VIRTUAL CLASS SEBAGAI STRATEGI PEMBELAJARAN UNTUK PENINGKATAN KUALITAS STUDENT-CENTERED LEARNING DI PERGURUAN TINGGI

ABSTRAK Metode pembelajaran berpusat pada siswa (Student-Centered Learning) memberikan ruang gerak lebih bagi mahasiswa untuk dapat berpartisipasi aktif dalam aktivitas perkuliahan di Perguruan Tinggi sesuai dengan kompetensi yang ingin dicapai. Melalui penerapan Student-Centered Learning ini maka mahasiswa dapat mengoptimalkan kemampuannya dalam belajar kreatif dan mandiri sehingga peran dosen dalam proses pembelajaran lebih diarahkan sebagai pendamping dan fasilitator belajar mahasiswa. Untuk mendukung penerapan Student-Centered Learning ini dapat dilakukan dengan memanfaatkan Information and Communication Technology (ICT) dalam berbagai macam strategi pembelajaran, salah satunya adalah dengan mengimplementasikan konsep kelas virtual (Virtual Class). Kata kunci : student-centered learning, information and communication technology, strategi pembelajaran, virtual class ABSTRACT Student Centered Learning (SCL) gives students some extra space for actively participating in every learning activity in the university according to their aiming competence. In this SCL application, student hopefully could optimize their ability in creative and self study learning so that lecturers are just stay as their study supervisor and facilitator. Information and Communication Technology (ICT) is then used to draw on this as a SCL booster in every learning strategy, such as implementing Virtual Class. Key words : student centered learning, information and communication technology, learning strategy, virtual clas

Hybrid iterated local search algorithm for optimization route of airplane travel plans

The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the Tabu-SA algorithm

Hyper-heuristics and fairness in examination timetabling problems

Examination timetabling is a challenging optimisation problem in operations research and artificial intelligence. The main aim is to spread exams evenly throughout the overall time period to facilitate student comfort and success; however, existing examination timetabling solvers neglect fairness by optimising the sum or average of the objective function value without considering its distribution among students or other stakeholders. The balance between quality of the overall timetable and fairness (global fairness and within a cohort) is a major concern, thus the latter is added as a new objective function and quality indicator of examination timetables. The objective function is also considered from the perspectives of multiple stakeholders of examination timetabling (i.e. students, invigilators, markers and estates), as opposed to viewing the objective function as an aggregate function. These notions make the problem become a multi-objective optimisation problem. We study sum of power rather than linear summation to enforce fairness and concurrently minimise the objective function, using some perturbation-based hyper- heuristics approaches to optimise the standard objective function. Secondly, multi-stage approach is studied (generating initial feasible solution, improving the standard quality of solution and then improving fairness), to improve the fairness objective function. Given that the standard objective function and fairness objective function conflict, we then studied several multi-objective algorithms employed within the framework of hyper-heuristics. The proposed hyper-heuristic algorithms mainly can be divided into two approaches: classical scalarisation technique-based weighted sum and Tchebyce↵; and population-based non-dominated sorting memetic algorithm II (NSMA-II) and artificial bee colony and strength pareto evolutionary 2 (SPEA2) hybrid (ABC-SPEA2). The experiments were conducted over two multi-objective examination timetabling problem formulations (i.e. with fairness and with multiple stakeholder perspectives), tested over problem instances from four different datasets: Carter, Nottingham, Yeditepe and ITC 2007. The experimental results over multi-objective examination timetabling problem with fairness showed that in terms of the standard objective function the proposed approach could produce results comparable with the best known solutions reported in the literature, whilst in the same time could be forced to be fairer that does or does not compensate on worsening the standard objective function. Fairness within a cohort could be improved much better than global fairness and treating as multi-objective problem could help the search for near-optimal standard objective function escape from local optima trap. The scalarisation technique based hyper-heuristics outperforms the population-based hyper-heuristic. The advantage of treating examination timetabling problem as multi-objective problem is that approximations of the Pareto optimal solutions give the optimal trade-o↵ between standard objective function, fairness among all students, and fairness within a cohort. In addition, the decision maker also can view the solution from multiple stakeholders view. We believe that by giving this more detailed information, the decision maker of examination timetable could make better decisions

WHY WE SHOULD TALK? THE POTENTIALS OF COMMUNITY DIALOG IN GROUNDING AN INTEGRATED RURAL DEVELOPMENT

Rural development is a social process. It involves local community in all stages of development. Community dialog is a means for facilitating community involvement in determining a development direction, potential development plan and development sus-tainability in the future. Frequently, local community is considered as the development target. This position puts them just being development watchers, spectators, silent and passive recipients. Moreover, these silent roles make them remain unempowered since they do not know how to determine their future, how to take part in collective decision and feel being neglected. This study examines potentials of community involvement in dialog. A qualitative research paradigm is adopted. The data are collected byrecording, transcribing and analyzing community dialog at Klagen, Nganjuk, Jawa Timur.  The study finds that community dialog offers considerable potentials. The first potential of community dialog is generating local community commitment, awareness, sense of belongingness and supportive character to build their own homeland. These positive development psychological states,characters and ethos are soft human dimensions which can be critical drivers in rural development. The second is creation of local knowledge and scientific knowledge joint enabling innovation and collective learning process. This joint-knowledge allows the combination of local wisdom and scientific insight. The third is building shared or collective development vision and plan. This plan and vision allow the development prioritizing process and development of rural strength, potential competitive advantage and resource building. The fourth is expanding rural networking and exercising rural people capacity to build wider internal and external social relationship. 

