Analisis Strategi Penerimaan Peserta Didik Baru Menggunakan K-Means Clustering dengan Optimasi Elbow

Le résumé fourni par la source

New Student Admission is an annual routine activity at schools, including SMP Citra Negara Depok. Every year, the school receives a large amount of registration data, but it has not been optimally utilized to determine the new student admission strategy for the following year, resulting in a decrease in the number of applicants. This study aims to cluster effective new student admission strategies using the K-Means clustering method optimized with the Elbow method and min-max normalization. The dataset used is derived from the registration data for the 2021–2022 and 2022–2023 academic years, with attributes including school name, number of classes, number of registrants, and difference. The Elbow Method results indicate that the optimal number of clusters is three. The K-Means process stops at the 7th iteration out of a maximum of 10 iterations. The clustering results in three strategies: a 55% discount during the October–December period, a brochure distribution strategy, and a 50% discount during the January–June period. The research findings indicate that the K-Means method optimized with the Elbow Method can help determine a more effective new student admission strategy for the school.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.