PERAMALAN JUMLAH PENJUALAN SEPEDA MOTOR MENGGUNAKAN METODE TIME SERIES STUDI KASUS: DEALER MOTOR NUSANTARA SURYA SAKTI (NSS) SUMBAWA

Authors

  • Silvia Firda Utami Universitas Teknologi Sumbawa
  • Sis Yanti Arisma Universitas Teknologi Sumbawa
  • Koko Hermanto Universitas Teknologi Sumbawa
  • Eki Ruskartina Universitas Teknologi Sumbawa

Keywords:

Forecasting methodology, Single Moving Average, Single Exponential Smoothing.

Abstract

Nusantara Sakti Sumbawa (NSS) is a Honda agent company which sells motorcycles. The company encounters difficulty in determining the monthly sales target of their products. Therefore, the study aims to investigate the appropriate methodology to estimate the sales target for the next five months in NSS by involving 13 different brands of Honda. To achieve the goal, the researcher conducted several stages in forecasting methodology, including collecting the previous sales data of the company, plotting the collected data, and identifying the proper methods of forecasting to analyse the data. Based on the plotting data process, the researcher found that the Time Series methodology, consisting of Single Moving Average and Single Exponential Smoothing was the most suitable method to analyse the data. The results of the study showed that the prediction for the future sales target of the 13 different brands of Honda in the next five months in NSS was 182 units in total (Beat Sporty CW: 35 units,  Beat street: 19 units, Vario 110: 14 units, Vario 125: 17 units, Vario 150: 15 units, Scoopy: 38 units, Revo: 1 unit, Blade: 1 unit, Supra X 125: 12 units, Supra GTR 150: 9 units, Sonic: 16 units, and CBR150R: 5 units). The study also revealed that the Single moving average was a proper method to predict the future sales target for the three brands of Honda, namely Vario 110, Revo, and Blade. While the other 10 brands, including Beat Sporty CW, Beat street, Vario 125, Vario 150, Scoopy, Supra X 125, GTR 150, Sonic, and CBR150R were appropriately predicted using the Single Exponential Smoothing method.

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Published

2020-07-02