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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Informatics and Communication Technology (JICT) Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) Journal of International Multidisciplinary Research Jurnal Pengabdian kepada Masyarakat Journal of Informatics and Communication Technology (JICT) RUBINSTEIN IKRAM: Jurnal Ilmu Komputer Al Muslim ALMUISY: Journal of Al Muslim Information System
Yakub
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Web-Based Car Sales Prediction System Using the ARIMA (Autoregressive Integrated Moving Average) Model for Optimizing Automotive Marketing Strategies Kurnia, Yusuf; Yakub; Rudy Arijanto; Winson Layanda; Dwi Putra, Dicky Surya
RUBINSTEIN Vol. 4 No. 1 (2025): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i1.4039

Abstract

This study aims to develop a web-based car sales prediction system using the ARIMA (Autoregressive Integrated Moving Average) model to support the optimization of marketing strategies in the automotive sector. With the rapid growth of the automotive industry in Indonesia, companies, particularly car showrooms, face the challenge of accurately forecasting vehicle demand. Therefore, an ARIMA-based prediction system can assist in estimating future sales based on historical data, thereby improving stock management, distribution, and marketing strategies. The system was developed using five years of historical sales data and implemented the ARIMA model to forecast car sales for upcoming periods. It was built with the Python programming language, employing Flask for the backend and HTML, CSS, and JavaScript for the frontend. The prediction results are presented in the form of interactive graphs, enabling users to make data-driven decisions more effectively. System evaluation was carried out by measuring prediction accuracy using MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) metrics. The testing results indicate that the ARIMA model can generate predictions with a high level of accuracy, assisting showrooms in planning stock and promotional activities more efficiently. Furthermore, the system is equipped with a responsive user interface, making it easily accessible via mobile devices. This research contributes to the utilization of technology in sales planning, particularly in the automotive sector, by enabling more precise, efficient, and data-driven decision-making.
Co-Authors Arif Rahman Hakim Basri, Amat Dahlan lama bawa Dahlia Amelia Dwi Putra, Dicky Surya Hendratna, Gempa Jonathan Gunawan Jusmawati Khaerunnas Marini Umi Atmaja, Dewi Oktavianus Gunawan, Aldi Pralantina, Peny Raharjo, Dasa Rakhim, Indra Paripurna Rendy Agustian Hidayat Rimbawan Oprasto, Raditya Rudy Arijanto Saputri, Rena Sayyaf Kholillullah Siti Nurbaiti Siti Nurhayati Suhadarliyah Swastika GKD, Puri Syahruddin, Muhammad Tonggiroh, Mursalim Winson Layanda Yusuf Kurnia, Yusuf