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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Elementer (Elektro dan Mesin Terapan) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pengabdian Kepada Masyarakat dan Desa (PASSA) SNTE
Emansa Hasri Putra
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Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Analisis Rekomendasi Media Promosi PPDB SMA Nurul Falah Pekanbaru dengan Algoritma K-Means Clustering Hidayat, Muhammad Taufiq; Emansa Hasri Putra; Dini Nurmalasari
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10499

Abstract

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.
Co-Authors Azwar, Hamid Banurea, Waldemar Hamid Azwar Hidayat, Muhammad Taufiq Muhammad Haikal Satria Muhammad Saputra Nababan, Citra Yanti Nurmalasari, Dini Rianda, Rendy Rizadi Sasmita Darwis Rizadi Sasmita Darwis, Rizadi Sasmita Saputra, Muhammad Sari, Indah Vusvita Wira Indani Wiwin Styorini