Data Segmentation and Concatenation for Controlling K-Means Clustering-Based Gamelan Musical Nuance Classification

• Published in: International journal of advanced computer science & applications Vol. 16; no. 3
• Main Authors: Himawan, Heribertus, Syarif, Arry Maulana, Dewi, Ika Novita, Karim, Abdul
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2025
• Subjects: Algorithms; Classification; Cluster analysis; Clustering; Complexity; Composition; Computer science; Data points; Datasets; Emotions; Euclidean geometry; Melody; Music; Musical performances; Musicians & conductors; Recommender systems; Segmentation; Segments; Similarity; Vector quantization; Indonesia
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2025.0160335

The musical nuance classification model is proposed using a clustering-based classification approach. Gamelan, a traditional Indonesian music ensemble, is used as the subject of this study. The proposed approach employs initial and final data segmentation to analyze symbolic music data, followed by concatenation of the clustering results from both segments to generate a more complex label. Structural-based segmentation divides the composition into an initial segment, representing theme introduction, and a final segment, serving as a closing or resolution. This aims to capture the distinct characteristics of the initial and final segments of the composition. The approach reduces clustering complexity while maintaining the relevance of local patterns. The clustering process, performed using the K-Means algorithm, demonstrates strong performance and promising results. Furthermore, the classification rules derived from data segmentation and concatenation help mitigate clustering complexity, resulting in an effective classification outcome. The model evaluation was conducted by measuring the similarity within the classes formed from data merging using Euclidean distance score, where values below three indicate high similarity, and values greater than ten indicate strong dissimilarity. Three of the 13 formed classes with more than one data point, Class 5, Class 12, and Class 18, demonstrate high similarity with a value below three. Five other classes, Class 7, Class 10, Class 11, Class 15, and Class 20, exhibit near-high similarity, with values ranging from three to four, while the remaining five classes fall within the range of four to five.