Fuzzy MADM Method for Decision Support System based on Artificial Neural Network to Water Quality Assessment in Surabaya River

Bima Prihasto, Mohammad Isa Irawan, Ali Masduqi

Abstract


The pollution of the Surabaya River has increased along with the rapid development of the industry in Surabaya. This causes the water quality decreases Surabaya. Surabaya is expected to meet the quality standards of water quality class II. At this time most of the monitoring sites recorded that the quality of water in times of Surabaya exceed the quality standards of raw water quality class II. With details for parameter DO exceed 4 mg / l, BOD exceed 3 mg / l, and COD exceed 25 mg / l. The condition has not been met quality standards of water quality does not occur at any time, but occur at a specific time. However, from the data collected, the current frequency exceeds the quality standards of water quality are very common. Besides, we cannot determine the general conditions in times of Surabaya, because at a time for water quality parameters DO and BOD is to meet water quality standards, while the COD is not fulfilling. So we need a decision support system to see the general picture on 8 monitoring sites in Surabaya. In this study begins backpropagation algorithm for classification from the parameters DO, BOD, and COD. Then the results are followed by Fuzzy Multi-Attribute Decision Making (MADM) to get the most polluted sites in times of Surabaya. The results from the this study by using the Simple Additive Weighting Method (SAW), Weighted Product (WP) and TOPSIS showed that location is the 5th most polluted locations to produce value for each 32.2917, 0.1139, and 0.2753.


Keywords


Fuzzy Multi-Attribute Decision Making (MADM), Simple Additive Weighting Method (SAW), Weighted Product (WP), TOPSIS, Backpropagation, Water Quality

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