Repository Universitas Negeri PadangPrediksi Harga Sewa Rumah Kos di Kota Padang Menggunakan Algoritma Random ForestRoskiWahyudiauthorMeningkatnya jumlah pendatang di Kota Padang, baik untuk tujuan pekerjaan maupun pendidikan, telah menyebabkan meningkatnya permintaan hunian sementara seperti rumah kos. Namun, variasi harga sewa yang sering kali tidak sebanding dengan fasilitas dan lokasi yang disediakan menyebabkan ketidakkonsistenan dalam penentuan harga. Penelitian ini bertujuan untuk mengembangkan model prediksi harga sewa rumah kos di Kota Padang menggunakan algoritma Random Forest, serta mengidentifikasi faktor-faktor yang paling berpengaruh harga sewa dan mengevaluasi tingkat akurasi model yang dihasilkan. Penelitian ini menggunakan pendekatan machine learning dengan metode Random Forest Regression. Dataset terdiri dari 226 sampel rumah kos yang diperoleh dari platform Mamikos dan wawancara langsung dengan pemilik kos. Variabel yang dianalisis meliputi tipe kos, luas kamar, jarak ke fasilitas umum, serta fasilitas pendukung seperti kamar mandi dalam, Wi-Fi, kasur, pendingin ruangan (AC), dan akses 24 jam. Tahapan pengolahan data meliputi normalisasi, pembagian data menjadi data latih dan data uji, bootstrap sampling, serta evaluasi model menggunakan metrik Mean Absolute Percentage Error (MAPE) untuk mengukur tingkat akurasi prediksi. The research results indicate that the Random Forest Regression algorithm, using 80% of the data for training and 20% for testing, is capable of predicting boarding house rental prices with good accuracy, as shown by a MAPE value of 9.68%. Based on the feature importance analysis, the features that have the most significant influence on rental prices are AC, distance to public facilities, and the presence of an indoor bathroom. In contrast, features such as the type of boarding house and 24-hour access have a relatively small impact due to low variability and correlations with other features.QA Mathematics2025Universitas Negeri Padang;Matematika FMIPA UNPThesis

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