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


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.


Business metric; Hybrid model; Multi-criteria decision making; Multi-objectives genetic algorithm complexity prediction model

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).