PREDICTING RENTAL PRICE RANGES USING THE NAIVE BAYES METHOD FOR BMN OPTIMIZATION (CASE STUDY IN BUKITTINGGI CITY AND SURROUNDINGS) - Luthfi, Faiz; Raharjo, Taufik (Inovasi Pembangunan : Jurnal Kelitbangan, 2024)
This study aims to develop a predictive model for determining the rental price range of State Property (BMN), including accommodations and ATM rooms, using the Naive Bayes algorithm to mitigate bias in rental optimization within the KPKNL Bukittinggi work area. By utilizing market transaction data for ATM room rentals and market offering data for accommodation rentals, the authors constructed a predictive model to estimate the rental price range of BMN. The model achieved accuracy rates of 61.85% for accommodation rentals and 70.15% for ATM room rentals. The originality of this study lies in addressing the potential bias risk in BMN rental assessments, which can lead to inefficient and suboptimal transactions. The findings reveal that of the 7 BMN accommodations analyzed, 4 were undervalued. Additionally, out of 24 BMN ATM room rentals, 3 were undervalued, and 3 were overvalued, indicating the model's potential to enhance the accuracy of rental value assessments.