PREDIKSI RISIKO KETERLAMBATAN PADA PROYEK BANGUNAN TINGGI DI SURABAYA MENGGUNAKAN BERBAGAI METODE MACHINE LEARNING
DOI:
https://doi.org/10.9744/duts.13.1.95-114Keywords:
keterlambatan proyek, prediksi risiko keterlambatan, machine learningAbstract
Industri konstruksi merupakan kontributor utama pertumbuhan ekonomi suatu negara yang memiliki karakteristik unik karena memiliki banyak variasi bergantung pada karakteristik setiap proyek. Tingginya kompleksitas proyek menyebabkan peningkatan risiko keterlambatan proyek. Penelitian ini melakukan prediksi risiko keterlambatan pada proyek bangunan tinggi di Surabaya dengan metode Artificial Neural Network (ANN), Support Vector Machine (SVM), dan Classification and Regression Tree (CART). Sebanyak 35 data proyek di Surabaya dihimpun melalui penyebaran kuesioner dengan 21 variabel input faktor risiko penyebab keterlambatan proyek dan 1 variabel output yaitu tingkat keterlambatan proyek. Pembuatan model prediksi dilakukan dengan transformasi data variabel input menjadi 3 model yaitu probability x impact, matriks risiko 5 zona, dan matriks risiko 3 zona. Proses evaluasi metode prediksi dilakukan dengan 7 parameter evaluasi. Hasil penelitian menunjukkan bahwa metode ANN dengan kombinasi model 2 memiliki kinerja terbaik dibandingkan dengan kedua metode prediksi lainnya yang menghasilkan tingkat akurasi 100% dalam melakukan klasifikasi tingkat keterlambatan proyek.
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Copyright (c) 2026 Ambrosius Matthew Junius Reynaldo, Nathanael Kenneth Suryawan, Willy Husada
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