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All Journal Jurnal Algoritma
Siti Luthfiah Khoirotunnisa
Institut Teknologi Garut
Computer Science & IT

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Analisis Kinerja Perhitungan Jarak Hamming pada Model Klasifikasi Penyakit Paru-Paru Menggunakan Algoritma K-Nearest Neighbor (KNN) Fitri Nuraeni; Siti Luthfiah Khoirotunnisa; Ridwan Setiawan; Muhammad Rikza Nashrulloh
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.2578

Abstract

Lung diseases are among the leading causes of death worldwide and require early, accurate diagnosis to minimize the risk of complications. In the digital era, developing artificial intelligence–based classification models has become a potential solution to support the diagnostic process, particularly for categorical data that represent symptoms such as coughing, shortness of breath, and smoking history. This study proposes a lung disease classification model using the K-Nearest Neighbor (K-NN) algorithm with a simple categorical distance approach, namely the Hamming distance. The dataset used is imbalanced; therefore, data balancing was performed using the random oversampling method. Model evaluation was carried out using two schemes—data splitting and 10-fold cross-validation—by testing multiple values of parameter k. The best results were obtained at k = 7 with an accuracy of 94.58%, precision of 95.25%, recall of 94.39%, and an F1-score of 94.53%. These findings demonstrate that the combination of the K-NN algorithm, Hamming distance, and oversampling can produce high and stable classification performance for categorical datasets in lung disease prediction.
Co-Authors Fitri Nuraeni Muhammad Rikza Nashrulloh Ridwan Setiawan