Sistem Rekomendasi Pekerjaan Berbasis Kompetensi Mahasiswa Menggunakan Pendekatan Content-Based Filtering
DOI:
https://doi.org/10.35746/jtim.v8i2.997Kata Kunci:
career recommender system, Content-Based Filtering, student competency, Cosine Similarity, academic performanceAbstrak
Students often experience difficulties in obtaining job recommendations that match their academic competencies and personal interests. This problem is caused by the suboptimal utilization of stu-dents' academic data in the job matching process, resulting in recommendations that are often less relevant to students' profiles and abilities. Therefore, a job recommendation system is needed that can process academic data and user preferences to produce more appropriate job recommenda-tions that meet students' needs. This study aims to develop a web-based job recommendation sys-tem that utilizes Grade Point Average (GPA) with a Content-Based Filtering approach. The devel-oped system matches student profiles—including GPA, major, skills, and interests—with job characteristics in the information technology field. The level of match between student profiles and job profiles is calculated using the Cosine Similarity algorithm. Evaluation of system performance is carried out by implementing a 5-fold cross-validation scheme and using Top-3 Accuracy and Exact Accuracy metrics. The test results show that the system obtained an average Top-3 Accuracy score of 85.0% and Exact Accuracy of 54.7%. These results demonstrate that the developed system is capable of generating relevant job recommendations with a high level of consistency. Therefore, this system is expected to contribute to supporting students' decision-making process in planning career paths that align with their academic achievements and individual preferences.
Unduhan
Referensi
B. P. S. (BPS), “Tingkat Pengangguran Terbuka Berdasarkan Daerah Tempat Tinggal,” 2024.
N. P. Mulya, “Navigating the Tides of Change: Analyzing Indonesia’s Employment and Unemployment Dynamics from 2011 to 2023,” Indones. J. Innov. Multidisipliner Res., vol. 2, no. 3, pp. 356–364, 2024, https://doi.org/10.69693/ijim.v2i3.189.
R. Mayapada and F. Fardinah, “Tingkat Pengangguran Terbuka Periode Sebelum hingga Sesudah Pandemi Covid-19 dengan Pendekatan Non-Parametrik,” Estim. J. Appl. Stat. Math. Data Sci., vol. 2, no. 1, pp. 1–9, 2024, https://doi.org/10.31537/estimator.v2i1.1829.
Y. Zhang, “Path of career planning and employment strategy based on deep learning in the information age,” PLoS One, vol. 19, no. 10, p. e0308654, 2024, https://doi.org/10.1371/journal.pone.0308654.
C. Weijia and A. Xiaojie, “The Improvement of College Students ’ Career Development Planning Skills,” Advances in Vo-cational and Technical Education, vol. 5, pp. 48–52, 2023, https://doi.org/10.23977/avte.2023.051108.
Y. Xiaoqing and Z. Bin Zainudin, “Integrating Career Planning and Guidance to Foster Career Readiness: A Conceptual Perspective on Student Development,” J. Digit. Realis. Mastery, vol. 4, no. 05 SE-Articles, pp. 8–26, May 2025, https://doi.org/10.56982/dream.v4i05.310.
S. I. Adlya and N. Zola, “Holland’s theory to guiding individual career choices,” J. Neo Konseling, vol. 4, no. 4, pp. 30–35, 2022, https://doi.org/10.24036/00698kons2022.
F. Omar, A. R. A. Rahim, and N. I. A. Hafit, “Perceived career counselling establishing career adaptability among newly hired graduates,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 12, no. 9, pp. 1170–1184, 2022, https://doi.org/10.6007/IJARBSS/v12-i9/14836.
Y. Huang, “Individualized career planning intelligent teaching system for college students based on adaptive artificial intelligence,” J. Comput. Methods Sci. Eng., vol. 25, no. 4, pp. 3121–3136, 2025, https://doi.org/10.1177/14727978251315731.
S. Gowsika, S. Aparna, V. Kiruthika, and K. Lavanya, “Personalized Career Guidance System for Students,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 5, no. 3, p. 48175, 2025, https://doi.org/10.48175/IJARSCT-23904.
D. Çelik Ertuğrul and S. Bitirim, “Job recommender systems: a systematic literature review, applications, open issues, and challenges,” J. Big Data, vol. 12, no. 1, p. 140, 2025, https://doi.org/10.1186/s40537-025-01173-y.
A. I. Gufroni, P. Purwanto, and F. Farikhin, “Academic performance prediction using supervised learning algorithms in university admission,” JOIV Int. J. Informatics Vis., vol. 9, no. 1, pp. 184–194, 2025, http://dx.doi.org/10.62527/joiv.9.1.2974.
E. Y. Varshavskaya and U. S. Podverbnykh, “Impact Of Job Mismatches On Job Satisfaction And Turnover Intention : Case Of Russia,” Russ. Manag. J., vol. 21, no. 1, pp. 115–132, 2023, https://doi.org/10.21638/spbu18.2023.106.
