OPTIMASI PENJADWALAN PERKULIAHAN MENGGUNAKAN METODEAUTO GENERATE TIMETABLE DENGAN ARRAY

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Hani Dewi Ariessanti Dwi Sloria Suharti Ary Budi Warsito

Abstract

Scheduling is one of the many problems that has been done in research for many years. Problems preparing the schedule in college are about college scheduling. The timing process should be done for each semester, which is a tiring and time-consuming task. The overall allocation of events in timeslots and spaces is done by the course scheduling process by considering the list of hard constraints and soft constraints, so there is no conflict created in allocating the schedule. Therefore, it is necessary to create a lecture scheduling application that is able to facilitate and overcome the problems in organizing the lecture schedule. The proposed scheduling system design proposed in this study is to optimize the schedule of lectures using the Auto Generate Timetable method with arrays to find the best candidates for college scheduling with the aim of minimizing the conflict and optimizing the scheduling schedule. This method is based on the process of lecturing process that has been conducted in college. Every curriculum, space, day / time, is needed to arrange the schedule of students and lecturers as part of the scheduling variable that is the solution candidate. Then the process of adjusting to constraints has been made with various parameters. The research method is to collect the data, analysis, design, coding, testing and up to the maintenance phase by using the Waterfall System Development Lifecycle method. The waterfall model provides a life-cycle approach to the development of software systems in sequential or sequential form. So with the existence of this application, it is hoped that the arrangement of lectures will not find problems as a constraint in arranging the lecture schedule

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How to Cite
[1]
H. Ariessanti, D. Suharti, and A. Warsito, “OPTIMASI PENJADWALAN PERKULIAHAN MENGGUNAKAN METODEAUTO GENERATE TIMETABLE DENGAN ARRAY”, CCIT Journal, vol. 11, no. 2, pp. 257-266, Aug. 2018.
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References

1. Ariani, D., Fahriza, A., & Prasetyaningrum, I. (2011). Optimasi Penjadwalan Mata Kuliah di Jurusan Teknik Informatika Pens dengan Menggunakan Algoritma Particle Swarm Optimization (PSO). EEPIS Repository, 1–11.
2. Aziz, A. (2015). Optimasi Penjadwalan Perkuliahan Menggunakan Metode Simulated Annealing (Studi Kasus: Program Studi Teknik Informatika Universitas Yudharta Pasuruan). EXPLORE IT : Jurnal Keilmuan Dan Aplikasi Teknik Informatika, 7(2).
3. Chu, S., & Chen, Y. (2006). Timetable Scheduling Using Particle Swarm Optimization. IEEE Xplore, 0–3. https://doi.org/10.1109/ICICIC.2006.541
4. Gani, T. A. (2004). Optimizing examination timetabling using a hybrid evolution strategies Optimizing Examination Timetabling using a Hybrid Evolution Strategies. 2nd International Conference on Autonomous Robots and Agents, (June 2017), 345–349.
5. Gunawan, C. A., & Toba, H. (2016). Pembangkitan Solusi Penjadwalan Berprioritas Melalui Penerapan Constraint Satisfaction Problem (Studi Studi Kasus : Laboratorium Fakultas Teknologi Informasi Universitas XXX ). Jurnal Teknik Informatika Dan Sistem Informasi, 2(April), 43–52.
6. Islam, T., Shahriar, Z., Perves, M.A. and Hasan, M. (2016). University Timetable Generator Using Tabu Search. Journal of Computer and Communications, 4, 28–37. https://doi.org/10.4236/jcc.2016.416003
7. Kumar, K., Sharma, R., & Mehta, K. (2012). Genetic Algorithm Approach to Automate University Timetable, 1(1).
8. Montero, E. (2011). A PSO algorithm to solve a Real Course + Exam Timetabling Problem. International Conference on Swarm Intelligence, 2, 1–9.
9. Norberciak, M. (2008). Universal Method for Timetable Construction based on Evolutionary Approach. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 2(3), 174–179.
10. Poole, L., David, Alan K., M. (2010). Artificial Intelligence : Foundations of Computational Agents. UK: Cambridge University Press. Retrieved from www.cambridge.org/9780521519007
11. Pressman, R. S. (2010). Software Engineering : A Prectitioner’s Approach. (F. M. Schilling, Ed.) (7th ed.). New York: McGraw-Hill.
12. Putri, Y. (2014). Pemanfaatan Matriks Jarang dengan Metode Algoritma Genetika Menggunakan Program Pascal. Jurnal Matematika UNAND, 3(1), 98–106.
13. Siswono, T. (2013). Kombinasi Algoritma Genetika dengan Algoritma Palgunadi untuk Penjadwalan Mata Kuliah di Universitas Sebelas Maret. ITSMart, 2(2), 7–12. https://doi.org/http://dx.doi.org/10.20961/its.v2i2.624
14. Warsito, A. B., & Yusup, M. (2014). Kajian Yii Framework dalam Pengembangan Website Perguruan Tinggi. CCIT Journal, 7(40), 437–451. Retrieved from http://raharja.ac.id/acid/karyailmiah/karyailmiah/view/id/7030814
15. Yudihartanti, Y., Syukur, A., & Wahono, R. S. (2011). Analisis Komparasi Metode Mamdani dan Sugeno dalam Penjadwalan Mata Kuliah. Jurnal Teknologi Informasi, 7(2), 109–116

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