Open AccessPlanning Total Veener Production PT. XYZ
Author(s) -
Krisna Irawan,
Tedjo Sukmono
Publication year - 2021
Publication title -
procedia of engineering and life science
Language(s) - English
Resource type - Journals
ISSN - 2807-2243
DOI - 10.21070/pels.v1i2.1025
Subject(s) - support vector machine , production (economics) , computer science , matlab , mean squared error , least squares support vector machine , data mining , statistics , artificial intelligence , mathematics , economics , macroeconomics , operating system
PT. XYZ is engaged in the manufacture and sale of wood veneers. Starting from the constant occurrence of over stock, now the company must make improvements to the production forecasting process so that over stock can be avoided. It can be seen that accurate production forecasting can create conditions for an effective and efficient production system. This study aims to obtain a more accurate forecast of material requirements using the Support Vector Regression (SVR) method, which is the result of the development of a Support Vector Machine (SVM) which has good performance in predicting time series data. Application of the Support Vector Regression (SVR) method with the RBF kernel in predicting the need for veneer production using the MATLAB application, it produces the smallest error rate with a MAPE of 5%, RMSE of 4364.63 and of 0.748274147. on 67 training data and 20 testing data.