Information System Forecasting Number of Visitors Using Double Exponential Smoothing Method (Case Study of Body Gym Kota Malang)

  • Muhammad Hafidz Ilham Priambudi Politeknik Negeri Malang
  • Eka Larasati Amalia Politeknik Negeri Malang
  • Agung Nugroho Pramudhita Politeknik Negeri Malang


This information system is used to carry out daily activities in Body Gym kota Malang where the process is currently still using manual methods and requires time which makes it inefficient. This is needed for a system capable of processing data by computerization. From the problems above, the author makes an information system in the form of a website that has a feature to manage all the information on the website.

This system is expected to make the owners and employees of the fitness center more effective in carrying out daily activities. This information system has features for financial recording, showing income graphs, recording the number of visitors, PT requests, and reports for data. This information system also features the main features, which can predict the number of visitors fitness, zumba, and aerobics using the Double Exponential Smoothing method and can give recommendations for gym owners. The data used to predict the number of fitness visitors is the actual data from 2015 to 2019.

From the results of the calculation of fitness visitor data using a 0.06 constant produced forecasting results for 1879 visitors in December 2019 where these results are the best results after being calculated with an MAPE error of 11.64% and with an accuracy accuracy of 88.36%. As for other activity features, this website application already has features that are quite complete and in accordance with its function after testing the system and the user to carry out daily activities with a computerized.


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How to Cite
M. H. I. Priambudi, E. L. Amalia, and A. N. Pramudhita, “Information System Forecasting Number of Visitors Using Double Exponential Smoothing Method (Case Study of Body Gym Kota Malang)”, JIP, vol. 7, no. 1, pp. 23-28, Nov. 2020.