Studi Komparatif Performa Model Ensemble Learning dalam Klasifikasi Kepuasan Penumpang Maskapai Penerbangan
DOI:
https://doi.org/10.52436/1.jpti.1504Keywords:
ensemble learning, kepuasan penumpang, klasifikasi, maskapai penerbangan, XGBoostAbstract
Penelitian ini bertujuan untuk mengevaluasi dan membandingkan performa beberapa model ensemble learning dalam klasifikasi kepuasan penumpang maskapai penerbangan menggunakan dataset publik dari Kaggle. Dataset terdiri dari 25.893 observasi dengan 23 variabel prediktor yang mencakup karakteristik pelanggan, kualitas pelayanan, dan pengalaman penerbangan. Tiga algoritma yang digunakan adalah Random Forest, Gradient Boosting, dan XGBoost. Proses pemodelan dilakukan menggunakan skema train-test split 80:20 serta validasi silang, dan evaluasi menggunakan metrik accuracy, precision, recall, F1-Score, dan ROC-AUC. Hasil penelitian menunjukkan bahwa XGBoost memberikan performa terbaik pada seluruh metrik evaluasi dengan akurasi sebesar 0,963 dan ROC-AUC sebesar 0,995. Analisis feature importance menunjukkan bahwa variabel online boarding, tipe perjalanan bisnis, serta kualitas layanan dalam penerbangan merupakan faktor dominan yang memengaruhi kepuasan penumpang. Kontribusi utama penelitian ini adalah penyajian evaluasi komparatif model ensemble learning yang terintegrasi dengan analisis interpretabilitas untuk mengidentifikasi determinan utama kepuasan pelanggan pada data tabular. Temuan ini memberikan implikasi bahwa peningkatan layanan digital dan kualitas pengalaman kabin menjadi prioritas strategis dalam meningkatkan kepuasan pelanggan maskapai.
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