Automated Matching Skills to Improve the Accuracy of Job Applicant Selection Using Indonesian National Work Competency Standards

Abdul Azzam Ajhari - Universitas Siber Asia, Jakarta, 12550, Indonesia
Dimas Febriyan Priambodo - National Cyber and Crypto Polytechnic, Bogor, 16120, Indonesia
Henny Yulianti - Universitas Siber Asia, Jakarta, 12550, Indonesia

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The high number of cyberattack anomalies and data leaks in Indonesia increases the need for cybersecurity in various companies. Cybersecurity capabilities and skills in Indonesia are divided into three categories based on the Indonesian National Work Competency Standards (SKKNI), namely Security Operation Center (SOC), Cybersecurity test/Penetration testing (Pentest), and Information Security Audit. Although various approaches have been applied in different companies to select job applicants, a new method with automated matching is explored in this study. This method matches the skills possessed by prospective job applicants with the profile of their job task requirements based on the SKKNI Decree of the Minister of Manpower of the Republic of Indonesia using Machine Learning (ML) models. The empirical comparison of results comes from automated matchmaking processed by Multinomial Naive Bayes (MNB) and Decision Tree algorithm models. Before modeling, the data is trained and evaluated for testing. Then to assess the most optimal algorithm between MNB and Decision Tree, a confusion matrix is proposed and used to find the best model. From the evaluation results, both models performed well and were highly accurate during training and test evaluation. The Decision Tree model performs slightly better than the MNB model, but both still provide satisfactory results in classifying data based on the Indonesian National Work Competency Standards (SKKNI) categories. This study offers a solution to minimize the number of potential applicants who are not competent in the three SKKNI cybersecurity job categories due to the mismatch of their abilities and skills.


Indonesian national work competency standards (SKKNI); automated matching; machine learning; multinomial naïve bayes; decision tree

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E. Budi, D. Wira, and A. Infantono, “Strategi Penguatan Cyber Security Guna Mewujudkan Keamanan Nasional di Era Society 5.0,” Pros. Semin. Nas. Sains Teknol. dan Inov. Indones., vol. 3, no. November, pp. 223–234, 2021, doi: 10.54706/senastindo.v3.2021.141.

K. K. R. Indonesia, Keputusan Menaker Nomor 391 Tahun 2020 tentang Penetapan Standar Kompetensi Kerja Nasional Indonesia Kategori Informasi dan Komunikasi Golongan Pokok Aktivitas Pemrograman, Konsultasi Komputer, dan Kegiatan yang Berhubungan Dengan Itu (YBDI) Bidang Securi. Indonesia, 2020.

Kementerian Ketenagakerjaan Republik Indonesia, Keputusan Menaker Nomor 23 Tahun 2022 tentang Standar Kompetensi Kerja Nasional Indonesia Kategori Informasi Dan Komunikasi Golongan Pokok Aktivitas Pemrograman, Konsultasi Komputer Dan Kegiatan yang Berhubungan Dengan Itu (YBDI) Bidang Uji Keamanan Siber. Indonesia, 2022.

Kementerian Ketenagakerjaan Republik Indonesia, Keputusan Menaker Nomor 24 Tahun 2022 tentang Standar Kompetensi Kerja Nasional Indonesia Kategori Informasi dan Komunikasi Golongan Aktivitas Pemrograman, Konsultasi Komputer dan Kegiatan yang Berhubungan Dengan Itu (YBDI) Bidang Audit Keamanan Infor. 2022.

J. Martinez-Gil, A. L. Paoletti, and M. Pichler, “A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles,” Inf. Syst. Front., vol. 22, no. 6, pp. 1265–1274, 2020, doi: 10.1007/s10796-019-09929-7.

N. Kamaruddin, A. W. A. Rahman, and R. A. M. Lawi, “Jobseeker-industry matching system using automated keyword selection and visualization approach,” Indones. J. Electr. Eng. Comput. Sci., vol. 13, no. 3, pp. 1124–1129, 2019, doi: 10.11591/ijeecs.v13.i3.pp1124-1129.

A. Mital, N. Siltala, E. Järvenpää, and M. Lanz, “Web-based solution to automate capability matchmaking for rapid system design and reconfiguration,” Procedia CIRP, vol. 81, no. March, pp. 288–293, 2019, doi: 10.1016/j.procir.2019.03.050.

F. F. Pratama and Y. I. Nurhasanah, “Penggunaan Metode Profile Matching Dan Naïve Bayes Untuk Menentukan Starting Eleven Pada Sepak Bola,” J. Tekno Insentif, vol. 14, no. 2, pp. 59–68, 2020, doi: 10.36787/jti.v14i2.268.

R. Rachman and R. N. Handayani, “Klasifikasi Algoritma Naive Bayes Dalam Memprediksi Tingkat Kelancaran Pembayaran Sewa Teras UMKM,” J. Inform., vol. 8, no. 2, pp. 111–122, 2021, doi: 10.31294/ji.v8i2.10494.

