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All Journal Sharia Oikonomia Law Journal Journal Emerging Technologies in Education Journal of Computer Science Advancements Journal of Moeslim Research Technik Journal of Tecnologia Quantica Journal of Social Entrepreneurship and Creative Technology
Cale, Wolnough
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Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Physics Other

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Journal : Journal of Computer Science Advancements

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The Effect of Educational Podcasts on Increasing Understanding of Concepts Among Students Nugroho, Budi; Cale, Wolnough; Jie, Lie
Journal of Computer Science Advancements Vol. 2 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i4.1320

Abstract

The rapid evolution of digital media has introduced various educational tools, among which educational podcasts have gained popularity. Podcasts offer an innovative and flexible method for students to engage with content outside traditional classroom settings. Despite their potential, there is limited empirical research on the effectiveness of educational podcasts in enhancing students’ understanding of concepts. This study aims to evaluate the impact of educational podcasts on students’ comprehension of academic concepts. Specifically, it investigates whether regular podcast exposure improves students’ conceptual understanding compared to traditional instructional methods. A quasi-experimental design involving 120 students from various educational levels was employed. Participants were divided into two groups: the experimental group, which used educational podcasts as supplementary material, and the control group, which continued with conventional teaching methods. Pre-tests and post-tests were administered to assess conceptual understanding before and after the intervention. Data analysis was conducted using quantitative methods, including t-tests and ANOVA. The results indicated a significant improvement in the experimental group’s conceptual understanding compared to the control group. The average score increase for the experimental group was 20% higher than that of the control group, suggesting that educational podcasts positively affect learning outcomes. Educational podcasts can effectively enhance students’ understanding of academic concepts. They provide an engaging and accessible way for students to reinforce learning outside classroom hours. The study highlights the potential of integrating podcasts into educational practices to support and improve student learning.
Optimization of Grid Computing for Big Data Processing in Biomedical Research Sope, Devi Rahmah; Cale, Wolnough; Aini, M. Anwar; Yusuf, Nur Fajrin Maulana; Zoraida, Masli Nurcahya
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1619

Abstract

The rapid growth of biomedical research has generated massive volumes of data, creating significant computational challenges. Traditional high-performance computing systems struggle to efficiently process, analyze, and manage such large-scale datasets. Grid computing, with its distributed architecture, offers a promising solution by enabling scalable and cost-effective data processing. This study explores the optimization of grid computing frameworks for big data processing in biomedical research, focusing on enhancing computational efficiency, scalability, and fault tolerance. The research aimed to evaluate the performance of optimized grid computing systems in processing diverse biomedical datasets, including genomic, proteomic, and imaging data. A combination of experimental and comparative approaches was employed, integrating grid computing frameworks such as Apache Hadoop and Globus Toolkit with biomedical data pipelines. Key metrics, including processing time, resource utilization, and error rates, were analyzed to assess the system’s performance. The findings demonstrated that optimized grid computing systems reduced processing time by an average of 35% compared to traditional methods while maintaining high accuracy. Scalability tests confirmed the framework’s ability to handle datasets up to 15 times larger without significant performance degradation. Fault tolerance improved through adaptive resource allocation, minimizing workflow interruptions. The study concludes that optimized grid computing is a transformative approach for big data processing in biomedical research. Its ability to enhance computational efficiency and scalability positions it as a crucial tool for addressing the growing data demands of modern biomedical science.
Co-Authors Aini, M. Anwar Arifin, Agus Zainul Barroso, Uwe Budi Nugroho Cale, Woolnough Haryanto Tanuwijaya Intes, Amina Jie, Lie Kathryn, Morse Li, Xie Nainggolan, Christine Dewi Nitin, Mahon Nur Fajrin Maulana Yusuf Oscar, Schersclight Ramadhona, Rossa Sope, Devi Rahmah Xu, Shanshan Zoraida, Masli Nurcahya