Application of Genetic Algorithm to Optimize Lecture Scheduling Based on Lecturers’ Teaching Day Willingness

Abstract

The scheduling of lectures is an important activity that is carried out every beginning of the semester. The process of making a lecture schedule in the Informatics Engineering Study Program of Universitas Catur Insan Cendekia Cirebon still uses office applications, so it takes a long time to complete. This is because certain criteria or conditions that make the scheduling of lectures complicated, such as the willingness of teaching days and courses for each lecturer to differ and the availability of parallel classes. The research methods used consist of several stages, namely data collection, analysis, and system design, as well as the implementation of software created. The method of data collection used is observation and library study. The method of analysis and design of the system used is Object-Oriented Analysis (OOA) using Unified Modelling Language (UML). Applications created using the PHP programming language with the Yii Framework and MySQL as databases, as well as applying genetic algorithms to solve scheduling problems. The result of this research is a lecture scheduling application used by BAAK Staff to create a lecture schedule in the Informatics Engineering Study Program. The application of this application can save time in the creation of lecture schedules.

Optimization in University Student Timetables: A Comprehensive Literature Review

“…Figure 1 below shows the flow diagram for the comprehensive review process. Reference Title Year Country [11] Developing a mobile-based application system to accelerate the efficiency of the course rescheduling process Malaysia [12] Modified and hybridised bi-objective firefly algorithms for university course scheduling Thailand [13] A general mathematical model for university courses timetabling: Implementation to a public university in Malaysia Malaysia [14] A genetic algorithm for the real-world university course timetabling problem Malaysia [15] Grouping and heuristics for a multi-stage class timetabling system Malaysia [16] Hybrid whale optimization algorithm for solving timetabling problems of ITC 2019 Indonesia [17] Investigation of heuristic orderings with a perturbation for finding feasibility in solving real-world university course timetabling problem Malaysia [18] Lecturer-course assignment model in national joint courses program to improve education quality and lecturers' time preference Indonesia [19] A compromise programming for multi-objective task assignment problem Vietnam [20] A hybrid of heuristic orderings and variable neighbourhood descent for a real-life university course timetabling problem Malaysia [21] An SHO-based approach to timetable scheduling: a case study Vietnam [22] Application of genetic algorithm to optimize lecture scheduling based on lecturers' teaching day willingness Indonesia [23] Automation and optimization of course timetabling using the iterated local search hyper-heuristic algorithm with the problem domain from the 2019 international timetabling competition Indonesia [24] Class scheduling framework using decorator and facade design pattern Philippines [25] Effective solution of university course timetabling using particle swarm optimizer based hyper heuristic approach Malaysia [26] Lecturer teaching scheduling that minimizes the difference of total teaching load using goal programming Indonesia [27] Multi-agent class timetabling for higher educational institutions using Prometheus platform Philippines [28] Particle swarm optimisation variants and its hybridisation ratios for generating cost-effective educational course timetables Thailand [29] Stemming the educational timetable problems Indonesia [18] University course timetabling model in joint courses program to minimize the number of unserved requests Indonesia [30] An effective hybrid local search approach for the post enrolment course timetabling problem Malaysia...…”

“…Subagio et al, [22] presented a Genetic Algorithm to solve lecture scheduling in Universitas Catur Insan Cendekia Cirebon. The outcome managed to save time and consider the constraints criteria like teaching days, courses, lecturer preferences, and availability of parallel classes.…”

“…Reference Dataset description [11] UCSI University, Malaysia [12] Eleven datasets obtained from Naresuan University, Thailand [13] Semester 1 2017/2018, Universiti Malaysia Terengganu (UMT), Malaysia [14] Semester 1 2018/2019, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia [15] Semester 2016/s6 and semester 2016/s2, i-Cats University College (ICATS), Malaysia [16] ITC 2019 [17] ITC 2007 Semester 1 2019/2020 and semester 2 2019/2020, Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), Malaysia [18] Lecturers-courses assignment simulation [19] Spring semester of 2020 for Computing Fundamental Department, FPT University, Vietnam [20] Semester 1 2019/2020 and semester 2 2019/2020, Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), Malaysia [21] Faculty of Information Technology, Nong Lam University (NLU), Vietnam [22] Even semester 2018/2019, Informatics Engineering Study Program of Universitas Catur Insan Cendekia Cirebon, Indonesia [23] ITC 2019 [24] Generate 4 datasets based on assumptions [25] ITC 2002 and ITC 2007 [26] Department of Mathematics XYZ University [27] Semester 1 2018/2019, semester 2 2017/2018, semester 1 2017/2018, semester 2 2016/2017, University of the East, Philippines [28] Eleven datasets obtained from Naresuan University, Thailand [29] Socha STKIP (a medium-size Indonesian college from the year 2014 to the year 2016) Lewis Carter ITC 2007 [30] ITC 2002 ITC 2007 Socha Hard [31] Semester 2 2014/2015 and semester 1 2015/2016, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia [32] Odd and Event semester, Operational Division of Universitas Teknologi Yogyakarta, Indonesia [33] Semester 1 2017/2018 and semester 2 2017/2018 from Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak (UNIMAS), Malaysia [35] Socha [36] Semester 1 2016/2017, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia [37] Semester 2 2014/2015 and semester 1 2015/2016, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia…”

Analisis Sistem Informasi Panda (SIP) Terhadap Penerimaan PenggunaMenggunakan Metode UTAUT

“…Hampir semua aktivitas atau aktivitas manusia dibantu oleh computer [1] [2]. Penggunaan sistem informasi dalam organisasi saat ini semakin meningkat [5] [6]. Pertumbuhan ini tidak hanya berasal dari tuntutan organisasi modern, tetapi juga dari masyarakat yang membutuhkan informasi [7][8] [9].…”