THE IMPLEMENTATION OF A SIMPLE LINIER REGRESSIVE ALGORITHM ON DATA FACTORY CASSAVA SINAR LAUT AT THE NORTH OF LAMPUNG

• Main Author: Dwi Marisa Efendi
• Format: Article
• Language: English
• Published: STMIK Pringsewu 2018-04-01
• Series: IJISCS (International Journal of Information System and Computer Science)
• Subjects: sugar production, data mining, determination, simple linear, independent, dependent
• Online Access: http://ojs.stmikpringsewu.ac.id/index.php/ijiscs/article/view/549

Cassava is one type of plant that can be planted in tropical climates. Cassava commodity is one of the leading sub-sectors in the plantation area. Cassava plant is the main ingredient of sago flour which is now experiencing price decline. The condition of the abundant supply of sago or tapioca flour production is due to the increase of cassava planting in each farmer. With the increasing number of cassava planting in farmer's plantation cause the price of cassava received by farmer is not suitable. So for the need of making sago or tapioca flour often excess in buying raw material of cassava This resulted in a lot of rotten cassava and the factory bought cassava for a low price. Based on the problem, this research is done using data mining modeled with multiple linear regression algorithm which aim to estimate the amount of Sago or Tapioca flour that can be produced, so that the future can improve the balance between the amount of cassava supply and tapioca production. The variables used in linear regression analysis are dependent variable and independent variable . From the data obtained, the dependent variable is the number of Tapioca (kg) symbolized by Y while the independent variable is milled cassava symbolized by X. From the results obtained with an accuracy of 95% confidence level, then obtained coefficient of determination (R2) is 1.00. While the estimation results almost closer to the actual data value, with an average error of 0.00.