Early Detection Of Melanoma Skin Cancer Using Gray Level Co-Occurrence Matrix And Ensemble Support Vector Machine: Deteksi Kanker Kulit Berbasis Analisis Fitur dan Metode Ensemble Machine Learning

Mustagfirin Mustagfirin; Rony Wijanarko; Arif Rifan  Rudiyanto; Abdullah Afnil  Hisbana; Fitrotin Na’imul  Farida

doi:10.52436/1.jutif.2026.7.2.5389

Authors

Mustagfirin Department of Informatics Engineering , Universitas Wahid Hasyim, Indonesia
Rony Wijanarko Department of Informatics Engineering , Universitas Wahid Hasyim, Indonesia
Arif Rifan Rudiyanto Department of Informatics Engineering , Universitas Wahid Hasyim, Indonesia
Abdullah Afnil Hisbana Department of Informatics Engineering , Universitas Wahid Hasyim, Indonesia
Fitrotin Na’imul Farida Department of Informatics Engineering , Universitas Wahid Hasyim, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5389

Keywords:

Gaussian Mixture Models, Gray Level Co-occurrence Matrix, machine learning, melanoma, skin cancer, Support Vector Machine, Random Forest

Abstract

Skin cancer is a major global health problem with incidence rates increasing every year. Melanoma, the most aggressive form of skin cancer, requires accurate early detection to reduce mortality risk. Conventional diagnostic methods such as visual examination and biopsy still face limitations in precision and consistency, highlighting the need for more objective and efficient technological approaches. This study proposes a classification method for melanoma using an ensemble of Support Vector Machine (SVM) and Random Forest (RF), supported by feature extraction through the Gray Level Co-occurrence Matrix (GLCM) and dimensionality reduction using Linear Discriminant Analysis (LDA). The research stages include image preprocessing using grayscale conversion to reduce data complexity, followed by GLCM-based texture feature extraction, and LDA transformation to enhance class separability. The classification model is developed using an ensemble voting mechanism that combines predictions from SVM and RF to produce a more stable and robust decision. Experimental results with a 60:40 train–test ratio show that the proposed method achieves an accuracy of 88.75%, outperforming each individual model tested. These findings indicate that the integration of GLCM–LDA features with the SVM-RF ensemble effectively improves melanoma detection performance. Overall, this study provides a significant contribution to the development of early detection systems in health informatics, offering potential improvements in patient safety and survival rates for individuals affected by skin cancer.

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