Claim Missing Document
Check
Articles

Found 12 Documents
Search

Analisis Usability Pada Aplikasi SatuSehatMenggunakan Mixed Methods Abel Gilang Saputra; Ngurah Agus Sanjaya ER; I Made Satria Bimantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p23

Abstract

SATUSEHAT mobile application is the official health application of the Ministry of Health of the Republic of Indonesia (Kemenkes RI). This application is a development of the previous application, PeduliLindungi, which only focused on handling the COVID-19 pandemic in 2020-2021. However, the focus of the SATUSEHAT app has been expanded. Some of its main focuses are accessing medical records, viewing vaccination status, and checking personal health status. Unfortunately, from several reviews, there are still many users who are not satisfied with their experience using this application, so this study aims to analyze the usability value using a combined method, of which two are Usability Testing and Post-Study System Usability Questionnaire. This research will involve 10 respondents, who will work on a short task, then answer a questionnaire to measure Usability Testing scores, and finally answer some questions to measure Post-Study System Usability Questionnaire scores. It is hoped that this research can help the developer of the application to see what aspects still have shortcomings, so that in the future it can be improved and developed again. Keywords: SATUSEHAT, Usability Testing, Post-Study System Usability Questionnaire
Analisis Trade-off Pendekatan Greedy dan Metaheuristic dalam Seleksi Fitur Terhadap Model Ensemble Anak Agung Gede Ngurah Ananda Wirasena; I Wayan Supriana; I Made Satria Bimantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2026.v04.i03.p11

Abstract

The increasing volume and dimensionality of medical data pose challenges for effective machine learning model development. Feature selection techniques (FST) are crucial for improving model performance, computational efficiency, and interpretability. This study analyzes the trade-off between greedy and metaheuristic FST approaches in optimizing Decision Tree-based ensemble models. We compare Mutual Information-Sequential Backward Selection (MI-SBS) as a greedy method and Binary Grey Wolf Optimization (BGWO) as a metaheuristic method. FST fitness is evaluated using a Decision Tree Classifier with 5-fold cross-validation. Final classification performance is assessed using AdaBoost and XGBoost on three distinct medical datasets. Results indicate that MI-SBS offers faster feature selection and stable accuracy, often outperforming the baseline. BGWO, while slower in selection, achieves greater feature reduction, leading to significantly faster final model training at the cost of a minor accuracy decrease. This research provides insights into selecting appropriate FST based on desired trade-offs between computational efficiency and classification accuracy in health informatics.