Article Per Year (5 Year)
p-Index From 2021 - 2026
1.607
P-Index
This Author published in this journals
All Journal Hunafa: Jurnal Studia Islamika Jurnal SOLMA Abdimasku : Jurnal Pengabdian Masyarakat Conten : Computer and Network Technology Jurnal Islam Nusantara Inkunabula: Journal of Library Science and Islamic Information Journal Global Technology Computer Contemporary Society and Politics Journal Proceeding Of International Conference On Education, Society And Humanity
Ulumuddin Ulumuddin
Usuluddin IAIN Palu
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal Global Technology Computer

 1 
Analisis Kinerja Algoritma Naive Bayes dalam Klasifikasi Data pada Pasien Tuberkulosis Berbasis Data Mining Ulumuddin, Ulumuddin; Widodo, Pudji
Journal Global Technology Computer Vol 5 No 1 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v5i1.8999

Abstract

Tuberculosis (TB) is one of the infectious diseases that remains a major public health problem in Indonesia, particularly at the primary healthcare level such as public health centers. The increasing amount of patient data stored in health information systems requires effective analytical methods to support accurate and efficient decision-making. This study aims to analyze the performance of the Naive Bayes algorithm in classifying tuberculosis patient data. The dataset used in this research was obtained from medical records of TB patients and non-TB patients, which were processed through several preprocessing stages, including data cleaning, data integration, data transformation, and normalization to ensure data quality. The data were then divided into training and testing datasets for classification purposes. The Naive Bayes algorithm was implemented to classify patient status based on selected clinical and demographic attributes. Model performance was evaluated using a confusion matrix and several evaluation metrics, including accuracy, precision, recall, and F1-score. The experimental results show that the Naive Bayes algorithm achieves satisfactory performance in classifying tuberculosis patient data and demonstrates good efficiency when applied to real-world healthcare data. However, the algorithm still has limitations related to the assumption of independence among attributes, which may affect classification accuracy. The findings of this study are expected to contribute to the development of a decision support system that can assist healthcare professionals at public health centers in performing early classification and analysis of tuberculosis patient data more effectively and efficiently.
Co-Authors Abdul Kodir Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Fransiska, Olan Ihya, Dimas Irawan Khoir, Abu Lukman Hakim Mahfuddin, Choirul Muttaqin, Aqiq Pudji Widodo Rd. Heri Solehudin Rudy Gunawan S, Rezkia Siti Nur Aisyah Solihin, Iramadhana Suhermanto, Suhermanto Taufiq Hidayat Tolkhah, Lalu M.