Application Of The Weighted Product (WP) Method In The Selection Of Prospective New Employees Using Assessment Indicators At PT. Delta Sukses Sejahtera - Rizky, Aldi; Cipta, Hendra (Journal of Computer Networks, Architecture and High Performance Computing, 2024)
In running its business, the success of a company is largely determined by the quality of its employees. To obtain quality employees who meet the required qualifications, companies must carry out a selection process in recruiting qualified new employee candidates. PT. Delta Sukses Sejahtera is a private company that operates in the field of recruitment services. This recruitment service serves companies by taking over the job of recruiting candidates. Each assessment is taken into account and considered according to the company's needs. So far, the selection process for prospective employees has experienced difficulties because they are still comparing the test results of prospective employees one by one to determine potential new employees. This process takes a long time. Apart from that, the old employee selection system created an element of subjective assessment. So it is necessary to build a decision support system using the Weighted Product (WP) method as an alternative solution, so that it runs effectively and reduces the occurrence of subjective assessments. Weighted Product (WP) is a method in a decision making system that connects attribute ratings using a multiplication technique, where the rating for each attribute needs to be raised to the power of the relevant attribute weight before calculation, this process is similar to the normalization stage. Based on the results of this research, it shows that from the five orders of candidates for recruiting new employees, it can be seen that the scores obtained by each prospective new employee are not too far apart from first to fifth. In the first and fifth places, they have similarities in the final psychological and educational criteria. Where the first and fifth psychological test criteria have almost perfect scores.