Metode Profile Matching Dalam Mendukung Keputusan Perpanjangan Asisten Dosen

Authors

  • Martini Universitas Bina Sarana Informatika
  • Nani Agustina Universitas Bina Sarana Informatika
  • Entin Sutinah Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31599/2xhq7861

Keywords:

Assistant Performance, Decision, Matching Profile

Abstract

Lecturer assistants are apprentice students whose job is to assist lecturers in teaching and learning activities in class. There are 10 students who are doing internships as teaching assistants. At the end of each semester the Training Division in a university conducts an evaluation of the assessment of students who are apprentices as teaching assistants. However, currently the training department has not yet found the right method for making decisions to determine teaching assistants who deserve to be extended for internships in the following semester. Currently, decision making is based on an assessment of the ability to explain the material when the lecturer concerned is unable to attend, although there are still many assessment criteria that can be considered in decision making. To solve this problem, this study adopts a method in making decisions on the extension of teaching assistants by using the Profile Matching method which includes several assessment criteria including attendance, neatness of dress, loyalty, work performance, responsibility, obedience, honesty, cooperation, initiative and leadership. From this criterion, it will be included in the Core Factor and Secondary Factor with the criteria of very good, good, pretty good, not good and very bad scores, so that in this research a decision was made to extend teaching assistants in the following semester, namely by choosing 4 teaching assistants who each has the following assessment results: AST9 with a value of 4.9, AST10 with a value of 4.8, AST1 with a value of 4.4 and AST8 with a value of 4.4.

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Published

2024-04-19

How to Cite

Metode Profile Matching Dalam Mendukung Keputusan Perpanjangan Asisten Dosen. (2024). Journal of Students‘ Research in Computer Science, 3(2), 229 – 240. https://doi.org/10.31599/2xhq7861