Using Case Based Reasoning, in the Student Graduation Prediction System (CBR)

Untung Rahardja

doi:10.34306/ajri.v4i1.759

Authors

Untung Rahardja University of Raharja

DOI:

https://doi.org/10.34306/ajri.v4i1.759

Keywords:

prediction, case-based reasoning, k-nearest neighbor

Abstract

The performance of an educational institution is considered to have a negative impact due to the high percentage of students who do not graduate on time. Therefore, the number of students must be calculated and anticipated, so this paper needs to propose a model for predicting student graduation rates and predicting the time of graduation for each student. The K-Nearest Neighbor (KNN) method and algorithm were developed using Case-Based Reasoning (CBR). The research was conducted with training data taken from data from students who had graduated, and as a trial, the sixth-semester student data was used. The research attributes required to consist of the grade 1 to semester 5 of the achievement index (IPS) and the students' age. Model testing is carried out with four scenarios where the distribution of training data and testing data is also needed as support. The experimental results show that the stability of the data accuracy value on CBS using the KNN algorithm is found in the 90% training data scenario and 10% test data, namely 90.24% with the closest neighbors K = 5, K = 7, and K = 9. It can be concluded that the model. The resulting prediction system can predict whether students will graduate on time or not. This research will be helpful if implemented in higher education performance to increase student achievement to graduate on time.

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References

W. L. G. Costa, E. Pereira, and G. Kontonatsios, “Use of Smartphones for Hypoglycaemia Prediction-A Feasibility Study,” Procedia Comput. Sci., vol. 151, pp. 139–146, 2019.

A. Ajimat, D. Sunarsi, and F. Sidiq, “Berwirausaha Memanfaatkan Media Sosial Pada Daerah Sepatan,” ADI Pengabdi. Kpd. Masy., vol. 1, no. 1, pp. 69–76, 2020.

R. Hardjosubroto, U. Rahardja, N. Anggraini, and W. Yestina, “Penggalangan Dana Digital Untuk Yayasan Disabilitas Melalui Produk UMKM Di Era 4.0,” ADI Pengabdi. Kpd. Masy., vol. 1, no. 1, pp. 1–13, 2020.

A. Maharani, S. Aninda, and S. Millah, “Pembuatan Kartu Ujian Online Sebagai Pengabdian Perguruan Tinggi,” ADI Pengabdi. Kpd. Masy., vol. 1, no. 2, pp. 8–14, 2021.

Y. Anggara, “Cegah COVID-19 Di Era New Normal Pada KP. Sambengan,” ADI Pengabdi. Kpd. Masy., vol. 1, no. 1, pp. 59–68, 2020.

D. A. Kurniawan and A. Z. Santoso, “Pengelolaan Sampah di daerah Sepatan Kabupaten Tangerang,” ADI Pengabdi. Kpd. Masy., vol. 1, no. 1, pp. 31–36, 2020.

E. A. Sukma, A. N. Hidayanto, A. I. Pandesenda, A. N. Yahya, P. Widharto, and U. Rahardja, “Sentiment Analysis of the New Indonesian Government Policy (Omnibus Law) on Social Media Twitter,” in 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2020, pp. 153–158.

Y. Altujjar, W. Altamimi, I. Al-Turaiki, and M. Al-Razgan, “Predicting critical courses affecting students performance: a case study,” Procedia Comput. Sci., vol. 82, pp. 65–71, 2016.

J. L. Arroyo-Barrigüete, S. Carabias-López, T. Curto-González, and A. Hernández, “Portability of Predictive Academic Performance Models: An Empirical Sensitivity Analysis,” Mathematics, vol. 9, no. 8, p. 870, 2021.

T. A. Cardona, “Predicting student retention using support vector machines,” Procedia Manuf., vol. 39, pp. 1827–1833, 2019.

G. Lesinski and S. Corns, “Multi-objective evolutionary neural network to predict graduation success at the United States military academy,” Procedia Comput. Sci., vol. 140, pp. 196–205, 2018.

P. Achimugu, A. Selamat, and R. Ibrahim, “ReproTizer: A fully implemented software requirements prioritization tool,” in Transactions on computational collective intelligence XXII, Springer, 2016, pp. 80–105.

U. Rahardja, E. P. Harahap, and S. R. Dewi, “The strategy of enhancing article citation and H-index on SINTA to improve tertiary reputation,” TELKOMNIKA, vol. 17, no. 2, pp. 683–692, 2019.

