Prediction of Student Work Readiness using Artificial Neural Network and Decision Tree
- Bilqiis Shahieza Andini Telkom University, Bandung, Indonesia
-
Kemas Muslim Lhaksmana
*
Telkom University, Bandung,
Indonesia
(*) Corresponding Author
Abstract
The readiness of students for the workforce is a critical metric in evaluating the quality of higher education. Forecasting students' work readiness before their entrance into the industry calls for a data-driven approach since they often lack the necessary skills and experience until graduation. Using two machine learning techniques—Artificial Neural Network (ANN) and Decision Tree (DT)—this research aims to create a classification model to predict students' employment readiness. Among the several aspects the data covers are academic knowledge, professional attitude, soft skills, hard skills, and socio-economic background. Data preparation, data cleansing, feature selection, model training, and performance evaluation make up the study approach. The ANN model comprised four hidden layers, while the DT was refined with RandomizedSearchCV. The test results showed that DT had an accuracy of 90.80%, and ANN had 90.69%, indicating that both performed very well and can be selected based on what the user needs most. This research contributes a predictive model for educational institutions to assess students' employment preparedness in a more objective and systematic way.
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References
U. N. Fajriah and K. Sudarma, “Pengaruh Praktik Kerja Industri, Motivasi Memasuki Dunia Kerja, dan Bimbingan Karir pada Kesiapan Kerja Siswa,” Econ. Educ. Anal. J., vol. 6, no. 2, pp. 421–432, 2017, [Online]. Available: http://journal.unnes.ac.id/sju/index.php/eeaj
M. J. Kavanagh and Richard David Johnson, Human resource information systems : Basics, applications, and future directions. Los Angeles: Sage, 2018.
B. Gabrikova, L. Svabova, and K. Kramarova, “Machine Learning Ensemble Modelling for Predicting Unemployment Duration,” Appl. Sci., vol. 13, no. 18, 2023, doi: 10.3390/app131810146.
D. Kartini, J. A. Yani Km, and K. Selatan, “Penerapan Data Mining dengan Algoritma Neural Network (Backpropagation) Untuk Prediksi Lama Studi Mahasiswa,” Pros. Semin. Nas. Sisfotek, vol. 3584, pp. 235–241, 2017.
D. Galih Pradana, M. L. Alghifari, M. Farhan Juna, and S. Dwisiwi Palaguna, “Klasifikasi Penyakit Jantung Menggunakan Metode Artificial Neural Network,” Indones. J. Data Sci., vol. 3, no. 2, pp. 55–60, 2022.
H. Noviyanto and B. Mukti, “Prediksi Kesiapan Kerja Mahasiswa menggunakan Algoritme K-Means dan C4.5,” JOSTECH J. Sci. Technol., vol. 2, no. 2, pp. 179–188, 2022, doi: 10.15548/jostech.v2i2.4422.
F. R. Yudana, M. Suyanto, and A. Nasiri, “Model Klasifikasi Untuk Menentukan Kesiapan Kerja Mahasiswa Dan Kelulusan Tepat Waktu Dengan Metode Machine Learning,” IJITECH Indones. J. Inf. Technol., vol. 1, no. 1, pp. 1–12, 2023, doi: 10.37680/ijitech.v1i1.xx.
I. Supriadi, A. Hariyanti, M. Z. Abidin, Rinrin, and D. Gustian, “Penerapan regresi linier berganda dalam kesiapan kerja mahasiswa,” Semin. Nas. Inform. 2020, vol. 1, no. 1, pp. 204–211, 2020.
Muhammad Hadiza Baffa, Muhammad Abubakar Miyim, and Abdullahi Sani Dauda, “Machine Learning for Predicting Students’ Employability,” UMYU Sci., vol. 2, no. 1, pp. 001–009, 2023, doi: 10.56919/usci.2123_001.
G. ElSharkawy, Y. Helmy, and E. Yehia, “Employability Prediction of Information Technology Graduates using Machine Learning Algorithms,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 10, pp. 359–367, 2022, doi: 10.14569/IJACSA.2022.0131043.
A. Malik et al., “Forecasting students’ adaptability in online entrepreneurship education using modified ensemble machine learning model,” Array, vol. 19, no. July, p. 100303, 2023, doi: 10.1016/j.array.2023.100303.
V. Prikshat, A. Nankervis, J. Burgess, and S. Dhakal, “Conceptualising graduate work-readiness: theories, concepts and implications for practice and research,” Transit. from Grad. to Work challenges Strateg. Twenty-first Century Asia Pacific beyond, pp. 15–29, 2019.
F. Nasril, D. Indiyati, and G. Ramantoko, “Talent performance analysis using people analytics approach,” Budapest Int. Res. Critics Inst. Humanit. Soc. Sci., vol. 4, no. 1, pp. 216–230, 2021.
M. Thite, e-HRM: Digital approaches, directions & applications. Routledge, 2018.
R. L. Cross, J. Singer, S. Colella, R. J. Thomas, and Y. Silverstone, The organizational network fieldbook: Best practices, techniques and exercises to drive organizational innovation and performance. Hoboken, NJ: John Wiley & Sons, 2010.
F. K. Wardani et al., “The Construction of Work Readiness Scale,” 14th Univ. Res. Colloqium, pp. 78–87, 2021, [Online]. Available: http://repository.urecol.org/index.php/proceeding/article/view/1774
F. F. Kirani, P. R. S. Prakasa, F. F. Vivany, G. A. Arsi, and H. D. Humairo, “Description and Efforts to Increase Work Readiness in Early Adult Individuals Who Have Not Get A Job,” J. Abdi Insa. Univ. Mataram, vol. 9, no. 1, pp. 160–168, 2022.
I. Ahmad, S. Samsugi, and Y. Irawan, “Implementasi Data Mining Sebagai Pengolahan Data,” J. Teknoinfo, vol. 16, no. 1, p. 46, 2022, [Online]. Available: http://portaldata.org/index.php/portaldata/article/view/107
O. V. William Chan, Navdeep Jaitly, Quoc Le, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” IEEE Int. Conf. Acoust. Speech, Signal Process. 2016, pp. 4960–4964, 2016.
Z. ``Leo’’ Liu, “Artificial Neural Networks,” in Artificial Intelligence for Engineers: Basics and Implementations, Cham: Springer Nature Switzerland, 2025, pp. 175–190. doi: 10.1007/978-3-031-75953-6_7.
H. Sharma and S. Kumar, “A Survey on Decision Tree Algorithms of Classification in Data Mining,” Int. J. Sci. Res., vol. 5, no. 4, pp. 2094–2097, 2016, doi: 10.21275/v5i4.nov162954.
Y. Y. Song and Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Arch. Psychiatry, vol. 27, no. 2, pp. 130–135, 2015, doi: 10.11919/j.issn.1002-0829.215044.
H. D. Bhakti, “Aplikasi Artificial Neural Network (ANN) untuk Memprediksi Masa Studi Mahasiswa Program Studi Teknik Informatika Universitas Muhammadiyah Gresik,” Eksplora Inform., vol. 9, no. 1, pp. 88–95, 2019, doi: 10.30864/eksplora.v9i1.234.
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