Prediction of Graduate Career Relevance Based on Academic and Non-Academic Aspects using Machine Learning

Muhammad Yusuf Luthfi Ijlal, Arif Setiawan, Diana Laily Fithri

Abstract


One of the key indicators for assessing the quality of higher education is the career success of its graduates. However, not all graduates succeed in securing employment in fields relevant to their area of study. This study aims to analyze the influence of academic and non-academic factors on career alignment and to develop a predictive model using machine learning algorithms. The data used in this study were obtained from an alumni tracer study and student academic records at Universitas Muria Kudus (UMK), comprising a total of 311 records after data transformation. The predictor variables include Grade Point Average (GPA), length of study, teamwork skills, and scholarship participation, with a single target variable representing career alignment. Three classification algorithms were applied: Decision Tree, Random Forest, and Support Vector Machine (SVM). The results show that teamwork skills and scholarship participation have a moderate positive correlation with career alignment, while length of study has a negative correlation, and GPA has a very weak positive correlation. Random Forest demonstrated the best performance with an AUC score of 0.86 and a Cohen’s Kappa score of 0.62, indicating good and stable classification quality.
The findings from the variable analysis and classification model can be utilized in developing a career alignment prediction system and as a basis for decision-making in strategies to improve graduate quality in line with labor market needs.

Keywords


Machine Learning; Decision Tree; Random Forest; Support Vector Machine; Prediksi Ketereratan Karier

Full Text:

PDF

References


K. D. Pramesti, N. I. Meisya, And R. Amrillah, “Relevansi Lulusan Perguruan Tinggi dengan Dunia Kerja,” J. Pendidik. Islam Dan Sos. Agama, Vol. 03, No. 04, Pp. 236234–243, 2024.

I. Ilyas, “Strategi Peningkatan Kompetensi Profesional Guru,” J. Inovasi, Eval. Dan Pengemb. Pembelajaran, Vol. 2, No. 1, Pp. 34–40, 2022.

A. R. Safiih And A. Waliyudin, “Pengaruh Kompetensi dan Disiplin Kerja Terhadap Kinerja Karyawan Pt Kerta Mulya Sejahtera Di Rumpin Bogor,” Jorapi J. Res. Publ. Innov., Vol. 2, No. 1, Pp. 1384–1394, 2024, [Online]. Available: Https://Jurnal.Portalpublikasi.Id/Index.Php/Jorapi/Index

Anis Dela Desela, Rianti Setiadi, And Yekti Widyaningsih, “Analisis Faktor-Faktor yang memengaruhi Kompetensi Karyawan Sos Children’s Villages Indonesia,” J. Stat. Dan Apl., Vol. 4, No. 2, Pp. 95–108, 2020.

Nur Maghfirah Aesthetika, “Komunikasi Interpersonal,” Komun. Interpers., 2018, [Online]. Available: Https://Doi.Org/10.21070/2018/978-602-5914-13-3

Widiyaningrum and Harnanik, “Faktor-Faktor yang mempengaruhi Kreativitas Belajar Siswa Kelas XII Pemasaran pada Pembelajaran Produktif Pemasaran di SMK Negeri 1 Purbalingga,” Econ. Educ. Anal. J., Vol. 5, No. 3, Pp. 729–735, 2016.

D. I. K. Sukasari And E. R. Tem-, “Human Capital: A Theoretical and Empirical Analysis With Special Reference to Education (3rd Edition),” No. Suppan, Pp. 1–16, 1994.

A. L. Kristof, “Person-Organization Fit: An Integrative Review of Its Conceptualizations, Measurement,and Implications,” Pers. Psychol., Vol. 49, No. 1, Pp. 1–49, 1996.

A. Isma, A. Isma, A. Isma, And A. Isma, “Peta Permasalahan Pendidikan Abad 21 di Indonesia,” J. Pendidik. Terap., Vol. 01, No. September, Pp. 11–28, 2023.

