Implementasi Metode TOPSIS dalam Pemilihan Supplier Bibit Ayam Broiler pada Peternak Mandiri Bleber

Abstract

Selecting a broiler chicken breeding stock supplier is a critically important process because it affects breeding stock quality, chicken health level, productivity, and the success of the farming business. The decision to select a supplier at Peternak Mandiri Bleber is still very subjective because it relies solely on experience, so the results are often not optimal. This research aims to present a decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. With this system, determining the best broiler chicken breeding stock supplier can be carried out in a more rational and transparent manner. This research is grounded in a quantitative approach carried out through sequential procedures. The initial stage is defining the problem and gathering data using observation and interviews. Criteria and weights are established as the basis for preparing the decision matrix, which then goes through the process of normalization and weighting. Determination of positive and negative ideal solutions is carried out, followed by calculation of the distance from each solution, and finally closed with calculation of preference values. This research establishes four assessment factors, namely breeding stock quality, price, delivery timeliness, and supplier service quality. The three suppliers serving as alternatives are then evaluated based on these criteria. From the calculation results, Supplier B occupies first rank with a preference score of 0.861, making it the top choice. Supplier A follows in second position with a value of 0.612, while Supplier C obtained a score of 0.000 in last place. These findings prove that TOPSIS can carry out the supplier selection process in a more orderly, measurable, and impartial manner, which ultimately improves the quality of decisions at Peternak Mandiri Bleber. For future research, it is suggested to add the number of suppliers and evaluation criteria, as well as compare TOPSIS with other methods so that the recommendation results become more complete and precise.

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