Classification of Symptoms of Disease in Early Childhood Using the Decision Tree Algorithm
DOI:
https://doi.org/10.61536/ambidextrous.v4i02.494Keywords:
disease classification, early childhood symptoms, decision tree, data miningAbstract
Diseases in early childhood often have similar symptoms, making it difficult to process early diagnosis. This study aims to classify disease symptoms in early childhood using the Decision Tree algorithm. The data used is in the form of child health symptom data which is processed through the pre-processing stage and divided into training data and testing data. The results of the study show that the Decision Tree algorithm is able to classify disease symptoms well and can help the early diagnosis process faster and more systematically. The results of the evaluation show that the implementation of immunization of school children in various regions has quite good achievements, with the percentage of immunization coverage in the range of 66% to more than 90%. This high percentage shows that most children have successfully received immunizations in accordance with the set targets, so that the immunization program can be said to be running consistently and effectively.
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Copyright (c) 2026 Nissa Albantaniyah, Dede Brahma Arianto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
