Article Sidebar

Submitted
18 February 2022
Accepted
23 May 2022
Published
31 May 2022
Download
PDF (Bahasa Indonesia)
Statistic
Read Counter : 186 Download : 94

Downloads

Download data is not yet available.

Main Article Content

Abstract

Existing research on course scheduling was conducted only from the institutional side.  However, students usually have other considerations, such as routine activities outside of class, course time, holidays in a week of study, and lead time between courses. These conditions had never been taken into consideration in existing research.  In this paper, a recommendation system was proposed using Depth First Search and Simple Multi Attribute Ranking Technique methods.  Depth First Search method was used to find all possible alternative schedules. All the possible alternative schedules were used to determine the schedule that best suits student preferences using Simple Multi Attribute Ranking Technique method. The system performance was measured through simulation to get course schedule recommendations for 28 students.  The simulation results were compared with the ideal schedule desired by the students and the real course schedule for those students. The accuracy of the recommended schedule against the ideal schedule desired by students was 70.8% with an average processing time of 1.05 seconds. The accuracy of the recommended schedule increased to about 91% when compared to the actual student courses schedule.  So it can be concluded that the research can help to recommend students' weekly class schedules in real terms.

Keywords

recommendation system course scheduling student preferences depth first search simple multi attribute ranking technique sistem rekomendasi, penjadwalan mata kuliah, preferensi mahasiswa, depth first search, simple multi attribute ranking technique

