Open AccessFuzzy Multi-Attribute Decision Making untuk Klasifikasi Potensi Kewirausahaan Berdasarkan Theory of Planned Behavior (Fuzzy Multi-Attribute Decision Making for Classifying Entrepreneurial Potential based on Theory of Planned Behavior)
Author(s) -
Nova Rijati,
Diana Purwitasari,
Surya Sumpeno,
Mauridhi Hery Purnomo
Publication year - 2020
Publication title -
jurnal nasional teknik elektro dan teknologi informasi
Language(s) - English
Resource type - Journals
eISSN - 2460-5719
pISSN - 2301-4156
DOI - 10.22146/jnteti.v9i1.118
Subject(s) - topsis , ranking (information retrieval) , ideal solution , theory of planned behavior , weighting , fuzzy logic , mathematics , artificial intelligence , similarity (geometry) , computer science , data mining , machine learning , mathematical optimization , operations research , control (management) , medicine , physics , radiology , image (mathematics) , thermodynamics
Indonesia government has launched a program to encourage youth entrepreneurship as a strategy to improve national economy. This paper proposes a method to find an entrepreneurial potential based on academic behavior features that are extracted from the Higher Education Database PDDikti. The proposed approach applies the Fuzzy Multi-Attribute Decision Making (FMADM) technique. Rules for extracting features of student academic behavior were following Theory of Planned Behavior (TPB) and resulting in 14 features. The FMADM model combines Fuzzy Simple Additive Weighting and Fuzzy Technique for Order Preference by Similarity to Ideal Solution, which is called FSAW-TOPSIS. Friedman Test demonstrated that FSAW-TOPSIS gives more optimal solution with the highest Mean Rank of the potential entrepreneurial value of 2.96. Besides, through Hamming Distance Test, FSAW-TOPSIS results the best order with a 98% percentage and ranking of the smallest Squared Error of 0.3%, which makes the proposed model offered a better solution. It can be concluded that using TPB variables in PDDikti environment with FSAW-TOPSIS technique provides an optimal recommendation on student entrepreneurship potential, which can be used as a part of a decision-making system for higher education management.