Penerapan Metode Simple Additive Weighting dan Fuzzy Logic dalam Menganalisa Mitigasi Risiko

Muhammad Rozakh; Diffri Siregar; Gunadi Widi Nurcahyo; Rini Sovia; Zumardi  Rahman

doi:10.35134/komtekinfo.v12i1.621

Authors

Muhammad Rozakh Universitas Putra Indonesia YPTK Padang
Diffri Siregar Universitas Putra Indonesia YPTK Padang
Gunadi Widi Nurcahyo Universitas Putra Indonesia YPTK Padang
Rini Sovia Universitas Putra Indonesia YPTK Padang
Zumardi Rahman Universitas Metamedia

DOI:

https://doi.org/10.35134/komtekinfo.v12i1.621

Keywords:

Risk, Simple Additive Weighting, Fuzzy Logic, Data, Technology

Abstract

Risk management is a stage to identify and address risks affecting a system or project. The risk mitigation process takes time and must be carried out periodically to be effective. In the context of education, information technology plays an important role in increasing the speed and accuracy of decision-making, including in risk mitigation. This study aims to apply the Simple Additive Weighting (SAW) and Fuzzy Logic methods to provide recommendations for risk mitigation that must be prioritized in a university environment. This research method uses a combination of Simple Additive Weighting (SAW) and Fuzzy Logic. Starting with using SAW to determine the criteria, weights, and suitability ratings, followed by making a decision matrix and normalization. The ranking data is then processed with Fuzzy Logic to handle uncertainty and produce objective decisions through the formation of a rule-base, inference, and defuzzification. The research dataset consists of 50 risk records and criteria used in the risk mitigation process obtained from the University. The results of the study indicate that the application of DSS using the SAW and Fuzzy Logic methods provides recommendations for risk mitigation with the results of 1 data not recommended for risk mitigation, 8 data highly recommended, and 4 data recommended for mitigation. This study contributes to designing an effective decision support system, allowing university leaders to make appropriate risk mitigation decisions based on relevant and accurate data using the SAW and Fuzzy Logic methods

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