APPLICATION OF THE FUZZY TIME SERIES MODEL IN CLOTHING MATERIAL STOCK FORECASTING

-The application of fuzzy time series is used to view the stock of clothing materials. As for the problem so far, CV Duta Express does not have a model for seeing the stock of complete materials in the warehouse, so the process is not optimal. This will have an impact on orders that come in at the same time and in large quantities. to avoid stock shortages, which resulted in the company experiencing losses. The purpose of this study is to make it easier to predict the stock of clothing materials and to be able to analyze every stock management at CV Duta Express with a fuzzy time series model. The variables used are stock needs, the amount of stock of school clothes, batik clothes, and pants. The research methodology in data collection consists of product type data from 2018–2021 at CV Duta Express Aceh Utara. Data analysis needs consist of school clothes, school pants, and clothes. Next, the fuzzy time series process determines the actual data is the type of school clothes, and what is forecast is sales for January 2018–2021 at the end of December. For a value of 1108 A2 fuzzification value, 172 A4 fuzzification value for batik clothes, and 894 pants with an A1 fuzzification value, then the value of the universe set used is U = [26, 323]. The value of forming a linguistic set is based on the length of the interval U3 = [111,153], U4 = [153,196], and U5 = [196,238]. The result of the fuzzification value from historical data for the value of 172 fuzzification is A4, for data of 133 fuzzification is A3. The formation of Fuzzy Logic Relationship (FLR) values for the period 1/7/2021 to 1/13/2021 is obtained from the data range A4=174 and range A3=125 in each period to be related. The results of forecasting with fuzzy time series testing at the end of December 2021 are 196 stocks of clothes that must be optimized in the following month. The test results in this study are to see if the error value using the AFER model is 0.4511% while the RMSE test value has an error value of 5.0199. After being calculated for the forecast every month, the average obtained for AFER is 0.50154 % and the RMSE is 9.86518..


INTRODUCTION
Computer technology and forecasting models are widely used to see the level of forecasts in the stock of clothing materials in a company.CV Duta Express is engaged in meeting the needs of meeting the stock of clothing materials and selling the clothing products.So far, CV Duta Express is still manual, starting from the system of recording incoming and outgoing goods and not using a model in viewing the stock of raw materials [1].Along with the development of its business, CV Duta Express has no control over the needs for goods that are often used by employees, and the large number of incoming and outgoing goods is no longer controlled.Therefore, a strategy is needed to see stock and the right sales and stock forecasting models for future stock inventories Sales is one element in exchanging products with customers and with other people [5].The existence of a prediction model can help predict future sales and can minimize losses on a product so that management can predict inventory optimally [6].Inventory control should not be reduced so that demand for spare parts is always available [7].A program technology is needed to predict the amount of inventory in the warehouse that does not reach capacity, causing losses because many items are not used [8].
Prediction results can be used to see a gold price trend in the future.time series prediction model for data accuracy in the following month to invest [9].Furthermore, the forecasting model can use machine learning and SVM models that can be used in forecasting [10] [11].Furthermore, fuzzy time series forecasting can predict in the following month the determination of rainfall with Chen's FTS series [12].The Fuzzy Time Series method can be used in forecasting the demand for the amount of inventory in the following month with the input variables consisting of demand, inventory, and sales [13].The fuzzy time series method is used in predicting world oil prices and the interval value in the fuzzy time series is very influential on the subsequent prediction results, so that the results of the formation of fuzzy values in the use of intervals are precise and effective in determining further forecasts [14] [15].The FTS method can predict the amount of demand for stock for the next month based on historical data and capture patterns from previous data [16] [17].There is an accuracy value in seeing the quality of test results using the AFFER and MSE equation models in the final test of the application of obesity prediction level testing [18].Therefore, a forecasting application is needed to forecast the stock distribution of fuzzy time series clothing models.

Destination
The aims of this research are: 1.To facilitate product forecasting and analysis of the results of incoming and outgoing stock of goods obtained from the implementation of CV Duta Express by using the time series method.2. With the stock forecasting of product sales using the average best fuzzy time series method on the CV Ambassador Express in Krueng Geukuh, the forecasting process can be done quickly and precisely based on predetermined variables.

