| dc.description.abstract | Evolving Connectionist System (ECoS) is a model of Evolving Intelligence
Systems (EIS) which has an adaptive and continuous learning method by
developing its own structure and function in a machine learning structure.
Multivariate Adaptive Regression Splines (MARS) is a model that has been
developed for several regulations that produce a functional basis and can be said to
be an optimal predictive model and often uses business problem data. Where,
Financial Technology, which is often referred to as FinTech, is included in big data
problems in the business sector, which can be modeled on the predictions of users
and the FinTech industry. FinTech with various criteria that can be utilized, namely
P2P Lending. So that the trend of FinTech in its use can be predicted by the industry
using the MARS approach. However, the MARS results, which are a functional
basis for predicting the behaviour of the FinTech industry, have been successful
and obtained accuracy with an MSE of 0.020 and a GCV of 0.058. After obtaining
the results of the functional basis, it is followed by a machine learning algorithm
that is based on the ECoS principle and has been successfully carried out with
various combinations of parameters where the maximum accuracy is in the learning
rate 1 parameter with 0.3, learning rate 2 with 0.6, sensitivity threshold with 0.3 and
error threshold with 0.1. of 0.433175207. and seen in the MARS model which is
then followed by ECoS, the actual value is not much different from the predicted
value. So that this algorithm is able to predict the behaviour of the FinTech industry. | en_US |
