Penerapan Metode Choice Based Conjoint

Studi Kasus: Preferensi Mahasiswa Program Studi Statistika Jurusan Matematika FMIPA Universitas Mulawarman Terhadap Ciri-ciri Dosen yang Diminati

  • Hidaya Annur Laboratorium Statistika Ekonomi dan Bisnis FMIPA Universitas Mulawarman
  • Desi Yuniarti Laboratorium Statistika Ekonomi dan Bisnis FMIPA Universitas Mulawarman
  • Ika Purnamasari Laboratorium Statistika Ekonomi dan Bisnis FMIPA Universitas Mulawarman


Lecturer is an important factor in the process of teaching and learning process in universities. This study was conducted with the aim to know the characteristics of students of Statistics Program Department of Mathematics at FMIPA Mulawarman University on the characteristics of the expected lecturers. One method that can be used to know the options is the conjoint-based optional method. Choice Based Conjoint (CBC) is a conjoint analysis that measures preferences based on conceptual choices and is used to determine the concept of attributes of lecturer characteristics expected by students. Attributes used in this study are the background of lecturers, lecturer characters, learning methods and interaction in the class. The data analysis technique used in the conjoint-based optional method is the conditional logit model. The result of CBC analysis shows that the attribute that is considered most important by the respondents based on attribute importance value is classroom interaction with percentage of 48,41% and seen from the value of the utility of interaction in the class with a positive value is the interaction in the active class with a value of 1.331. The characteristics of lecturers that are expected to be possessed by lecturers are casual lecturer character, last doctoral education, creative teaching methods and active classroom interaction.

How to Cite
ANNUR, Hidaya; YUNIARTI, Desi; PURNAMASARI, Ika. Penerapan Metode Choice Based Conjoint. JURNAL EKSPONENSIAL, [S.l.], v. 10, n. 1, p. 11 - 20, june 2019. ISSN 2085-7829. Available at: <>. Date accessed: 14 dec. 2019.