Penerapan Data Mining Untuk Memprediksi Pemesanan Bibit Pohon Dengan Regresi Linear Berganda
Devi Sari Oktavia Panggabean, Efori Buulolo, Natalia Silalahi
Abstract
Data mining, often also called knowledge discovery in database (KDD), is an activity that includes collecting, using historical data to find order, patterns or relationships in large data sets. Outputs from data mining can be used to improve future decision making. Problems that often occur in BPDASHL are estimation problems such as weather, difficulties in planting, lack of labor, lack of experience in tree nurseries, and different soil conditions. Another problem that is found in agencies is that they do not have a system to predict estimated tree seedlings orders every year so that a method is needed, namely the Multiple Linear Regression Algorithm. So with this was made the Application of Data Mining To Predict Ordering Tree Seeds With Multiple Linear Regression. Multiple Linear Regression Algorithms which are methods that support estimating or predicting order targets for the coming period. Algorithm testing is done using SPSS software. From the results of the research that has been done, it can help BPDASHL to make it easier to predict the ordering of seeds using SPSS Software