Cluster Analysis Of Emotions In Quranic Translations Using K-Means Clustering

Muhammad Faisal Fiqri, Raditya Muhammad, Mochamad Iqbal Ardimansyah

Abstract


Al-Qur’an as the word of Allah is a comprehensive source of knowledge, covering spiritual, moral, social, and psychological aspects, including instructions on the recognition of emotions that have a significant impact on a person's emotional intelligence. This research aims to identify and categorize verses in Indonesian translation of the Quran that contain basic emotions such as anger, disgust, fear, happiness, sadness, and surprise. The process involves data preprocessing, verse search using Vector Space Model, and application of K-Means Clustering algorithm. As a result, the verses can be grouped into four main clusters. The characteristics of the clusters formed include, cluster 0 shows the grouping of verses containing the word “happy”, clusters 1 and 2 respectively show the word “fear”, and cluster 3 shows the word “sad”. The cluster evaluation results obtained using Silhouette Score is 0.442 and Calinski-Harabasz Index is 251.653, which indicates that there is a sign of cluster but there is still some overlap between clusters. In conclusion, this clustering makes an important contribution to the understanding of Quranic interpretation and opens up opportunities for further development in academic studies and religious learning.

Keywords


K-Means Clustering; Basic Emotion; Al-Qur’an Transtation; Text Mining

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DOI: https://doi.org/10.17509/seict.v5i2.75942

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