ESTIMASI RENCANA ANGGARAN BIAYA PROYEK TELEKOMUNIKASI BERBASIS ALGORITMA CATBOOST DAN DATA BOQ
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Abstract
Estimasi Rencana Anggaran Biaya (RAB) yang akurat merupakan elemen krusial dalam keberhasilan proyek infrastruktur telekomunikasi. Penentuan harga satuan pada dokumen Bill of Quantities (BOQ) secara konvensional sering kali tidak efisien dan subjektif. Tantangan utama otomatisasi estimasi ini adalah tingginya kardinalitas fitur kategorikal berupa teks deskriptif. Penelitian ini mengusulkan penerapan algoritma CatBoost untuk memprediksi harga satuan pekerjaan berbasis data BOQ. Tahapan penelitian meliputi pembersihan data historis sebanyak 20.611 item, transformasi logaritma natural pada variabel target, serta pelatihan model dengan pembagian data 80% latih dan 20% uji. Hasil eksperimen menunjukkan CatBoost mampu menghasilkan Coefficient of Determination (R2) sebesar 97,66% dan Mean Absolute Error (MAE) sebesar Rp 13.289. Kinerja ini unggul dibandingkan algoritma pembanding XGBoost (R2 85,93% dan MAE Rp 23.614). Validasi manual mengonfirmasi rasio kesalahan prediksi hanya 0,05 dari total nilai proyek, yang membuktikan kelayakan model untuk otomatisasi RAB secara presisi.
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