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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA)
M Sahyudi
Universitas Teknokrat indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education 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.
Design and Implementation of a Public Sentiment Prediction Framework on Budget Efficiency Policy using Support Vector Machine, Naïve Bayes, and Random Forest M Sahyudi; Sampurna Dadi Riskiono; Ryan Randy Suryono
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.13561

Abstract

Public sentiment toward government budget efficiency policies has become increasingly visible through social media platforms, where citizens actively express opinions, support, and criticism. This study aims to analyze public sentiment toward budget efficiency policies using data collected from the social media platform X (formerly Twitter). A total of 2,000 public comments related to budget efficiency policies were collected through web scraping using the X API. The data were preprocessed through normalization, case folding, text cleaning, tokenization, stopword removal, and stemming. Sentiment classification was conducted using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that SVM achieved the highest accuracy, while Random Forest demonstrated superior recall in identifying positive sentiment. These findings suggest that Random Forest is particularly suitable for sentiment analysis tasks where minimizing false negatives is important, while SVM performs well in overall classification accuracy. This research contributes to the comparative evaluation of machine learning models for public sentiment analysis on policy-related issues using social media data.  
Co-Authors Amarudin Ryan Randy Suryono Sampurna Dadi Riskiono