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All Journal Jurnal Fisika dan Terapannya
Imanuel Purba, Chrisman
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Physics

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Klasifikasi Kanker Kulit dari Citra Dermoskopi menggunakan Fitur Gray Level Co-occurence Matrix (GLCM) dengan Algoritma Machine Learning Imanuel Purba, Chrisman; Alrizal, Alrizal; Fendriani, Yoza
JFT : Jurnal Fisika dan Terapannya Vol 12 No 1 (2025): Juni 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jft.v12i1.56651

Abstract

This study aims to classify skin cancer based on dermoscopic images using texture feature extraction through the Gray Level Co-occurrence Matrix (GLCM) technique by comparing the performance of four machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree, and Random Forest. This approach was developed to address the limitations of previous studies, which typically employed only a single algorithm without comprehensive comparison. The evaluation results show that Random Forest achieved the best performance, with an accuracy of 92.72%, precision of 94.44%, recall of 92.39%, and an F1-score of 93.40%. This is attributed to its ensemble nature, which combines multiple decision trees through a voting mechanism, making it effective in handling imbalanced data and complex texture patterns. Conversely, Support Vector Machine (SVM) demonstrated the lowest performance, with an accuracy of 66.06%, precision of 84.44%, recall of 64.40%, and an F1-score of 73.07%, indicating its limitations in recognizing nonlinear in high-dimensional data. Based on these results, the combination of GLCM and Random Forest has proven to be effective and optimal for medical image classification, and holds significant potential to support more accurate clinical decision-making in the early detection of skin cancer
Co-Authors Alrizal, Alrizal Yoza Fendriani