DETERMINATION OF BUSINESS PLUG SALES LEVEL USING NAVE BAYES ALGORITHM (Case Study: PT Multi Prima Sejahtera Tbk)

PUTRI RIONALDO, CRYSTALIA (2022) DETERMINATION OF BUSINESS PLUG SALES LEVEL USING NAVE BAYES ALGORITHM (Case Study: PT Multi Prima Sejahtera Tbk). Tugas Akhir thesis, University of Technology Yogyakarta.

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Abstract

ABSTRACT One of the factors in creating a healthy and growing company is sales. Spark plugs are one of the important components in motor vehicles. The number of types of spark plugs that are produced results in a buildup of the availability of spark plugs from the type that is not selling well. Based on these problems, this research builds a system to make comparisons, using sales data taken in the last two years and dividing it every three months. From that period, a class will be formed, namely description of behavior or lack of behavior. The method used is Naïve Bayes to determine the probability. Nave Bayes is one of the classification methods of branching artificial intelligence. The Naïve Bayes algorithm calculates the probability value of each attribute based on sales data from Champion spark plugs from PT. Multi Prima Sejahtera. Determining the level of sales is the success of sales, because with this result it can be used as a reference for the process of making spark plugs. The system built has several features, namely train data features, test data features, calculation features with Naïve Bayes, results features that display calculation results using the Naïve Bayes algorithm, and performance features that display accuracy, recall, and precision. The data used in this study were 16 datasets which were divided into 13 training data and 3 test data. From this research, the experiment was conducted twice to produce the highest accuracy obtained in the first experiment, namely the 100% accuracy value, 50% recall value and 100% precision value. Keywords: spark plug, Naïve bayes

Item Type: Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User: Kaprodi S1 Informatika UTY
Date Deposited: 28 Nov 2022 02:30
Last Modified: 28 Nov 2022 02:30
URI: http://eprints.uty.ac.id/id/eprint/11225

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