Predicting Potential Car Buyers using Logistic Regression Algorithm
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B. P. Siahaan and E. A. Prasetio, “Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry,” Asian J. Technol. Manag. AJTM, vol. 15, no. 1, pp. 49–66, 2022, doi: 10.12695/ajtm.2022.15.1.4.
M. Vanhala, C. Lu, J. Peltonen, S. Sundqvist, J. Nummenmaa, and K. Järvelin, “The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research,” J. Bus. Res., vol. 106, pp. 46–59, Jan. 2020, doi: 10.1016/j.jbusres.2019.09.009.
S. Nusinovici et al., “Logistic regression was as good as machine learning for predicting major chronic diseases,” J. Clin. Epidemiol., vol. 122, pp. 56–69, Jun. 2020, doi: 10.1016/j.jclinepi.2020.03.002.
A. D. Nurhalim, “Analisis Faktor-Faktor yang Mempengaruhi Perilaku Konsumen dalam Keputusan Pembelian Mobil Toyota Avanza di Kota Tangerang,” Jambura Econ. Educ. J., vol. 5, no. 1, pp. 51–59, Dec. 2022, doi: 10.37479/jeej.v5i1.15263.
S. N. Untari, S. Djaja, and J. Widodo, “Strategi Pemasaran Mobil Merek Daihatsu pada Dealer Daihatsu Jember,” J. Pendidik. Ekon. J. Ilm. Ilmu Pendidik. Ilmu Ekon. Dan Ilmu Sos., vol. 11, no. 2, p. 82, Jan. 2018, doi: 10.19184/jpe.v11i2.6451.
M. P. LaValley, “Logistic Regression,” Circulation, vol. 117, no. 18, pp. 2395–2399, May 2008, doi: 10.1161/CIRCULATIONAHA.106.682658.
F. Harahap, A. Y. N. Harahap, E. Ekadiansyah, R. N. Sari, R. Adawiyah, and C. B. Harahap, “Implementation of Naïve Bayes Classification Method for Predicting Purchase,” in 2018 6th International Conference on Cyber and IT Service Management (CITSM), Parapat, Indonesia: IEEE, Aug. 2018, pp. 1–5. doi: 10.1109/CITSM.2018.8674324.
S. P. Shakti, M. K. Hassan, Y. Zhenning, R. D. Caytiles, and I. N.Ch.S.N, “Annual Automobile Sales Prediction Using ARIMA Model,” Int. J. Hybrid Inf. Technol., vol. 10, no. 6, pp. 13–22, Jun. 2017, doi: 10.14257/ijhit.2017.10.6.02.
A. Hasyim, M. Fatchan, and W. Hadikristanto, “Penerapan Algoritma Naïve Bayes Dalam Memprediksi Tingkat Penjualan Mobil Tahun 2022,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 4, no. 02, pp. 207–215, Nov. 2022, doi: 10.46772/intech.v4i02.872.
S. A. Sinaga, “Implementasi Metode Arima (Autoregressive Moving Average) Untuk Prediksi Penjualan Mobil,” J. Glob. Technol. Comput., vol. 2, no. 3, pp. 102–109, Aug. 2023, doi: 10.47065/jogtc.v2i3.4013.
F. G. Walelang, C. E. J. C. Montolalu, and D. Hatidja, “Penerapan Metode Autoregressive Integrated Moving Average (ARIMA) dalam Model Intervensi Fungsi Step untuk Memprediksi Penjualan Mobil di PT. Hasjrat Abadi Cabang Tendean Manado.” Indonesian Journal of Intelligence Data Science, May 15, 2023.
I. Hadjar, “Regresi Logistik: Menaksir Probabilitas Peristiwa Variabel Binari,” Phenom. J. Pendidik. MIPA, vol. 7, no. 2, pp. 137–163, Jan. 2018, doi: 10.21580/phen.2017.7.2.1385.
D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression, Third edition. in Wiley series in probability and statistics. Hoboken, NJ: Wiley, 2013.
D. G. Kleinbaum and M. Klein, Logistic Regression. in Statistics for Biology and Health. New York, NY: Springer New York, 2010. doi: 10.1007/978-1-4419-1742-3.
M. Nourelahi, A. Zamani, A. Talei, and S. Tahmasebi, “A Model to Predict Breast Cancer Survivability Using Logistic Regression”.
Z. Khemais, D. Nesrine, and M. Mohamed, “Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression,” Int. J. Econ. Finance, vol. 8, no. 4, p. 39, Mar. 2016, doi: 10.5539/ijef.v8n4p39.
G. M. Allenby and P. J. Lenk, “Modeling Household Purchase Behavior with Logistic Normal Regression,” J. Am. Stat. Assoc., vol. 89, no. 428, pp. 1218–1231, Dec. 1994, doi: 10.1080/01621459.1994.10476863.
C. Mood, “Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It,” Eur. Sociol. Rev., vol. 26, no. 1, pp. 67–82, Feb. 2010, doi: 10.1093/esr/jcp006.
G. Rajagopalan, A Python Data Analyst’s Toolkit: Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics. New York: Apress, 2021.
D. R. S. Saputro and P. Widyaningsih, “Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method for the parameter estimation on geographically weighted ordinal logistic regression model (GWOLR),” Yogyakarta, Indonesia, 2017, p. 040009. doi: 10.1063/1.4995124.
J. T. Townsend, “Theoretical analysis of an alphabetic confusion matrix,” Percept. Psychophys., vol. 9, no. 1, pp. 40–50, Jan. 1971, doi: 10.3758/BF03213026.
N. A. Obuchowski, “ROC Analysis,” Am. J. Roentgenol., vol. 184, no. 2, pp. 364–372, Feb. 2005, doi: 10.2214/ajr.184.2.01840364.
D. Phung, G. I. Webb, and C. Sammut, Encyclopedia of Machine Learning and Data Science. New York, NY: Springer US : Imprint: Springer, 2020.
DOI: https://doi.org/10.32520/stmsi.v13i3.4068
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