KLASIFIKASI EMOSIONAL SAAT MENDENGARKAN MUSIK TURKI

Abstract

This study aims to classify emotions felt by listeners while listening to Turkish music based on acoustic features. The dataset used includes various acoustic features, such as intensity, tempo, frequency, and sound spectrum, extracted from Turkish musical compositions. The analysis was carried out using machine learning methods to map the relationship between these acoustic features and perceived emotions, such as happy, sad, angry, or calm. The results showed that features such as tempo and intensity have significant correlations with certain emotions. The resulting classification model has a high level of accuracy, indicating the potential use of this approach in music psychology and therapy applications. This study contributes to the understanding of the interaction between acoustic characteristics and listeners' emotional responses, especially in the context of traditional Turkish music