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Öğe Financial technology forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior(Institute of Advanced Engineering and Science, 2024) Al-Khowarizmi; Watts, Michael J.; Efendi, Syahril; Kamil, Anton AbdulbasahFinancial technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. FinTech can make any transactions anywhere with the pillars of peer-to-peer (P2P) lending, merchants, and crowdfunding. In the P2P lending pillar, there are borrowers and lenders who are digitized in FinTech devices. FinTech in Indonesia is controlled by a state agency called the financial services authority or otoritas jasa keuangan (OJK). In the movement of P2P lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as evolving connectionist system (ECoS), which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a mean absolute percentage error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1=0.6 and a learning rate 2=0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders. © 2024, Institute of Advanced Engineering and Science. All rights reserved.Öğe Sample median approximation on stochastic data envelopment analysis(Inderscience Publishers, 2020) Nasution, Marah Doly; Mawengkang, Herman; Kamil, Anton Abdulbasah; Efendi, Syahril; SutarmanThis paper study a new approximation model to solving stochastic data envelopment analysis (SDEA) problem. The proposed approach is based on problems that might occur in everyday life. This paper discusses the approach in determining the efficiency and super efficiency ratings of a decision making unit (DMU) in the DEA model with stochastic data. In determining efficiency, SDEA is first transformed into an equivalent deterministic DEA by changing its chance constraints in such a way that the SDEA problem can be solved easily. The author proposes an approach technique called a sample median approximation (SMA) to change the chance constraints so that it will be easy to get the optimal solution in determining the efficiency of DMUs. In the process, the data to be processed first is determined by the median average which will later be considered to represent the actual sample average. As a numerical example, the author resolves the vendor selection problem as presented by Wu and Olson (2006) in their paper. By taking the same parameter value (a = 0.2 and beta = 0.9), the efficiency score and super efficiency of the problem are obtained. © 2020 Inderscience Enterprises Ltd.. All rights reserved.