Research Content 2.1 Research methodology
Research on the application of fuzzy time series models in forecasting the stock of clothing materials and distribution of clothing on CV Ambassador Express Aceh.This research data collection is quantitative.2.2: Data collection techniques The data needed in the study is forecasting data on product types at CV Duta Express from 2018 to 2021.The data provided includes product type data such as batik clothes, school clothes, and school pants.2. 3Data Analysis and DesignAnalysis of data sorting at CV Duta Express based on data analysis in the clothing stock section according to research needs, then designing an application/program to complete product sales stock forecasting using the fuzzy time series (FTS) method and making a detailed application of the fuzzy time series model according to system requirements [19]

Research Flowchart
The research flow chart of the application of the fuzzy time series model in forecasting the stock distribution of clothing on the cv ambassador express [20]

Figure 1. Research Flowchart
The forecasting stages in the fuzzy time series are as follows: the first step is to input data on clothing stock, calculate the formation of the U universe set of clothing stock data in the universe of conversation and then divide it into several intervals with the same distance.If U is the universal set, U = {u1, u2, ... , un}, then a fuzzy set Ai of U is defined as : Ai = Ai(u1)/u1 + Ai(u2)/u2 + … + Ai(un)/un .…(1) Where A is the membership function of the fuzzy set Ai , so that Ai: U → [0,1].The next step is the formation of intervals and calculating the number of intervals and the length of each interval from the universal set (U) and in calculating the number of intervals with the conditions k=1+3,3 Log n …………….
(2) Next is the determination of fuzzy sets in the universe of conversation and fuzzification of historical data on clothing stock.The next step is to establish a fuzzy logic relation based on historical data on clothing stock and form a sequential fuzzy set Ai(t-1) and Aj(t) can be expressed as: (FLRG) Ai → Aj1, Aj2 …………….
(3) Classifying fuzzy logic relations with all relationships.FLRs that have the same LHS can be grouped into FLR groups.For example, Ai→Aj, Ai→Ak, Ai→Am can be grouped into Ai→Aj, Ak, Am.The next step is to create a group of fuzzy relationships so as to create relational groups from each of these fuzzy relationships [21].Calculation results Defuzzification of data to obtain forecasts based on FLRG and probability matrix [22].Adjustment of the final value of the forecast in viewing the forecast data for the following month.

Conclusion 5.1 Conclusion
The conclusions of the application of the fuzzy time series model in forecasting the stock distribution of clothing in Aceh are as follows: 1.The application of the fuzzy time series model can predict the demand for stock of clothing materials consisting of school clothes, batik clothes and pants and each of these data shows a low number of errors and the predicted data will be better for the prediction results of future plans for better results.optimal.2. The results for each process of forecasting stock data for pants have an error value for AFER of 0.22927 % and RMSE of 26.10036.Furthermore, for the process of forecasting school clothes stock data, the AFER error value is 0.23640% and the RMSE is

Figure 3 .
Figure 3. Clothing Stock Chart . 2.4 Types and sources of data The following data are required for the application of the fuzzy time series model in forecasting stock distributions for clothing stock at CV Duta Express from January 2019 to December 2021: Tablel 1.Stock of clothes

Table Description :
-A total of 172 is included in the historical data fuzzification of group A4, between the range of A4 values, namely 153-196 -The number of 221 is included in the historical data fuzzification of group A5, between the range of A5 values, namely 196-238 -The number of 93 is included in the historical data fuzzification of group A2, between the range of A2 values, namely 26-68 -The number of 104 is included in the historical data fuzzification of group A3, between the range of A3 values, namely 111-153 -The number of 277 is included in the historical data fuzzification of the A6 group, between the A6 value range, which is 238-281 6. Fuzzy Logic Relationship Group (FLRG)The formation of Fuzzy Logic Relationship Group (FLRG) values is as follows: Table3.FLR (Fuzzy Logical Relatinship))

Table Description -
The value of A1 206.