Hybrid Model Based on Technology Acceptance Model (TAM) & Information System Success Model (ISSM) in Analyzing the Use of E-Health

Electronic health or commonly known as e-health is defined as the use of information and communication technology in supporting the health and health-related fields. The outbreak of the Covid-19 virus in 2019 has led to a massive increase in the use of e-health, therefore it is important to know how users accept e-health. To analyze e-health acceptance, we combined the extended TAM model with enhanced care and increased accessibility and ISSM. A total of 121 data were collected using a structured questionnaire. The data that has been collected was analyzed using PLS-SEM. From the tests that have been carried out, it is known that the enhanced care, perceived usefulness, perceived ease of use, attitude, information quality, satisfaction have a significant influence on usage intentions, while the increased accessibility, net benefit, service quality, and system quality factors have no significant effect on intention to use. © 2022 EDP Sciences. All rights reserved.

Predicting Creditworthiness of Smartphone Users in Indonesia during the COVID-19 pandemic using Machine Learning

In this research work, we attempted to predict the creditworthiness of smartphone users in Indonesia during the COVID-19 pandemic using machine learning. Principal Component Analysis (PCA) and Kmeans algorithms are used for the prediction of creditworthiness with the used a dataset of 1050 respondents consisting of twelve questions to smartphone users in Indonesia during the COVID-19 pandemic. The four different classification algorithms (Logistic Regression, Support Vector Machine, Decision Tree, and Naive Bayes) were tested to classify the creditworthiness of smartphone users in Indonesia. The tests carried out included testing for accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristics (AUCROC) assesment. Logistic Regression algorithm shows the perfect performances whereas Naïve Bayes (NB) shows the least. The results of this research also provide new knowledge about the influential and non-influential variables based on the twelve questions conducted to the respondents of smartphone users in Indonesia during the COVID-19 pandemic. © 2022 IEEE.