Decision Support System to Select The Best Customers Using Analytical Hierarchy Process (AHP) Methods, Simple Additive Weighting (SAW) Method, Weight Aggregated Sum Product Assessment Method (WASPAS) at kebaya Shop

− Style Queen Kebaya Store (SQ Kebaya) is a store that is engaged in apparel, its product sales focus includes adult and children's kebaya. The negative impact of the Covid 19 Pandemic has proven that the Store (SQ Kebaya) has experienced a decline in sales turnover in 2020, therefore the SQ Kebaya Store's efforts to restore its sales activities are by giving gifts for customer appreciation during the COVID 19 season through selecting the best customers for the 2020 period. However, the problem faced by SQ Kebaya Stores in the process of evaluating the best customer selection is that there is no criterion weight so that the decision making is not right on target, making the best customer decisions less efficient because they have to look for customer sales records manually in the sales record book. This study produces a web-based decision support system for selecting the best customers at SQ Kebaya Stores using the AHP (criteria weight), SAW and WASPAS (best customer ranking) methods, this study produces priority weights and importance levels of each criterion, namely status (0.37 ), method of payment (0.23), total spending (0.14), quantity (0.13), intensity of visits (0.07), length of subscription (0.07) and the result of ranking the percentage of the largest alternative value is the alternative SAW method with an average of 0.6952 , while the WASPAS method is 0.6405. It can be concluded that the right method used to obtain the best alternative value is the SAW method.


INTRODUCTION
Store SQ or SQ Kebaya Store is a store engaged in the sale of apparel, the focus of its product sales includes adult and children's kebaya, batik skirts, songket skirts, adult men's shirts and children of various sizes. Competition in the world of trade, especially in the era of COVID 19, is getting tougher, one of which is in the Tanah Abang Wholesale Center area, Central Jakarta, apparel traders are very aggressively carrying out various attractive product marketing promotions, which aim to increase purchasing power and customer enthusiasm for the products being marketed. , maintain the existence of stores, as well as keep buying and selling activities running during the COVID 19 season. The negative impact of this COVID 19 Pandemic has made SQ Kebaya Store's revenue decrease in turnover by 40% to 60% each month from January to August 2020. In responding to this The owner of the SQ Kebaya Store plans to give gifts to the best wholesale and retail customers who meet the shop owner's evaluation criteria during the 2020 period for their loyal appreciation of being customers at SQ Kebaya Stores during the COVID 19 season. This gift-giving activity has taken place before in 2017. 2019, but not be going well and not on target giving gifts to customers, because shop owners have problems choosing the best customers, some of the obstacles faced by SQ Kebaya shop owners in determining the best customers are still using hardcopy (notebooks) in collecting sales transaction data, there is no weight For each criterion used in the assessment of the best customer selection, it is difficult to make efficient decisions due to the absence of a decision support system for selecting the best customer. From some of the obstacles obtained, a Decision Support System (DSS) is needed that can help SQ Kebaya Stores in choosing the best customers. This study tries to develop a Decision Making System using the Analytical Hierarchy Process (AHP) method for weighting criteria, Simple Additive Weighting (SAW) and the Weight Aggregated Sum Product Assessment (WASPAS) method. to find out the ranking of the best customer selection decisions. Several previous studies that have the same object of study as [1] make a Decision Support System (DSS) application design using the Simple Additive Weighting (SAW) to determine the best customer, then research from [2] made a Decision Support System (DSS) application with the Analytchical Hierarchy Process (AHP) & Weight Sum Model (WSM) method for the selection of the Customer Award Recipient, and [3] made a Decision Support System (SPK) to determine the best customer in a building store using the WASPAS method.

A. Decision Support System
According to Penerapan Metode Profile Matching Untuk Menentukan Pemberian Reward Terhadap Pelanggan Pada Bisnis Ritel [4] Decision Support System (DSS) is a system designed to assist decision makers in making decisions. With the DSS, a decision is expected to be more similar to a decision that should be based on complete and perfect information. Two elements contained in the DSS are boundaries and guidelines. The limit in question is the extent and method of SPK in limiting the decisions of its users. Meanwhile, guidelines mean the extent and way of SPK providing guidance for users in making decisions.

B. Customers
Explaining that customers or customers are individuals or groups who are accustomed to buying a product or service based on their decisions based on considerations of benefits and prices who then make contact with the company via telephone, mail, and other facilities to get a new offer from the company [5].

C. Analytical Hierarchy Process (AHP) Method
According to [6], [7]AHP is a decision-making method that involves a number of criteria and alternatives selected based on consideration of all related criteria in a hierarchical form. With a hierarchy, a complex problem can be broken down into groups which are then arranged hierarchically so that the problem will look more structured and systematic. In detail, describes the procedures and steps of the Analytical Hierarchy Process (AHP), namely: a. Creating a pairwise comparison matrix of each specified criterion. b. Specifies the priority of the element. The consistency of the hierarchy. If the value is more than 10%, then the data judgment assessment must be corrected. However, if the consistency ratio (CI/IR) is less or equal to 0.1 then the results of the calculations that have been carried out can be declared correct.

