Bulletin of Electrical Engineering and Informatics Vol. 12, No. 6, December 2023, pp. 3697~3705 ISSN: 2302-9285, DOI: 10.11591/eei.v12i6.5380  3697 Journal homepage: http://beei.org Complexity prediction model: a model for multi-object complexity in consideration to business uncertainty problems Rahmad B. Y. Syah, Habib Satria, Marischa Elveny, Mahyuddin K. M. Nasution Excellent Centre of Innovations and New Science-PUIN, Faculty of Engineering, Universitas Medan Area, Medan, Indonesia Data Science and Computational Intelligent Research Group, Medan, Indonesia Article Info ABSTRACT Article history: Received Dec 1, 2022 Revised May 28, 2023 Accepted Jun 5, 2023 In a competitive environment, the ability to rapidly and successfully scale up new business models is critical. However, research shows that many new business models fail. This research looks at hybrid methods for minimizing constraints and maximizing opportunities in large data sets by examining the multivariable that arise in user behavior. E-metric data is being used as assessment material. The analytical hierarchy process (AHP) is used in the multi-criteria decision making (MCDM) approach to identify problems, compile references, evaluate alternatives, and determine the best alternative. The multi-objectives genetic algorithm (MOGA) role analyzes and predicts data. The method is being implemented to expand the information base of the strategic planning process. This research examines business sustainability along two critical dimensions. First, consider the importance of economic, environmental, and social evaluation metrics. Second, the difficulty of gathering information will be used as a predictor for making long-term business decisions. The results show that by incorporating the complexity features of input optimization, uncertainty optimization, and output value optimization, the complexity prediction model (MPK) achieves an accuracy of 89%. So that it can be used to forecast future business needs by taking into account aspects of change and adaptive behavior toward the economy, environment, and social factors. Keywords: Business metric Hybrid model Multi-criteria decision making Multi-objectives genetic algorithm complexity prediction model This is an open access article under the CC BY-SA license. Corresponding Author: Rahmad B. Y. Syah Excellent Centre of Innovations and New Science-PUIN, Faculty of Engineering, Universitas Medan Area Medan, Indonesia Email: rahmadsyah@uma.ac.id 1. INTRODUCTION Increasing the productivity of business processes has become a major issue, both in academia and in business, because organizations must provide effective and efficient results [1], [2]. The impact of commercial digital business actors' characteristic patterns varies greatly and is highly competitive for users with diverse desires [3]. This is demonstrated by the continued expansion of business actors by prioritizing profit-oriented, revenue-generating activities, as well as consideration in increasing the competitiveness of the number of merchants obtained electronically, thereby providing users with a plethora of options for facilitating their transactions [4], [5]. There must be anticipation for business actors by preventing disruptions in order for the business to be sustainable, specifically by providing a significant social impact caused by business actors and users [6], [7]. The current issue is that people have a variety of digital shopping options that business actors must consider; as a result, business metrics are used in determining investment in long-term business opportunities [8], [9]. Multi criteria decision making (MCDM) is a decision making technique that chooses the