Development of a Decision Support System on Employee Performance Assessment Using Weighted Performance Indicators Method
Автор: Terttiaavini, Yusuf Hartono, Ermatita, Dian Palupi Rini
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 3 vol.15, 2023 года.
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Employee Performance Assessment is a part of the Decision Support System. One of the decision support system methods that are most used in performance assessment is Simple Additive Weighting (SAW). In the SAW method, each criterion has a weight value to show the interest level. The determination of the criteria on the SAW method is subjective and the final result is on the ranked system and creates many problems. The study utilizes the Weighted Performance Indicators (WPI) method to solve the problems in the SAW method. The criterion is determined based on the respondent's opinion so that it will be more realistic to achieve the target. The population of the study is the employee of Indo Global Mandiri University which reach 30 persons. WPI method consists of 9 steps. The research result is shown that 4 employees has a performance below MSV and 36 employee has above MSV. The general value of the employee performance value = is 0.69. It shows that the performance of the employee at Indo Global Mandiri University is good enough. However, it needs to be increased, so that the target could be achieved. WPI method is easy to implement, it is not just limited to the employee performance assessment only, but it could be implemented for the other performance assessment, for example, human resource performance, finance, company, industry, system, etc.
Decision support system, employee performance assessment, simple additive weighting method (saw), weighted performance indicators method (wpi), respondents opinion
Короткий адрес: https://sciup.org/15018869
IDR: 15018869 | DOI: 10.5815/ijieeb.2023.03.01
Список литературы Development of a Decision Support System on Employee Performance Assessment Using Weighted Performance Indicators Method
- Nicolaescu SS, Florea A, Kifor CV, Fiore U, Cocan N, Receu I, et.al. Human capital evaluation in knowledge-based organizations based on big data analytics. Futur Gener Comput Syst 2020:654–67. https://doi.org/10.1016/j.future.2019.09.048.
- Dangol P. Role of Performance Appraisal System on Employees Motivation. Quant Econ Manag Stud 2021;2:13–26. https://doi.org/10.9790/487x-0846683.
- Idowu AO. Effectiveness of Performance Appraisal System and its Effect on Employee Motivation Idowu,. Nile J Bus Econ 2017;5:15–39, http://dx.doi.org/10.20321/nilejbe.v3i5.88.
- Sharma A, Sharma T. HR analytics and performance appraisal system: A conceptual framework for employee performance improvement. Manag Res Rev 2017;40:684–97. https://doi.org/10.1108/MRR-04-2016-0084.
- Kahya E. A wage model consisted of job evaluation, employee characteristics and job performance. Pamukkale Univ J Eng Sci 2018;24:720–9. https://doi.org/10.5505/pajes.2017.92609.
- Aziz TFA, Sulistiyono S, Harsiti H, Setyawan A, Suhendar A, Munandar TA. Group decision support system for employee performance evaluation using combined simple additive weighting and Borda. IOP Conf Ser Mater Sci Eng 2020;830. https://doi.org/10.1088/1757-899X/830/3/032014.
- Ibatova AZ, Kuzmenko VI, Klychova GS. Key performance indicators of management consulting. Manag Sci Lett 2018;8:475–82. https://doi.org/10.5267/j.msl.2018.3.004.
- Majumder M. Multi Criteria Decision Making. Int. J. Res. Appl. Sci. Eng. Technol., vol. 49.98, 2015, hal. 35–47. https://doi.org/10.1007/978-981-4560-73-3_2.
- Terttiaavini T, Zamzam F, Ramadhan M, Saputra TS. Design a Decision Support System to Evaluate The Performance of Indonesian Lecturers by Developing a Simple Additive Weighting Method. Test Eng Manag 2019;28:36–41.
- Taufiq R, Septarini RS, Hambali A, Yulianti Y. Analysis and Design of Decision Support System for Employee Performance Appraisal with Simple Additive Weighting (SAW) Method. openjournal.unpam.ac.id 2020;5:275–80.
- Subagyo HD, Ariani A, Qoriani HF, Widodo G. Decision Supporting System Employee Performance Appraisal Narotama University with Simple Additive Weighting Method (SAW). Int Conf Green Technol 2017;8:273–7.
- Adler S, Campion M, Colquitt A, Grubb A, Murphy K, Ollander-Krane R, et.al. Getting rid of performance ratings: Genius or folly? A debate. Ind Organ Psychol 2016;9:219–52. https://doi.org/10.1017/iop.2015.106.
- Terttiaavini, Hartono Y, Ermatita, Rini DP. Building a Weighted Performance Indicator Concept utilized The Respondent ’ s Opinion Approach. 2021 3rd Int. Conf. Electron. Represent. Algorithm. 3rd ed., IEEE; 2021, hal. 137–42. https://doi.org/10.21203/rs.3.rs-178466/v1.
- Terttiaavini, Ermatita. Sistem Penilaian Kinerja Dosen menggunakan Decision Maker Respondent Opinion Model. J Inform Glob 2022;13:1–6.
- Terttiaavini. Potensial Ability. 1 ed. Palembang: NoeFikri Offset; 2017.
- Terttiaavini T, Hamim S amariena, Agustri S. Aplikasi sistem pakar penentu bidang studi ditingkat perguruan tinggi berbasis web. J Ilm Inform … 2016;7:67–72.
- Benešová A, Tupa JT. Requirements for Education and Qualification of People in Industry. 27th Int. Conf. Flex. Autom. Intell. Manuf. FAIM2017, vol. 11, Modena, Italy: 2017, hal. 2195–202. https://doi.org/10.1016/j.promfg.2017.07.366.
- Hecklau F, Galeitzke M, Flachs S, Kohl H. Holistic Approach for Human Resource Management in Industry 4.0. Procedia CIRP 2016;54:1–6. https://doi.org/10.1016/j.procir.2016.05.102.
- Störmer E, etc. The Future of Work : Jobs and skills in 2030. 2014.
- Joerres J. The Future of Jobs Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution. 2016.
- Gehrke L. Industri 4.0. A discussion of Qualifications and Skills in Factory of the Future. 2015.
- Morgan J. The Future of work. Wiley; 2014.
- Pompa C. Jobs for the Future. 2015.
- Mintasih D. Mengembangkan Literasi Infomrasi melalui belajar berbasis kehidupan tertintergrasi PBL untuk menyiaokan calon pendidik dalam menghadapi era Revolusi industri 4.0. Elemtary Islam Teach J 2018;6:271–90.
- Bauer H. Industry 4.0 How to navigate digitization of the manufacturing sector. 2015.
- Li X, Zhong Y. An Overview of Personal Credit Scoring : Techniques and Future Work. Int J Intell Sci 2012;2012:181–9.
- Chen M, Mao S, Liu Y. Big data: A survey. Mob Networks Appl 2014;19:171–209. https://doi.org/10.1007/s11036-013-0489-0.
- Bamakan SMH, Nurgaliev I, Qu Q. Opinion leader detection: A methodological review. Expert Syst Appl 2019;115:200–22. https://doi.org/10.1016/j.eswa.2018.07.069.
- Karami A, Bennett LS, He X. Mining Public opinion about economic issues: Twitter and the U.S. Presidential election. arXiv 2018;9. https://doi.org/10.4018/IJSDS.2018010102.
- Malonda R. Opini Publik Terhadap Pencitraan Politik dalam Meningkatkan Tingkat Elektabilitas Politik Pada Pemilu Presiden Tahun 2019 di Kabupaten Mihanasa. J Polit 2019;8:1–15.
- Juariyah J, Wijayanti N. Opini Mahasiswa Dalam Pemilu Presiden 2019 (Studi Kasus Aktifis Bem Fisip Tentang #2019Gantipresiden Pada Lima (5) Perguruan Tinggi Di Kabupaten Jember). Mediakom 2020;4:43–57. https://doi.org/10.32528/mdk.v4i1.3571.
- Schakel AH, Brown AJ. Dissecting Public Opinion on Regional Authority: Four Types of Regionalists Based on Citizens’ Preferences for Self-Rule and Shared Rule. Publius J Fed 2021;52:310–28. https://doi.org/10.1093/publius/pjab020.
- Hinderks A, Schrepp M, Domínguez Mayo FJ, Escalona MJ, Thomaschewski J. Developing a UX KPI based on the user experience questionnaire. Comput Stand Interfaces 2019;65:38–44. https://doi.org/10.1016/j.csi.2019.01.007.
- Nastišin IĽ. Research on the most important KPIs in social media that should be tracked. J Glob Sci 2017:1–6.
- Zhou X. Hierarchical Item Response Models for Analyzing Public Opinion. Polit Anal 2019;27:481–502. https://doi.org/10.1017/pan.2018.63.
- Siregar D, Arisandi D, Usman A, Irwan D, Rahim R. Research of Simple Multi-Attribute Rating Technique for Decision Support Research of Simple Multi-Attribute Rating Technique for Decision Support. Int. Conf. Inf. Commun. Technol., 2017, hal. 1–6.
