Implementasi Regresi Linier Berganda Untuk Prediksi Harga Mobil Bekas Di Indonesia Berbasis Gradio
DOI:
https://doi.org/10.47065/bulletincsr.v6i4.1097Keywords:
Gradio; Used Car; MSE; Mean Squared Error; Price Prediction; Multiple Linear RegressionAbstract
The price of a used vehicle depends on various aspects that cause changes in the selling value in the market, such as model, year, transmission, mileage, fuel, tax, mpg, and cc. A common problem in used car transactions is determining prices that are still not fully based on measurable data analysis. The purpose of this study is to design a model to estimate the price of a used car through the multiple linear regression method and implement it in the User Interface. The data used in this study is secondary data obtained from the Kaggle public repository, and collected from several used car buying and selling forums in Pekanbaru and social media platforms such as Facebook that contain vehicle price information. The dataset contains 400 rows of data with a range of car years from 2005 to 2025. The research stages include data preprocessing in the form of categorical variable encoding and data normalization. Data is divided into training data and testing data, followed by the process of model building and model performance assessment. Evaluation is carried out using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The model was built using several independent variables, namely model, year, transmission, kilometer, fuel, tax, mpg, and cc, with vehicle price as the dependent variable. Based on the test results, the multiple linear regression method shows the ability to produce used car price estimates and has potential for application in decision support systems. The test results show that the MSE value on the training data is 0.004 and the testing data is 0.010, MAE on the training data is 0.046 and the testing data is 0.071, and RMSE on the training data is 0.062 and 0.100 on the testing data, the coefficient of determination (R²) on the training data is 0.985 and on the testing data is 0.955. The next model is implemented using the Python Gradio library so that users can predict vehicle prices through the User Interface.
Downloads
References
A. S. Bachti, A. T. Agustama, H. Wiranata, E. Daniati, and A. Ristyawan, “Prediksi Harga Mobil Toyota Bekas Menggunakan Algoritma Regresi Linear Berganda,” Pros. SEMNAS INOTEK (Seminar Nas. Inov. Teknol., vol. 8, no. 1, pp. 173–183, 2024, doi: 10.29407/inotek.v8i1.4924.
D. Miftahul Huda, G. Dwilestari, and A. R. Rinaldi, “Prediksi Harga Mobil Bekas Menggunakan Algoritma Regresi Linear Berganda,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 6, no. 1, pp. 150–157, 2024, doi: 10.36499/jinrpl.v6i1.10266.
Ilham Amansyah, Jamaludin Indra, Euis Nurlaelasari, and Ayu Ratna Juwita, “Prediksi Penjualan Kendaraan Menggunakan Regresi Linear: Studi Kasus pada Industri Otomotif di Indonesia,” Innov. J. Soc. Sci. Res., vol. 4, no. 4, pp. 1199–1216, 2024, doi: 10.31004/innovative.v4i4.12735.
P. K. Dewi and Yoviapridiansyah, “Penerapan Algoritma Linear Regression dalam Prediksi Harga Mobil Bekas Berbasis Machine Learning,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 5, no. 2, pp. 473–480, 2026, doi: 10.53513/jursi.v5i2.12494.
E. Dewi, S. Mulyani, F. Mulady, D. Ramadhan, A. Ariyantono, and D. Ramdani, “Estimasi Harga Jual Mobil Bekas Menggunakan Metode Regresi Linier Berganda,” e-Jurnal JUSITI (Jurnal Sist. Inf. dan Teknol. Informasi), vol. 9, no. 1, pp. 1–8, 2020, doi: 10.36774/jusiti.v9i1.649.
Y. Haliza, Putri, R. Rafiza, and J. Simanullang, “Penerapan Regresi Linier Berganda dalam Memprediksi IPM Berdasarkan Faktor Ekonomi dan Sosial di Sumatera Barat,” Socius J. Penelit. Ilmu-Ilmu Sos., vol. 2, no. 12, pp. 544–554, 2025, doi: 10.5281/zenodo.15696487.
P. Aulia Azhar, M. Arya Pratama, and R. Fitriani, “Prediksi Harga Mobil Audi Bekas Menggunakan Model Regresi Linear dengan Framework Streamlit,” J. Technol. Informatics, vol. 6, no. 1, pp. 22–28, 2024, doi: 10.37802/joti.v6i1.763.
