Penerapan Algoritma K-Means Clustering Untuk Harga Rumah di Jakarta Selatan
DOI:
https://doi.org/10.30640/trending.v1i2.753Keywords:
K-Means Clustering, Secondary Data, House Prices, Hous, Dbi ValueAbstract
The price of a house is the value or cost assigned to a residential property, usually expressed in a certain currency. Home prices are determined by various factors, such as location, size, condition, facilities owned, as well as market factors such as demand and competition. House prices are a useful tool for analyzing and understanding the housing market in a given area by displaying information about house prices in that area in an organized and easy-to-read format. The k-means clustering algorithm is used to classify house price data based on features such as location, size, type of house, and so on. The purpose of using the k-means clustering algorithm is to find out the price differences between groups and determine the appropriate price for each group. The results of this analysis can be used to assist in the decision-making process in industrial property, including marketing, determining the selling price, and property development. The data collection technique that the researcher chose used secondary data techniques, which are sources of research data that can produce a number of collected data. during research conducted quickly and informally through the use of intermediary media. The research source that the researchers chose came from Kaggle, with a total of 1010 data on house prices. Based on the research results, it can be concluded that house prices in South Jakarta can be grouped into 10 clusters according to the best dbi value that is 0.129.
References
(Y. R. Sari et al., 2020)Al-rizki, M. F. I., Widaningrum, I., & Buntoro, G. A. (2020). Prediksi Penyebaran Penyakit TBC dengan Metode K-Means Clustering Menggunakan Aplikasi Rapidminer. 5(1), 1–10. https://doi.org/10.31544/jtera.v5.i1.2020.1-10
Apuilino Iman Seno Aji, F., Achmadi, S., & Ariwibisono, F. (2021). Penerapan Metode Clustering Pada Analisis Realisasi Pendapatan Asli Daerah Dengan Algoritma K-Means. JATI (Jurnal Mahasiswa Teknik Informatika), 5(2), 443–451. https://doi.org/10.36040/jati.v5i2.3741
Bastian, A., Sujadi, H., & Febrianto, G. (n.d.). Penerapan Algoritma K-Means Clustering Analysis Pada Penyakit Menular Manusia (Studi Kasus Kabupaten Majalengka). 1, 26–32.
Dinata, R. K., Safwandi, S., Hasdyna, N., & Azizah, N. (2020). Analisis K-Means Clustering pada Data Sepeda Motor. INFORMAL: Informatics Journal, 5(1), 10. https://doi.org/10.19184/isj.v5i1.17071
Fatmawati, K., & Windarto, A. P. (2018). Data Mining: Penerapan Rapidminer Dengan K-Means Cluster Pada Daerah Terjangkit Demam Berdarah Dengue (Dbd) Berdasarkan Provinsi. Computer Engineering, Science and System Journal, 3(2), 173. https://doi.org/10.24114/cess.v3i2.9661
Ginting, F., Buulolo, E., & Siagian, E. R. (2019). Implementasi Algoritma Regresi Linear Sederhana Dalam Memprediksi Besaran Pendapatan Daerah (Studi Kasus: Dinas Pendapatan Kab. Deli Serdang). KOMIK (Konferensi Nasional Teknologi Informasi Dan Komputer), 3(1), 274–279. https://doi.org/10.30865/komik.v3i1.1602
Mohede, R. M., Rotinsulu, D. C., Tumangkang, S. Y. L., Pembangunan, J. E., Ekonomi, F., & Ratulangi, U. S. (2020). DAERAH TERHADAP PENINGKATAN PENDAPATAN ASLI DAERAH ( PAD ) DI KABUPATEN KEPULAUAN SANGIHE. 20(01), 45–54.
Purba, W., Siawin, W., & . H. (2019). Implementasi Data Mining Untuk Pengelompokkan Dan Prediksi Karyawan Yang Berpotensi Phk Dengan Algoritma K-Means Clustering. Jurnal Sistem Informasi Dan Ilmu Komputer Prima(JUSIKOM PRIMA), 2(2), 85–90. https://doi.org/10.34012/jusikom.v2i2.429
Ramdhan, D., Dwilestari, G., Dana, R. D., Ajiz, A., & Kaslani, K. (2022). Clustering Data Persediaan Barang Dengan Menggunakan Metode K-Means. MEANS (Media Informasi Analisa Dan Sistem), 7(1), 1–9. https://doi.org/10.54367/means.v7i1.1826
Sagala, R. M. (2021). Prediksi Kelulusan Mahasiswa Menggunakan Data mining Prediction of college subject using K-means Algorithm in Data mining. Jurnal TeIKa, 11(2), 131–142.
Sari, R. M., Tasril, V., & M, Y. A. (2020). Prediksi Jumlah APBD Kota Payakumbuh dengan metode K-Means. 1, 45–50.
Sari, Y. R., Sudewa, A., Lestari, D. A., & Jaya, T. I. (2020). Penerapan Algoritma K-Means Untuk Clustering Data Kemiskinan Provinsi Banten Menggunakan Rapidminer. CESS (Journal of Computer Engineering, System and Science), 5(2), 192. https://doi.org/10.24114/cess.v5i2.18519
Studi, P., & Informatika, T. (2022). Vol . 15 No . 1 September 2022 ISSN : 1979-8415 PENERAPAN ALGORITMA K-MEANS UNTUK MENGELOMPOKKAN DATA PENGIRIMAN PAKET DI KANTOR POS CIREBON ISSN : 1979-8415. 15(1), 23–27.
