Implementation of Forward Chaining and Naive Bayes to Determine the Severity of Measles in Toddlers

Andi Husnul  Khatimah; St. Hajrah  Mansyur; Siska  Anraeni

Andi Husnul Khatimah Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Indonesia
St. Hajrah Mansyur Department of Information System, Faculty of Computer Science, Universitas Muslim Indonesia, Indonesia
Siska Anraeni Department of Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Indonesia

Keywords: Measles, Children, Severity Assessment, Forward Chaining

Abstract

This study aims to determine the severity level of measles in children using the Forward Chaining and Naive Bayes methods. Sixteen symptoms were analyzed through decision tables, weighted likelihood tables, and simulated patient data. Forward Chaining was applied to match symptoms with predefined diagnostic rules, while Naive Bayes calculated severity probabilities based on symptom likelihoods. All computations were carried out using Microsoft Excel to ensure traceability and accuracy. The results indicate that both methods produce generally consistent classifications, with minor variations in cases involving overlapping symptoms. Forward Chaining provides deterministic outputs, whereas Naive Bayes offers more detailed probabilistic assessments. Overall, the combination of both approaches is effective for supporting early identification of measles severity in children.

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