Model Klasifikasi Calon Mahasiswa Baru Untuk Sistem Rekomendasi Program Studi Sarjana Berbasis Machine Learning

Penulis

Ahmad R Pratama, Rio Rizky Aryanto, Arif Taufiq M Pratama

Abstrak

Proses pemilihan program studi bagi calon mahasiswa baru, khususnya bagi mereka yang masih duduk di bangku SMA atau sederajat, merupakan salah satu momen pengambilan keputusan penting. Tak jarang pilihan yang salah berujung pada kegagalan studi atau kesulitan lain selepas menamatkan studi. Meski sudah mulai marak dilakukan di berbagai negara maju, sistem rekomendasi program studi berbasis machine learning untuk calon mahasiswa baru masih belum banyak dikembangkan di Indonesia. Penelitian ini dilakukan sebagai upaya rintisan sistem rekomendasi tersebut dengan menggunakan data pribadi dan akademik dari semua mahasiswa dan alumni program sarjana di Universitas Islam Indonesia (UII), di mana data prestasi akademik di masing-masing program studi digunakan sebagai ground truth label. Dari hasil penelitian ini, didapatkan sebuah model berbasis Random Forest (RF) dengan tingkat akurasi 86%, presisi 84%, recall 86%, dan AUC 97%. Model ini memiliki kinerja yang jauh lebih baik jika dibandingkan dengan model berbasis Multinomial Logistic Regression (MLR) maupun Support Vector Machine (SVM). Sesuai peta jalan penelitian, model yang dihasilkan dari penelitian ini akan digunakan untuk pengembangan sistem rekomendasi yang dapat membantu calon mahasiswa baru dalam memilih program studi saat proses penerimaan mahasiswa baru (PMB), khususnya di lingkungan UII.

 

Abstract

Choosing a major for the prospective undergraduate students is one of the most important moments in their life, especially for the high school graduates. Not infrequently, a wrong choice can lead to academic failure or even other difficulties after graduating from college. While a machine learning-based college major recommendation system is not strange in some developed countries, it is not the case in Indonesia. This study aims to serve as a pilot project for such a recommendation system by using personal and academic data of all students and alumni of the undergraduate programs in Universitas Islam Indonesia (UII) where academic achievement data is used as the ground truth label. Out of three models used and evaluated in this study, we found that Random Forest-based model to be the best option with an accuracy of 86%, precision on 84%, recall of 86%, and AUC of 97%. We also found that this model has a much better performance than other models with Multinomial Logistic Regression (MLR) or Support Vector Machine (SVM). The resulting model from this study will be deployed to develop a college major recommendation system that can help the prospective students choose their majors during college admission process, particularly in the context of UII as per research roadmap.

 


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Referensi


BISHOP, C.M., 2006. Pattern Recognition and Machine Learning. Switzerland: Springer New York.

FAIZAL, E., 2015. Analisis Pemilihan Jurusan Favorit Menggunakan Metode Promethee (Studi Kasus Pada STMIK El Rahma Yogyakarta). Jurnal Fahma, 13.

FORTUNA, B., FORTUNA, C. dan MLADENIĆ, D., 2010, September. Real-time news recommender system. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 583-586). Springer, Berlin, Heidelberg.

HOSSIN, M. dan SULAIMAN, M.N., 2015. A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process (IJDKP), 4-5

JAYANTH, S.B., ANTHONY, A., ABHILASHA, G., SHAIK, N. dan SRINIVASA, G., 2018. A team recommendation system and outcome prediction for the game of cricket. Journal of Sports Analytics, 4(4), pp.263-273.

KHANVILKAR, G. dan VORA, D., 2018. Sentiment analysis for product recommendation using random forest. International Journal of Engineering & Technology, 7(3), pp.87-89.

KHANAM, Z., dan ALKHALDI, S., 2019. An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System. In International Conference on Inventive Computation Technologies (pp. 711-722). Springer, Cham.

