GDL

Journal from gdlhub / 2020-06-03 11:36:00
Oleh : Mariana Rospilinda Siki, Nisa Hanum Harani, Cahyo Prianto, Kursor
Dibuat : 2020-06-03, dengan 1 file

Keyword : Electronic Data Interchange (EDI), Decision Tree Method, Vendors, Procurement
Url : http://kursorjournal.org/index.php/kursor/article/view/217
Sumber pengambilan dokumen : WEB

Electronic Data Interchange (EDI) is an electronic data exchange mechanism between a company and another company or Business to Business (B2B) in a supply chain cycle. In this study, EDI's role in managing the procurement of goods as well as the EDI model has been applied. Determination of vendor recommendations is one element of vendor performance evaluation of the procurement process. Lack of information and analysis obtained by PT. Cinovasi Rekaprima makes it difficult to predict vendor recommendations. Predicted vendor recommendations can help the Procurement Division in developing appropriate strategies to determine recommended vendors. This problem can be applied to data mining techniques to make predictions using the classification method. Decision Tree is a method that converts facts into decision trees that represent rules that can be interpreted by humans. Attributes that influence the determination of vendor recommendations consist of the availability of goods, services, ease of ordering and product quality. Sample data obtained directly from the Procurement Division of PT. Cinovasi Rekaprima is primary data in the form of vendor data (quotation) and secondary data in the form of vendor performance evaluation forms. The result of the EDI application is a classification consisting of 2 classes, namely recommended vendors and non-recommended vendors and the Procurement Division can use it for decision making to determine the right vendor, so that the procurement process becomes easier and increases company profitability. The testing model uses k-fold cross-validation with the k value is 1 to 10 fold. This application can determine vendor recommendations with the highest accuracy 87.00 % on k-3 and k-5 fold.

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