Bulletin of Computer Science Research
Vol. 6 No. 3 (2026): April 2026

Kombinasi Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) dan Simple Additive Weighting (SAW) Pada Sistem Pendukung Keputusan Seleksi Magang

Arya Fauzan Adima (Universitas Teknokrat Indonesia, Bandar Lampung)
Parjito Parjito (Universitas Teknokrat Indonesia, Bandar Lampung)



Article Info

Publish Date
30 Apr 2026

Abstract

The internship selection process at XZ University is still carried out conventionally, thus reducing time efficiency in decision making. The purpose of this study is to design a web-based Decision Support System (DSS) by applying a combination of Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) and Simple Additive Weighting (SAW) methods to increase the objectivity and efficiency of the selection process. The MOORA method functions to normalize and optimize alternative values as the basis for calculating SAW in internship selection, while the SAW method is used for normalizing and ranking alternatives based on weight criteria. This study uses a quantitative approach with a descriptive experimental method. Data were obtained through observation, interviews, and documentation. The criteria used include GPA, semester, collaboration time, video editing skills, camera operating skills, photography skills, videography skills, graphic design skills, creativity, attitude, discipline, responsibility. The results of data processing show that the five best alternatives with the highest preference values are A24 (100%), A3 (97%), A9 (96%), A5 (95%), dan A22 (93%.). These results show that the combination of the MOORA and SAW methods is able to provide the best alternative recommendations objectively and structured in the internship selection process.

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Journal Info

Abbrev

bulletincsr

Publisher

Subject

Computer Science & IT

Description

Bulletin of Computer Science Research covers the whole spectrum of Computer Science, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer ...