Kolaborasi Metode Naive Bayes dan MPE dalam Pengambilan Keputusan Pemilihan Supplier Ban Motor

Authors

  • Nani Agustina Universitas Bina Sarana Informatika, DKI Jakarta
  • Entin Sutinah Universitas Bina Sarana Informatika, DKI Jakarta
  • Martini Martini Universitas Bina Sarana Informatika, DKI Jakarta

DOI:

https://doi.org/10.30865/mib.v8i2.7538

Keywords:

MPE Method, Naives Bayes, Supplier Selection, Decision System

Abstract

Almayra Jaya Ban is a company operating in the trading business sector which faces complexity in managing various products originating from several different suppliers. The main problem faced by this company is the difficulty in processing different supplier data for optimal evaluation in terms of supplier potential, quality of goods, and availability of goods from various suppliers. Supplier selection is an important decision that can affect operational performance and profits for the company. To increase accuracy and objectivity in decision making, this research uses collaboration between the Naive Bayes Method and the Exponential Comparison Method (MPE). Through experiments and sensitivity analysis, the results show that the integration of these two methods produces more stable and reliable decisions. Validation is carried out using historical data and trials to ensure accuracy. So by collaborating between the Naive Bayes Method and MPE it can be an effective approach in the decision making system for selecting motorbike tire suppliers, by providing more accurate, objective and accountable decisions for this company. The results of this research obtained a decision in selecting suppliers by selecting 7 alternative suppliers to be retained in the motorbike tire procurement cooperation, namely: Dermaga Jaya Motor with a score of 218.90, Ardendi Jaya Sentosa with a score of 217.90, Kumala Motor with a score of 213.08, Sinar Maju Motor got a score of 209.90, Raissa Jaya Motor got a score of 200.90, Nias Jaya Motor got a score of 198.18, Ciruas Jaya Motor got a score of 195.00.

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Published

2024-04-30

Issue

Vol. 8 No. 2 (2024): April 2024

Section

Articles

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