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All Journal JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI
Rachmat Firman
Universitas Budi Luhur
Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Public Health

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Evaluasi Multi-Metrik Kinerja K-Means, K-Medoids, dan K-Modes untuk Segmentasi dan Strategi Promosi Mahasiswa Baru Tepat Sasaran Rachmat Firman; Utomo Budiyanto
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol. 11 No. 3 (2026): Inpress
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i3.5149

Abstract

Categorical student segmentation often suffers from bias due to subjective cluster (K) determination and vulnerability to local optima. This research proposes a robust comparative framework among K-Means, K-Modes, and K-Medoids to optimize university promotion strategies. Model robustness was tested via 30 independent iterations and evaluated fairly in an "Equivalent Evaluation Space" based on Hamming distance. Optimal architecture determination was automated using Multi-Criteria Decision Making (MCDM) integrating Borda Score and Z-Score Composite. Regarding computational Output, K-Means (K=10) dominated with the strongest cluster density (BetaCV=1.8705), highest initialization stability (ARI=0.5010), and maximum Borda score (12 points). As a managerial Outcome to mitigate Customer Acquisition Cost (CAC) inefficiency, the ten clusters were synthesized into three strategic macro-segments. Promotion tactics are directed towards: (1) Regional Network (25.47%) driven by Word-of-Mouth referrals; (2) Urban Premium (25.00%) via Omnichannel Ads; and (3) Cost-Sensitive Vocational (49.53%) requiring proactive school roadshows and scholarships. This integrated modeling successfully transforms intuitive mass marketing into measurable, efficient, and data-driven precision marketing.
Co-Authors Utomo Budiyanto