Resume Classification System using Machine Learning Method
Article Sidebar
Main Article Content
Abstract
The objective of this research is to 1) create the model and evaluate the performance of a model for classifying job applicants' resumes, 2) develop a web application system for job position recommendations with machine learning method, and 3) study user satisfaction with the web application system for job position recommendations with machine learning method. The sample group consists of 30 fourth-year students majoring in Information Technology at Rajabhat Maha Sarakham University who will be undertaking an internship. The research tools used include 1) a dataset of job applicants' resumes, 2) a web application system for job position recommendations, and 3) a questionnaire to assess user satisfaction with the web application system for job position recommendations. The algorithms used to classify job applicants' resumes include Naïve Bayes, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Multilayer Perceptron.
The evaluation metrics used for the model include Precision, Recall, F1-Score, and Accuracy. The statistics used to study satisfaction were mean and standard deviation.
The research findings revealed the following: 1) The creation and evaluation of the model for classifying job applicants' resumes showed that Multilayer Perceptron and Support Vector Machine methods achieved the highest performance with a precision of 99.64%, recall of 99.59%, F1-score of 99.596%, and accuracy of 99.59%. 2) Development of a web application system for recommending job positions with machine learning methods. The system can work as designed. It consists of five components: a membership system, a PDF file processing system for job applicants' resumes, a data processing system integrated with a model, a system that displays job titles corresponding to job applicants' resumes, and a system that shows job vacancies from recruitment companies. 3) The assessment of user satisfaction with the web application system for job position recommendations with the machine learning method indicated an extremely high satisfaction level, with an average score of 4.69 and a standard deviation of 0.47.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
กระทรวงการอุดมศึกษา วิทยาศาสตร์ วิจัยและนวัตกรรม. (2565). โครงการส่งเสริมการจัดสหกิจศึกษาและการศึกษาเชิงบูรณาการกับการทำงาน (Cooperative and Work Integrated Education หรือ CWIE). สืบค้น 11 มกราคม 2566,
จาก https://www.mhesi.go.th/index.php/flagship-project/6820-CWIE.html
Goindani, M., Liu, Q., Chao, J., & Jijkoun, V. (2017). Employer Industry Classification Using Job Postings. IEEE
International Conference on Data Mining Workshops, ICDMW, 2017-November, 183–188.
Retrieved from https://doi.org/10.1109/ICDMW.2017.30
Daryani, C., Chhabra, G. S., Patel, H., Chhabra, I. K., & Patel, R. (2020). AN AUTOMATED RESUME SCREENING SYSTEM USING NATURAL LANGUAGE PROCESSING AND SIMILARITY. Topics In Intelligent Computing And Industry Design, 2(2): 99-103. Retrieved from https://doi.org/10.26480/ETIT.02.2020.99.103
Roy, P. K., Chowdhary, S. S., & Bhatia, R. (2020). A Machine Learning approach for automation of Resume
Recommendation system. Procedia Computer Science, 167, 2318–2327. Retrieved from
https://doi.org/10.1016/J.PROCS.2020.03.284
Javed Mehedi Shamrat, F. M., Tasnim, Z., Ghosh, P., Majumder, A., & Hasan, M. Z. (2020). Personalization of Job
Circular Announcement to Applicants Using Decision Tree Classification Algorithm. 2020 IEEE International
Conference for Innovation in Technology, INOCON 2020. Retrieved from
https://doi.org/10.1109/INOCON50539.2020.9298253
van Huynh, T., van Nguyen, K., Nguyen, N. L. T., & Nguyen, A. G. T. (2020). Job Prediction: From Deep Neural
Network Models to Applications. Proceedings - 2020 RIVF International Conference on Computing and
Communication Technologies, RIVF 2020. Retrieved from https://doi.org/10.1109/RIVF48685.2020.9140760
Tran, H. T., Vo, H. H. P., & Luu, S. T. (2021). Predicting Job Titles from Job Descriptions with Multi-label Text
Classification. Proceedings - 2021 8th NAFOSTED Conference on Information and Computer Science, NICS
, 513–518. Retrieved from https://doi.org/10.1109/NICS54270.2021.9701541
กนิษฐา อินธิชิต, ภควัฒน์ ปิยวงษ์ และสุภาพร สุขใส, (2565). การพัฒนาแอปพลิเคชันช่วยตัดสินใจในการเลือกเรียนสาขาวิชา
คอมพิวเตอร์ ในมหาวิทยาลัยราชภัฏศรีสะเกษ บนระบบปฏิบัติการแอนดรอยด์ โดยใช้เทคนิคต้นไม้ตัดสินใจ.
วารสารวิชาการ การจัดการเทคโนโลยี มหาวิทยาลัยราชภัฏมหาสารคาม , 9(2), 97–107. สืบค้นจาก https://ph02.tci-thaijo.org/index.php/itm-journal/article/view/247879/168306
Kaggle. (2021). Resume Dataset. Retrieved January 11, 2023, from https://www.kaggle.com/datasets/snehaanbhawal/resume-dataset
Zhu, C. (2021). The basics of natural language processing. Machine Reading Comprehension, 27–46.
Retrieved from https://doi.org/10.1016/B978-0-323-90118-5.00002-3
NLTK. (2023). nltk.tokenize.casual module. Retrieved January 11, 2023, from https://www.nltk.org/api/nltk.tokenize.casual.html
Jabri, S., Dahbi, A., Gadi, T., & Bassir, A. (2018). Ranking of text documents using TF-IDF weighting and association rules mining. Proceedings of the 2018 International Conference on Optimization and Applications, ICOA
, 1–6. Retrieved from https://doi.org/10.1109/ICOA.2018.8370597
Google. (n.d.). Colaboratory. Retrieved January 11, 2023, from https://research.google.com/colaboratory/faq.html
Shalev-Shwartz, S., & Ben-David, S. (2013). Understanding machine learning: From theory to algorithms. In
Understanding Machine Learning: From Theory to Algorithms (Vol. 9781107057). Retrieved from
https://doi.org/10.1017/CBO9781107298019
Kohavi, R. (n.d.). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.
Retrieved January 11, 2023, from http//roboticsStanfordedu/"ronnykLikert,
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22 140, 55.
