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Susilo, Anto
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Computer Science & IT

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Use of Virtual Reality Technology for Learning Mechanical Skills Susilo, Anto; Barra, Ling; Wang, Yuanyuan
Journal of Computer Science Advancements Vol. 2 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i5.1323

Abstract

Background: With the technological landscape in education shifting rapidly, Virtual Reality presents a novel approach to practical skill development, particularly in mechanical engineering. This study explores the potential of Virtual Reality to enhance the learning of specific mechanical skills, such as Mechanical Skills, which are crucial in the increasingly automated industry. The main objective of this research was to assess the effectiveness of Virtual Reality technology in teaching mechanical skills compared to traditional hands-on methods. The study employed a quasi-experimental design involving 100 mechanical engineering students from two universities. Using conventional training methods, which included [insert specific methods], participants were randomly assigned to a VIRTUAL REALITY training group and a control group. Both groups were tested on their ability to perform specific mechanical tasks, such as [insert specific tasks], before and after the training sessions. The Virtual Reality group demonstrated a statistically significant improvement in performance accuracy and speed compared to the control group. Post-study surveys also indicated higher satisfaction and engagement levels among the Virtual Reality group. The findings suggest that Virtual Reality technology can significantly enhance the learning of mechanical skills, offering a more effective and engaging approach than traditional methods. The potential of Virtual Reality to revolutionize mechanical engineering education is inspiring, offering a new and exciting way to teach and acquire practical skills.
Big Data Analysis to Predict Consumption Patterns in Smart Cities Susilo, Anto; Prasetiyo, Rachmat; Aslam, Bilal; Farah, Rina
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1535

Abstract

The rapid development of smart cities has increased the demand for efficient resource management and personalized services, where understanding consumption patterns is crucial. Big data analysis offers a powerful tool for predicting these patterns, enabling city planners and service providers to make data-driven decisions to enhance urban living quality. This study aims to utilize big data analytics to predict consumption patterns across various sectors in smart cities, including energy, water, and transportation. By leveraging large datasets, this research seeks to provide actionable insights for optimizing resource allocation and anticipating future consumption demands. The methodology involves collecting and analyzing data from multiple sources, such as IoT sensors, public utility records, and social media, to identify consumption trends. Machine learning algorithms, including time series analysis and clustering, were applied to detect patterns and forecast demand. Results indicate that big data analytics can accurately predict consumption fluctuations, with an 85% accuracy in energy demand forecasting and a 78% accuracy in water usage prediction. The findings highlight correlations between demographic factors and consumption, providing a comprehensive understanding of urban needs. The study concludes that big data analysis is a valuable approach to managing resources effectively in smart cities. By predicting consumption patterns, city planners can proactively address demand surges, reduce waste, and improve resource distribution, ultimately supporting sustainable urban growth. Implementing these insights could significantly enhance smart city efficiency and resilience.
Co-Authors Aslam, Bilal Barra, Ling Farah, Rina Prasetiyo, Rachmat Yuanyuan Wang, Yuanyuan