Penerapan Random Forest Regression Untuk Memprediksi Harga Jual Rumah Dan Cosine Similarity Untuk Rekomendasi Rumah Pada Provinsi Jawa Barat
DOI:
https://doi.org/10.22441/fifo.2022.v14i2.003Keywords:
Random Forest Regression, Cosine Similarity, Prediksi, Rekomendasi, Harga, RumahAbstract
Penelitian ini bertujuan untuk mengimplementasikan sebuah algoritma machine learning yaitu Random Forest Regression dalam memprediksi harga rumah dan algoritma Cosine Similarity dalam memberikan rekomendasi rumah. Data yang diambil menggunakan teknik web scrapping, lalu data tersebut akan di olah menggunakan metode CRISP-DM (Cross-Industry Standard Process dor Data Mining) dengan tahap business understanding, data understanding, data preparation, modeling, evaluation dan deployment. Hasil akurasi dari proses prediksi dengan tuning parameter sebesar 85,29%, sedangkan hasil akurasi rekomendasi mendapatkan hasil 89,99% pada data uji. Penelitian berhasil mengimplementasikan model berupa website yang dapat digunakan oleh pengguna dalam mencari kebutuhan harga rumah di daerah provinsi Jawa Barat kemudian rekomendasi diberikan dengan peralihan link menuju website rumah123.com untuk informasi lebih lengkap. Website yang sudah dibangun telah diuji dengan pengujian inferensial mendapatkan nilai precision yang didapatkan oleh sistem 75%, recall 100%, akurasi sistem 80%, sedangkan f-measure 86%, sedangkan pada sistem rekomendasi mendapatkan nilai precision yang didapatkan oleh sistem 78%, recall 100%, akurasi sistem 80%, sedangkan f-measure 88%. Pengujian dengan user acceptance test pada website mendapatkan persentase sebesar 89.29% dengan kategori sangat baik.
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