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Journal : Jurnal Teknik Informatika (JUTIF)

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; Wijanarko, Rony; Rudiyanto, Arif Rifan; Hisbana, Abdullah Afnil; Farida, Fitrotin Na’imul
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5389

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.
Co-Authors Achmad Munib Achmad Munib, Achmad Affan Hensetiaji Widya Agung Riyantomo Ahmad Nurman Khoir Ahmad Sobirin Ahmad Yusuf Ali Ikwan Apriantoro Apriantoro Arbanto, Bonifacius Ardian Fachreza Arif Rifan Rudiyanto ARIF RIFAN RUDIYANTO, ARIF RIFAN Arifiani, Rina Aris Abdul Ghoni Aufar, Syauqina Nashihi Chamidy, Ardian Nurrasyid Cocon, Cocon Dani Setiawan Darmanto Darmanto Dede Hermawan Deded Sarip Nawawi Dwi Puji Prabowo, Dwi Puji Dyah Kusumawati EDI SARWONO Eliyana Zid Naily Syifa Eri Kurniawan Esti Astutik Fajar Slamet Budiono Farida, Fitrotin Na’imul Fattah, Abdulloh Ghoni, Aris Abdul Hisbana, Abdullah Afnil Indah Hartati Is Solikhatun Is Solikhatun, Is Ismayati, Maya Ismi Syarif Iwan Riyanto Jeni Nadik Khamadi Khamadi Khamadi Khoironi, Habib Gufron Kusumah, Sukma Surya Laeli Kurniasari Layla Sriningtyas Marjono Marjono Martha, Marlia Chandra Miftahus Surur Miftahus Surur, Miftahus Miftakhul Huda Misbahudin Misbahudin Moch Subchan Mauludin Moch. Subchan Mauluddin Moch. Subchan Mauludin Mohamad Fahris Muhamad Arifuddin Muhamad Nurkhafid Munasik Munasik Nasutian Nasutian Ningrum, Riska Surya Noor Azharul Fuad, Noor Azharul Noor Prasetyo, Moh Dwi Norma Saputra, Ari Wibawa Novianti, Nurlita Dwi Nugroho Eko Budiyanto Nugroho Eko Budiyanto Nur Azizah Nur Kholis Prabowo, Handoko Agung Prasetyo, Sandif Pratama, Fandy Indra Pratama, Yudhi Priyono, Andhen Rifan Rudiyanto, Arif Rois, Nur Rony Wijanarko Rudi Setiawan Saputra, Ari Wibawa Nurma Sifiyana, Selfi Silmi Yudhistira Siti Maghfiroh Sobirin, Ahmad Sri Mulyo Bondan Respati Sri Umiatun Andayani Sri Wulan Sriningtyas, Layla Sumardi . Sumardi Sumardi Sutiawan, Jajang Teguh Edhy Wibowo Toto Haryadi, Toto Ulumuddin, dimas irawan ih'ya Utami Dian Tri Venanda Arif Budiman Wikantyoso, Bramantyo Yance Anas