C. Whenjaya, H. Susanto, and Herman, “Model Prediksi Harga Mobil Bekas Menggunakan Regresi Linear Berganda Berdasarkan Fitur Kendaraan,” J. Digit. Ecosyst. Nat. Sustain., vol. 4, no. 2, pp. 95–99, 2024, doi: 10.63643/jodens.v4i2.263.
Nusyirwan, Irfadli, and Menhard, “Apakah Bauran Pemasaran Berpengaruh Terhadap Keputusan Pembelian Mobil Pada Agung Toyota Cabang Duri,” J. Ekon. Akunt. dan Manaj. Nusant., vol. 4, no. April, pp. 403–408, 2026, doi: 10.55338/jeama.v4i3.456.
A. Sajad and N. Nurmalitasari, “Analisis Faktor–Faktor Yang Mempengaruhi Harga Mobil Bekas Menggunakan Metode Regresi Linier,” Pros. Semin. Nas. Teknol. Inf. dan Bisnis, pp. 266–270, 2023, [Online]. Available: https://www.ojs.udb.ac.id/Senatib/article/view/3195
N. O. Idris and F. Pontoiyo, “Evaluasi Model Machine Learning untuk Prediksi Harga Mobil dengan Perbandingan Ensemble dan Regresi Linear,” J. Ilmu Komput. dan Sist. Inf., vol. 4, no. 1, pp. 129–143, 2025, doi: 10.70340/jirsi.v4i1.181.
M. Irpan, U. Rio, Karpen, and Hamdani, “Model Prediksi Jumlah Produksi Kelapa Sawit Menggunakan Regresi Linear Berganda Di PT . Surya Argolika Reksa,” J. Ilm. BETRIK, vol. 16, no. 02, pp. 122–130, 2025, doi: 10.36050/dv9sd116.
I. J. Matondang, E. Budianita, F. Syafria, and I. Afrianty, “BULLETIN OF COMPUTER SCIENCE RESEARCH Sistem Prediksi Produksi Kelapa Sawit Berbasis Gradio Menggunakan Algoritma Regresi Linear Berganda,” Bull. Comput. Sci. Res., vol. 6, no. 2, pp. 664–672, 2026, doi: 10.47065/bulletincsr.v6i2.994.
M. Imam and N. Pasaribu, “Implementasi Data Mining Dengan Metode Regresi Linier Untuk Memprediksi Laju Pertumbuhan Penduduk Kota Binjai Implementation of Data Mining Using Linear Regression Method to Predict Population Growth Rate of Binjai City,” J. Ilmu Komput. dan Sist. Inf., no. September, pp. 405–415, 2025, [Online]. Available: https://jurnal.unity-academy.sch.id/index.php/jirsi/article/view/247
A. A. Asriadi, C. A. Anggraini, F. I. Puspita, H. Juan, M. A. Imbiri, and U. Papua, “Prediksi Partisipasi Jemaat dalam Ibadah Penutupan Tahun 2025 Menggunakan Metode Regresi Linier Sederhana: Studi Kasus di Jemaat Efrata,” Sci-Tech J., vol. 4, no. 2, pp. 246–256, 2025, doi: 10. 56709/stj.v4i3.809.
M. U. Riyal et al., “Analisa Perbandingan Metode Trend Moment dan Regresi Linear dalam Prediksi Kurs Mata Uang Rupiah terhadap,” J. Comput. Syst. Informatics, vol. 6, no. 3, pp. 563–571, 2025, doi: 10.47065/josyc.v6i3.7400.
F. F. Az-zahra, T. Informatika, U. M. Sukabumi, K. Sukabumi, R. L. Berganda, and A. Regresi, “Prediksi hasil panen wortel menggunakan algoritma regresi linear berganda,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 5, pp. 10255–10262, 2024, doi: 10.36040/jati.v8i5.10954.
A. J. Karlina, M. Irsyad, F. Insani, and E. P. Cynthia, “Estimasi Hasil Panen Ayam Pedaging Menggunakan Algoritma Regresi Linear Berganda,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 3, no. 6, pp. 966–976, 2023, doi: 10.30865/klik.v3i6.920.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Regresi Linier Berganda Untuk Prediksi Harga Mobil Bekas Di Indonesia Berbasis Gradio
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 M Ridho Alfani; Elvia Budianita; Lestari Handayani, Siti Ramadhani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