C. A. Chavadi, M. Sirothiya, and V. MR, “Mediating role of job satisfaction on turnover intentions and job mismatch among millennial employees in Bengaluru,” Bus. Perspect. Res., vol. 10, no. 1, pp. 79–100, 2022, https://doi.org/10.1177/227853372199471.
V. Sam, “Impacts of educational mismatches on job satisfaction: The case of university graduates in Cambodia,” Int. J. Manpow., vol. 41, no. 1, pp. 84–99, 2020, https://doi.org/10.1108/IJM-07-2018-0229.
M. Jiang, Y. Fang, H. Xie, J. Chong, and M. Meng, “User click prediction for personalized job recommendation,” World Wide Web, vol. 22, no. 1, pp. 325–345, 2019, https://doi.org/10.1007/s11280-018-0568-z.
Y. Saito and K. Sugiyama, “Leveraging multiple behaviors and explicit preferences for job recommendation,” Expert Syst. Appl., vol. 258, p. 125149, 2024, https://doi.org/10.1016/j.eswa.2024.125149.
Y. Zayed, Y. Salman, M. Awad, and A. Hasasneh, “Employment Recommendation System for Graduates Using Machine Learning.,” Int. J. Eng. Appl., vol. 11, no. 5, 2023, https://doi.org/10.15866/irea.v11i5.23492.
H. Chang and T. Shi, “Research and Design of a Job Search Service Platform Based on Recommendation Algorithm,” J. Comput. Technol. Electron. Res., vol. 1, no. 1, 2024, https://doi.org/10.25236/FSST.2023.051414.
Y. Mashayekhi, N. Li, B. Kang, J. Lijffijt, and T. De Bie, “A challenge-based survey of e-recruitment recommendation sys-tems,” ACM Comput. Surv., vol. 56, no. 10, pp. 1–33, 2024, https://doi.org/10.48550/arXiv.2209.05112.
N. L. Cezar, I. Gasparini, D. Lichtnow, G. M. Lunardi, and J. P. M. de Oliveira, “Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping.,” Proc. 26th Int. Conf. Enterp. Inf. Syst. SCITEPRESS, pp. 965–972, 2024, https://doi.org/10.5220/0012550700003690.
H. Yuan and A. A. Hernandez, “User cold start problem in recommendation systems: A systematic review,” IEEE access, vol. 11, pp. 136958–136977, 2023, https://doi.org/10.1109/ACCESS.2023.3338705.
J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Syst. Appl., vol. 69, pp. 29–39, 2017, https://doi.org/10.1016/j.eswa.2016.09.040.
V. K. Matam and N. M. Reddy, “Overcoming the cold-start challenge in recommender systems: A novel two-stage framework,” Open Comput. Sci., vol. 15, no. 1, p. 20250038, 2025, https://doi.org/10.1515/comp-2025-0038.
A. Saini, J. Tripathi, M. Faraz, and M. Kaif, “Job recommendation system based on content-based filtering,” in Advances in AI for Biomedical Instrumentation, Electronics and Computing, CRC Press, 2024, pp. 30–35. https://www.taylorfrancis.com/chapters/edit/10.1201/9781032644752-6/job-recommendation-system-based-content-based-filtering-anu-saini-jyoti-tripathi-mohd-faraz-mohd-kaif
F. Manganello, E. Rasca, A. Villa, A. Maddalena, and G. Boccuzzi, “Machine learning for post-diploma educational and career guidance: a scoping review in AI-driven decision support systems,” in Frontiers in Education, Frontiers Media SA, 2025, p. 1578979. https://doi.org/10.3389/feduc.2025.1578979.
L. B. Surya, D. F. Fadhilah, K. A. A. Pahlevi, M. Imaduddin, A. Nailurrizqi, and V. Atina, “Perancangan Sistem Rekomen-dasi Produk Herbal Menggunakan Content-Based Filtering,” in Prosiding Seminar Nasional Teknologi Informasi dan Bisnis, 2025, pp. 221–230. https://doi.org/10.47701/c0nsjt07.
N. G. Gupta, S. Tajne, G. Parate, P. Mune, A. Wankhade, and A. Mohod, “Student Career Recommendation Using Machine Learning,” Int. J. Ingenious Res. Invent. Dev., vol. 1, no. 2, pp. 21–25, 2023, https://doi.org/10.5281/zenodo.7811524.
A. H. A. Rashid, M. Mohamad, S. Masrom, and A. Selamat, “Student career recommendation system using content-based filtering method,” in 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS), IEEE, 2022, pp. 60–65. https://doi.org/10.1109/AiDAS56890.2022.9918766.
V. N. June, B. Foo, H. Goh, A. Quek, and C. Cham, “Dynamic Job Recommendation by Profiling Undergraduates Academic Performances,” Journal of Informatics and Web Engineering, vol. 4, no. 2, 2025. https://doi.org/10.33093/jiwe.2025.4.2.12
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Intan Sulistyaningrum Sakkinah, Muhammad Ainul Fikri, Rosyida Arian Rahma
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
