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All Journal dCartesian: Jurnal Matematika dan Aplikasi d'Cartesian: Jurnal Matematika dan Aplikasi
Langi , Yohanes A. R.
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Computer Science & IT Mathematics

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Segmentasi Motivasi Mahasiswa Luar Provinsi di Universitas Sam Ratulangi Menggunakan Analisis Klaster Langit, Patricia Sukma; Kekenusa , John S.; Langi , Yohanes A. R.
d'Cartesian Vol. 15 No. 1 (2026): Maret 2026
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.15.1.2026.67089

Abstract

This study aims to identify the factors influencing students from outside North Sulawesi Province in choosing Sam Ratulangi University and to classify them based on their motivational patterns. A quantitative approach was employed using questionnaire data collected from 107 undergraduate students from the 2024–2025 cohorts. The research instrument was tested and found to be valid and reliable, with a Cronbach’s Alpha of 0.861. Data were analyzed using the Two-Step Cluster method based on five motivational dimensions: academic, economic, social, experience, and regional opportunity. The results revealed two optimal clusters with fair model quality: a low-motivation cluster (38.3%) and a high-motivation cluster (61.7%). Academic motivation emerged as the most dominant factor distinguishing the clusters. These findings indicate that students exhibit heterogeneous motivational patterns in selecting higher education institutions.
Segmentasi Motivasi Mahasiswa Luar Provinsi di Universitas Sam Ratulangi Menggunakan Analisis Klaster Langit, Patricia Sukma; Kekenusa , John S.; Langi , Yohanes A. R.
d'Cartesian Vol. 15 No. 1 (2026): Maret 2026
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.15.1.2026.67089

Abstract

This study aims to identify the factors influencing students from outside North Sulawesi Province in choosing Sam Ratulangi University and to classify them based on their motivational patterns. A quantitative approach was employed using questionnaire data collected from 107 undergraduate students from the 2024–2025 cohorts. The research instrument was tested and found to be valid and reliable, with a Cronbach’s Alpha of 0.861. Data were analyzed using the Two-Step Cluster method based on five motivational dimensions: academic, economic, social, experience, and regional opportunity. The results revealed two optimal clusters with fair model quality: a low-motivation cluster (38.3%) and a high-motivation cluster (61.7%). Academic motivation emerged as the most dominant factor distinguishing the clusters. These findings indicate that students exhibit heterogeneous motivational patterns in selecting higher education institutions.
Co-Authors Kekenusa , John S. Langit, Patricia Sukma