Sensitivity Analysis of Various AHP Process: A Case Study on Consumption Fish Farming

Abstract

The utilization of a decision support system has successfully helped many businesses in increasing their product sales. By conducting product evaluations, the sales potential of each product will be seen more accurately, thereby helping strategic decision-makers. As one of the algorithms in product selection, AHP has been proven to solve complex problems involving multi-criteria, as many studies have successfully used it to rank products. However, in AHP implementation there are two different ways of calculating weights and consistency ratios. Due to the various AHP processes available, this paper performs testing with the most frequently used variations to determine product potential and compare the methods for multi-criteria decision-making. The criteria are harvest duration, selling price, feed production, weather conditions, and target market. The research results show that the weights of the two methods are different, but the resulting ranks are the same. The best choice type of fish to be farmed by fish farmers is catfish with the highest weight and the most difficult type of fish to farm is giant gourami. The result also show that the best way of the normalization process is squares of comparison matrices because its sensitivity does not easily change the ranking order.