* Corresponding Author
Hide
Show all
Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning
https://doi.org/10.22146/ijccs.58919 Annisaa Utami(1*), Yohanes Suyanto(2), Agus Sihabuddin(3)
(1) Master Program in Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration.
This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods.
The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance. The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%.
Keywords
Full Text:
PDFReferences
[1] S. Hendra and S. Kusumadewi, “Case-based system model for counseling students,” Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 2016. [Online]. Available: https://ieeexplore.ieee.org/document/7407806. [Accessed: 18-May-2019].
[2] A. Jesus, M. J. Gomes, and A. Cruz, “Case based learning for therapeutics: Student’s performance in face to face vs blended learning,” Iberian Conference on Information Systems and Technologies, CISTI, 2016. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7521471?part=1 . [Accessed: 18-May-2019].
[3] M. Muslim, A. Alwi, and E. Erika, “A Framework of Counseling System for Student Guardianship using Case Based Reasoning (CBR) Inference,” IOP Conference Series: Materials Science and Engineering, 2018. [Online]. Available: https://www.researchgate.net/publication/326350001_A_Framework_of_Counseling_System_for_Student_Guardianship_using_Case_Based_Reasoning_CBR_Inference. [Accessed: 18-May-2019].
[4] H. Supic, “Case-based reasoning model for personalized learning path recommendation in example-based learning activities,” Proceedings - 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2018, 2018. [Online]. Available: https://ieeexplore.ieee.org/document/8495930. [Accessed: 18-May-2019].
[5] F. Tempola, A. Musdholifah, and S. Hartati, “Case Based Reasoning for Determining the Feasibility of Scholarship Grantees Using Case Adaptation,” Proceedings - 2018 5th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2018, 2018. [Online]. Available: https://ieeexplore.ieee.org/document/8576898/. [Accessed: 18-May-2019].
[6] S. H. Merawati N.L.P, “Sistem rekomendasi topik skripsi menggunakan metode case based reasoning,” 2018. [Online]. Available: http://journal.widyatama.ac.id/index.php/jitter/article/view/164. [Accessed: 12-May-2019].
[7] U. S. Aesyi and R. Wardoyo, “Prediction of Length of Study of Student Applicants Using Case Based Reasoning,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2019. [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/28076. [Accessed: 25-May-2020].
[8] S. Ahmad, P. Singh, and A. P. Singh, “Case Based Reasoning Model in the Diagnosis of Psychiatric Disorder,” IEEE, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/7889876. [Accessed: 13-Dec-2020].
[9] S. H. Eka Wahyudi, “Case-Based Reasoning untuk Diagnosis Penyakit Jantung,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2017. [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/15523/11717. [Accessed: 25-May-2020].
[10] N. Rokhman, “A Survey on Mixed-Attribute Outlier Detection Methods,” vol. 13, no. 1, pp. 39–44, 2019.Available: https://journal.binus.ac.id/index.php/commit/article/view/5558 [Accessed: 19-Jun-2020].
[11] A. Rohmadi, “Case Based Reasoning untuk Pemilihan Social Media Bagi Penjual Online,”Thesis, Jurusan Ilmu Komputer FMIPA UGM , 2017.
[12] H. Shi and W. Dong, “A Kind of Case Similarity Evaluation Model Based on Case-Based Reasoning,” pp. 5–9, 2011.
[13] K. Tan, “The Colorectal Cancer Recurrence Support (CARES) System,” vol. 11, pp. 175–188, 1997.
[14] B. G. De Soto and B. T. Adey, “Investigation of the Case-based Reasoning Retrieval Process to Estimate Resources in Construction Projects,” Procedia Eng., vol. 123, pp. 169–181, 2015.
[15] U. A. Mancasari, “Sistem pakar Menggunakan Penalaran Berbasis Kasus Untuk Mendiagnosa Penyakit Saraf Pada Anak,” Skripsi,Jurusan Ilmu Komputer FMIPA UGM, 2012.
DOI: https://doi.org/10.22146/ijccs.58919
Article Metrics
Abstract views : 4037
| 
views : 3003
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1