Decision Tree Implementation in IT Job Recommendation System

Authors

  • Yohana Tri Widayati Universitas AKI
  • Stephanus Widjaja STMIK AKI Pati
  • Adityo Putro Wicaksono Universitas AKI
  • Jutono Gondohanindijo Universitas AKI
  • Christine Cecillia Putri Universitas AKI

DOI:

https://doi.org/10.26623/transformatika.v21i2.8328

Keywords:

AI Project Cycle, Decision Tree, Information Technology (IT), Python Programming Language, Recommendation System

Abstract

Employment is the primary activity that humans engage in to generate income. With the advancement of technology and research, there are many new job opportunities leading to confusion in choosing a job path. This leads to individual confusion in making job choices. Ignorance of one's own talents and personality, as well as ignorance of the various options available, can be the source of this ignorance. This research aims to develop a Decision Tree model to assist users in determining the appropriate IT field. The system uses AI Project Cycle and data processing tools such as Google Collaboratory, which is based on Python programming language. The results show that the Decision Tree algorithm can be applied to recommend jobs in the IT field to help users find suitable fields in the IT field.

Author Biographies

  • Yohana Tri Widayati, Universitas AKI
    Program Studi Sistem Informasi, Universitas AKI.
  • Stephanus Widjaja, STMIK AKI Pati
    Teknik Informatika
  • Adityo Putro Wicaksono, Universitas AKI
    Fakultas Teknik dan Informatika
    Program Studi Teknik Informatika
  • Jutono Gondohanindijo, Universitas AKI
    Fakultas Teknik dan Informatika
    Program Studi Teknik Informatika
  • Christine Cecillia Putri, Universitas AKI
    Fakultas Teknik dan Informatika
    Program Studi Teknik Informatika

References

Badarudin, “SISTEM REKOMENDASI PEKERJAAN BAGI ALUMNI TEKNIK INFORMATIKA DENGAN METODE WEIGHTED PRODUCT (WP),” BMC Public Health, vol. 5, no. 1, pp. 1–8, 2017.

V. B. Kusnandar, “Mayoritas Pengangguran Indonesia Berusia Muda pada Agustus 2022,” Databoks, 2023. https://databoks.katadata.co.id/datapublish/2023/01/12/mayoritas-pengangguran-indonesia-berusia-muda-pada-agustus-2022#:~:text=Menurut data Badan Pusat statistik,yakni 2%2C54 juta orang

M. M. Reddy, “Career Prediction System,” Int. J. Sci. Res. Sci. Technol., vol. 8, no. 4, pp. 54–58, 2021.

A. Solichin, “Rekomendasi Keterampilan Teknologi Informasi Menggunakan Metode User-Based Collaborative Filtering dan Log-Likelihood Similarity,” Cogito Smart J., vol. 6, no. 2, pp. 141–154, 2020.

K. S. Roy, K. Roopkanth, V. U. Teja, V. Bhavana, and J. Priyanka, “Student Career Prediction Using Advanced Machine Learning Techniques,” Int. J. Eng. Technol., vol. 7, no. 2, 2018, doi: https://doi.org/10.14419/ijet.v7i2.20.11738.

E. Budiman, Haviluddin, N. Dengan, A. H. Kridalaksana, M. Wati, and Purnawansyah, “Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation,” Lect. Notes Electr. Eng., vol. 488, no. April, pp. 380–389, 2018, doi: 10.1007/978-981-10-8276-4_36.

R. Latifah, E. S. Wulandari, and P. E. Kreshna, “Model Decision Tree Untuk Prediksi Jadwal Kerja Menggunakan Scikit-Learn,” J. Univ. Muhammadiyah Jakarta, pp. 1–6, 2019, [Online]. Available: https://jurnal.umj.ac.id/index.php/semnastek/article/download/5239/3517

U. Kamath and J. Liu, Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning, 1st ed. Springer Cham, 2021. doi: https://doi.org/10.1007/978-3-030-83356-5.

N. VidyaShreeram and D. A. Muthukumaravel, “Student Career Prediction Using Decision Tree and Random Forest Machine Learning Classifiers,” 2021. doi: 10.4108/eai.7-6-2021.2308621.

A. Handa, R. Negi, and S. K. Shukla, Implementing Enterprise Cybersecurity with Open-Source Software and Standard Architecture. River Publishers, 2021.

Surahman, M. Rachmat, and S. Supardi, Metodologi Penelitian. 2016.

F. Azimah and K. R. N. Wardani, “Klasifikasi Deteksi Gejala Awal COVID-19 Dengan Metode Logistic Regression, Random Forest Classifier dan Support Vector Machine,” J. Locus Penelit. dan Pengabdi., vol. 1, no. 6, 2022, doi: https://doi.org/10.58344/locus.v1i6.135.

J. J. Faraway, “Does data splitting improve prediction?,” Stat. Comput., vol. 26, no. 1–2, pp. 49–60, 2016, doi: https://doi.org/10.1007/s11222-014-9522-9.

R. Fox, Information Technology: An Introduction for Today’s Digital World. CRC Press, 2013. [Online]. Available: https://ocw.ui.ac.id/pluginfile.php/11951/mod_folder/content/0/Fox%2C Richard - Information Technology _ An Introduction for Today’s Digital World-CRC Press %282013%29.pdf?forcedownload=1

K. Nongthombam, “Data Analysis Using Python,” Int. J. Eng. Res. Technol., vol. 10, no. 07, pp. 463–468, 2021.

F. Nelli, Python Data Analytics, 2nd ed. Apress Berkeley, CA, 2018. doi: https://doi.org/10.1007/978-1-4842-3913-1.

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Published

2024-02-05

Issue

Vol. 21 No. 2 (2024): Januari 2024

Section

Artikel

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