Implementasi Simple Additive Weighting Untuk Rekomendasi Karir Mahasiswa Berdasarkan Hasil Tes Psikologi

Moh Nur Iman Siyus Setyowati; Dihin Muriyatmoko; Eko Prasetio Widhi

doi:10.61132/prosemnasproit.v2i2.207

Authors

Moh Nur Iman Siyus Setyowati Universitas Darussalam Gontor
Dihin Muriyatmoko Universitas Darussalam Gontor
Eko Prasetio Widhi Universitas Darussalam Gontor

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.207

Keywords:

career recommendations, decision support system, psychological testing, SAW method, UNIDA students

Abstract

Career selection is an important process for students at Darussalam Gontor University (UNIDA) because it influences their academic development and future employment. However, many UNIDA students experience difficulties in determining suitable careers due to a lack of understanding of their psychological characteristics. This study aims to build a Decision Support System (DSS) for career recommendations for UNIDA students based on psychological test results using the Simple Additive Weighting (SAW) method. The psychological data used are non-clinical test results collected through a structured questionnaire from six respondents and converted into numerical scores. The research stages include determining criteria and weights, compiling a decision matrix, normalization process, calculating preference values, and ranking career alternatives using SAW. The career alternatives used consist of academics, corporate professionals, entrepreneurs, managers, and social/public services. The results show that the managerial career alternative obtained the highest preference value of 0.861, followed by entrepreneurs at 0.824, corporate professionals at 0.778, social/public services at 0.737, and academics at 0.703. These findings demonstrate that the SAW method is capable of providing objective and systematic career recommendations based on the psychological profiles of UNIDA students. This research is expected to assist UNIDA students and academics in making more informed career decisions tailored to individual characteristics

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