Novi Sentia Putri
Sekolah Tinggi Manajemen Informatika dan Komputer Royal

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Sales Prediction at Malco Fashion using the Least Square Approach (Case Study: Malco Fashion Store) Novi Sentia Putri; Herman Saputra; Ari Dermawan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.4018

Abstract

Increasing and changing customer demand every day requires companies to be more detailed in planning strategies to increase sales every day, characterized by the need to produce relevant reports, every company must have the ability to do this. However, so far the bookkeeping of sales of goods at Malco Fashion Store still uses a manual system that is written in a ledger. Malco Fashion shop owners cannot know the amount of goods to be provided for the next period. Therefore, the utilization of a sales forecasting system will greatly assist the store in processing data and transactions regarding sales. Because with the data related to the sale of goods from the previous period, it can conclude or predict the sales volume in the coming period. Therefore, the utilization of a sales forecasting system will greatly assist stores in data processing and transactions regarding sales. Because with the data related to the sale of goods from the previous period can conclude or predict the sales volume in the coming period. Accurate predictions in the sale of goods can be achieved using the least squares method or Least Square method based on historical data on sales of goods for the last few months. This method was chosen because it is the most commonly used method to determine the similarity of data trends. With the Least Square Method at Malco Fashion store can help in forecasting sales of goods in the next period accurately and quickly so that business owners can predict stock of goods to minimize shortages of goods and reduce excess stock of goods