F. M. Syam and D. Anggrayni, “E-Training System To Improve HR Capability at Indofood Factory Using Codeigniter Framework,” ADI J. Recent Innov., vol. 3, no. 2, pp. 217–225, 2022.

X. Gao et al., “AgeGuess, a methylomic prediction model for human ages,” Front. Bioeng. Biotechnol., vol. 8, p. 80, 2020.

M. R. Khosravani, S. Nasiri, and K. Weinberg, “Prediction of fracture in sandwich-structured composite joints using case-based reasoning approach,” Procedia Struct. Integr., vol. 13, pp. 168–173, 2018.

V. Agarwal, M. C. Lohani, A. S. Bist, E. P. Harahap, and A. Khoirunisa, “Analysis Of Deep Learning Techniques For Chest X-Ray Classification In Context Of Covid-19,” ADI J. Recent Innov., vol. 3, no. 2, pp. 208–216, 2022.

A. M. Abdulaziz, “The effective seismic attributes in porosity prediction for different rock types: Some implications from four case studies,” Egypt. J. Pet., vol. 29, no. 1, pp. 95–104, 2020.

L. A. Deveza, A. Downie, J. G. Tamez-Pena, F. Eckstein, W. E. Van Spil, and D. J. Hunter, “Trajectories of femorotibial cartilage thickness among persons with or at risk of knee osteoarthritis: development of a prediction model to identify progressors,” Osteoarthr. Cartil., vol. 27, no. 2, pp. 257–265, 2019.

N. Wang et al., “Herb target prediction based on representation learning of symptom related heterogeneous network,” Comput. Struct. Biotechnol. J., vol. 17, pp. 282–290, 2019.

B. Rawat, N. Mehra, A. S. Bist, M. Yusup, and Y. P. A. Sanjaya, “Quantum Computing and AI: Impacts & Possibilities,” ADI J. Recent Innov., vol. 3, no. 2, pp. 202–207, 2022.

B. Rawat, A. S. Bist, U. Rahardja, C. Lukita, and D. Apriliasari, “The Impact Of Online System on Health During Covid 19: A Comprehensive Study,” ADI J. Recent Innov., vol. 3, no. 2, pp. 195–201, 2022.

U. Rahardja, G. Maulani, M. H. Henderi, and A. S. Bist, “BUSINESS INTELLIGENCE ANALYSIS USING DIGITAL DASHBOARD IN COLLEGE MANAGEMENT WITH SIMPLE ROI METHOD,” J. Nat. Remedies, vol. 21, no. 7 (S3), pp. 1–16, 2020.

I. U. Rahardja, M. M. MTI, Q. Aini, and I. Handayani, WEB BASED ACCOUNTING SYSTEM. NIRWANA NUSANTARA, 2020.

U. Rahardja, “Decision Support System for Ranking of Students in Learning Management System (LMS) Activities using Analytical Hierarchy Process (AHP) Method,” in Journal of Physics: Conference Series, 2020, vol. 1477, no. 2, p. 22022.

V. Elmanda, A. E. Purba, Y. P. A. Sanjaya, and D. Julianingsih, “Efektivitas Program Magang Siswa SMK di Kota Serang Dengan Menggunakan Metode CIPP di Era Adaptasi New Normal Pandemi Covid-19,” ADI Bisnis Digit. Interdisiplin J., vol. 3, no. 1, pp. 5–15, 2022.

D. Rifai, S. Fitri, and I. N. Ramadhan, “Perkembangan Ekonomi Digital Mengenai Perilaku Pengguna Media Sosial Dalam Melakukan Transaksi,” ADI Bisnis Digit. Interdisiplin J., vol. 3, no. 1, pp. 49–52, 2022.

N. Sari, W. A. Gunawan, P. K. Sari, I. Zikri, and A. Syahputra, “Analisis Algoritma Bubble Sort Secara Ascending Dan Descending Serta Implementasinya Dengan Menggunakan Bahasa Pemrograman Java,” ADI Bisnis Digit. Interdisiplin J., vol. 3, no. 1, pp. 16–23, 2022.

A. González-Briones, J. Prieto, F. De La Prieta, E. Herrera-Viedma, and J. M. Corchado, “Energy optimization using a case-based reasoning strategy,” Sensors, vol. 18, no. 3, p. 865, 2018.

M. R. Khosravani and S. Nasiri, “Injection molding manufacturing process: Review of case-based reasoning applications,” J. Intell. Manuf., vol. 31, no. 4, pp. 847–864, 2020.