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All Journal Jurnal Riset Rumpun Ilmu Teknik (JURRITEK)
Hanif Azhar Ramadhan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT

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Integrasi Metode Hybrid Recommendation dan Random Forest Regression untuk Optimasi Prediksi Durasi Menginap pada Sistem Pemesanan Kos Berbasis Web Mufti Ari Bianto; Hanif Azhar Ramadhan; Ardian Hudi Ramadhani; Tsalits Wildan Hamid
JURNAL RISET RUMPUN ILMU TEKNIK Vol. 4 No. 3 (2025): Desember : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v4i3.6591

Abstract

This study proposes the integration of a Hybrid Recommendation method (combining Content-Based and Collaborative Filtering) with Random Forest Regression (RFR) to improve the accuracy of stay duration prediction in web-based boarding house booking systems. The main issue in online boarding booking systems is the inaccuracy of predicting user stay duration, affecting room allocation efficiency and customer satisfaction. The dataset was sourced from the hotel sector due to its attribute similarities and data validity. The research process includes data preprocessing (missing value imputation, normalization, and one-hot encoding), temporal and contextual feature engineering, hybrid recommendation system construction with CBF and CF score weighting, and RFR model training optimized through Grid Search and 10-fold cross-validation. Evaluation was conducted using MAE, RMSE, R² metrics, as well as recommendation metrics such as Precision@5, Recall@5, and Mean Reciprocal Rank (MRR). Results show that this integrated model achieved an R² of 0.7239 and an MAE of 1.0537 days, as well as a Precision@5 of 0.9636. This integration proves effective in improving prediction accuracy and recommendation relevance and contributes to the development of AI-based intelligent systems in the accommodation domain.
Co-Authors Ardian Hudi Ramadhani Mufti Ari Bianto Tsalits Wildan Hamid