Open AccessFuzzy Inference System In Predicting Unemployment Rate In City X Using Sugeno Method
Author(s) -
Yosdarso Afero
Publication year - 2021
Publication title -
ijistech (international journal of information system and technology)
Language(s) - English
Resource type - Journals
ISSN - 2580-7250
DOI - 10.30645/ijistech.v5i4.151
Subject(s) - seekers , unemployment , inference , set (abstract data type) , process (computing) , constant (computer programming) , fuzzy inference , defuzzification , computer science , unemployment rate , fuzzy set , fuzzy logic , mathematics , operations research , fuzzy number , artificial intelligence , fuzzy control system , economics , adaptive neuro fuzzy inference system , political science , law , programming language , economic growth , operating system
Unemployment in Indonesia is currently increasing. This can be seen from the number of unemployed and workers who are looking for job vacancies either directly or through applications. The problem in this study is the lack of understanding of job applicants about the terms or criteria for applying for a job so that they are not accepted in a company. There are five criteria that must be considered in applying for a job, namely, education, vacancies, age, opportunities and knowledge. The purpose of this study is to help job seekers to complete and understand the criteria that have been set. This study uses the Sugeno method with the final result in the form of linear or constant. The working process of the Sugeno method is fuzification, inference engine, application of implication and defuzzification functions to obtain results in accordance with the decision-making system.