Beranda > Algoritma dan Metode, Ilmu Komputer, Informatika, Teknologi > Support Vector Regression and Multi-Attribute Decision Making for Productivity Prediction and Renewable Energy Development Regional Rank

Support Vector Regression and Multi-Attribute Decision Making for Productivity Prediction and Renewable Energy Development Regional Rank

123The limitations of energy in Indonesia, especially in Riau Province, has became polemic in the future, almost every period of the energy generated from fossil is diminishing and expensive. The government was given the mandate to develop alternative energies, one of them is the palm. The important role of goverment is to make decision regarding to the development of renewable energy. Riau Province has 8.91 million ha area potentially generating Indonesia’s largest palm oil plantation as it has 2.26 million hectares of plantation land with an average production of 6.93 million ton per year dispersing in the various districts.

This study is divided into three parts, i.e, pre-decision making, prediction, and post-decision making. Multi Attribute Decision Making (MADM) model consisting of Simple Additive Weighting (SAW) produces Bagan Sinembah as the best alternative with pre-prediction value at 0.7605 and post-prediction value at 0.7361, and followed by Mandau and Tapung. This method is reinforced by Analytic Hierarchy Process (AHP) as weighting and decision-making validation with global value at 0.2731. Weighting using AHP eigenvalue has less possibility change in every decision compared with the direct weight-given preference by decision makers.

1234The results of decision are independent to main criteria, that is Agricultural Sector Size (LSP) and Plantation Production (HPP) as determinant of the best decision, even though these two criteria are the most important criteria in decision-making. Related to post-production for next period production, it is yielded the best model Support Vector Regression (SVR) which is the kernel of Radial Basis Function (RBF) with 95% coefficient of determination (R2), 6% galat error (MSE) in fold 1 ranging γ = 20 to C = 23.

The potential of generation for each waste that is based on 50% waste utilization estimation and calculation of steam flow rate, dry waste composition with 6.6% water content, 50% shrinkage levels for Shells and Stems, and 65% for Empty Fruit Bunches.If this simulation is implemented and developed as alternative energy made from palm oil waste, it will be able to produce the electrification ratio at 21% to Bagan Sinembah; therefore, the total electrification ratio is 70%, or equivalent to 27 thousand out of 32 thousand residents in a district can enjoy electricity.

 Keywords:

Analytic Hierarchy Process (AHP), Multi-attribute Decision Making (MADM), Renewable Energy, Oil Palm, Simple Additive Weighting (SAW), Support Vector Regression (SVR)

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