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All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING CCIT (Creative Communication and Innovative Technology) Journal Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Journal of Social Science IWJ : Inland Waterways Journal The Indonesian Journal of Computer Science
Nouval Trezandy Lapatta
Universitas Tadulako
Chemistry Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Physics Social Sciences Transportation

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Journal : Sistemasi: Jurnal Sistem Informasi

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Predicting Potential Car Buyers using Logistic Regression Algorithm Nouval Trezandy Lapatta; Abdullah Husin
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4068

Abstract

This research aims to develop a predictive model to identify individuals with a high potential to become car buyers, employing logistic regression algorithm. The primary objective is to support the automotive industry in devising more efficient and focused marketing strategies. The choice of logistic regression is based on its superiority in handling categorical dependent variables and its practicality in result interpretation. The data processed in this study derive from demographic information, consumption habits, brand preferences, and various other factors that influence car buying decisions. The main data source is the outcome of online surveys participated in by individuals predicted to have the potential to buy a car within the next 12 months. The analysis results indicate that factors such as income, age, previous vehicle ownership status, gender and marriage status play significant roles in predicting the likelihood of someone becoming a car buyer. The developed model achieved an accuracy and precision of 95%, proving its significant capability in identifying potential car buyers with a high success rate. These findings provide valuable insights for the automotive industry in formulating more targeted and efficient marketing strategies, as well as contributing to the academic literature on the application of logistic regression in consumer behavior prediction.
Co-Authors Abdillah Sani, Ilham Abdullah Husin Andi Hendra Angraeni, Dwi Shinta Deny Wiria Nugraha Djohari, Riyandi Dwitama Fahlevi, Mohammad Fazrin Firzatullah, Raden Muhamad Hajra Rasmita Ngemba Hanama, Ikhsan Wahyudin Pratama, Septiano Anggun Raden Muhammad Firzatullah Sukirman Sukirman Syaiful Hendra Yuri Yudhaswana Joefrie