Selection of prospective workers using profile matching algorithm on crowdsourcing platform

Cucus, Ahmad and Aji, Luhur Bayu and Al Fahim, Mubarak Ali and Aminuddin, Afrig and Farida, Lilis Dwi (2022) Selection of prospective workers using profile matching algorithm on crowdsourcing platform. In: ICOIACT 2022 - 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding; 5th International Conference on Information and Communications Technology, ICOIACT 2022 , 24-25 August 2022 , Yogyakarta. pp. 122-126. (185076). ISBN 978-166545140-6

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Abstract

The use of a crowdsourcing platform is an option to get workers who will help complete the work. Crowdsourcing is the process of gathering work, information, or opinions from a large number of individuals using the internet, social media, or smartphone apps. Whether crowdsourcing is used for programming, design, content creation, or any other task, requesters are putting their trust in individuals who are unfamiliar with their knowledge and have unknown histories and skills. Requesters do not have the time or resources to screen all of the crowd's qualities, unlike employing full-time personnel. In this study, we try to minimize the risks faced by requesters when using a crowdsourcing platform to complete their work, namely by increasing the match between the profile of workers and the jobs offered on the crowdsourcing platform. The researcher implemented the profile matching method using a dataset consisting of several fields that became the criteria for finding a match. The criteria used to find a match between workers and the work offered consist of two parts, core factors and secondary factors. Core Factor Criteria as skill, designation, location, and the secondary factor is the number of years of work experience. These criteria become variables that are used in the profile matching algorithm to find workers who best match the profiles offered. This algorithm is able to select worker profiles from 10,000 datasets, up to 1148 people who are most suitable for the tasks offered. And the results obtained indicate an increase in the match between workers and the needs of the work offered by the requester.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Crowdsourcing; Profile matching; Worker
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	College of Engineering Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	21 Nov 2023 00:30
Last Modified:	21 Nov 2023 00:30
URI:	http://umpir.ump.edu.my/id/eprint/39339
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