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All Journal Jakadimas Journal of Computer Science Advancements
Zoraida, Masli Nurcahya
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education

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Education on the impact of dangerous chemicals in food on health at Galesong Community Health Center, Galesong District, Takalar Regency, South Sulawesi: Edukasi dampak bahan kimia berbahaya pada makanan terhadap kesehatan di Puskesmas Galesong, Kecamatan Galesong, Kabupaten Takalar, Sulawesi Selatan Surgani, Mishbah Nurul Fajri Surgani; ., Rahmawati; Dwijayanti, Endah; ., Mustaina; Zoraida, Masli Nurcahya
JAKADIMAS (Jurnal Karya Pengabdian Masyarakat) Vol. 3 No. 1 (2025): JAKADIMAS
Publisher : Fakultas Teknologi dan Industri Pangan Unisri Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33061/jakadimas.v3i1.12026

Abstract

Food Additives (FA) are ingredients added to food to influence the nature and shape of the food. The FAs that are often used are formalin, borax, artificial sweeteners and artificial coloring. This outreach program aims to provide education to the community at the Galesong Health Center about the dangers of adding hazardous chemicals. The method used is in the form of outreach with a two-way interactive method between the extension worker and the participants. The target of this outreach activity is the surrounding community and health center officers. The results of the community service show that the enthusiasm of the extension participants is very high with the activeness of the participants in the question and answer session. The education provided will become public knowledge in choosing good, safe and halal additives.
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 ., Mustaina Aini, M. Anwar Cale, Wolnough Dwijayanti, Endah Nur Fajrin Maulana Yusuf Rahmawati . Sope, Devi Rahmah Surgani, Mishbah Nurul Fajri Surgani