Open AccessData Prediction For Coffee Harvest Using Least Square Method
Author(s) -
Edi Surya Negara,
Keni Keni,
Ria Andryani
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1007/1/012193
Subject(s) - yield (engineering) , statistics , mean squared error , quality (philosophy) , square (algebra) , mean absolute percentage error , mathematics , mean squared prediction error , computer science , philosophy , materials science , geometry , epistemology , metallurgy
Pagaralam is one of the highest quality coffee producing regions in Indonesia. But the problem that is often found by farmers is the lack of knowledge and predictions about the coffee harvest they will produce in the next period. The solution that can be given is developing an application to be able to analyze and predict coffee yield data for the next harvest period. This study produces a calculation using the Least Square method which can produce a prediction algorithm for coffee yields with the lowest prediction error rate with an MPE of 13.72 and the greatest accuracy using a MAPE of 0.0166 which is implemented in a Coffee Harvest Prediction Application.