Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JSAI (Journal Scientific and Applied Informatics)
Lemi
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimasi Metode Naïve Bayes Menggunakan Smoothing dan Feature Selection Untuk Penyakit Demam Berdarah Dengue Lemi; Ilma Hasana Kunio, Nurul; Sukma Wati, Ade
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7208

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease caused by the Dengue virus and has emerged as a significant health issue in many tropical countries, including Indonesia. Early identification of the disease is crucial to prevent further spread and complications. This study aims to refine the Naïve Bayes methodology to improve the accuracy of early detection of medical data related to patients suffering from DHF. The application of Naïve Bayes is expected to enhance predictive accuracy and facilitate healthcare professionals in diagnostic procedures. The data used in this research consists of clinical patient information, including laboratory findings and experienced symptoms. The results show that the optimization of the Naïve Bayes method successfully increased prediction accuracy to 92%, which could serve as an effective diagnostic alternative for early DHF detection. The conclusion of this study is that Naïve Bayes can be relied upon to identify DHF more quickly and accurately, ultimately contributing to the medical decision-making process.
Co-Authors Ilma Hasana Kunio, Nurul Sukma Wati, Ade