M. F. Rifai, H. Jatnika, and B. Valentino, “Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS),” Petir, vol. 12, no. 2, pp. 131–144, 2019, doi: 10.33322/petir.v12i2.471.

F. A. Bachtiar, F. Pradana, and R. D. Yudiari, “Employee Recruitment Recommendation Using Profile Matching and Naïve Bayes,” in 2019 International Conference on Sustainable Information Engineering and Technology (SIET), 2019, pp. 94–99. doi: 10.1109/SIET48054.2019.8985988.

A. Ivanov, “Decision trees for evaluation of mathematical competencies in the higher education: A case study,” Mathematics, vol. 8, no. 5, 2020, doi: 10.3390/MATH8050748.

H. Elfaham, J. Grothoff, T. Deppe, M. Azarmipour, and U. Epple, “Recipe Based Skill Matching,” in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, vol. 1, pp. 500–507. doi: 10.1109/INDIN41052.2019.8972204.

A. Alibasyah, A. Ajiz, G. Dwilestari, K. Kaslani, and E. Wahyudin, “Penerapan Algoritma Decision Tree dalam Penentuan Karyawan Kontrak,” MEANS (Media Inf. Anal. dan Sist., vol. 7, no. 1, pp. 124–129, 2022, doi: 10.54367/means.v7i1.1844.

C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang, “Is ChatGPT a General-Purpose Natural Language Processing Task Solver?,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, pp. 1339–1384. [Online]. Available:

Y. Zhu, P. Zhang, and E. Haq, Can ChatGPT Reproduce Human-Generated Labels ? A Study of Social Computing Tasks, vol. 1, no. 1. Association for Computing Machinery, 2016. doi: 10.48550/arXiv.2304.10145.

O. Azeroual, M. Jha, A. Nikiforova, K. Sha, and M. Alsmirat, “A Record Linkage-Based Data Deduplication Framework with DataCleaner Extension,” Mltimodal Technol. Interact. Artic., vol. 6, no. 4, pp. 1–18, 2022, doi: 10.3390/mti6040027.

R. Friedman, “Tokenization in the Theory of Knowledge,” Encyclopedia, vol. 3, no. 1, pp. 380–386, 2023, doi: 10.3390/encyclopedia3010024.

J. Liao, S. Cheng, and M. Tan, “Text Polishing with Chinese Idiom: Task, Datasets and Pre-Trained Baselines,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 22, no. 6, Jun. 2023, doi: 10.1145/3593806.

E. V. Beshenkova, “Lowercase or uppercase letter in the names of fairytale heroes and literary characters (from the materials of the academic description of Russian spelling),” 2021.

D. J. Ladani and N. P. Desai, “Stopword Identification and Removal Techniques on TC and IR applications : A Survey,” in 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 466–472. doi: 10.30515/0131-6141-2021-82-6-79-86.

A. K. Singh and M. Shashi, “Vectorization of Text Documents for Identifying Unifiable News Articles,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 7, pp. 305–310, 2019.

T. K. Garg and P. Mittal, “Logistics networks: a sparse matrix application for solving the transshipment problem,” J. Math. Comput. Sci., vol. 11, no. 6, pp. 7511–7522, 2021, doi: 10.28919/10.28919/JMCS/6654.

J. Chen, Z. Dai, J. Duan, H. Matzinger, and I. Popescu, “Naive Bayes with Correlation Factor for Text Classification Problem,” in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019, pp. 1051–1056. doi: 10.1109/ICMLA.2019.00177.

S. Bagui and W. Florida, “MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier,” vol. 16, no. 2, pp. 1–23, 2020, doi: 10.4018/IJIIT.2020040101.

T. Yu, “Decision tree: basic theory, algorithm formulation and implementation,” in International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2022, vol. 12163, p. 121631F. doi: 10.1117/12.2628025.

Y. Meng, “Decision tree model in supervised learning,” in Proc.SPIE, May 2022, vol. 12259, p. 1225960. doi: 10.1117/12.2639432.

Y. Lu, T. Ye, and J. Zheng, “Decision Tree Algorithm in Machine Learning,” in 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), 2022, pp. 1014–1017. doi: 10.1109/AEECA55500.2022.9918857.

F. Cheng, “Application of decision tree in student information management system,” in 2022 International Conference on Artificial Intelligence in Everything (AIE), 2022, pp. 316–321. doi: 10.1109/AIE57029.2022.00068.

H. Turki, M. Ali, H. Taieb, and M. Ben Aouicha, “Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures,” in Proceedings of the Workshop on Data meets Applied Ontologies in Explainable AI, 2021, vol. 2014, pp. 0–3. doi: 10.48550/arXiv.2011.00109.

I. Markoulidakis, G. Kopsiaftis, and N. Doulamis, “Confusion Matrix Analysis for NPS,” in Proceedings of the 24th Pan-Hellenic Conference on Informatics, 2021, pp. 192–196. doi: 10.1145/3437120.3437305.