Prediksi Harga Mobil Bekas Menggunakan Algoritma Regresi Linear Berganda

Dea Miftahul Huda, Gifthera Dwilestari, Ade Rizki Rinaldi, Iin Solihin

Abstract


The lack of information regarding used car prices creates obstacles for people in buying and selling vehicles because they don't understand the market prices that are used as a reference. This information is very important to know price predictions with the range of variables that can be considered. The aim is to process an algorithm model that is capable of carrying out statistics using appropriate techniques to make predictions. Prediction is a very important technique in decision making. The linear regression algorithm is a model building technique used to predict the value of a given dataset. In this study, a multiple linear regression algorithm was used to predict used car prices. The dataset used to create a prediction model with a linear regression algorithm was sourced from the Kaggle repository for used car prices and then the results were visualized in Rapminer. The prediction process uses a comparison of testing data and training data with a ratio of 90 training data and 10 testing data in the process of building the model and evaluating the model that has been produced. The result of the prediction process using the linear regression algorithm is a prediction model of Price 1637.49. The prediction model will be evaluated with 2 assessment indicators, namely RMSE and Relative Error. The results obtained from this model, in the Price category, the RMSE value is 1637.49 and the Relative Error value is 11.89%. And the application of the regression model to new data from the independent variables used is the attribute Age (Age) 24 X1, Kilometers (KM), 783764 X2, Horse power (HP) 100 X3, Transmission (Automaitc) 0 X4, Engine capacity (CC) 1500 regression equation Y = b1 + b2X1 + b3X2 + b4X3 + b5X4 +b6X5 +b7X6.


Keywords


prediction, price, dataset, multiple linear regression, regression

Full Text:

PDF

References


Adiguno, S., Syahra, Y. And Yetri, M. (2022) ‘Prediksi Peningkatan Omset Penjualan Menggunakan Metode Regresi Linier Berganda’, Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 1(4), P. 275. Available At: Https://Doi.Org/10.53513/Jursi.V1i4.5331.

Akmal, K., Faqih, A. And Dikananda, F. (2023) Perbandingan Metode Algoritma Naïve Bayes Dan K-Nearest Neighbors Untuk Klasifikasi Penyakit Stroke, Jurnal Mahasiswa Teknik Informatika. Available At: Www.Researchgate.Net.

Arif, M. And Syukur, D. (2023) ‘Penerapan Model Regresi Linear Untuk Estimasi Mobil Bekas Menggunakan Bahasa Python’, Jurnal Ilmiah Matematika, Sains Dan Teknologi, 11(2), Pp. 182–191.

Bramasto, S. And Khairiani, D. (2022) ‘Prediksi Daya Output Sistem Pembangkit Listrik Tenaga Surya ( PLTS ) Menggunakan Regresi Linear Berganda’, Faktor Exacta, 15(3).

Ginanto (2021) ‘Prediksi Penjualan Kendaraan Niaga Berdasarkan Kinerja Purnajual Dan Pertumbuhan Pasar’, 14(4), Pp. 214–224. Available At: Https://Doi.Org/10.30998/Faktorexacta.V14i4.9447.

Ginting, F., Buulolo, E. And 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), Pp. 274–279. Available At: Https://Doi.Org/10.30865/Komik.V3i1.1602.

Hasibuan, E. (2022) ‘Implementasi Machine Learning Untuk Prediksi Harga Mobil Bekas Dengan Algoritma Regresi Linear Berbasis Web’, Jurnal Ilmiah Komputasi, 21(4), Pp. 595–602. Available At: Https://Doi.Org/10.32409/Jikstik.21.4.3327.

Karlina, D. (2023) ‘Estimasi Hasil Panen Ayam Pedaging Menggunakan Algoritma Regresi Linear Berganda’, Kajian Ilmiah Informatika Dan Komputer, 3(6), Pp. 966–976. Available At: Https://Doi.Org/10.30865/Klik.V3i6.920.

Kemal (2021) Pengaruh Minat Beli Pada Produk Mobil Wuling.

Nur Wahyudin, D. (2020) Penerapan Algoritma Regresi Linear Berganda Pada Estimasi Penjualan Mobil Astra Isuzu Implementation Of Double Linear Regression Algorithm On Sales Estimation Of Astra Isuzu Car.

Ristamaya, W., Studi Sistem Informasi, P. And Triguna Dharma, S. (2020) ‘Penerapan Data Mining Untuk Memprediksi Penjualan Sepeda Motor Pada PT Mitra Pinasthika Mustika Di Periode Yang Akan Datang Menggunakan Metode Regresi Linier Berganda’, X. No.X.

Rizky, Dkk, . (2019) ‘Implementasi Data Mining Untuk Memprediksi Target Pemakaian Stok Barang Menggunakan Metode Regresi Linier Berganda’, Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 18(2), P. 167. Available At: Https://Doi.Org/10.53513/Jis.V18i2.156.

Susanti, P. And Sussolaikah, K. (2022) Penerapan Metode Regresi Linear Untuk Memprediksi Harga Jual Mobil Bekas Yaris Dan Jazz Pada Wilayah Dki Jakarta Application Of Linear Regression Method To Predictable The Selling Price Of Yaris And Jazz Brand Car In The Dki Jakarta Area, Jurnal Ilmiah Nero.

Triyanto Ervan, D. (2019) ‘Implementasi Algoritma Regresi Linear Berganda Untuk Memprediksi Produksi Padi Di Kabupaten Bantul’, Jurnal Teknologi dan Sistem Informasi Univrab Volume 4 No. 2, 4(2), pp. 73–86.




DOI: http://dx.doi.org/10.36499/jinrpl.v6i1.10266

Refbacks

  • There are currently no refbacks.


INDEXED BY :

Google Scholar GarudaCrossrefBASE Dimensions DOAJOne Search Scilit Sinta

 

 

 


Address : :

Fakultas Teknik Universitas Wahid Hasyim

JL. Menoreh Tengah X / 22, Sampangan, Gajahmungkur, Kota Semarang, Jawa Tengah 50232, Indonesia
Handphone: 0815-6529-309
Email: jinformatika@unwahas.ac.id
 
 
RJI JournalStories Main logo
View My Stats
ISSN : 2656-2855   E-ISSN : 2685-5518