<![CDATA[PT Dasema Digi Persada is a company involved in the field of instrumentation, engineering, construction, and industrial processing. PT Dasema Digi Persada provides facilities for the supply of industrial equipment needs, installation services and equipment maintenance. In its operations, PT Dasema Digi Persada had business problems relating to data utilization. Sales information of collected goods was unable to provide information on the outcome of sales which will be used as the basis for decision-making about the quantity of inventory or stock products to be sold. The decision support system is required to classify the sold goods data into two clusters representing the best-selling category and the less-selling item so that the company can prevent the occurrence of product limitations and surplus stock products as both have the potential to cause losses. Therefore, in the work of this thesis developed a decision support system for the grouping of such data. The methods used in data grouping are is K-Means Clustering which is considered suitable as it has advantages in terms of performance, speed and ease of use. K-Means Clustering supports the grouping of large amounts of data by allocating data to a cluster based on the proximity of each cluster's datacenter location. A built-in web-based decision support system has been tested by implementing functional testing with black box and accuracy testing with the silhouette coefficient. The black box testing result states a 100% functional system is valid and the silhouette coefficient testing result generates a silhouette index value of 0.61 which is categorized as a good structure.]]>
Copyrights © 2020