Y. Mardi, “Data Mining : Klasifikasi menggunakan Algoritma C4.5,” Edik Inform., Vol. 2, No. 2, Pp. 213–219, 2017, Doi: 10.22202/Ei.2016.V2i2.1465.

A. Rahmawati, D. Wintana, and D. A. Ambarsari, “Penerapan Algoritma Iterative Dichotomiser Three ( ID3 ) dalam Mendiagnosa Kesehatan Kehamilan,” Vol. 06, No. 02, Pp. 116–127, 2019.

D. Truong, Data Science And Machine Learning For Non-Programmers: Using Sas Enterprise Miner. In Chapman & Hall/Crc Data Mining And Knowledge Discovery Series. Crc Press, 2024. [Online]. Available: Https://Books.Google.Co.Id/Books?Id=Jghzeaaaqbaj

A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support Vector Machine,” 2003.

W. Wijiyanto And S. Sopingi, “Kontribusi Keluarga dalam Prediksi Mahasiswa Lulus Tepat Waktu menggunakan Model Support Vector Machine,” Dutacom, Vol. 17, No. 1, Pp. 25–36, 2023.

M. L. Mu’tashim And A. Zaidiah, “Klasifikasi Ketepatan Lama Studi Mahasiswa dengan Algoritma Random Forest dan Gradient Boosting (Studi Kasus Fakultas Ilmu Komputer Universitas Pembangunan Nasional Veteran Jakarta),” Semin. Nas. Mhs. Bid. Ilmu Komput. Dan Apl., Vol. 4, No. 1, Pp. 155–166, 2023.

R. I. Arumnisaa And A. W. Wijayanto, “Comparison Of Ensemble Learning Method: Random Forest, Support Vector Machine, Adaboost For Classification Human Development Index (Hdi),” Sistemasi, Vol. 12, No. 1, P. 206, 2023.

E. Novianto, S. Suhirman, And D. Prasetyo, “Perbandingan Metode Klasifikasi Random Forest dan Support Vector Machine dalam Memprediksi Capaian Studi Mahasiswa,” Vol. 9, No. 4, Pp. 1821–1833, 2024.

N. Wati And S. Mamase, “Simulasi Analisis Hubungan Antara Kompetensi Belajar dan Kesesuaian Pekerjaan Alumni Lulusan Kampus Vokasi Menggunakan Metode Decision Tree,” J. Teknol. Inf. Indones., Vol. 7, No. 2, Pp. 106–111, 2023, Doi: 10.30869/Jtii.V7i2.1094.

B. Santoso, “Penerapan Teknik Data Mining Algoritma C4. 5 untuk Prediksi Kesesuaian Tingkat Pendidikan Dengan Bidang Pekerjaan Pada Alumni Universitas Sriwijaya,” 2021.

R. P. Oktarian, A. Desiani, and I. Irmeilyana, “Penerapan Algoritma Iterative Dichotomiser 3 (Id3) untuk memprediksi Kesesuaian Tingkat Pendidikan dengan Bidang Pekerjaan …,” 2018, [Online]. Available: Https://Repository.Unsri.Ac.Id/4889/%0ahttps://Repository.Unsri.Ac.Id/4889/1/Rama_44201_08011381419053_0011127702_0017057403_01_Front_Ref.Pdf

D. R. Utari, “Prediksi Bidang Kerja bagi Lulusan Program Studi Vokasi Sekretaris menggunakan Teknik Klasifikasi Data Mining,” J. Sekr. Adm., Vol. 16, No. 2, Pp. 115–123, 2018.

I. M. Hamdani1 Et Al., “Intisari Jurnal Inovasi Pengabdian Masyarakat Edukasi dan Pelatihan Data Science dan Data Preprocessing,” Juni, Vol. 2, No. 1, Pp. 19–26, 2024.

