Predicting Startup Success, a Literature Review

Harjo Baskoro (Doctor of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia)
Harjanto Prabowo (Doctor of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia)
Meyliana Meyliana (Doctor of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia)
Ford Lumban Gaol (Doctor of Computer Science Program, Bina Nusantara University, Jakarta, Indonesia)

Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In

The development and growth of startups around the world nowadays have become a global phenomenon. Startups have become an essential element of innovation and economic growth in many countries. But literature shows that the failure rate of a startup is around 90%. Therefore it is crucial for investors, financial advisors, and the government to spot the 10% which eventually will generate higher return rates, bring in greater revenue and ensure economic growth. This research aim is to study what are the critical factors of the startup’s success that can be used to make a predictive model using a machinelearning algorithm to predict the success of a startup.