Selection of the Best Village Crop Potential Using the Multi-Attribute Border Approximation Area Comparison (MABAC) Method

Khalid Muhammad Abdul Khalid

doi:10.59934/jaiea.v3i1.341

Authors

Khalid Muhammad Abdul Khalid STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v3i1.341

Keywords:

Decision Support Systems, Potential Agricultural Products, MABAC Method, PHP Programming, MySQL Database

Abstract

The difference in the location of each geographical condition of each village resulted in many different types of superior agricultural products in each village, this resulted in not all villages in Langkat Regency being able to utilize the crops in their village. This has caused the Community and Village Empowerment Service of Langkat Regency to work very hard in providing support for the progress of each village in developing existing agricultural products. The support provided by the government through the Langkat Regency DPMD is subsidized fertilizer, subsidized seeds and so on. Based on the results of research that has been conducted at the DPMD of Langkat Regency, the selection process to determine the village with the best agricultural products which is done manually can slow down the results of the decisions given and the results obtained are ineffective and inefficient. In overcoming this, it is necessary to build a system to streamline the process of selecting villages with the best agricultural products that have been properly computerized by utilizing the process of the Decision Support System (DSS). In this study a Decision Support System (DSS) will be built using the Multi-Attribute Border Approximation Area Comparison (MABAC) method which in this method is known as a method that can provide solutions in making a decision compared to other methods. The system was successfully built using the PHP programming language with a MySQL database. In the system built, the appropriate criteria to be used in supporting the final results of decisions that have been successfully analyzed and applied to the system are land area, income per harvest, number of workers and number of harvests each year. Based on the results of the research that has been done, the MABAC method is able to determine the ranking of the processed data based on the results of the total value of the criterion function.

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