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Fuzzy Time Series (FTS) has been growing rapidly in recent years. There are many models that were developed. In this paper, we propose a new method to forecast exchange rate data by combining some models. Firstly, we use the average-based interval to make optimal interval numbers. Secondly, we use frequency density-based partitioning for optimal partitioning. In this part, we divide the three highest frequency of intervals into four, three, and two sub-intervals, respectively, and discarding intervals if there is no data distributed. And thirdly, we use k-means clustering to construct the Fuzzy Logical Relationship Group (FLRG). We divide Fuzzy Logical Relationship (FLR) into 16 initial clusters. Then we evaluate model by calculating the error value using MSE (Mean Squared Error) and AFER (Average Forecasting Error Rates). The study case of this paper is daily exchange rate data (USD to IDR) started from January until May with its unstable fluctuation caused by Pandemic Covid-19. The study aims to obtain a forecasting model of exchange rate data as the preparation and evaluation for future conditions.

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Analyzing customer purchasing patterns can help minimarket expand marketing strategies by gaining insight into which items are often bought together by customers. Also, transaction data is a source of information available at the convenience store and one thing that can be used for business decision making. In this paper, we aim to use the Apriori algorithm method to obtain consumer purchasing patterns to analyze consumer purchasing patterns. This system uses a priori algorithm calculation method where the input data is consumer transaction data. Transaction data will be sorted and calculated by providing a minimum support value and a minimum confidence value. In this study, the authors conclude that the results of the analysis of information systems in determining consumer purchasing patterns can be as information to determine sales and in the application of Apriori algorithms can provide information in the form of a combination of consumer purchase patterns based on consumer transaction data with a minimum support value above 10% and the minimum confidence value is above 65%.

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Forecasting method based on fuzzy time series has been widely developed in recent years. In this paper, we propose a new improvement at determining universe of discourse, variation historical data and partitioning stage. At early stage, we define the universe of discourse then calculate the basis value to find out how much interval should be used with variatin historical data. Secondly, we are partitioning the main intervals into several numbers of sub-intervals. The empirical analysis shows that sub-interval caused the fuzzy number getting closer to crisp value. It causes the better forecasting value. We use the data of yearly production petrolium Indonesia for simulation. We compare the forecasting results and error value of the method with previous existing methods The modifications give better forecasting results than previous methods indicated with smaller The Means Squared Error (MSE) and Average Forecasting Error (AFER).

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