Application of Case Based Reasoning for Student Recommendations Drop Out (Case Study: Adisutjipto College of Technology)

Harliyus Agustian


The data of non-active students is an obstacle in a university because it is counted as a student body, thus affecting the lecturer ratio. For that reason, in order to improve the lecturer ratio, a way is needed in addition to adding lecturers, but also by evaluating the data of students who are not active and active to be filtered back by looking at academic data that is known so that students can continue their studies or should be advised to resign or also drop out . To solve these problems a system model is needed that can recommend students as drop out students and can also provide other recommendations that can be used as evaluations. Case-based reasoning method is used to see new data matching with old data, where active student data to be evaluated will be matched with student data that has been dropped out or received a warning letter, so that it will be used as a new solution. Case-based reasoning methods can help in recommending students to drop out or get a warning letter.


Case based reasoning, Drop out, Similarity


Main, J.; Dillon, T.S.; Shiu, S.(2001). A Tutorial on Case-Based Reasoning : Soft Computing in Case-Based Reasoning (Eds), Sprenger-Verlag, London, pp. 1-28

Mulyana, S., & Hartati, S. (2015, July). Tinjauan Singkat Perkembangan Case–Based Reasoning. In Seminar Nasional Informatika (SEMNASIF) (Vol. 1, No. 4).

Vásquez-Morales, G. R., Martínez-Monterrubio, S. M., Moreno-Ger, P., & Recio-García, J. A. (2019). Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning. IEEE Access, 7, 152900-152910.

B. C. -. Gimenez, W. Jouini, S. Bayat and M. Cuggia.(2013). Improving Case based reasoningSystems by Combining k-Nearest Neighbor Algorithm with Logistic Regression in the Prediction of Patients' Registration on the Renal Transplant Waiting List. PLoS ONE, vol. 8, no. 9, pp. 1-10

Hidayah, I., Syahrina, A., & Permanasari, A. E. (2012). Student modeling using case-based reasoning in conventional learning system. arXiv preprint arXiv:1211.0749.

ADMODT, A. and PLAZA, E. (1994). Case-based reasoning: Foundational issues, methodological variations and system approaches. AI Communications, 7(1), pp. 35~39.

Gu D., Liang C. and Zhao H. (2017). A Case-Based Reasoning System Based on Weighted Heterogeneous ValueDistance Metric for Breast Cancer Diagnosis. Artificial Intelligence in Medicine. 77:31-47

A. M. Talib and N. E. M. Elshaiekh.(2014).Multi agent system-based on casebased reasoning for cloud computing system. Acad. Platf. J. Eng. Sci.,vol. 2, no. 2, pp. 34–38, 2014.

Nurdiana, O., Jumadi, J., & Nursantika, D. (2016). Perbandingan Metode Cosine Similarity Dengan Metode Jaccard Similarity Pada Aplikasi Pencarian Terjemah Al-Qur’an Dalam Bahasa Indonesia. Jurnal Online Informatika, 1(1), 59-63.

Agustian, H. (2018). Two Level Clustering Untuk Analisis Kuesioner Akademik DI STTA Yogyakarta. Angkasa: Jurnal Ilmiah Bidang Teknologi, 10(1), 29-40.

Article Metrics

Abstract view : 23 times


  • There are currently no refbacks.