Enhancing Recruitment Transparency Using Simple Additive Weighting in Smart City Governance

Authors

  • Rahimi Fitri Indonesia
  • Nitami Lestari Putri Indonesia
  • Abdul Rozaq Indonesia
  • Agus Setiyo Budi Nugroho Indonesia
  • Upik Upik Indonesia
  • Masyita Ratu Diba Indonesia
Pages Icon

DOI:

https://doi.org/10.63158/journalisi.v8i1.1396

Keywords:

Simple Additive Weighting, Decision Support System, Smart City, Workforce Recruitment

Abstract

The advancement of digital governance requires municipal recruitment processes that are transparent, accountable, and based on measurable criteria. In many local government environments, recruitment remains manual or semi-structured, increasing subjectivity, reducing efficiency, and limiting the traceability of decision outcomes. Although Decision Support Systems (DSS) using the Simple Additive Weighting (SAW) method are widely applied for candidate ranking, prior work often emphasizes technical scoring accuracy with limited attention to Smart City governance needs such as transparency, auditability, and accountable decision justification. This study develops and evaluates a SAW-based DSS to support objective, transparent, and traceable recruitment decisions within a Smart Governance context. Using a quantitative system development approach, candidate attributes were transformed into numerical scores and assessed through weighted criteria: education, work experience duration, English proficiency, age (cost criterion), and relevance of work experience. The SAW computation produced consistent and interpretable rankings, with the highest preference score reaching 98.462, indicating reduced reliance on unstructured subjective judgment. Usability testing using the System Usability Scale (SUS) yielded an average score of 87.6 (“Excellent”), demonstrating strong acceptance and practical feasibility across stakeholder roles. Overall, the proposed system functions as a governance-support tool that strengthens transparency and accountability in public-sector recruitment.

Downloads

Download data is not yet available.

References

[1] E. Knies, P. Boselie, J. Gould-Williams, and W. Vandenabeele, “Strategic human resource management and public sector performance: Context matters,” Int. J. Hum. Resour. Manag., vol. 28, no. 24, pp. 5192–5213, 2017, doi: 10.1080/09585192.2017.1407088.

[2] B. Z. Poljašević, A. M. Gricnik, and S. Š. Žižek, “Human resource management in public administration: The ongoing tension between reform requirements and resistance to change,” Adm. Sci., vol. 15, no. 3, p. 94, 2025, doi: 10.3390/admsci15030094.

[3] H. Konateh, E. K. Duramany-Lakkoh, and E. Udeh, “Cost and administrative effectiveness of recruitment and selection practices on public service delivery in public sector institutions,” Eur. J. Bus. Manag. Res., vol. 8, no. 2, pp. 21–30, 2023.

[4] M. T. P. Lubis, U. T. Handayani, N. T. Aganta, R. F. Dalimunthe, and P. Lumbanraja, “Analysis of civil servant recruitment in Indonesia: Challenges and opportunities in the digital era (Analisis rekrutmen pegawai negeri sipil di Indonesia: Tantangan dan peluang di era digital),” Int. J. Econ. Manag. Sci., vol. 1, no. 4, pp. 446–454, 2024, doi: 10.61132/ijems.v1i4.399.

[5] B. Baykurt, “Algorithmic accountability in U.S. cities: Transparency, impact, and political economy,” Big Data Soc., vol. 9, no. 2, 2022, doi: 10.1177/20539517221115426.

[6] H. Taherdoost, “Analysis of simple additive weighting method (SAW) as a multi-attribute decision-making technique: A step-by-step guide,” J. Manag. Sci. Eng. Res., vol. 6, no. 1, pp. 21–24, 2023, doi: 10.30564/jmser.v6i1.5400.

[7] N. Vafaei, R. A. Ribeiro, and L. M. Camarinha-Matos, “Assessing normalization techniques for simple additive weighting method,” Procedia Comput. Sci., vol. 199, pp. 1229–1236, 2021, doi: 10.1016/j.procs.2022.01.156.

[8] D. Pibriana, “Application of the simple additive weighting (SAW) method in employee recruitment decision-making at PT. ABC (Penggunaan metode simple additive weighting (SAW) dalam pengambilan keputusan rekrutmen karyawan pada PT. ABC),” Techno.Com, vol. 19, no. 1, pp. 45–55, 2020, doi: 10.33633/tc.v19i1.2771.

[9] R. A. Saputri, A. N. Sianturi, S. Mutmainnah, and E. R. Yulia, “Decision support system for new employee recruitment using the simple additive weighting (SAW) method at PT Crestec Indonesia Cikarang (Sistem penunjang keputusan penerimaan karyawan baru menggunakan metode simple additive weighting (SAW) pada PT Crestec Indonesia Cikarang),” JIKO (J. Inform. dan Komput.), vol. 6, no. 2, p. 207, 2022, doi: 10.26798/jiko.v6i2.627.

[10] M. Saputra and L. Bachtiar, “Analysis of employee recruitment at PT. Srikandi Diamond Indah Motors Sampit using the analytical hierarchy process (AHP) and simple additive weighting (SAW) methods (Analisis penerimaan karyawan pada PT. Srikandi Diamond Indah Motors Sampit dengan metode analytical hierarchy process (AHP) dan simple additive weighting (SAW)),” J. Sisfokom (Sist. Inf. dan Komput.), vol. 10, no. 3, pp. 312–319, 2021, doi: 10.32736/sisfokom.v10i3.1239.

