One of the Cooperative of Financial Services is disbursed loans to debtors (members and prospective members). In lending (provision of credit) is likely to arise the problem, namely the possibility of debt default by the debtor. To anticipate the risk of default (credit risk), to prospective debtors applying for credit risk analysis was performed using credit scoring. In this paper the analysis of credit scoring is done using logistic regression model, which is estimated using genetic algorithms. As a numerical illustration, the method used to analyze the credit scoring on a cooperative of financial services in Indonesia. Of the eight factors were analyzed, it was only six factors that significantly influence to the risk of default. Six of these factors include: number of dependents, the amount of savings, the value of collateral, monthly income, credit limit is realized, and the loan repayment period.
Economic conditions in Indonesia are still unstable, causing the US dollar exchange rate to increase. This is because most international transactions in Indonesia use US dollars. Prediction or forecasting is chosen as one of the important things in choosing a market to invest in buying and selling. This research will focus on making forecasting applications and analyzing the exchange rate of USD against rupiah based on time series data or temporal datasets from the Investing.com site using machine learning methods, namely Extreme Gradient Boosting (XGBoost). Applications created using the python programming language and streamlit framework. Modeling is carried out using the Knowledge Discovery in Database (KDD) methodology with the stages of dividing the dataset with a 50:50 percentage share into test and train data. The modeling uses hyperparameter tuning values, namely n_estimators = 1000, max_depth = 1, x_colsample_bytree = 0.9894, x_gamma = 0.9989, x_min_child = 1.0, x_reg_lamda = 0.2381, and x_subsample = 0.7063 with best loss or RMSE 451.4151. The values of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when making the model were 6.61374% and 3.95485%. Meanwhile, when testing the model, the RMSE is 0.23577% and MAPE is 0.11643%.
Until now, the pandemic conditions of Covid-19 are still ravaging the world, even in Indonesia and West Java. Various attempts have been made to stop it. West Java implements Large Scale Social Restrictions, is known as Pembatasan Sosial Skala Besar (PSBB). However, over time, a discourse emerged to loosen PSBB. One of the World Health Organization’s (WHO) requirements to loosen is the effective reproduction rate of Corona Virus cases below 1. Therefore, this study focuses on predicting the number of cases in West Java. The methods based on multi-layer perception (MLP) and linear regression (LR). The data were obtained from the C Covid -19 positive case from March to mid-August 2020 in West Java. The experiments show that MLP reaches optimal if it used 13 hidden layers with learning rate and momentum = 0.1. The MLP had a smaller error than LR. Both of them predict the number of cases in the next 30 days from August 14, 2020. The results show that West Java will still have an increase in the number of new cases of Covid -19.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.