Analisis Faktor Penentu Keberhasilan Kelulusan Mahasiswa Menggunakan Pendekatan Statistik Multivariat
Keywords:
Conceptual Review, Item Response Theory, Three-Parameter Logistic Model, Item Quality, Educational Assessment, Keberhasilan Kelulusan, Statistik Multivariat, Analisis Faktor, Regresi Logistik, Analisis Diskriminan, MahasiswaAbstract
Student graduation success is a key indicator of higher education quality, influenced by various complex and interrelated factors. This study aims to identify and analyze the determinant factors of student graduation success using a multivariate statistical approach. Data were collected from 450 final-year students at Nusa Cendana University Kupang in the 2023/2024 academic year through structured questionnaires and secondary academic records. Variables analyzed encompassed internal factors (learning motivation, time management, initial academic ability, physical and mental health) and external factors (family support, teaching quality, campus facilities, student organizational involvement). The analysis employed three multivariate statistical techniques: Factor Analysis, Discriminant Analysis, and Binary Logistic Regression. The findings reveal five dominant factors significantly influencing student graduation success: intrinsic learning motivation (β = 0.421; p < 0.001), family social support (β = 0.389; p < 0.001), quality of academic interaction (β = 0.312; p < 0.01), time management and discipline (β = 0.287; p < 0.01), and physical and psychological health conditions (β = 0.256; p < 0.05). The developed logistic regression model demonstrates a prediction accuracy of 86.7% with a Nagelkerke R² value of 0.612. Discriminant analysis yields a function capable of classifying on-time and non-on-time graduating students with 84.2% accuracy. These findings offer practical implications for higher education institutions in designing academic policies, student guidance programs, and data-driven interventions to improve on-time graduation rates.
Keywords: Graduation Success, Multivariate Statistics, Factor Analysis, Logistic Regression, Discriminant Analysis, Students.
Keberhasilan kelulusan mahasiswa merupakan indikator kunci mutu pendidikan tinggi yang dipengaruhi oleh berbagai faktor kompleks dan saling berkaitan. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis faktor-faktor penentu keberhasilan kelulusan mahasiswa menggunakan pendekatan statistik multivariat. Data dikumpulkan dari 450 mahasiswa tingkat akhir di Universitas Nusa Cendana Kupang pada tahun akademik 2023/2024 melalui kuesioner terstruktur dan data akademik sekunder. Variabel yang dianalisis mencakup faktor internal (motivasi belajar, manajemen waktu, kemampuan akademik awal, kesehatan fisik dan mental) dan faktor eksternal (dukungan keluarga, kualitas pengajaran dosen, fasilitas kampus, keterlibatan organisasi mahasiswa). Analisis dilakukan menggunakan tiga teknik statistik multivariat yaitu: Analisis Faktor (Factor Analysis), Analisis Diskriminan (Discriminant Analysis), dan Regresi Logistik Biner (Binary Logistic Regression). Hasil penelitian menunjukkan bahwa terdapat lima faktor dominan yang secara signifikan memengaruhi keberhasilan kelulusan mahasiswa, yaitu: motivasi belajar intrinsik (β = 0,421; p < 0,001), dukungan sosial keluarga (β = 0,389; p < 0,001), kualitas interaksi akademik (β = 0,312; p < 0,01), manajemen waktu dan kedisiplinan (β = 0,287; p < 0,01), serta kondisi kesehatan fisik dan psikologis (β = 0,256; p < 0,05). Model regresi logistik yang dikembangkan memiliki akurasi prediksi sebesar 86,7% dengan nilai Nagelkerke R² = 0,612. Analisis diskriminan menghasilkan fungsi yang mampu mengklasifikasikan mahasiswa lulus tepat waktu dan tidak lulus tepat waktu dengan tingkat akurasi 84,2%. Temuan penelitian ini memberikan implikasi praktis bagi lembaga pendidikan tinggi dalam merancang kebijakan akademik, program bimbingan mahasiswa, serta intervensi berbasis data untuk meningkatkan angka kelulusan tepat waktu.
Keywords: Keberhasilan Kelulusan, Statistik Multivariat, Analisis Faktor, Regresi Logistik, Analisis Diskriminan, Mahasiswa
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