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Predicting Interest in Working Abroad using Naïve Bayes and Decision Tree Algorithms

by Nur Widiastuti, Arif Pramudwiatmoko
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 40
Year of Publication: 2023
Authors: Nur Widiastuti, Arif Pramudwiatmoko
10.5120/ijca2023923206

Nur Widiastuti, Arif Pramudwiatmoko . Predicting Interest in Working Abroad using Naïve Bayes and Decision Tree Algorithms. International Journal of Computer Applications. 185, 40 ( Nov 2023), 40-45. DOI=10.5120/ijca2023923206

@article{ 10.5120/ijca2023923206,
author = { Nur Widiastuti, Arif Pramudwiatmoko },
title = { Predicting Interest in Working Abroad using Naïve Bayes and Decision Tree Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2023 },
volume = { 185 },
number = { 40 },
month = { Nov },
year = { 2023 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number40/32955-2023923206/ },
doi = { 10.5120/ijca2023923206 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:17.722242+05:30
%A Nur Widiastuti
%A Arif Pramudwiatmoko
%T Predicting Interest in Working Abroad using Naïve Bayes and Decision Tree Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 40
%P 40-45
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In order to reduce the unemployment rate, especially in Central Java Province, the Provincial Manpower Office Central Java created an innovation, namely the Central Java e-Makaryo application or https://bursakerja.jatengprov.go.id/. This application is useful for connecting job seekers and employers. However, the application has not been able to analyze how many job seekers are interested in working abroad and job seekers who are not interested in working abroad. Meanwhile, this is very much needed by the government to prepare job vacancies as needed. In this regard, the data of job seekers who are interested in working abroad are analyzed using the RapidMiner application with the Naïve Bayes algorithm classification method and the Decision Tree algorithm. The number of data used is 11,464. The accuracy results after testing on job seeker data classification using the Decision Tree algorithm has accuracy rate of 77,35%. +/_ 0,10% (micro average: 77.35%), higher than the Naïve Bayes algorithm which has an accuracy rate of 75.99%. Thus, the accuracy performance using the Decision Tree algorithm is better than Naïve Bayes.

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Index Terms

Computer Science
Information Sciences

Keywords

Algorithm classification machine learning Naïve Bayes Decision Tree data mining unemployment job seekers.

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