.J. was recognized as one of the most prolific authors. Keyword network analysis indicates that the dominant research themes focus on the Metaverse, Virtual Reality, Augmented Reality, learning, and immersive learning experiences. Furthermore, emerging topics such as digital twins, artificial intelligence, and technology acceptance have begun to gain attention as potential directions for future research. These findings suggest that Metaverse research in education is developing in a multidisciplinary manner and holds significant potential to support the transformation of digital learning in the future.
Co-Authors Bagastian Dian Sri Purwanti M Sahyudi Muhammad Fadli Rifka Simbolon Riyan Maruly Ryan Randy Suryono Valdi Mughni Budiman Windia Hanifah