Proceeding of the Electrical Engineering Computer Science and Informatics
Vol 7, No 2: EECSI 2020

Data Mining Implementation to Predict Sales Using Time Series Method

Agung Triayudi (Universitas Nasional Jakarta)
Sumiati Sumiati (Universitas Serang Raya Banten)
Thoha Nurhadiyan (Universitas Serang Raya)
Vidila Rosalina (Universitas Serang Raya)



Article Info

Publish Date
01 Oct 2020

Abstract

Sales transaction data histories can be used to predict the possibility of sales transaction that will occur in the future. These characteristics are in accordance with forecasting using time series method where this method uses previous data as tools to predict transaction value that will appear in the present time. Company X that runs its business by sell their product through distributors has sales data that is not optimally utilized. The average number of sales per year ranges from 5000 transactions which is not use to forecast transactions hereafter. Transaction data is stored in the company database so that data mining technology can be applied to support company X transaction data collection from previous year. The data is processed in applications where the results of forecasting are compared with real data in 2018 to see the accuracy of the forecasting results. The graphic that shown in application has pattern which can use for forecasting. From the forecasting method used, it can be seen that the forecasting results show data that came out did not produce data that matched the real data where the highest level of accuracy was 99.68% and the lowest accuracy was still above 50%.

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Journal Info

Abbrev

EECSI

Publisher

Subject

Computer Science & IT Electrical & Electronics Engineering

Description

Proceeding of the Electrical Engineering Computer Science and Informatics publishes papers of the "International Conference on Electrical Engineering Computer Science and Informatics (EECSI)" Series in high technical standard. The Proceeding is aimed to bring researchers, academicians, scientists, ...