T. Utami, D. A. Shofiana, And Y. Heningtyas, “Penerapan Algoritma C4 . 5 untuk Prediksi Churn Rate Pengguna Jasa Telekomunikasi,” Vol. 8, No. 2, Pp. 69–76, 2020.

S. Diantika, F. Teknik, U. Bina, And S. Informatika, “Penerapan Teknik Random Oversampling untuk mengatasi Imbalance Class dalam Klasifikasi Website Phishing,” Vol. 7, No. 1, Pp. 19–25, 2023.

Ciciana, “Metode Resampling Dan Random Forest Untuk Klasifikasi Data Program Studi Matematika Universitas Sulawesi Barat Tahun 2024,” 2024.

N. V Chawla, K. W. Bowyer, L. O. Hall, And W. P. Kegelmeyer, “Smote : Synthetic Minority Over-Sampling Technique,” Vol. 16, Pp. 321–357, 2002.

I. M. Nasir Et Al., “Pearson Correlation-Based Feature Selection For Document Classification using Balanced Training,” Sensors (Switzerland), Vol. 20, No. 23, Pp. 1–18, 2020.

And K. P. Y. Widyaningsih, G. Puspita Arum, “Aplikasi K-Fold Cross Validation dalam Penentuan Model Regresi Binomial Negatif Terbaik Application Of K-Fold Cross Validation In Determining The Best Negative Binomial Regression Model,” Vol. 15, No. 2, Pp. 315–322, 2021.

F. Sodik, B. Dwi, And I. Kharisudin, “Perbandingan Metode Klasifikasi Supervised Learning pada Data Bank Customers menggunakan Python,” Vol. 3, Pp. 689–694, 2020.

G. Valentini And F. Masulli, “Ensembles Of Learning Machines,” Pp. 3–4, 2002.

R. Indransyah Et Al., “Klasifikasi Sentimen Pergelaran Motogp di Indonesia menggunakan Algoritma Correlated Naïve Bayes Clasifier,” Pp. 60–66, 2022.

H. A. J. I. Pratama Et Al., “Implementasi Algoritma C4.5 dan Naïve Bayes Classification untuk Mengklasifikasi Mahasiswa Berpotensi Drop Out,” 2024.

O. Arifin And T. B. Sasongko, “Analisa Perbandingan Tingkat Performansi Metode Support Vector Machine dan Naïve Bayes Classifier,” No. 2016, Pp. 67–72, 2018.

A. Setiawan, Z. H. Nst, Z. Khairi, And L. Efrizoni, “Klasifikasi Tingkat Risiko Diabetes menggunakan Algoritma,” Vol. 7, No. 2, Pp. 263–271, 2024.

Fatmawati, “Perbandingan Algoritma Klasifikasi Data Mining Model C4 . 5 dan Naive Bayes untuk Prediksi Penyakit Diabetes,” Pp. 50–59, 2016.

F. Bahiyyah And I. Indiati, “Analisis Kesalahan Siswa SMP dalam Menyelesaikan Soal Literasi Matematika berdasarkan Metode Newman ditinjau dari Kemandirian Belajar Pendahuluan Kemampuan untuk Memahami dan menggunakan Matematika Dasar dalam Kegiatan Sehari-Hari Dikenal Sebagai Literasi,” Vol. 12, No. 3, Pp. 436–446, 2021.

W. M. C. And Hardeli, “Development Of E-Module For Material Classification and Its Changes Based On A Scientific Approach In Grade Seven Of Junior High Schools”.

S. Boslaugh, Statistics In A Nutshell: A Desktop Quick Reference. “ O’reilly Media, Inc.,” 2012.

R. Kohavi, “A Study Of Cross-Validation and Bootstrapfor Accuracy Estimation and Model Selection,” Ijcai Int. Jt. Conf. Artif. Intell., Vol. 2, No. June, Pp. 1137–1143, 1995




DOI: https://doi.org/10.32520/stmsi.v14i4.5364

Article Metrics

Abstract view : 106 times
PDF - 39 times

Refbacks



Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.