AYDIN, MUHAMMAD ASADEL (2026) KLASIFIKASI POLA KARIER ALUMNI BERDASARKAN LABEL CLUSTER DATA TRACER STUDY MENGGUNAKAN RANDOM FOREST, XGBOOST, DAN REGRESI LOGISTIK. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Tracer studies are widely used by higher education institutions to evaluate graduates' career outcomes. This study aims to analyze the factors influencing alumni career patterns using a classification approach based on cluster labels derived from tracer study data at Universitas Mercu Buana. The research process included data preprocessing, class balancing using the Synthetic Minority Oversampling Technique (SMOTE), and model development using Logistic Regression, Random Forest, and XGBoost. The classification labels were organized into a twocluster scheme as the primary analysis and a four-cluster scheme for comparison. Model performance was evaluated using accuracy, precision, recall, and F1-score, while influential factors were identified through feature importance analysis. The results indicate that Random Forest achieved the best performance, with an accuracy of 79.11% in the two-cluster scheme. Teamwork was identified as the most influential factor, followed by self-development, information technology skills, communication, entrepreneurship, leadership, number of job applications, number of responses, and number of interviews. Keywords: Tracer Study, alumni career patterns, classification, Random Forest, feature importance. Tracer study merupakan salah satu instrumen evaluasi yang digunakan perguruan tinggi untuk mengetahui gambaran pola karier alumni. Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi pola karier alumni melalui metode klasifikasi berdasarkan label cluster hasil clustering data tracer study Universitas Mercu Buana. Tahapan penelitian meliputi prapemrosesan data, penyeimbangan kelas menggunakan Synthetic Minority Over-sampling Technique (SMOTE), serta pembangunan model menggunakan Logistic Regression, Random Forest, dan XGBoost. Label klasifikasi disusun dalam skema dua cluster sebagai hasil utama dan empat cluster sebagai pembanding. Evaluasi model dilakukan menggunakan akurasi, presisi, recall, dan F1-score, sedangkan analisis faktor dilakukan melalui feature importance. Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 79,11% pada skema dua cluster. Faktor yang paling berpengaruh terhadap pola karier alumni adalah kerja sama tim, diikuti pengembangan diri, penggunaan teknologi informasi, komunikasi, kewirausahaan, kepemimpinan, jumlah lamaran, jumlah respons, dan jumlah wawancara. Kata kunci: Tracer Study, pola karier alumni, klasifikasi, Random Forest, feature importance.
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