Klasifikasi Nilai Nominal Uang Logam Indonesia Menggunakan Support Vector Machine - Triee Salsabila; Riyadhul Fajri; Heri Gustami (Jurnal Ilmu Komputer Aceh, 2026)
This study focuses on the classification of Indonesian Rupiah coin denominations using the Support Vector Machine (SVM) method based on digital image processing. The research objects consist of Rp100, Rp200, Rp500, and Rp1,000 coins issued from 2016 to the present. The pre-processing stage includes resizing the images to 128×128 pixels and converting them into grayscale to ensure data uniformity. Feature extraction is performed by combining shape features, Haralick texture, Local Binary Pattern (LBP), and HSV color features to represent the main characteristics of each coin. The classification model is developed using an SVM with a Radial Basis Function (RBF) kernel, with 80% of the data used for training and 20% for testing. The experimental results show an accuracy of 75%, indicating that the proposed approach is reasonably effective in distinguishing Indonesian coin denominations. However, further improvements can be achieved through parameter optimization and dataset expansion in future studies.