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All Journal Jurnal Medika Malahayati Computer Science (CO-SCIENCE) Journal Medical Informatics Technology
Ernawati, Muji
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Computer Science & IT Dentistry Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Journal : Journal Medical Informatics Technology

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Advances in Machine Learning and Deep Learning towards Medical Data Analysis Vebiyatama, Andicha; Ernawati, Muji
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.32

Abstract

Artificial intelligence uses advanced algorithms such as deep learning and machine learning methods to help doctors make more accurate diagnoses, identify potential health risks, and customize personalized treatment plans for patients. This literature review explores machine learning and deep learning methods applied to medical datasets over the past five years. The paper discusses the advancements, challenges, and future directions in utilizing ML and DL techniques for medical data analysis. It synthesizes recent research findings, highlighting key methodologies, datasets, and outcomes.
Analysis of Service Quality on User Satisfaction in BPJS Kesehatan Website Trihardo, Rendra; Jumadi, J; Ernawati, Muji
Journal Medical Informatics Technology Volume 2 No. 4, December 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i4.56

Abstract

BPJS Kesehatan plays a vital role in providing health insurance to millions of Indonesians, making it essential to assess the quality of service on their website to ensure efficient and accessible healthcare delivery. This study evaluates of service quality on user satisfaction with the BPJS Kesehatan website by analyzing 10 hypotheses related to information quality, system usability, and service effectiveness. The research employed a quantitative approach, utilizing a structured questionnaire and regression analysis with data from 32 respondents. Significant findings include a strong positive effect of service quality on system use (β = 0.928, p = 0.002) and a notable impact of system use on net benefits (β = 0.337, p = 0.014). The model's high R² value of 0.796 indicates that nearly 80% of the variance in net benefits is explained by the predictors, demonstrating that improved service quality and increased system use substantially enhance user satisfaction and perceived benefits. These results underscore the importance of focusing on service quality and user engagement to optimize outcomes from the BPJS Kesehatan website.
Co-Authors Ariyanti, Novi Aryanti, Tanty Heni Hermaliani, Eni Jayatmi, Irma Jumadi, J Kustari, N Amelinda Lestari, Fitri Wahyu Lestari, Restu Senja Maryam Syarah Mardiyah Ridwan, Ridwan Trihardo, Rendra Vebiyatama, Andicha