Perbandingan Metode Learning Vector Quantization Dan Backpropagation Dalam Klasifikasi Personality Pada Anak - Novita, Rita; Sujana, Teguh; Agusviyanda, Agusviyanda; Fitri, Triyani Arita; Susanti, Susanti (Computer Science and Information Technology, 2024)
This research focuses on classifying children's personalities at Rumah Bermain Bilal using Artificial Neural Network algorithms, specifically Learning Vector Quantization (LVQ) and Backpropagation. The primary objective of this study is to evaluate the effectiveness of these algorithms in categorizing children's personality data and to identify the most accurate method for educational settings. The experiments were conducted with various configurations, including the number of iterations and learning rate, to assess the performance of each algorithm comprehensively. The findings show that the LVQ method demonstrates higher accuracy than Backpropagation. For training data, LVQ achieved an accuracy of 73.47%, whereas Backpropagation reached only 40.82%. For test data, LVQ achieved an accuracy of 84.62%, significantly outperforming Backpropagation's 53.85%. These results indicate that LVQ is more effective in personality classification, especially in an educational context. It is hoped that these findings will assist educational institutions in implementing artificial intelligence-based methods to understand children's personality traits better, thereby supporting the development of more targeted teaching strategies.