Aplikasi Penentuan Tunjangan Kinerja dan Rekomendasi Pegawai Universitas Singaperbangsa Karawang - Aldi Jakaria; Ade Andri Hendriadi; Nina Sulistiyowati (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2019)
Universitas Singaperbangsa Karawang does not yet have a system and criteria for assessing the performance of non-P3K employees. Currently the staffing office at the University of Singaperbangsa Karawang does not yet have a way to determine how an employee is entitled to a Performance Allowance. Based on these facts, a website-based employee performance allowance information system will be created with a case study of the staff of the singaperbangsa karawang university. The system to be built includes the definition of criteria, data processing to become the best employee recommendation / promotion and determination of Employee Performance Allowances. The methodology used is software engineering and uses the Software Development Life Cycle (SDLC) method with the waterfall model because it is in accordance with the research that will be carried out with a relatively short stage of system usage. The calculation process is done by using the method of fuzzy multiple attribute decision making with weighted product because this method determines the weight value for each attribute, then proceed with a ranking process that will select the best alternative from a number of alternatives. The system created can provide information about the amount of employee performance benefits and recommendations for promotion for employees. After evaluating the user, this system gets a response that is easy to understand and easy to understand on each menu on the system. Looking at the benefits of this system is useful when it will provide performance allowances to employees and at the time will determine the employees who are reconditioned for promotions. The design of employee recommendation decision support systems using fuzzy multiple attribute decision making is done by completing the weighted product to produce alternatives after verification with the existing data getting 60% accuracy.