Analisis Perbandingan Metode Exponential Smoothing dan Least Square pada Peramalan Penjualan Produk Smartphone Merek Vivo di Solusi Cash & Kredit Cabang Karanganyar

Mahendra Mei Utami; Sunarso Sunarso; Sumaryanto Sumaryanto

doi:10.55606/mri.v2i4.3174

Authors

Mahendra Mei Utami Universitas Slamet Riyadi Surakarta
Sunarso Sunarso Universitas Slamet Riyadi Surakarta
Sumaryanto Sumaryanto Universitas Slamet Riyadi Surakarta

DOI:

https://doi.org/10.55606/mri.v2i4.3174

Keywords:

Sales Forecasting, Exponential Smoothing, Least Square

Abstract

MSMEs are productive economic businesses run by individuals or small business entities to grow and develop their businesses in order to build the economy, so that MSMEs become the most important pillars in the Indonesian economy. The large number of competitors in the business world requires entrepreneurs to find strategies that can increase the sales cycle and fulfill the number of requests. Solusi Cash & Kredit is a company engaged in cash and credit sales of smartphones, furniture, and electronics. According to the owner of Solusi Cash & Kredit, Vivo smartphones are one of the best-selling brands on the market. The phenomenon that occurred in the company during August 2023 to June 2024 was the occurrence of fluctuations or instability in product sales levels. The purpose of this study was to determine the results of the comparison of sales forecasting with the Exponential Smoothingi and Least Square methods. The results obtained were that the exponential smoothing alpha 0.3 method had a MAPE value of 18.27% with a forecast value for the next period of 34 units per month. Alpha 0.5 had a MAPE value of 14.74% with a forecast value for the next period of 36 units per month. Alpha 0.7 had a MAPE value of 12.64% with a forecast value for the next period of 36 units per month. Alpha 0.9 had a MAPE value of 11.7% with a forecast value for the next period of 37 units per month. The least square method had a MAPE value of 7.2% with a forecast value for the next period of 41 units per month.

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