<![CDATA[Rapid developments in voice-based emotion recognition have made positive contributions to human-computer interaction. This research aims to compare the performance of two algorithms, namely Multilayer Perception (MLP) and Support Vector Machine (SVM), in recognizing emotions based on sound. The data used in this research was taken from Kaggle, which amounted to 1440 voice data. The data is then collected into several emotions which will then be feature extracted from the dataset to eliminate irrelevant information and reduce noise so that the classification results are optimal. The research results show that the classification accuracy using the Multilayer Perception (MLP) algorithm reaches 83%, while the Support Vector Machine (SVM) reaches 82%. Based on the accuracy results of both methods, it can be concluded that the Multilayer Perception algorithm is superior to the Support Vector Machine algorithm in the context of voice-based emotion recognition. Keyword: Emotional Expression, Voice, Mfcc, Multi-Layer Perceptron, Support Vector Machine]]>
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