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All Journal Jurnal Pendidikan Fisika Indonesia Geoplanning : Journal of Geomatics and Planning Malikussaleh Journal of Mechanical Science Technology Proceeding ISETH (International Summit on Science, Technology, and Humanity) International Journal for Disaster and Development Interface Journal Medical Informatics Technology
Jumadi, J
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Automotive Engineering Computer Science & IT Control & Systems Engineering Dentistry Earth & Planetary Sciences Education Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Journal Medical Informatics Technology

 1 
Cervical Cancer Papsmear Classification through Meta-Learning Technique using Convolution Neural Networks. Mahendra, M; Jumadi, J; Riana, Dwiza
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.23

Abstract

This study uses convolutional neural networks (CNNs) and meta-learning techniques to create an accurate and efficient model for classifying the risk factors of cervical cancer. The dataset includes four types of cervical lesions, and the main objective is to categorize these lesions as either benign or malignant. This classification is essential for early and succesfull treatment of cervical cancer. The challenge arises from the complexity and variations in the images, resulting in the inability of conventional machine learning and deep learning approaches to provide correct classifications. Meta ensemble learning approaches are employed to improve the model's classification accuracy. The dataset of cervical cancer risk factors is preprocessed before being used to train and evaluate numerous CNNs utilizing pre-trained models and various architectures. Subsequently, a meta-learning is employed to optimize the learning process, and used to aggregate the outputs of the multiple CNNs. Moreover, the assessment findings show the model achieves high accuracy and effectiveness. Finally, the suggested model's accuracy score will be contrasted against the current cutting-edge methods used by other existing systems.
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 A.P, Muhammad Iqbal Anastasia, D P Arif Rohman Ariswan, A Choirul Amin Dabet, Abubakar Danardono, Danardono Dwiza Riana Efri Roziaty Ernawati, Muji Fikriyah, Vidya N. Hamim Zaky Hadibasyir Hendra Wahyudi Hikmah, F N Kuswaji Dwi Priyono Kuswanto, H Lotfata, Aynaz Mahendra, M Masruroh, Heni Moh. Irma Sukarelawan Muhammad Yusuf Nawaz, Muhammad Ndoa, Y A A Nurjannah, T Putra, Reza Putri, R Z Ramadhan, M F Sattar, Farha Setiawan, I. N. Trihardo, Rendra Zulkarnaen, Amay Velda