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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi CSRID (Computer Science Research and Its Development Journal) Magisma: Jurnal Ilmiah Ekonomi dan Bisnis JSAI (Journal Scientific and Applied Informatics) Technologia: Jurnal Ilmiah Journal of Information Systems and Informatics Teknomatika (Jurnal Teknologi dan Informatika) Jurnal Sisfotek Global Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Pengabdian Mandiri Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Jurnal Pengabdian Masyarakat Tekno E-Amal: Jurnal Pengabdian Kepada Masyarakat Buffer Informatika Jurnal Ekonomi, Akuntasi dan Manajemen Indonesia(JEAMI) Widya Cipta : Jurnal Sekretari dan Manajemen
R Burham Isnanto
Institut Sains Dan Bisnis Atma Luhur
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Carprice Intelligence: Prediction Price of Second Car using Machine Learning Rahmat Sulaiman; Burham Isnanto
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37602

Abstract

Determining a fair market price for a used vehicle is a significant challenge for both sellers and buyers due to a lack of data transparency and the variety of influencing factors such as brand, production year, and mileage. This research aims to address this issue by developing a market data-based used vehicle price prediction system using a machine learning approach. The methodology adapts the CRISP-DM framework. Data is collected through web scraping from leading online marketplaces and processed through cleaning, normalization, and encoding before being used for modeling. Various regression algorithms were implemented, and the Linear Regression model was chosen for its optimal performance. The model was evaluated using the R Squared metric, yielding a score of 74% on the training data and 76% on the test data, demonstrating good accuracy and adaptability to new data. The best model was then implemented into a simple user interface based on Streamlit, allowing users to get a more objective recommendation for buying and selling prices. Overall, this system has great potential to facilitate more efficient and transparent transactions in the used automotive market, helping users make smarter and more profitable decisions.
Co-Authors Agus Dendi Rachmatsyah Agus Dendi Rachmatsyah Alkodri, Amir Amri Andreani Andreani Ari Amir Alkodri Bambang Adi Winoto Bambang Adiwinoto Benny Wijaya Elly Yanuarti Elly Yanuarti Erwin Ferry Bakti Ferry Bakti Atmaja Gita Aditia Hengki Kiswanto Laurentinus, Laurentinus Lili Indah Sari Maxrizal R Ferry Bakti Atmaja R.Ferry Bakti Atmaja Rahmat Sulaiman Rahmat Sulaiman Riyan Nugraha Sarwindah Sarwindah Seno Hadi Saputro Sujono Supardi Supardi Wishnu Aribowo Probonegoro Yulianti