Electronic Data Interchange (EDI) Applications Use the Decision Tree Method to Determine Vendor Recommendations
DOI:
https://doi.org/10.21107/kursor.v10i2.217Keywords:
Electronic Data Interchange (EDI), Decision Tree Method, Vendors, Procurement.Abstract
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.
Downloads
References
[2] P. Susetyorini, “Pelaksanaan sistem elektronic data interchange (edi) di pelabuhan tanjung emas sebagai alternatif prosedur kepabeanan,†Pandecta: Research Law Journal, vol. 5, no. 2, 2010.
[3] T. Mukhopadhyay, S. Kekre, and S. Kalathur, “Business value of information technology: a study of electronic data interchange,†MIS quarterly, pp. 137–156, 1995.
[4] S. Palaniswami and B. Lingaraj, “Procurement and vendor management in the global environment,†International Journal of Production Economics, vol. 35, no. 1-3, pp. 171–176, 1994.
[5] E. A. Purwanto, “e-procurement di indonesia pengembangan pelayanan pengadaan barang dan jasa pemerintah secara elektronik,†2007.
[6] R. Angeles and R. Nath, “Business-to-business e-procurement: success factors and challenges to implementation,†Supply Chain Management: An International Journal, vol. 12, no. 2, pp. 104–115, 2007.
[7] M. Pal and P. M. Mather, “An assessment of the effectiveness of decision tree methods for land cover classiï¬cation,†Remote sensing of environment, vol. 86, no. 4, pp. 554–565, 2003.
[8] R. Ariadni and I. Arieshanti, “Implementasi metode pohon keputusan untuk klasiï¬kasi data dengan nilai ï¬tur yang tidak pasti,†2015.
[9] S. Vincenzi, M. Zucchetta, P. Franzoi, M. Pellizzato, F. Pranovi, G. A. De Leo, and P. Torricelli, “Application of a random forest algorithm to predict spatial distribution of the potential yield of ruditapes philippinarum in the venice lagoon, italy,†Ecological Modelling, vol. 222, no. 8, pp. 1471–1478, 2011.
[10] G. Izmirlian, “Application of the random forest classiï¬cation algorithm to a selditof proteomics study in the setting of a cancer prevention trial,†ANNALS-NEW YORK ACADEMY OF SCIENCES, vol. 1020, pp. 154–174, 2004.
[11] C. L. Iacovou, I. Benbasat, and A. S. Dexter, “Electronic data interchange and small organizations: Adoption and impact of technology,†MIS quarterly, pp. 465–485, 1995.
[12] A. S. Budiman, “Kajian penerapan edi dalam pengelolaan rantai pasokan di industri manufaktur,†Jurnal Ilmiah Teknologi Infomasi Terapan, vol. 3, no. 3, 2017.