<![CDATA[Emotion plays a crucial role in human communication, and recognizing emotions from speech (Speech Emotion Recognition or SER) has broad applications in various fields. This study designs and implements a speech emotion recognition application to support tourism promotion events in Majalengka. A 1D Convolutional Neural Network (CNN) model is developed using public SER datasets (RAVDESS, CREMA-D, SAVEE, TESS) combined and augmented to improve cultural generalization. Key audio features, such as Mel Frequency Cepstral Coefficients (MFCC), are extracted for effective emotion classification. The resulting system achieves an accuracy of 74.4% on test data, successfully recognizing emotions like angry, sad, neutral, and happy with good precision. This automated emotion analysis assists judges in evaluating participants’ speeches objectively and efficiently. The integration of SER technology in tourism events demonstrates an innovative strategy to enhance the promotion of local culture and improve the overall visitor experience in Majalengka.]]>
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