Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time

Authors

  • Sonia Sri Muliani Universitas Labuhanbatu
  • Volvo Sihombing Universitas Labuhanbatu
  • Ibnu Rasyid Munthe Universitas Labuhanbatu

DOI:

10.33395/sinkron.v8i2.13658

Abstract

Almost all universities in Indonesia face the problem of a low number of students graduating on time. This will affect higher education accreditation. For this reason, universities must pay attention to the timely graduation of their students. The way that can be taken is to predict students' graduation on time. This research aims to predict students' timely graduations using a combination of exploratory data analysis and artificial neural networks. Exploratory data analysis is used to study the relationship between features that influence students' on-time graduation, while artificial neural networks are used to predict on-time graduation. This research goes through method stages, starting with determining the dataset, exploratory data analysis, data preprocessing, dividing training and test data, and applying artificial neural networks. From the research, it was found that Work features and GPS features greatly influence graduation on time. Students who study while working are less likely to graduate on time compared to students who do not work. Students who have an average GPS above 3.00 for eight consecutive semesters will find it easier to graduate on time. Meanwhile, Age and Gender features have no effect on graduating on time. With a percentage of 50% training data and 50% test data, epoch 100, and learning rate 0.001, the best network model was obtained to predict graduation on time with an accuracy rate of 69.84%. The research results also show that the amount of test data and the learning rate can influence the level of accuracy. Meanwhile, the number of epochs does not affect the level of accuracy.

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References

Abukmeil, M., Ferrari, S., Genovese, A., Piuri, V., & Scotti, F. (2021). A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning. ACM Comput. Surv., 54(5). https://doi.org/10.1145/3450963

Aldera, S., Emam, A., Al-Qurishi, M., Alrubaian, M., & Alothaim, A. (2021). Exploratory Data Analysis and Classification of a New Arabic Online Extremism Dataset. IEEE Access, 9, 161613–161626. https://doi.org/10.1109/ACCESS.2021.3132651

Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-0177-7

Apridiansyah, Y., Veronika, N. D. M., & Putra, E. D. (2021). Prediksi Kelulusan Mahasiswa Fakultas Teknik Informatika Universitas Muhammadiyah Bengkulu Menggunakan Metode Naive Bayes. JSAI : Journal Scientific and Applied Informatics, 4(2), 236–247. https://doi.org/10.36085/jsai.v4i2.1701

Athallah, H. (2023). Kelulusan Mahasiswa. Retrieved March 20, 2024, from kaggle website: https://www.kaggle.com/datasets/hafizhathallah/kelulusan-mahasiswa

Azahari, A., Yulindawati, Y., Rosita, D., & Mallala, S. (2020). Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 7(3), 443–452. https://doi.org/10.25126/jtiik.2020732093

Bangun, O., Mawengkang, H., & Efendi, S. (2022). Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa. Jurnal Media Informatika Budidarma, 6(4), 2006. https://doi.org/10.30865/mib.v6i4.4572

Basheer, M. Y. I., Mutalib, S., Hamid, N. H. A., Abdul-Rahman, S., & Malik, A. M. A. (2019). Predictive analytics of university student intake using supervised methods. IAES International Journal of Artificial Intelligence (IJ-AI), 8(4), 367–374. https://doi.org/10.11591/ijai.v8.i4.pp367-374

Bukhari, M. M., Ullah, S. S., Uddin, M., Hussain, S., Abdelhaq, M., & Alsaqour, R. (2022). An Intelligent Model for Predicting the Students’ Performance with Backpropagation Neural Network Algorithm Using Regularization Approach. Human-Centric Computing and Information Sciences, 12. https://doi.org/10.22967/HCIS.2022.12.044

Dengen, C. N., Kusrini, K., & Luthfi, E. T. (2020). Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu. SISFOTENIKA, 10(1), 1. https://doi.org/10.30700/jst.v10i1.484

Figueroa-Cañas, J., & Sancho-Vinuesa, T. (2020). Early Prediction of Dropout and Final Exam Performance in an Online Statistics Course. IEEE Revista Iberoamericana de Tecnologias Del Aprendizaje, 15(2), 86–94. https://doi.org/10.1109/RITA.2020.2987727

Fiqha, I., Yandris, G. J., & Nasution, F. A. (2022). Implementation of Neural Network Algorithms in predicting student graduation rates. Sinkron, 6(1), 248–255. https://doi.org/10.33395/sinkron.v7i1.11254

Gunawan, M., Zarlis, M., & Roslina, R. (2021). Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu. Jurnal Media Informatika Budidarma, 5(2), 513. https://doi.org/10.30865/mib.v5i2.2925

Harun, R. R., Septyanun, N., Yuliani, T., AM, J., Hamdi, & Rejeki, S. (2022). Lulus Tepat Waktu: Sebuah Motivasi Dan Kode Etik Belajar Bagi Mahasiswa di Perguruan Tinggi. JCES (Journal of Character Education Society), 5(3), 773–779. https://doi.org/10.31764/jces.v3i1.9118