D. Method Simple Additive Weighting (SAW)
The SAW method is often also known as the weighted addition method. The basic concept of the SAW method is to find the weighted sum of the performance ratings for each alternative on all attributes. The SAW method requires the process of normalizing the decision matrix (X) to a scale that can be compared with all existing alternative ratings. The steps of the SAW method are [8]: 1. Determine the criteria that will be used as a reference in making decisions. 2. Determine the suitability rating of each alternative on each criterion. 3. Make a decision matrix based on criteria (C), then normalize the matrix based on the equation that is adjusted to the type of attribute so that a normalized matrix R is 4. The final result is obtained from the ranking process, namely the addition of the multiplication of the normalized matrix R with the weight vector so that the largest value is chosen as an alternative best (A) as the solution. The WASPAS method is to find the priority of the most preferred location in accordance with using weighting [9] The Weighted Aggregated Sum Product Assessment (WASPAS) method is a method that can reduce errors or optimize the estimation for the selection of the highest and lowest values. Thus, the main objective of the MCDMapproaches approach is to select the best option from a set of alternatives in the face of various conflicting criteria.. The calculation process step applies the WASPAS method . 1. Create a decision matrix 2. Normalize the x matrix, benefit / cost 3. Calculate the value of Qi : Value from Q to i : Multiply the value of with a weight (w) 0.5 : The best alternative is the alternative that has the highest Qi value

F. Unfied Modeling Language (UML)
According to [9] Unified Modeling Language (UML) was introduced to analyze object-oriented modules and requirements withassistance use-case and actorUML is a widely used modeling language for software analysis, design, and implementation. Developers . can easily do software development using UML

G. Black Box Testing (BBT)
According to [10] Black box testing is testing software in terms of functional specifications without testing the design and program code. Black box testing is the stage used to test the smoothness of the program that has been created. This test is important to do so that there are no errors in the flow of the program that has been made.

H. Technology Acceptance Method (TAM)
The Technology Acceptance Model (TAM) is an adaptation of the Theory of Reasoned Action Model (TRA). This model was developed by Fred D. Davis in 1986. TAM is a theory that describes the behavior of technology users in accepting and using new technology. TAM has two main variables that are used to predict acceptance of use, namely perceived usefulness and perceived ease of use which will affect attitudes towards use, behavioral intentions to use and ultimately indicateactual system use [11].

A. Research Methods
This research is a quantitative research method where there are certain populations and samples to be processed. In more detail, the quantitative data in this study came from the results of weighting criteria in the process of selecting the best customers at SQ Kebaya Stores using the Analytical Hierarchy Process (AHP) method. In addition, the Simple Additive Weighting (SAW) method and the Weight Aggregated Sum Product Assessment (WASPAS) method are used to obtain alternative ranking results as one of the final decisions.

B. Population and Sample Selection Methods The
Population in this study are customers at the SQ Kebaya Store, the sample that will be used is the prospective customer data at the SQ Kebaya Store. The sample selection method used is non-probability sampling which depends more on the ability and limitations of the researcher in drawing samples. The non-probability sampling technique used is purposive sampling. Purposive sampling is a non-probability technique that is often used because of its simplicity. This sample selection method is considered more The method of collecting data is obtained by studying, researching, and reading books, information from the internet, journals, theses related to the selection of the best customers. 4 Internal Data The internal data used in this study is the customer data of the SQ Kebaya Store.

D. Instrumentation The instrumentation
Used in this study was a questionnaire designed to collect data and test the system. The instrumentation are: 1 Questionnaire weight criteria.
The criterion weight questionnaire was provided by the researcher to determine the assessment criteria in choosing the best customers at the SQ store kebaya. 2 Technology Technology Acceptance Model (TAM) Questionnaire The Acceptance Model (TAM) [12] questionnaire was provided by researchers to determine the level of user acceptance of the decision support system application to be developed.
E. Analysis Techniques, Design and Testing 1 Analytical Techniques The analysis technique used in this study uses an object-oriented analysis approach with UML. The analysis process is carried out on the results of the stages of data collection with interviews and literature studies to obtain specifications for the system requirements to be developed. In the analysis process, the analytical techniques used are: -Analysis of data and information obtained from interviews, questionnaires and literature studies.
-Analysis of functional, non-functional, and user requirements. Functional requirements modeling to describe the system functions and the users involved and what functions can be obtained by each user are modeled with use case diagrams.
-System actor analysis. At this stage, an analysis of system actors is carried out which is developed and modeled with use cases that run in the system.
-In this study, the best customer selection technique uses the Analytical Hierarchy Process (AHP) method for weighting the criteria, Simple Additive Weighting (SAW) and the Weight Aggregated Sum Product Assessment (WASPAS) method to determine the final total value of the sum of all the largest alternative values which will later be The SAW or WASPAS method is chosen to be used to determine the best customer ranking results.

