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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.

References
  1. S. H. Ing, A. A. Abdullah, M. Y. Mashor, Z. A. Mohamed-Hussein, Z. Mohamed, and W. C. Ang, “Exploration of hybrid deep learning algorithms for covid-19 mRNA vaccine degradation prediction system,” Int. J. Adv. Intell. Informatics, vol. 8, no. 3, pp. 404–416, 2022, doi: 10.26555/ijain.v8i3.950.
  2. A. Azaluddin and L. Hanifa, “Effect of Inflation and Economic Growth on The Rate of Unemployment,” Sang Pencerah J. Ilm. Univ. Muhammadiyah But., vol. 7, no. 4, pp. 609–617, 2021, doi: 10.35326/pencerah.v7i4.1559.
  3. L. Yanthiani, “The Impact of Unemployment on the Economy in Indonesia,” J. Islam. Econ. Bus., vol. 2, no. 2, pp. 112–130, 2023, doi: 10.15575/jieb.v2i2.21310.
  4. E. K. Oktafianto, N. A. Achsani, and T. Irawan, “The Determinant of Regional Unemployment in Indonesia: The Spatial Durbin Models,” Signifikan J. Ilmu Ekon., vol. 8, no. 2, pp. 179–194, 2019, doi: 10.15408/sjie.v8i2.10124.
  5. M. L. B. Ginting, “Perluasan Kesempatan Kerja Bagi Freshgraduate di Masa Pandemi Covid-19, Apa Peran Pemerintah?,” J. Ketenagakerjaan, vol. 16, no. 2, 2021, doi: 10.47198/naker.v16i2.106.
  6. BPS Kebumen, “Kabupaten Kebumen dalam Angka 2022,” p. 292, 2022.
  7. K. Wulandari, D. Hariani, and S. Sulandri, “Analisis E-Service Bursa Kerja Online,” J. Public Policy Manag. Rev., vol. 10, no. 2, pp. 1–19, 2021.
  8. F. D. S. Harahap, “Dampak Pandemi Covid-19 Terhadap Masyarakat Khususnya Dunia Ketenagakerjaan,” OSF., vol. 2019, 2020.
  9. B. Budiman, R. Nursyanti, R. Y. R. Alamsyah, and I. Akbar, “Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection,” Int. J. Quant. Res. Model., vol. 1, no. 3, pp. 123–134, 2020, doi: 10.46336/ijqrm.v1i3.72.
  10. C. E. Amos Pah, “Decision Support Model for Employee Recruitment Using Data Mining Classification,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 5, pp. 1511–1516, 2020, doi: 10.30534/ijeter/2020/06852020.
  11. A. Renear, S. Sacchi, and K. Wickett, “Definitions of dataset in the scientific and technical literature,” Proc. Am. Soc. Inf. Sci. Technol., vol. 47, pp. 1–4, Nov. 2010, doi: 10.1002/meet.14504701240.
  12. Normah, B. Rifai, S. Vambudi, and R. Maulana, “Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis SMOTE,” J. Tek. Komput. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022, doi: 10.31294/jtk.v4i2.
  13. S. Hendrian, “Algoritma Klasifikasi Data Mining Untuk Memprediksi Siswa Dalam Memperoleh Bantuan Dana Pendidikan,” Fakt. Exacta, vol. 11, no. 3, pp. 266–274, 2018, doi: 10.30998/faktorexacta.v11i3.2777.
  14. A. H. Nasrullah, “Implementasi Algoritma Decision Tree Untuk Klasifikasi Produk Laris,” J. Ilm. Ilmu Komput., vol. 7, no. 2, pp. 45–51, 2021, doi: 10.35329/jiik.v7i2.203.
  15. A. Oluwaseun and M. S. Chaubey, “Data Mining Classification Techniques on the analysis of student performance,” Glob. Sci. J., vol. 7, no. April, pp. 79–95, 2019.
  16. Y. T. Utami and E. Elisa, “Prediksi Kinerja Karyawan Berdasarkan Proses Trainer Menggunakan Data Mining,” J. Comasie, vol. 04, 2022.
  17. J. T. Samudra, R. Rosnelly, and Z. Situmorang, “Comparative Analysis of Support Vector Machine and Perceptron In The Classification of Subsidized Fuel Receipts,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 3, pp. 652–656, 2023, doi: 10.29207/resti.v7i3.4731.
  18. M. Muhasshanah, M. Tohir, D. A. Ningsih, N. Y. Susanti, A. Umiyah, and L. Fitria, “Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process,” CommIT (Communication Inf. Technol. J., vol. 17, no. 1, pp. 51–59, 2023, doi: 10.21512/commit.v17i1.8236.
  19. D. Syafira, S. Suwilo, and P. Sihombing, “Analysis of Attribute Reduction Effectiveness on the Naive Bayes Classifier Method,” J. Phys. Conf. Ser., vol. 1566, no. 1, 2020, doi: 10.1088/1742-6596/1566/1/012060.
  20. B. S. Prakoso, D. Rosiyadi, H. S. Utama, and D. Aridarma, “Klasifikasi Berita Menggunakan Algoritma Naive Bayes Classifer Dengan Seleksi Fitur Dan Boosting,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 227–232, 2019, doi: 10.29207/resti.v3i2.1042.
  21. M. Hadikristanto Wahyu ; Suprayogi, “SIGMA - Jurnal Teknologi Pelita Bangsa SIGMA - Jurnal Teknologi Pelita Bangsa,” SIGMA - J. Teknol. Pelita Bangsa 167, vol. 10, no. September, pp. 167–172, 2019.
  22. R. Adrian, M. A. J. S. Perdana, A. Asroni, and S. Riyadi, “Applying the Naive Bayes Algorithm to Predict the Student Final Grade,” Emerg. Inf. Sci. Technol., vol. 1, no. 2, pp. 49–57, 2020, doi: 10.18196/eist.127.
  23. Y. F. Safri, R. Arifudin, and M. A. Muslim, “K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor,” Sci. J. Informatics, vol. 5, no. 1, p. 18, 2018, doi: 10.15294/sji.v5i1.12057.
  24. R. Achmad and A. S. Girsang, “Implementation of naive bayes classifier algorithm in classification of civil servants,” J. Phys. Conf. Ser., vol. 1485, no. 1, 2020, doi: 10.1088/1742-6596/1485/1/012018.
  25. R. Sistem, R. Prabaswara, J. Lemantara, and J. Jusak, “Classification of Secondary School Destination for Inclusive Students,” Jurnal Resti., vol. 5, no. 158, pp. 1009–1019, 2023.
  26. C. S. Lee, P. Y. S. Cheang, and M. Moslehpour, “Predictive Analytics in Business Analytics: Decision Tree,” Adv. Decis. Sci., vol. 26, no. 1, pp. 1–29, 2022, doi: 10.47654/V26Y2022I1P1-30.
  27. I. M. Nasser and A. H. Alzaanin, “Machine Learning and Job Posting Classification: A Comparative Study,” Int. J. Eng. Inf. Syst., vol. 4, no. 9, pp. 6–14, 2020, [Online]. Available: www.ijeais.org
  28. D. Lavanya and K. U. Rani, “Performance Evaluation of Decision Tree Classifiers on Medical Datasets,” Int. J. Comput. Appl., vol. 26, no. 4, pp. 1–4, 2011, doi: 10.5120/3095-4247.
  29. H. Husni, “Kajian Literatur Mengenai Klasifikasi Blog,” J. Simantec, vol. 8, no. 2, pp. 63–77, 2020, doi: 10.21107/simantec.v8i2.7223.
Index Terms

Computer Science
Information Sciences

Keywords

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

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