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07 February 2025
DOI: 10.24246/itexplore.v4i1.2025.pp33-43
Penerapan algoritma K-Nearest Neighbors (KNN) untuk klasifikasi citra medis
IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Universitas Kristen Satya Wacana
đ 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.
đ Keywords
#algoritma; citra medis; klasifikasi; K-Nearest Neighbors; reduksi dimensi
âšī¸ Informasi Publikasi
Tanggal Publikasi
07 February 2025
Volume / Nomor / Tahun
Volume 4, Nomor 1, Tahun 2025
đ HOW TO CITE
Ujianto, Nur Tulus; Gunawan; Fadillah, Haris; Fanti, Azizah Permata; Saputra, Aryan Dandi; Ramadhan, Ilham Gema, "Penerapan algoritma K-Nearest Neighbors (KNN) untuk klasifikasi citra medis," IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi, vol. 4, no. 1, Feb. 2025.
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