Abstract

Based on anthropometric data, this study classified news stunts using the Learning Vector Quantization 3 (LVQ 3) technique. Kaggle's secondary data consists of 6500 data points with seven main variables. The research phases include data gathering, preprocessing, transformation, normalization, modeling, testing, and assessment. Normalization normalizes data value ranges, while data transformation transforms non-numeric variables into numerical representations. The two layers of the LVQ-3 architecture—input and competitive—are used for modeling. Based on the data class and learning level modification, the neurons' weights are changed. We run experiments with different values for the LVQ 3 parameters, such as window, maximum iteration, and learning level. Confusion, precision, recall, and F1 score matrixes were used in the evaluation process. A learning level of 0.1 to 0.6 produced the best results, with a consistent accuracy of 0.72. The greatest accuracy of 0.7420 was obtained by dividing the data by 70:30 using a window between 0.3 and 1. The findings from this study show that, despite being influenced by the common use of variables and the lack of data variance, LVQ 3 performs quite well and has significant accuracy. To identify the value that best matches the data set conditions, more parameter exploration is required.