Penerapan Data Mining dengan Algoritma Regresi Linear Berganda Untuk Memprediksi Omset Penjualan Minyak Goreng - Fadianty, Ramadita; Sriani, Sriani (Forum Kerjasama Pendidikan Tinggi, 2024)
In a competitive business world, companies must be able to develop effective strategies to increase sales and revenue. One approach that can be taken is to analyze sales transaction data to support more accurate decision making. PT. Siantar Bintang Perkasa is a company engaged in the field of basic necessities that sells various products, especially cooking oil. In facing the challenge of predicting revenue due to price fluctuations and market demand, data mining is used to find relevant patterns from historical sales data. Multiple Linear Regression techniques are implemented to predict company revenue and identify influential variables. The results of the study show that sales of Sania oil with 1 liter packaging and Sovia with the same packaging tend to get a slightly different sales prediction graph from the original value. And sales of oil other than Sania and Sovia with 1 liter packaging tend to get a fairly fluctuating prediction graph. The significance value in some calculations is still quite high on average. With this approach, accuracy in business strategy planning can be increased. This study also identified significant differences with previous studies, especially in terms of the research objects and variables used, as well as the analysis methods applied. These results are expected to help companies in making better decisions to improve their financial performance in the future.