Machine Learning Techniques for Predicting the Success of AI-Enabled Startups in the Digital Economy

Authors

  • Dariari Robert Eesp Incorporation Author
  • Fitra Putri Oganda University of Raharja Author
  • Asep Sutarman Universitas Muhammadiyah Prof. Dr. Hamka Author
  • Wahyu Hidayat University of Raharja Author
  • Anandha Fitriani University of Raharja Author

Keywords:

Smart-PLS, Machine Learning, StartUp, Artificial Intelligence, Digital

Abstract

AI-enabled startups in the digital economy have unique challenges in achieving success. Therefore, it is essential to understand the factors that influence the success of these startups. This study uses data analysis methods such as Smart-PLS 4.0, which has three stages: Outer Model Analysis, Inner Model Analysis, and Hypothesis Testing. By using five variables and ten indicators, 5x10 = 50 indicators. Of course, this cannot be separated from Machine Learning/Machine Learning which uses the Python programming language, which can be used to accurately predict consumer behavior, business performance, or decision-making from startup data. However, using these methods separately may result in a less accurate or inadequate model. The results show that a combination of Smart-PLS and Machine Learning/machine learning techniques can produce more accurate predictive models and can be used to predict the success rate of AI-enabled startups in the digital economy. That way, this model can assist startup entrepreneurs in making strategic decisions to increase their business success.

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Published

2024-02-29

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How to Cite

Machine Learning Techniques for Predicting the Success of AI-Enabled Startups in the Digital Economy. (2024). CORISINTA, 1(1), 61-69. https://journal.corisinta.org/index.php/corisinta/article/view/26