An Integrated Decision Support System Using Respond to Criteria Weighting and Root Assessment Method for Content Creator Selection

Temi Ardiansah; Iswan A. Thais; Aditia Yudhistira; Ahmad Ari Aldino; Setiawansyah

doi:10.34148/teknika.v15i2.1477

Authors

Temi Ardiansah Department of Informatics, Faculty Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Lampung, Indonesia
Iswan A. Thais Department Computer Engineering, Faculty of Engineering, Institut Teknologi Gamalama, Ternate, Maluku, Indonesia
Aditia Yudhistira Department of Informatics, Faculty Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Lampung, Indonesia
Ahmad Ari Aldino Centre for Learning Analytics, Monash University, Victoria, Australia
Setiawansyah Department of Informatics, Faculty Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Lampung, Indonesia

DOI:

https://doi.org/10.34148/teknika.v15i2.1477

Keywords:

Decision Support System, RECA Weighting, RAM Method, Content Creator Selection, Multi-Criteria Decision Making

Abstract

This study proposes an integrated decision support system using the RECA Weighting and RAM methods to improve the objectivity and accuracy of content creator selection in the digital marketing era. The problem addressed arises from the complexity of evaluating multiple candidates based on various criteria such as content quality, engagement, consistency, creativity, and brand alignment, which are often assessed subjectively. The RECA method is applied to determine criteria weights objectively based on data variation among alternatives, while the RAM method is used to rank candidates through normalization, weighting, and root-based transformation to ensure more stable and balanced results. The findings show that the proposed approach is capable of producing consistent and reliable rankings, where the top three alternatives are Nabila Putri as the first rank, followed by Sinta Maharani in second place, and Putri Ananda in third place, indicating their superior performance across all evaluation criteria. Furthermore, sensitivity analysis by adjusting criteria weights by ±0.05 indicates that the ranking results remain relatively stable, demonstrating the robustness of the model. Therefore, the integration of RECA and RAM provides an effective, transparent, and accountable solution for supporting decision-making in content creator selection.

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