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Marpaung, Ramadina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Application of Analytic Network Method for Employee Bonus Determination Process Sianipar, Baringin; Ndaraha, Juandi; Marpaung, Ramadina
Jurnal ICT : Information and Communication Technologies Vol. 15 No. 2 (2024): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v15i2.179

Abstract

Employee bonus system is one of the important strategies in human resource management (HRM) to improve employee motivation, performance, and loyalty. However, many companies face challenges in creating a fair, transparent, and effective bonus system, mainly due to reliance on traditional assessment methods that are subjective. This research aims to develop an employee bonus model based on the Analytic Network Process (ANP) method that is able to capture the relationship between criteria holistically. The research method involves data collection through observation, interviews, and literature study, and data analysis using ANP to evaluate criteria such as target achievement, productivity, work quality, initiative, teamwork, and attendance. The results showed that ANP was effectively able to produce objective and transparent prioritization calculations, with employee A03 (Dermawan) identified as the highest bonus recipient with a final score of 0.1632 (32.64%). The implications of this research suggest that the application of ANP can help companies design a more fair and strategic reward system, thereby increasing employee confidence in the bonus system. However, this research is limited to one case study and has not evaluated the long-term impact of the developed model. Future research is recommended to expand the coverage to various industry contexts and integrate advanced analytic technologies to improve the accuracy of the model.
Co-Authors Ndaraha, Juandi Sianipar, Baringin