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All Journal IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Saputra, Aryan Dandi
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Computer Science & IT Control & Systems Engineering

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Penerapan algoritma K-Nearest Neighbors (KNN) untuk klasifikasi citra medis Ujianto, Nur Tulus; Gunawan; Fadillah, Haris; Fanti, Azizah Permata; Saputra, Aryan Dandi; Ramadhan, Ilham Gema
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp33-43

Abstract

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.
Co-Authors Fanti, Azizah Permata Gunawan Haris Fadillah Ramadhan, Ilham Gema Ujianto, Nur Tulus