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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Anindyia Sitorus Pane
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Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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PIPELINE MACHINE LEARNING PREDIKSI RISIKOPENYAKIT DARI DATA KUESIONER Anggiat Roberto Sinaga; Siti Aisyah; Dheo Zakaria Harahap; Anindyia Sitorus Pane; Sendy Valiza A. Ginting
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6172

Abstract

This study aims to design a system capable of predicting disease risk levels using the Naive Bayes algorithm and questionnaire data. Respondent data was collected via Google Forms, covering variables such as age, lifestyle, stress levels, sleep quality, physical activity, and family health history. The data underwent preprocessing and was converted into a tabular format before being divided into 80 training data points and 20 testing data points. The Naive Bayes algorithm was used to classify disease risk into low, moderate, and high categories. Test results showed that the model could generate predictions with a high level of accuracy. The trained model was then implemented in a web-based system so that users could easily and quickly determine their disease risk.
Co-Authors Anggiat Roberto Sinaga Dheo Zakaria Harahap Sendy Valiza A. Ginting Siti Aisyah