PREDIKSI HARGA RUMAH DI JABODETABEK MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK

Muhammad Abdullah Hafizh, Subairi Subairi, Raditya Dimas Libriawan, Naufal Duta Maulana, Agung Mustika Rizki

Abstract


A house is a fundamental need for humans. Determining house prices is a crucial aspect of property transactions, especially in major areas like Jabodetabek, where property prices are consistently rising. Prediction is a suitable tool to assist in decision-making for determining house prices. There are numerous methods that can be applied for prediction; the author employs the Artificial Neural Network (ANN) method. ANN is known as a highly flexible predictive algorithm capable of accommodating various input features. The results of using the ANN method for predicting house prices in the Jabodetabek area show a Mean Absolute Error (MAE) of 0.209, Mean Squared Error (MSE) of 0.159, and Mean Absolute Percentage Error (MAPE) of 4.951.


Keywords


Prediksi; Harga Rumah; Spesifikasi; Artifical Neural Network; Jabodetabek

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References


M. Labib Mu’tashim, S. A. Damayanti, H. N. Zaki, T. Muhayat, and R. Wirawan, “Analisis Prediksi Harga Rumah Sesuai Spesifikasi Menggunakan Multiple Linear Regression,” vol. 3, p. 2021.

N. Agustina, C. Putri, and D. B. Arianto, “Komparasi Penggunaan Information Gain Pada Machine Learning untuk Memprediksi Harga Rumah di Jabodetabek,” Jurnal Sains dan Teknologi, vol. 5, no. 3, pp. 756–762, doi: 10.55338/saintek.v5i1.2052.

F. Febyanti, “Pemodelan Faktor-Faktor yang Mempengaruhi Harga Rumah di Jabodetabek Menggunakan Metode Regresi Probit,” Jurnal Riset Statistika, pp. 50–56, Jul. 2022, doi: 10.29313/jrs.vi.905.

A. Widyastuti, “Prediksi Harga Rumah Sesuai Spesifikasi Menggunakan Metode Multiple Linear Regression,” vol. 4, no. 1, pp. 30–35, 2024, [Online]. Available: http://ejurnal.unim.ac.id/index.php/submit

S. A. F. Harahap, M. F. Rafliansyah, A. Septyanto, and I. Pratama, “Studi Perbandingan Algoritma Machine Learning pada Kasus Image Classification Studi Perbandingan Algoritma Statistic, Ensemble, dan Neural Network Pada Kasus Image Classification,” Jurnal Rekayasa Elektro Sriwijaya, vol. 4, no. 2, pp. 46–51.

M. Haris Husni, R. Hendi Nofanolo Harefa, Ambiyar, and M. Agni Zaus, “Perbandingan Penggunaan Statistika Dan Machine Learning Dalam Teknik Pengolahan Data,” Jurnal Sistem dan Teknologi Informasi, vol. 6, pp. 440–450, Aug. 2024, [Online]. Available: https://journalpedia.com/1/index.php/jsti

W. Wilianto, Y. Yuliana, A. Suwandhi, J. Jimmy, and J. Putra, “Penerapan AI dalam Menentukan Harga Mobil Bekas Berdasarkan Tahun Perakitan,” Jurnal Minfo Polgan, vol. 13, no. 1, pp. 550–560, Jun. 2024, doi: 10.33395/jmp.v13i1.13728.

K. Alfin Yazi Bara Mentari, A. Falih Hartono, R. Phasca Sanggam Sinaga, A. Puspita Sari, and J. Timur, “Prediksi Harga Rumah Menggunakan Metode Regressi Lasso,” Prosiding Seminar Implementasi Teknologi Informasi dan Komunikasi, vol. 3, no. 2, 2024, doi: 10.31284/p.semtik.2024-2.6240.

C. P. S. Heru Supriyono, “Pemilihan Rumah Tinggal Menggunakan Metode Weighted Product,” Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika, vol. 1, no. 1, pp. 23–28, 2015.

D. Y. Descania, “Prediksi Pertumbuhan Penduduk Di Kecamatan Baregbeg Kabupaten Ciamis Menggunakan Metode Artificial Neural Network,” Jurnal Informatika dan Teknologi Pendidikan, vol. 2, no. 2, pp. 37–43, Dec. 2022, doi: 10.25008/jitp.v2i2.28.

Satrio Junaidi, R. Valicia Anggela, and D. Kariman, “Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwork (ANN),” Journal of Applied Computer Science and Technology, vol. 5, no. 1, pp. 109–119, Jun. 2024, doi: 10.52158/jacost.v5i1.489.

M. L. Mu’tashim, T. Muhayat, S. A. Damayanti, H. N. Zaki, and R. Wirawan, “Analisis Prediksi Harga Rumah Sesuai Spesifikasi Menggunakan Multiple Linear Regression,” Informatik : Jurnal Ilmu Komputer, vol. 17, no. 3, p. 238, 2021, doi: 10.52958/iftk.v17i3.3635.

W. Pangestu, A. Widodo, and B. Rahayudi, “Prediksi Jumlah Kendaraan Bermotor Di Indonesia Menggunakan Metode Average-Based Fuzzy Time Series,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 9, p. 964, 2018.




DOI: https://doi.org/10.31284/j.kernel.2024.v5i2.6806

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