KLASIFIKASI PENYAKIT MELANOMA MENGGUNAKAN WHALE OPTIMIZATION ALGORITHM SEBAGAI SELEKSI FITUR - Aiman Muhamad Basymeleh; Panca Mudjirahardjo; n/a Rahmadwati (Jurnal Mahasiswa TEUB, 2023)
The biggest challenge in melanoma is distinguishing between benign and malignant skin diseases with various problems related to time and patient health to differentiate lesions in patients. To reduce these problems, an image classification system is required using several feature extraction methods on images, namely Gray Level Co-occurrence Matrix, contour features, and HSV image features. In this study, feature selection was also carried out using a metaheuristic algorithm, namely the Whale Optimization Algorithm (WOA), as feature optimization in the next stage, which is the classification stage using the Multilayer Perceptron Neural Network method. However, the results of testing the Multilayer Perceptron Neural Network on these feature extractions showed very good performance, especially in the case of HSV color feature extraction and Gray Level Co-occurrence Matrix (GLCM). In addition, the feature selection also showed the same results from the same feature extraction with a relatively faster prediction time. Keywords : Melanoma, Feature Extraction, Feature Selection, Whale Optimization Algorithm, Classification