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Implementasi Metode OCRA Penentuan Dosen Terbaik Dimasa Pandemi Covid-19 Tanjung, Dewi Maulida Sari; Al-Adawiyah, Robiah; Saputra, Imam
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i1.1607

Abstract

Each lecturer has a different way of teaching, some are preferred by students and some are not liked by students. A lecturer is required to improve his performance in teaching and imparting the knowledge he has to his students, so there are several requirements that are used as criteria in determining the best lecturers including Doc Scopus, Position, Competence, Age, and Warning Letters. So that in this study a money system was made using a decision support system that could determine the best lecturers during the covid-19 pandemic. In its determination, data such as criteria, weights and alternatives are needed. So, in this study using the OCRA (Optional Competitiveness Rating Analysis) method to determine the Best Lecturer through data such as alternatives and criteria, it can be concluded that the second alternative (A2) is Kennedi as the best Lecturer with the best preference value of 3,837.
Analisis Perbandingan Metode MAUT dan OCRA dalam Rekomendasi Produk Tabir Surya Terbaik Harahap, Amran Saleh; Tanjung, Dewi Maulida Sari; Soeb Aripin
Journal of Decision Support System Research Vol. 1 No. 2 (2024): January 2024
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/dss.v1i2.37

Abstract

This research aims to conduct a comparative analysis between the MAUT (Multi-Attribute Utility Theory) and OCRA (Order of Comparative Risk Assessment) methods in the context of recommending the best sunscreen products. A Decision Support System (DSS) is employed as the underlying tool for selecting the most suitable products based on predefined criteria. The research findings indicate that both methods yield the same best alternative, which is the product "The Originote" with an A8 ranking. The MAUT method produces the best preference value of 0.53, while the OCRA method yields the best preference value of 2.455. These results reveal that in the data testing conducted, the OCRA method takes the first rank with higher preferences compared to the MAUT method. This research offers valuable insights into the selection of the best sunscreen products by utilizing two different methods, which can assist consumers and stakeholders in making more informed and data-driven decisions. These findings can serve as a basis for further developments in decision analysis methods within the context of sunscreen products and similar items.