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biographies

Susana Limanto, Universitas Surabaya

Teknik Informatika

Heru Arwoko, University of Surabaya

Informatics Engineering

Jason Austin Juwono, University of Surabaya

Informatics Engineering
How to Cite
Limanto, S., Arwoko, H., & Juwono, J. A. (2022). Recommendation System for Collegian Student’s Weekly Course Schedule. Jurnal Komputer Terapan, 8(1), 24–35. https://doi.org/10.35143/jkt.v8i1.5279
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. A. L. aro Bolaji, A. T. Khader, M. A. Al-Betar, and M. A. Awadallah, “University course timetabling using hybridized artificial bee colony with hill climbing optimizer,†J. Comput. Sci., vol. 5, no. 5, pp. 809–818, 2014, doi: 10.1016/j.jocs.2014.04.002.
  2. A. Siame and D. Kunda, “University Course Timetabling using Bayesian based Optimization Algorithm,†Int. J. Recent Contrib. from Eng. Sci. IT, vol. 6, no. 2, p. 14, 2018, doi: 10.3991/ijes.v6i2.8990.
  3. H. Rudová, T. Müller, and K. Murray, “Complex university course timetabling,†J. Sched., vol. 14, no. 2, pp. 187–207, 2011, doi: 10.1007/s10951-010-0171-3.
  4. S. Kumar and R. Pandey, “Automated university course timetable generator,†Int. J. Ind. Syst. Eng., vol. 36, no. 1, pp. 1–16, 2020, doi: 10.1504/IJISE.2020.109133.
  5. Z. Lü and J. K. Hao, “Adaptive Tabu Search for course timetabling,†Eur. J. Oper. Res., vol. 200, no. 1, pp. 235–244, 2010, doi: 10.1016/j.ejor.2008.12.007.
  6. M. Chen, X. Tang, T. Song, C. Wu, S. Liu, and X. Peng, “A Tabu search algorithm with controlled randomization for constructing feasible university course timetables,†Comput. Oper. Res., vol. 123, p. 105007, 2020, doi: 10.1016/j.cor.2020.105007.
  7. F. K. S. Dewi, “Pembangunan Perangkat Lunak Pembangkit Jadwal Kuliah dan Ujian Dengan Metode Pewarnaan Graf,†J. Buana Inform., vol. 1, no. 1, pp. 57–68, 2010, doi: 10.24002/jbi.v1i1.295.
  8. M. Wiladi, N. A. Rizki, and B. M. Salindeho, “Pengembangan Algoritma Welsh Powell Pada Penyusunan Jadwal Kuliah,†Pros. Semin. Nas. Mat. Stat. dan Apl., pp. 75–81, 2019.
  9. N. G. A. H. Saptarini, P. I. Ciptayani, and I. B. I. Purnama, “A custom-based crossover technique in genetic algorithm for course scheduling problem,†TEM J., vol. 9, no. 1, pp. 386–392, 2020, doi: 10.18421/TEM91-53.
  10. S. Limanto, N. Benarkah, and T. Adelia, “Thesis examination timetabling using genetic algorithm,†Int. Electron. Symp. Knowl. Creat. Intell. Comput. IES-KCIC 2018 - Proc., pp. 6–10, 2019, doi: 10.1109/KCIC.2018.8628572.
  11. R. Ansari and N. Saubari, “Application of genetic algorithm concept on course scheduling,†IOP Conf. Ser. Mater. Sci. Eng., vol. 821, no. 1, 2020, doi: 10.1088/1757-899X/821/1/012043.
  12. B. Naderi, “Modeling and Scheduling University Course Timetabling Problems,†Int. J. Res. Ind. Eng., vol. 5, no. 4, pp. 1–15, 2016, doi: 10.22105/riej.2017.49167.
  13. A. Gülcü and C. Akkan, “Robust university course timetabling problem subject to single and multiple disruptions,†Eur. J. Oper. Res., vol. 283, no. 2, pp. 630–646, 2020, doi: 10.1016/j.ejor.2019.11.024.
  14. A. Rezaeipanah, S. S. Matoori, and G. Ahmadi, “A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search,†Appl. Intell., vol. 51, no. 1, pp. 467–492, 2021, doi: 10.1007/s10489-020-01833-x.
  15. S. L. Goh, G. Kendall, N. R. Sabar, and S. Abdullah, “An effective hybrid local search approach for the post enrolment course timetabling problem,†Opsearch, vol. 57, no. 4, pp. 1131–1163, 2020, doi: 10.1007/s12597-020-00444-x.
  16. A. M. Hambali, Y. A. Olasupo, and M. Dalhatu, “Automated university lecture timetable using Heuristic Approach,†Niger. J. Technol., vol. 39, no. 1, pp. 1–14, 2020, doi: 10.4314/njt.v39i1.1.
  17. M. Assi, B. Halawi, and R. A. Haraty, “Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem,†Procedia Comput. Sci., vol. 126, pp. 899–906, 2018, doi: 10.1016/j.procS.2018.08.024.
  18. Y. Sun, X. Luo, and X. Liu, “Optimization of A University Timetable Considering Building Energy Efficiency: An Approach based on the Building Controls Virtual Test Bed Platform using A Genetic Algorithm,†J. Build. Eng., vol. 35(102095), 2021.
  19. P. Yasari, M. Ranjbar, N. Jamili, and M. H. Shaelaie, “A two-stage stochastic programming approach for a multi-objective course timetabling problem with courses cancelation risk,†Comput. Ind. Eng., vol. 130, no. March, pp. 650–660, 2019, doi: 10.1016/j.cie.2019.02.050.
  20. D. S. Vianna, C. B. Martins, T. J. Lima, M. de F. D. Vianna, and E. B. M. Meza, “Hybrid VNS-TS heuristics for University Course Timetabling Problem,†Brazilian J. Oper. Prod. Manag., vol. 17, no. 1, pp. 1–20, 2020, doi: 10.14488/bjopm.2020.014.
  21. J. Nourmohammadi-Khiarak, Y. Zamani-Harghalani, and M. R. Feizi-Derakhshi, “Combined Multi-Agent Method to Control Inter-Department Common Events Collision for University Courses Timetabling,†J. Intell. Syst., vol. 29, no. 1, pp. 110–126, 2020, doi: 10.1515/jisys-2017-0249.
  22. A. Bouyer and N. Farajzadeh, “An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm,†J. Intell. Syst., vol. 29, no. 1, pp. 1–18, 2020, doi: 10.1515/jisys-2015-0009.
  23. H. Babaei, J. Karimpour, and A. Hadidi, “Applying Hybrid Fuzzy Multi-Criteria Decision-Making Approach to Find the Best Ranking for the Soft Constraint Weights of Lecturers in UCTP,†Int. J. Fuzzy Syst., vol. 20, no. 1, pp. 62–77, 2018, doi: 10.1007/s40815-017-0296-z.
  24. T. Thepphakorn and P. Pongcharoen, “Performance improvement strategies on Cuckoo Search algorithms for solving the university course timetabling problem,†Expert Syst. Appl., vol. 161, p. 113732, 2020, doi: 10.1016/j.eswa.2020.113732.
  25. S. Imran Hossain, M. A. H. Akhand, M. I. R. Shuvo, N. Siddique, and H. Adeli, “Optimization of University Course Scheduling Problem using Particle Swarm Optimization with Selective Search,†Expert Syst. Appl., vol. 127, pp. 9–24, 2019, doi: 10.1016/j.eswa.2019.02.026.
  26. C. Akkan and A. Gülcü, “A bi-criteria hybrid Genetic Algorithm with robustness objective for the course timetabling problem,†Comput. Oper. Res., vol. 90, pp. 22–32, 2018, doi: 10.1016/j.cor.2017.09.007.