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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Informatika Jurnal Teknologi Teknosains: Media Informasi Sains dan Teknologi Scientific Journal of Informatics Jurnal Komtika (Komputasi dan Informatika) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Telematika CCIT (Creative Communication and Innovative Technology) Journal Abdimasku : Jurnal Pengabdian Masyarakat Infotech: Journal of Technology Information Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Teknik Informatika dan Teknologi Informasi Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Jurnal Teknologi Buffer Informatika Jurnal Komtika (Komputasi dan Informatika) Jurnal Informatika Dan Tekonologi Komputer Jurnal Teknik Informatika dan Teknologi Informasi
Rahmat Sulaiman
ISB Atma Luhur
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture 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 Library & Information Science Mechanical Engineering Physics Social Sciences Other

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Carprice Intelligence: Prediction Price of Second Car using Machine Learning Sulaiman, Rahmat; Isnanto, Burham
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 Agustina Mardeka Raya Ahmad Faqih Alkayess Al Fathul, Rab Sahf Alkayess, Ahmad Faqih Aria Setiawan Cahya Saputra, Kornelius Eko Chrisna Putra, Rendy Rian Dwi Yuny Sylfania Elly Yanuarti Ellya Hellmud Fransiskus Panca Juniawan Harry Sudjikianto Hellmud, Ellya Indallah, Royhan Isnanto, Burham Juniawan, Fransiskus Panca Juwita, Ayu Ratna Kirana, Chandra Laurentinus Laurentinus, Laurentinus Marina Vebu Marini . Marini Marini Padli Padli Pradana, Harrizki Arie Pradana, Harrizki Arie Rab Sahf Al Fathul Romadiana, Parlia Royhan Indallah Sarwindah Sarwindah Sarwindah, Sarwindah Sudjikianto, Harry Sugihartono, Tri Sugihartono, Tri Yanuarti, Elly