KUMALA, A.T., BENARKAH, N. dan TJANDRA, E., 2015. Pembuatan Sistem Pendukung Keputusan Pemilihan Jurusan Kuliah Bagi Siswa SMA Berbasis Web dengan Metode Promethee. Calyptra, 4(2), pp.1-10

LIU, R., dan TAN, A., 2020. Towards interpretable automated machine learning for STEM career prediction. Journal of Educational Data Mining, 12(2), pp.19-32

MARDANI, A., LIAO, H., NILASHI, M., ALRASHEEDI, M., dan CAVALLARO, F. 2020. A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 275.

MESYA, 2019. JPNN [online]. Tersedia di: https://www.jpnn.com/news/hasil-survei-87-persen-mahasiswa-pilih-jurusan-tidak-sesuai-minat [Diakses 11 Mei 2021]

NGUYEN, L. V., HONG, M. S., JUNG, J. J., dan SOHN, B. S. 2020. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences, 10(12).

PICCIANO, A. G., 2012. The evolution of big data and learning analytics in American higher education. Journal of asynchronous learning networks, 16(3), pp.9-20.

PRADANA, Y.R., 2020. Sistem Rekomendasi Dosen Pembimbing Berdasarkan Latar Belakang Menggunakan Metode Multi-Class Support Vector Machine Dan Weighted Product (Doctoral dissertation, Universitas Brawijaya).

PUTRA, M.I., 2019. Sistem rekomendasi kelayakan kredit menggunakan metode Random Forest pada BRI Kantor Cabang Pelaihari (Doctoral dissertation, UIN Sunan Ampel Surabaya).

ROZI, A.F., and PURNOMO, A.S., 2018. Rekomendasi Pemilihan Minat Studi Menggunakan Metode Mamdani Studi Kasus: Program Studi Sistem Informasi FTI UMBY. INFORMAL: Informatics Journal, 2(3), pp.138-147.

RUMAISA, F., 2012. Penentuan Association Rule Pada Pemilihan Program Studi Calon Mahasiswa Baru Menggunakan Algoritma Apriori Studi Kasus pada Universitas Widyatama Bandung. In Seminar Nasional Aplikasi Teknologi Informasi 2012, Jurusan Teknik Informatika, Universitas Islam Indonesia, Yogyakarta.

SAM’AN, M., 2015. Implementasi Fuzzy Inference System sebagai Sistem Pengambilan Keputusan Pemilihan Program Studi di Perguruan Tinggi. UNNES Journal of Mathematics, 4(1).

SRIVASTAVA, T., 2019. Analytics Vidhya [online]. Tersedia di: https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/ [Diakses 11 Mei 2021]

STINEBRICKNER, T.R. dan STINEBRICKNER, R., 2011. Math or science? Using longitudinal expectations data to examine the process of choosing a college major (No. w16869). National Bureau of Economic Research

SURYADI, A., 2018. Sistem Rekomendasi Penerimaan Mahasiswa Baru Menggunakan Naive Bayes Classifier Di Institut Pendidikan Indonesia. Joutica, 3(2), pp.171-182.

WANG, Z., dan SHI, Y., 2016. Prediction of the admission lines of college entrance examination based on machine learning. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (pp. 332-335). IEEE.

WATERS, A. dan MIIKKULAINEN, R., 2014. Grade: Machine learning support for graduate admissions. AI Magazine, 35(1), pp.64-64.

WEI, J., HE, J., CHEN, K., ZHOU, Y., dan TANG, Z., 2017. Collaborative filtering and deep learning-based recommendation system for cold start items. Expert Systems with Applications, 69, pp.29-39.

WISWALL, M. dan ZAFAR, B., 2015. Determinants of college major choice: Identification using an information experiment. The Review of Economic Studies, 82(2), pp.791-824.




DOI: http://dx.doi.org/10.25126/jtiik.2022934311