M. Ananda Dzikrul Hakul Amri
Bachelor of Industrial Engineering, Universitas Bojonegoro, Indonesia

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Analysis Of Television (TV) Brand Switching Prediction In XYZ MSME Market Share Using Markov Chain M. Ananda Dzikrul Hakul Amri; Rizky Stighfarrinata; Rio Candra Pratama
International Journal of Technology and Education Research Vol. 4 No. 02 (2026): International Journal of Technology and Education Research (IJETER)
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v4i02.4460

Abstract

The rapid development of digital broadcasting technology and the implementation of the Analog Switch-Off (ASO) policy have intensified competition among television brands and increased the occurrence of brand switching behavior among consumers. This condition requires business owners to understand consumer switching patterns in order to anticipate market share changes and develop more effective marketing strategies. This study aims to analyze consumer switching patterns among television brands, predict future market share, and determine the long-term equilibrium condition (steady state) using the Markov Chain method. The research was conducted at UMKM XYZ using a quantitative approach with data collected from questionnaires distributed to 50 respondents over a 90-day observation period. The analysis involved constructing a transition probability matrix, determining the initial market share vector, forecasting market share across several periods, and identifying the steady-state condition. The results indicate that the initial market shares were 18% for Aqua, 32% for LG, 24% for Polytron, and 26% for Sharp. The forecast shows that Polytron experienced the highest market share growth and became the dominant brand, while LG experienced a significant decline. The steady-state condition was reached in the fifth period, with long-term market shares of 24% for Aqua, 17% for LG, 37% for Polytron, and 23% for Sharp. These findings demonstrate that the Markov Chain method is effective for predicting brand switching behavior and market share distribution, providing valuable support for marketing decisions and inventory management in UMKM XYZ.