Integrating Convolutional Neural Network Features Extraction with Extreme Learning Machines for Image Classification of Pandava Characters in Wayang Kulit - Alfiatul Fitria; Budi Prasetiyo (Journal of Student Research Exploration, 2026)
This research focuses on the utilization of image processing techniques—the Convolutional Neural Networks (CNNs) and Extreme Learning Machine (ELMs)—to classify the characters of Wayang Kulit automatically. The Pandava characters or casts are classified in accordance with the characters from traditional Indonesian puppets, commonly known as shadow puppets. The focus is to introduce such rich cultural heritage to younger generations by using technology. Prior research has utilized classification of characters using Convolutional Neural Networks(CNNs), Extreme Learnings Machines(ELMs), and Support Vector Machines(SVMs), which led to varied accuracy levels. In our subsequent experiments, three proposed models, with varying underlying model assumptions, were evaluated. The proposed models generated moderate accuracies ranging from 39 to 52%. The results suggest that our models have room for further development to enhance their performance. Strategies from parameter tuning to the in-depth analysis of the confusion matrix are discussed. Above all, the research is geared towards ensuring the appreciation and preservation of traditional cultural heritage in this digital era.