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Journal : Journal of Computer Science and Technology Application

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Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques Eryc; Nasib; Muh. Fahrurrozi; Ramzi Zainum Ikhsan; Parker, Jonathan
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/p8sbs746

Abstract

This study, titled Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques, explores how artificial intelligence (AI) particularly machine learning (ML) can enhance the accuracy and strategic impact of business forecasting in dynamic markets. Traditional statistical forecasting methods often fail to accommodate complex, nonlinear, and high-dimensional data. To address this gap, the research develops and validates a machine learning–based forecasting model designed to integrate predictive analytics into strategic decision-making. The study adopts a quantitative approach and employs Structural Equation Modeling (SEM) using SmartPLS 3 to examine the interrelationships among four latent variables: Market Trends (MT), Forecasting Accuracy (FA), Strategic Planning Efficiency (SPE), and Business Performance (BP). Each construct is measured using three indicators, forming a structural model that tests six hypothesized relationships. The results indicate that understanding market trends significantly improves forecasting accuracy and strategic planning efficiency, which in turn positively influences business performance. Furthermore, forecasting accuracy directly enhances both planning efficiency and overall performance, emphasizing the strategic value of data-driven insights. The findings validate the reliability and predictive power of the proposed model, offering a robust framework for organizations aiming to leverage machine learning in strategic forecasting. By bridging the gap between algorithmic prediction and managerial application, this study contributes to the growing field of AI-driven business analytics and supports the development of more agile, informed, and resilient business strategies in a data-centric economy.
Co-Authors Adrian Leonardus Joviyanto Saragi Alvin Siswanto Angeline Ardiansyah Ardiansyah Muhammad Calvin Decholas Valentino Wang Erved Indasari Indasari Deu Juliandry, Cindy Liegestu, Suryo Pramono Muh. Fahrurrozi Muhammad Ardiansyah Nasib Parker, Jonathan Ramzi Zainum Ikhsan Sandriawan, Elfan Santoso, Didi Santoso, Vincent Stanley Tjahyadi, Surya Tony Wibowo, Tony Wenky Willson Windi