Implementasi Weighted K-Nearest Neighbour untuk Klasifikasi Tingkat ISPA di Puskesmas Lhoksukon

Cut Dini Akhmalia; Zahratul Fitri; Maryana Maryana

doi:10.55606/juitik.v6i2.2303

Authors

Cut Dini Akhmalia Universitas Malikussaleh
Zahratul Fitri Universitas Malikussaleh
Maryana Maryana Universitas Malikussaleh

DOI:

https://doi.org/10.55606/juitik.v6i2.2303

Keywords:

Classification, Diagnosis, ISPA, Machine Learning, Weighted KNN Classification

Abstract

Acute Respiratory Tract Infection (ARI) is a common disease in the community and requires appropriate treatment to determine its severity. This study aims to implement the Weighted K-Nearest Neighbor (WKNN) algorithm in classifying the severity of ARI and designing a web-based system as a supporting medium for medical personnel. The research data were obtained from patient medical records at the Lhoksukon Community Health Center in 2024–2025. The classification process was carried out through preprocessing stages, data labeling based on WHO and Ministry of Health references validated by medical personnel, data normalization, and calculations using the WKNN method with a K value of 3, Euclidean distance, and weighting. The test results using a confusion matrix showed that the WKNN method was able to classify the severity of ARI into mild, moderate, and severe categories with an accuracy level of 95.06%. Thus, the WKNN algorithm proved effective as an aid in the process of diagnosing the severity of ARI.

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