HUSAIN, MUHAMMAD HAFIZH (2023) PERBANDINGAN AKURASI PREDIKSI HARGA RUMAH ALGORITMA K-NEAREST NEIGHBOR DENGAN ALGORITMA EXTREME GRADIENT BOOST (XGBOOST). S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Rumah merupakan suatu bangunan yang merupakan kebutuhan primer bagi manusia untuk berlindung dari gangguan luar, selain itu berfungsi sebagai hunian, tempat manusia melangsungkan kehidupannya dan berumah tangga. Dengan banyaknya variasi harga, ukuran, dan fasilitas rumah membuat calon pembeli rumah harus cermat dalam memilih rumah, Faktor budget juga yang menjadi alasan oleh calon pemilik rumah agar mendapatkan rumah yang nyaman, aman, serta layak untuk dihuni. Dengan dipilihnya algoritma K-Nearest Neighbor serta algoritma Extreme Gradient Boost diharapkan dapat membantu dalam melakukan prediksi harga sesuai dengan ukuran, fasilitas dan budget dari calon pembeli rumah tersebut. Hasil dari penelitian ini dengan prediksi harga rumah menggunakan algoritma K-Nearest Neighbor memiliki tingkat akurasi yang kurang yaitu sebesar 45,46%. sedangkan pada prediksi harga rumah menggunakan algoritma Extreme Gradient Boost (Xgboost) memiliki tingkat akurasi yang lebih baik yaitu sebesar 80,26% serta terdapat aspek apa saja yang mempengaruhi naik atau turunnya harga rumah. Dengan adanya prediksi harga menggunakan algoritma Extreme Gradient Boost (Xgboost) calon pembeli diharapkan dapat membeli rumah dengan harga yang sesuai dengan budget lalu mengetahui berapa kenaikan harga yang terjadi Kata kunci: Prediksi, Daftar harga rumah, Perbandingan, Algoritma K-Nearest Neighbor, Algoritma Extreme Gradient Boost House is a building which is a primary need for humans to protect themselves from external disturbances, besides that it functions as a residence, a place for humans to carry out their lives and have a household. With so many variations in price, size, and home facilities, potential home buyers must be careful in choosing a home. The budget factor is also the reason for prospective homeowners to get a home that is comfortable, safe, and suitable for habitation. By choosing the K-Nearest Neighbor algorithm and the Extreme Gradient Boost algorithm, it is hoped that it can help predict prices according to the size, facilities and budget of the prospective home buyer. The results of this study with predictions of house prices using the K-Nearest Neighbor algorithm have a less accurate level of 45.46%. while the prediction of house prices using the Extreme Gradient Boost (Xgboost) algorithm has a better accuracy rate of 80.26% and found what aspect that can influence the rise or fall in house prices. With price predictions using the Extreme Gradient Boost (Xgboost) algorithm, prospective buyers are expected to be able to buy a house at a price that fits their budget and then find out how much the price increase has occurred. Keywords: Prediction, House price list, Comparison, K-Nearest Neighbor Algorithm, Extreme Gradient Boost Algorithm
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO 23 023 |
| NIM/NIDN Creators: | 41519210007 |
| Uncontrolled Keywords: | Prediksi, Daftar harga rumah, Perbandingan, Algoritma K-Nearest Neighbor, Algoritma Extreme Gradient Boost |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 27 Sep 2023 03:39 |
| Last Modified: | 27 Sep 2023 03:39 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/81505 |
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