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Jurnal ICT : Information and Communication Technologies
Published by Marq & Cha Institute
ISSN : 20867867     EISSN : 28089170     DOI : https://doi.org/10.35335/jict
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering
Jurnal ICT : Information and Communication Technologies (p-ISSN: 2086-7867) is a scientific journal and open access journal published by Pusat Penelitian Teknoligi, Marqcha Institute, Indonesia. Jurnal JICT covers the field of Informatics, Computer Science, Information Technology and Communication.It was firstly published in 2010 for a printed version. The aims of Jurnal JICT are to disseminate research results and to improve the productivity of scientific publications. Jurnal JICT is published two times a year (April and October).
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 81 Documents
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Decision Support System in Marketing Strategy Using Data Mining Techniques Harahap, Leliana; Purba, Sartika Dewi; Panggabean, Jonas Franky R; Sirait, Kamson
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.299

Abstract

The increasing complexity of market competition and the rapid growth of enterprise data have made traditional marketing decision-making approaches inadequate in addressing information asymmetry and dynamic market changes. Conventional decision support systems (DSS) are often limited to data-level reporting and lack advanced analytical capabilities to uncover hidden patterns and strategic insights. This study aims to design and evaluate an intelligent marketing Decision Support System by integrating data warehousing, Online Analytical Processing (OLAP), and data mining techniques to enhance the quality and effectiveness of marketing decisions. The proposed method adopts a data-driven DSS architecture that performs extract–transform–load (ETL) processes to build a unified subject-oriented data warehouse, followed by multidimensional analysis and knowledge discovery using decision tree classification and neural network models. Experimental validation was conducted using FoodMart 2000 sales data to assess the predictive performance and decision support capability of the system. The results demonstrate that the three-layer BP neural network model achieved a mean absolute percentage error of 15.13% in sales prediction, indicating satisfactory forecasting accuracy, while simulation and sensitivity analyses reveal a positive correlation between promotional investment and corporate profit growth. These findings confirm that the proposed marketing DSS can effectively reduce information asymmetry, improve forecasting reliability, and support strategic marketing decisions related to pricing, promotion, and market expansion. The study implies that integrating data mining with DSS provides a robust analytical foundation for data-driven marketing management and sustainable enterprise competitiveness in complex market environments.

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