[11] L. Mazia, L. A. Utami, M. B. Himawan, A. D. Lestari, and M. Aprilia, “Decision support system for employee recruitment using the simple additive weighting (SAW) method at PT. Ponny Ekspress Suksestama Jakarta (Sistem pendukung keputusan penerimaan karyawan menggunakan metode simple additive weighting (SAW) pada PT. Ponny Ekspress Suksestama Jakarta),” IJIS (Indones. J. Inf. Syst.), vol. 6, no. 1, p. 1, 2021, doi: 10.36549/ijis.v6i1.122.

[12] S. G. Meshram, E. Alvandi, C. Meshram, E. Kahya, and A. M. F. Al-Quraishi, “Application of SAW and TOPSIS in prioritizing watersheds,” Water Resour. Manag., vol. 34, no. 2, pp. 715–732, 2020, doi: 10.1007/s11269-019-02470-x.

[13] F. N. Khasanah and H. Herlawati, “Culinary places recommendation system in Bekasi City using the simple additive weighting method,” PIKSEL, vol. 9, no. 1, pp. 63–74, 2021, doi: 10.33558/piksel.v9i1.2621.

[14] A. Sadeghi, A. Maleki, M. H. Ahmadi, and A. H. Kiani, “Comparative evaluation of renewable energy investments: A multi-criteria decision-making approach,” Energy Convers. Manag. X, vol. 28, 2025, doi: 10.1016/j.ecmx.2025.101190.

[15] M. Grdinić-Rakonjac and M. Lučić, “Electric vehicle selection with easy applicable MCDM methods,” Transp. Res. Procedia, vol. 82, pp. 782–789, 2025, doi: 10.1016/j.trpro.2025.06.079.

[16] H. Aljaghoub et al., “Comparative analysis of various oxygen production techniques using multi-criteria decision-making methods,” Int. J. Thermofluids, vol. 17, 2023, doi: 10.1016/j.ijft.2022.100261.

[17] M. R. T. Kurnia, E. P. W. Mandala, and R. Prawiro, “Decision support system for loan eligibility using the simple additive weighting (SAW) method,” J. Comput. Sci. Inf. Technol., vol. 9, no. 4, pp. 176–180, 2023, doi: 10.35134/jcsitech.v9i4.84.

[18] M. L. M. Cahigas, R. C. A. Robielos, and M. J. J. Gumasing, “Application of multiple criteria decision-making methods in the human resource recruitment process,” [details unavailable].

[19] P. Ziemba, “Comparison of multi-criteria decision aiding methods in the problem of employee recruitment,” Procedia Comput. Sci., vol. 225, pp. 2704–2713, 2023, doi: 10.1016/j.procs.2023.10.262.

[20] P. Sarangi, R. Mishra, and A. Padhi, “Balancing skills and expectations: AHP analysis of competency-based recruitment in the EdTech sector,” Future Bus. J., vol. 11, no. 1, 2025, doi: 10.1186/s43093-025-00483-0.

[21] G. Rinaldi, K. Theodorakos, F. Crema Garcia, O. M. Agudelo, and B. De Moor, “DSS4EX: A decision support system framework to explore artificial intelligence pipelines with an application in time series forecasting,” Expert Syst. Appl., vol. 269, 2025, doi: 10.1016/j.eswa.2025.126421.

[22] T. Singh, P. Pattnaik, S. R. Kumar, G. Fekete, G. Dogossy, and L. Lendvai, “Optimization on physicomechanical and wear properties of wood waste filled poly(lactic acid) biocomposites using integrated entropy-simple additive weighting approach,” S. Afr. J. Chem. Eng., vol. 41, pp. 193–202, 2022, doi: 10.1016/j.sajce.2022.06.008.

[23] M. Gao, P. Kortum, and F. L. Oswald, “Multi-language toolkit for the system usability scale,” Int. J. Hum.-Comput. Interact., vol. 36, no. 20, pp. 1883–1901, 2020, doi: 10.1080/10447318.2020.1801173.

[24] A. M. Deshmukh and R. Chalmeta, “Validation of system usability scale as a usability metric to evaluate voice user interfaces,” PeerJ Comput. Sci., vol. 10, 2024, doi: 10.7717/peerj-cs.1918.

[25] R. M. A. Putri, W. G. S. Parwita, I. P. S. Handika, I. G. I. Sudipa, and P. P. Santika, “Evaluation of accounting information system using usability testing method and system usability scale,” Sinkron, vol. 9, no. 1, pp. 32–43, 2024, doi: 10.33395/sinkron.v9i1.13129.

Downloads

Published

2026-02-18

Issue

Section

Articles

How to Cite

[1]
R. Fitri, N. L. Putri, A. Rozaq, A. S. B. Nugroho, U. Upik, and M. R. Diba, “Enhancing Recruitment Transparency Using Simple Additive Weighting in Smart City Governance”, journalisi, vol. 8, no. 1, pp. 432–455, Feb. 2026, doi: 10.63158/journalisi.v8i1.1396.

Most read articles by the same author(s)