Implementasi Algoritma XGBoost Classifier untuk Klasifikasi Kerumunan Jemaah Haji dan Umrah: Implementation of the XGBoost Classifier Algorithm for Classifying Crowds of Hajj and Umrah Pilgrims

Rahmattun Illaihiyah; Ginanjar Abdurrahman; Daryanto Daryanto

doi:10.57152/malcom.v6i1.2245

Authors

Rahmattun Illaihiyah Universitas Muhammadiyah Jember
Ginanjar Abdurrahman Universitas Muhammadiyah Jember
Daryanto Daryanto Universitas Muhammadiyah Jember

DOI:

https://doi.org/10.57152/malcom.v6i1.2245

Keywords:

Extreme Gradient Boosting, Haji dan Umrah, Kepadatan Kerumunan, Klasifikasi, Pembelajaran Mesin

Abstract

Efektivitas manajemen kerumunan menjadi aspek penting dalam pelaksanaan ibadah haji dan umrah, mengingat mobilitas jemaah yang sangat tinggi pada waktu dan lokasi tertentu. Kepadatan yang tidak terkendali berpotensi menimbulkan risiko keselamatan dan mengganggu kelancaran ibadah. Penelitian ini mengusulkan pendekatan klasifikasi tingkat kepadatan kerumunan (crowd density) dengan memanfaatkan data operasional yang merepresentasikan dinamika pergerakan, perilaku individu, dan kondisi lingkungan. Dataset yang digunakan terdiri dari 10.000 entri dengan 29 fitur. Algoritma Extreme Gradient Boosting (XGBoost) dipilih karena kemampuannya mengolah data kompleks dan heterogen. Hasil pengujian menunjukkan akurasi 98.7%, precision 99%, recall 99%, dan f1-score 99% untuk kategori rendah, sedang, dan tinggi, baik pada macro maupun weighted average. Temuan ini menunjukkan bahwa model yang dikembangkan memiliki tingkat akurasi dan keandalan yang tinggi dalam memprediksi kepadatan kerumunan.

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