Design Techniques
In designing and developing a prototype decision support system to choose the best customers at the SQ Kebaya Store, the author uses the prototyping proposed by Roger S Pressman, where there are 5 main stages in the process, namely communication, quick plan, modeling quick plan, construction of prototype and deployment delivery & feedback. The first stage of communication, the author tries to communicate and identify the general concept and design of the prototype by asking directly the owner of the SQ Kebaya Store which is adjusted to the results of the AHP, SAW and WASPAS analysis. In the second stage, the author will start planning the prototype of the DSS, namely the quick plan , then carry out the design, namely the quick plan modeling in the third stage. At the design stage, the author uses the Unified Modeling Language (UML) tool, while in the implementation stage the author uses several tools , namely PHP Hypertext Preprocessor (PHP) and database . The fourth stage is the construction of prototyping DSS, at this stage the prototype begins to be developed in accordance with the planning and design in the previous stage. The fifth stage is deployment delivery & feedback. At the last stage, the prototype is put into use and tested, repairs will be made immediately if there are deficiencies.

Testing Techniques
System testing is carried out using the blackbox testing [13] to identify the reliability and functionality of the decision support system application later. After it is deemed appropriate, a Test Acceptance Model (TAM) [14], [15] will then be carried out to find out how far the level of user acceptance is for using the application (Decision Support System) to choose the best customer. A TAM questionnaire will be prepared to be filled out by users containing their assessment of the application.

A. Alternative
The author uses a non-probablity sampling method. In this research, the population is shop customers proposed by the owner of the SQ Kebaya Store in 2020

B. Method Analytical Hierarchy Process (AHP)
At this stage the author begins to determine and weight the criteria, the determination of the criteria has been determined by the owner of the SQ Kebaya Store, and to determine the weight of the criteria obtained from the results of the criteria weight questionnaire which was previously filled in by the Store Owner. SQ Kebaya. The calculation results are as in table IV.     The calculation of alternative value of SAW method is as follows: = (0.07 * 0.33) + (0.07 * 1) + (0.23 * 1) + (0.14 * 0, 5) + (0.13 * 0.2) + (0.37 * 1) = 0.78933 = (0.07 * 0.33) + (0.07 * 0.75) + (0.23 * 1) + (0.14 * 0.25) + (0.13 * 0.2) + (0.37 * 1) = 0.73683 = (0.07 * 0.33) + (0.07 * 1) + (0.23 * 1) + (0.14 * 0. 5  From table XIV the results of the ranking above show the largest percentage of alternative values, namely the results of the alternative SAW method with an average of 0.6952, while the WASPAS method is 0.6405. It can be concluded that the right method and can be used to obtain the largest alternative in the case of making the decision to choose the best customer at the SQ Kebaya Store is the SAW method where the final value of a large alternative indicates that the best alternative is preferred [9]. It is hoped that this SAW method is appropriate to use to select the best customers at the SQ Kebaya Store.

A. Conclusion
Based on the problems, literature study, research reviews, research objects and research methodology in the decision support system to choose the best customers at SQ Stores Kebaya with AHP, SAW, and WASPAS methods. So it can be concluded as follows: 1. This research produces a web-based decision support system with the AHP method as a weighting method and gets the results of the priority weights and importance levels of each criterion, namely status (0.37), payment method (0.23), total spending (0.14).Store owners be SQ accurate (right on target) in choosing the best customers. 2. The results of ranking the best customers obtained the largest percentage of alternative values, namely the results of the alternative value of the SAW method with an average of 0.6952, while the WASPAS method was 0.6405. It can be concluded that the right method and can be used to obtain the optimal best alternative in the case of making the decision to choose the best customer at the SQ Store Kebaya is the SAW method. So it is hoped that this SAW method is used appropriately to choose the best customers at SQ Stores kebaya. 3. The results of the User Acceptance Test (UAT) test using the Technology Acceptance Method (TAM) by emphasizing on 3 aspects of the test. The result of the percentage score based on the Usability aspect is 84.7%, the percentage score for the Convenience aspect is 81.6% and the percentage score for the User Acceptance aspect is 82%. In general, the percentage of the UAT score in this study was 82.7% and based on the score interval it can be concluded that the user Strongly Agrees with the Decision Support System (DSS) for Selecting the Best Customers by Using the Analytical Hierarchy Process (AHP), Method Simple Additive Weighting Method ( PBUH) and Stores Weight Aggregated Sum Product Assessment (WASPAS) Method at SQ kebaya.

B. Suggestions
Based on the conclusions of existing research, the suggestions that the authors give for the development of a Decision Support System (DSS) to Choose the Best Customers Using the Simple Additive Weighting (SAW) and Method Weight Aggregated Sum Product Assessment (WASPAS) pada Toko SQ Kebaya yaitu: 1. This research can be continued with different decision support system methods in selecting the best customer, and the information system model can be developed even better. 2. The SQ Kebaya Store can provide input and suggestions for improvements to the research that has been done.