RANCANG BANGUN TEKNOLOGI CLOUD COMPUTING VIRTUALISASI PROSES BELAJAR MENGAJAR

  • Ahmad fatoni Universitas Banten Jaya
  • Rizki Fatullah Universitas Banten Jaya
Keywords: RPL, SPK, K-Nearest Neighbor, debtor, creditor

Abstract

There is a lot of business competition in this case credit sales, where credit sales are made using the gradual payment method with requirements that must be agreed upon by the credit provider and credit recipient. In this XYZ company there are problems that often occur including when officers select and determine credit recipients (debtors) in accordance with the requirements given by the company, this company also still uses traditional methods in promotion and there is no decision-making system to be able to assist in determining the prospective debtor In addition, creditors (creditors) find it difficult to receive debtor data reports. In determining the debtor to get credit sales, in this study we will use the SPK K-Nearest Neighbor method where this method is to calculate the closeness between old cases and new cases in order to facilitate officers in determining prospective borrowers with a decision support system using the K-Nearest method Neighbor. In addition to facilitating and helping creditors to be able to find out the debtor data report in more detail and to increase sales. The results of this study, get results from calculating the closeness between new cases with old cases using the K-Nearest Neighbor method. And from some calculations with that method, the greatest value will be used to predict the next cases.

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Published
2020-02-27
How to Cite
fatoni, A., & Fatullah, R. (2020). RANCANG BANGUN TEKNOLOGI CLOUD COMPUTING VIRTUALISASI PROSES BELAJAR MENGAJAR. Jurnal Ilmiah Sains Dan Teknologi, 4(1), 76-86. https://doi.org/10.47080/saintek.v4i1.829

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