Penerapan Algoritma K-Means Pada Aplikasi Pembelajaran Lagu Daerah Untuk Menentukan Lagu Dalam Pembelajaran Siswa - Adeng Ichwan Maulida; Carudin; Rasim; Mugiarso (Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya, 2023)
Introduction to regional songs is part of the arts and culture subject in the Elementary School curriculum. Currently, students learn regional songs through books that only provide the lyrics, and there are limitations in the song variations. The singing learning process is conducted by teachers. Teachers routinely teach the same regional songs every year, resulting in many other regional songs never being taught. The challenge arises when teachers have to determine which regional songs should be prioritized in learning, due to a lack of information about known and familiar regional songs among students. The research aims to support decisions on prioritizing regional songs in the teaching and learning process based on their popularity among different student groups. With a web-based system, it becomes easier to determine and categorize the popularity levels of regional songs among elementary school students, specifically at SDN Margahayu XIII in this case. The algorithm used to process and categorize these regional songs is K-Means, which is a non-hierarchical data clustering method aiming to partition data into two or more groups. The research results in a decision support system providing clustering results based on values or criteria that closely align with the popularity levels. The final outcome is a decision support system that can be used to determine which regional songs should be prioritized for learning in the teaching process based on their popularity levels.