<![CDATA[This research explores the application of Extreme Learning Machine (ELM) for classifying types of fashion footwear. The increasing number of e-commerce transactions and the use of visual media in marketing demand an efficient and accurate automated system to identify various types of footwear such as shoes, sandals, and slip-ons. Conventional classification systems often encounter challenges in handling variations in shape, color, and lighting conditions in footwear images. ELM, with its unique approach of assigning random weights in the hidden layer, offers a potential solution to these issues. In this study, a classification system was developed consisting of several stages, including the collection of diverse footwear image data, image preprocessing to improve quality and reduce noise, feature extraction relevant for distinguishing footwear types, and finally, classification using the ELM algorithm. The preprocessing process involved color conversion from RGB to HSV to reduce sensitivity to lighting variations, as well as thresholding to produce binary images. Extracted features included geometric characteristics such as area, perimeter, and aspect ratio. The system’s performance was evaluated using standard metrics such as accuracy, precision, and recall. The results showed an accuracy value of 83.3%. In addition, the model evaluation demonstrated very good results: precision reached 83.3%, recall 83.3%, and F1-Score 91%, indicating that ELM is effective in classifying types of fashion footwear. This study contributes to the development of intelligent, efficient, and accurate classification systems for applications in the fashion industry, while also opening opportunities for further research in optimizing ELM parameters and exploring more representative features]]>
