Analisis Rekomendasi Media Promosi PPDB SMA Nurul Falah Pekanbaru dengan Algoritma K-Means Clustering - Hidayat, Muhammad Taufiq; Emansa Hasri Putra; Dini Nurmalasari (Universitas Muhammadiyah Riau, 2025)
The advancement of information technology has significantly transformed how educational institutions conduct promotional activities and student admissions. The shift toward digital behavior among society requires schools to adopt more adaptive and data-driven marketing strategies. SMA Nurul Falah Pekanbaru, as one of the private high schools, has experienced a decrease in student enrollment after the Covid-19 pandemic. Conventional promotional methods such as banners, billboards, and printed brochures have proven less effective in reaching prospective students. This research aims to analyze the effectiveness of promotional media based on student characteristics using the K-Means Clustering algorithm as a segmentation method. The dataset was obtained from three years of PPDB registration records, including demographic, socioeconomic, school origin, and promotion media information. The analysis process involved several stages, namely data preprocessing, exploratory data analysis (EDA), determination of the optimal number of clusters using the Elbow and Silhouette methods, and the development of a web-based recommendation system using Python, PHP, and MySQL. The results indicate that the optimal number of clusters is k=4 with a Silhouette Score of 0.351. The four clusters represent distinct behavioral patterns in accessing educational information, with digital media emerging as the most effective channel. The developed recommendation system provides decision support for the school in designing promotional strategies that are more efficient, measurable, and accurately targeted through data analytics-based insights.