Indrakumari, R., Poongodi, T., & Jena, S. R. (2020). Heart Disease Prediction using Exploratory Data Analysis. Procedia Computer Science, 173, 130–139. https://doi.org/https://doi.org/10.1016/j.procs.2020.06.017

Kartarina, K., Sriwinarti, N. K., & Juniarti, N. luh P. (2021). Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 3(2), 107–113. https://doi.org/10.35746/jtim.v3i2.159

Kulkarni, A., & Shivananda, A. (2019). Exploring and Processing Text Data BT - Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python (A. Kulkarni & A. Shivananda, Eds.). Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-4267-4_2

Lagman, A. C., Alfonso, L. P., Goh, M. L. I., Lalata, J. A. P., Magcuyao, J. P. H., & Vicente, H. N. (2020). Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model. International Journal of Information and Education Technology, 10(10), 723–727. https://doi.org/10.18178/ijiet.2020.10.10.1449

Liao, S. N., Zingaro, D., Thai, K., Alvarado, C., Griswold, W. G., & Porter, L. (2019). A Robust Machine Learning Technique to Predict Low-performing Students. ACM Transactions on Computing Education, 19(3), 1–19. https://doi.org/10.1145/3277569

Masrizal, M., & Hadiansa, A. (2017). Prediksi Jumlah Lulusan Mahasiswa STMIK Dumai Menggunakan Jaringan Syaraf Tiruan. INFORMATIKA, 9(2), 9–14. https://doi.org/10.36723/juri.v9i2.98

Muliono, R., Lubis, J. H., & Khairina, N. (2020). Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time. SinkrOn, 4(2), 42. https://doi.org/10.33395/sinkron.v4i2.10480

Pelima, L. R., Sukmana, Y., & Rosmansyah, Y. (2024). Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review. IEEE Access, 12(February), 23451–23465. https://doi.org/10.1109/ACCESS.2024.3361479

Priyatman, H., Sajid, F., & Haldivany, D. (2019). Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(1), 62. https://doi.org/10.26418/jp.v5i1.29611

Putri, R. P. S., & Waspada, I. (2018). Penerapan Algoritma C4.5 pada Aplikasi Prediksi Kelulusan Mahasiswa Prodi Informatika. Khazanah Informatika : Jurnal Ilmu Komputer Dan Informatika, 4(1), 1–7. https://doi.org/10.23917/khif.v4i1.5975

Qisthiano, M. R., Kurniawan, T. B., Negara, E. S., & Akbar, M. (2021). Pengembangan Model Untuk Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes. Jurnal Media Informatika Budidarma, 5(3), 987. https://doi.org/10.30865/mib.v5i3.3030

Rahmat, F., Zulkafli, Z., Juraiza Ishak, A., Mohd Noor, S. B., Yahaya, H., & Masrani, A. (2020). Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data. Frontiers in Earth Science, 8(November), 1–14. https://doi.org/10.3389/feart.2020.00377

Ridwan, R., Lubis, H., & Kustanto, P. (2020). Implementasi Algoritma Neural Network dalam Memprediksi Tingkat Kelulusan Mahasiswa. Jurnal Media Informatika Budidarma, 4(2), 286. https://doi.org/10.30865/mib.v4i2.2035

Riyanto, V., Hamid, A., & Ridwansyah, R. (2019). Prediction of Student Graduation Time Using The Best Algorithm. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 2(1), 1–9. https://doi.org/10.24014/ijaidm.v2i1.6424

Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2, 100018. https://doi.org/https://doi.org/10.1016/j.caeai.2021.100018

Sahoo, K., Samal, A. K., Pramanik, J., & Pani, S. K. (2019). Exploratory Data Analysis using Python. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(12), 4727–4735. https://doi.org/10.35940/ijitee.L3591.1081219

Suhaimi, N. M., Abdul-Rahman, S., Mutalib, S., Hamid, N. H. A., & Malik, A. M. A. (2019). Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms. International Journal of Modern Education and Computer Science, 11(7), 1–13. https://doi.org/10.5815/ijmecs.2019.07.01

Tampakas, V., Livieris, I. E., Pintelas, E., Karacapilidis, N., & Pintelas, P. (2019). Prediction of Students’ Graduation Time Using a Two-Level Classification Algorithm. In M. Tsitouridou, J. A. Diniz, & T. A. Mikropoulos (Eds.), International Conference on Technology and Innovation in Learning, Teaching and Education (pp. 553–565). Cham: Springer International Publishing.

Tinggi, B. A. N. P. Matriks Penilaian LED Dan LKPS Program Sarjana. , Pub. L. No. Peraturan BAN-PT Nomor 5 tahun 2019, 1 (2019). Indonesia.

Yaqin, A., Laksito, A. D., & Fatonah, S. (2021). Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University. International Journal on Advanced Science, Engineering and Information Technology, 11(2), 610–617. https://doi.org/10.18517/ijaseit.11.2.11152

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Muliani, S. S. ., Sihombing, V. ., & Munthe, I. R. . (2024). Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1188-1199. https://doi.org/10.33395/sinkron.v8i2.13658

Issue

Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024

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Articles

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Copyright (c) 2024 Sonia Sri Muliani, Volvo Sihombing, Ibnu Rasyid Munthe

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