Journal of Artificial Intelligence and Technology Information
Vol. 4 No. 3 (2026): Volume 4 Number 3 September 2026

A Hybrid SITDE Weighting and HYBSO Method in Decision Support Systems for Warehouse Employee Selection

Tri Widodo (Universitas Teknokrat Indonesia)
Okma Arnilia (Universitas Islam Negeri Siber Syekh Nurjati Cirebon)
Iryanto Chandra (Universitas Islam Negeri Sunan Kalijaga)
Sahrial Ihsani Ishak (Universitas Dian Nusantara)
Setiawansyah Setiawansyah (Universitas Teknokrat Indonesia)



Article Info

Publish Date
21 Sep 2026

Abstract

Warehouse employee selection is a complex process because it involves various heterogeneous criteria, both quantitative and qualitative, such as physical tests, accuracy, work experience, discipline, and teamwork ability. A selection process that still relies on subjective judgment risks producing inconsistent and suboptimal decisions. This study proposes a hybrid method that integrates skewness impact through distributional evaluation (SITDE) for objective determination of criteria weights with a hybrid solution algorithm (HYBSO) for systematic ranking of alternatives. The results of applying this method indicate that Work Experience has the highest weight of 0.2813, followed by the Discipline Test (0.1969) and Physical Test (0.1926), while the Accuracy Test (0.1641) and Teamwork Test (0.1652) have lower weights. The final ranking results show that Candidate HW ranks first with a score of 0.3596, followed by Candidate FN (0.7066) and Candidate DK (0.9162). The sensitivity analysis evaluates the stability of the ranking results under changes in criterion weights across 20 scenarios, demonstrating the robustness of the proposed SITDE-HYBSO method and identifying candidates whose ranking positions are more sensitive to variations in criterion importance. These findings confirm that the SITDE-HYBSO hybrid method is capable of producing objective, consistent, and accountable employee selection decisions, while also providing an adaptive mechanism to respond to changes in organizational priorities.

Copyrights © 2026






Journal Info

Abbrev

jaiti

Publisher

Subject

Computer Science & IT Engineering

Description

Journal of Artificial Intelligence and Technology Information (JAITI) is a peer-review journal focusing on Artificial Intelligence and Technology Information issues. Journal of Artificial Intelligence and Technology Information (JAITI) invites academics and researchers who do original research in ...