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All Journal Al-Hijr: Journal of Adulearn World Journal International of Lingua and Technology World Psychology International Journal of Educational Narratives Journal of World Future Medicine, Health and Nursing Journal of Computer Science Advancements Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Humanities Research Sustainability Islamic Studies in the World
Nizam, Zain
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Journal of Computer Science Advancements

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MOBILE APPLICATION DESIGN BASED ON NATURAL LANGUAGE PROCESSING TO IMPROVE THE QUALITY OF HEALTH SERVICES Ridwan, Achmad; Nizam, Zain; Satybaldy, Daniyar
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.v3i1.1626

Abstract

The increasing demand for efficient and personalized health services has driven the integration of advanced technologies into healthcare systems. Mobile applications leveraging natural language processing (NLP) offer promising solutions to improve patient communication, diagnostic accuracy, and service delivery. Despite advancements, challenges remain in developing user-friendly applications that address diverse healthcare needs. This research focuses on designing a mobile application based on NLP to enhance the quality of health services, emphasizing usability, accuracy, and accessibility. The study employs a user-centered design approach combined with experimental evaluation. The application was developed using Python-based NLP libraries, integrating features such as symptom analysis, medical query responses, and appointment scheduling. A prototype was tested with 150 participants, including patients and healthcare professionals, to evaluate performance metrics such as response accuracy, user satisfaction, and system reliability. The findings indicate that the NLP-based application achieved an 85% accuracy rate in interpreting medical queries and a 90% user satisfaction rate. Participants reported improved communication with healthcare providers and faster access to relevant medical information. However, challenges such as handling complex medical terminology and ensuring data privacy were noted. The study concludes that NLP-powered mobile applications have significant potential to improve health service quality by enabling efficient and accurate communication between patients and providers. Addressing challenges related to data security and expanding linguistic capabilities will be essential for future development. The research underscores the importance of integrating advanced technologies to meet the evolving needs of the healthcare sector.
BIG DATA ANALYTICS FOR SUSTAINABLE GREEN SUPPLY CHAIN MANAGEMENT OPTIMIZATION MODELS Nizam, Zain; Rahman, Rashid; Hakim, Muhammad Arif Abdul
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3789

Abstract

The growing need for sustainable practices in global supply chains has driven the adoption of Big Data Analytics (BDA) to optimize performance and reduce environmental impact. Traditional supply chain management systems often fail to balance operational efficiency with sustainability goals, leading to increased waste and resource inefficiency. Big Data Analytics, by providing real-time insights, predictive models, and data-driven decision-making, offers a solution to this challenge. This research explores the application of BDA in the optimization of Sustainable Green Supply Chain Management (GSCM) models, focusing on how data-driven strategies can enhance both environmental and operational performance. The study employs a mixed-methods approach, combining case studies, performance metrics, and interviews with key industry stakeholders to assess the impact of BDA on supply chain efficiency, resource utilization, and waste reduction. The results show that BDA significantly improves key performance indicators, including a 20% increase in resource efficiency, a 25% reduction in waste, and a 15% decrease in operational costs. The study concludes that BDA is a crucial enabler for sustainable supply chains, providing organizations with the tools to optimize operations while minimizing their environmental footprint.
Co-Authors A, Chevy Herli Sumerli Achmad Ridwan, Achmad Anis, Nina Bambang Winardi Belangi, Siska Putri Busnawir Busnawir Chai, Nong Costa, Bruna Enda Wista Sinuraya Farah, Rina Fariq, Aiman Fitri, Aroviani Amsa Hussain, Sara Ibrahim, Nour Idris, Haziq Jumriyah, Jumriyah Liz Yanti Andriyani Malik, Fatima Muhammad Arif Abdul Hakim Muhammad Hazmi Muhammad Muhammad Nurul Huda Pahmi Pahmi Rahman, Rashid Razak, Faisal Rosita Endang Kusmaryani Rosul, Ahmad Satybaldy, Daniyar Silitubun, Evaristus Siti Aisyah Sun Wei Syaifullah Rahim WIJAYA WIJAYA, WIJAYA Zakka, Umar