Perbandingan Metode Penyelesaian Permasalahan Optimasi Lintas Domain dengan Pendekatan Hyper-Heuristic Menggunakan Algoritma Reinforcement-Late Acceptance

Sebuah organisasi terkadang membutuhkan solusi untuk permasalahan optimasi lintas domain. Permasalahan optimasi lintas domain merupakan permasalahan yang memiliki karakteristik berbeda, misalnya antar domain optimasi penjadwalan, rute kendaraan, bin packing, dan SAT. Optimasi tersebut digunakan untuk mendukung pengambilan keputusan sebuah organisasi. Dalam menyelesaikan permasalahan optimasi tersebut, dibutuhkan metode pencarian komputasi. Di literatur, hampir semua permasalahan optimasi dalam kelas NP-hard diselesaikan dengan pendekatan meta-heuristics. Akan tetapi meta-heuristic ini memiliki kekurangan, yaitu diperlukan parameter tunning untuk setiap problem domain yang berbeda. Sehingga pendekatan ini dirasa kurang efektif. Oleh karena itu diperlukan pendekatan baru, yaitu pendekatan hyper-heuristics. Metode hyper-heuristic merupakan metode pencarian komputasi approximate yang dapat menyelesaikan permasalahan optimasi lintas domain dengan waktu lebih cepat. Lintas domain permasalahan yang akan diselesaikan ada enam, yaitu satisfiability (SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, travelling salesman problem (TSP), dan vehicle routing problem (VRP). Dalam meningkatkan kinerja, penelitian ini menguji pengaruh dari adaptasi algoritma Reinforcement Learning (RL) sebagai strategi seleksi LLH dikombinasikan dengan algoritma Late Acceptance sebagai move acceptance, selanjutnya disebut algoritma Reinforcement Learning-Late acceptance (RL-LA). Untuk mengetahui efektivitas performa dari algoritma RL-LA, performa algoritma RL-LA yang diusulkan dibandingkan dengan algoritma Simple Random-Late Acceptance (SR-LA). Hasil dari penelitian ini menunjukan bahwa algoritma yang diusulkan, i.e. RL-LA lebih unggul dari SR-LA pada  4 dari 6 domain permasalahan uji coba, yaitu SAT, personnel scheduling, TSP, dan VRP, sedangkan pada domain lainnya seperti bin packing dan flow shop mengalami penurunan. Secara lebih spesifik, RL-LA dapat meningkatkan peforma pencarian dalam menemukan solusi optimal pada 18 instance dari 30 instance atau sebesar 64%, dan jika dilihat dari nilai median dan minimum metode RL-LA lebih unggul 28% dari metode SR-LA.  Kontribusi utama dari penelitian ini adalah studi performa algoritma hibrida reinforcement learning dan late acceptance dalam kerangka kerja hyper-heuristics untuk menyelesiakan permasalahan optimasi lintas domain. AbstractAn organization sometimes needs solutions to cross domain optimization problems. The problem of cross domain optimization is a problem that has different characteristics, for example between domain optimization scheduling, vehicle routes, bin packing, and SAT. This optimization is used to support an organization's decision making. In solving these optimization problems, a computational search method is needed. In the literature, almost all optimization problems in NP-hard class are solved by meta-heuristics approach. However, this meta-heuristic has drawbacks, namely tuning parameters are needed for each different problem domain. So this approach is considered less effective. Therefore a new approach is needed, namely the hyper-heuristics approach. Hyper-heuristic method is an approximate computational search method that can solve cross domain optimization problems faster. In this final project there are six cross domain problems to be solved, namely satisfaction (SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman problem (TSP), and vehicle routing problem (VRP). In improving performance, this study examines the effect of the adaptation of the Reinforcement Learning (RL) algorithm as LLH selection combined with the Late Acceptance algorithm as a move acceptance. The results of this study indicate that there are 4 out of six problem domains that have improved performance, namely the SAT, personnel scheduling, TSP, and VRP, while in other domains such as bin packing and flow shop has decreased

Fairness in examination timetabling: student preferences and extended formulations

Variations of the examination timetabling problem have been investigated by the research community for more than two decades. The common characteristic between all problems is the fact that the definitions and data sets used all originate from actual educational institutions, particularly universities, including specific examination criteria and the students involved. Although much has been achieved and published on the state-of-the-art problem modelling and optimisation, a lack of attention has been focussed on the students involved in the process. This work presents and utilises the results of an extensive survey seeking student preferences with regard to their individual examination timetables, with the aim of producing solutions which satisfy these preferences while still also satisfying all existing benchmark considerations. The study reveals one of the main concerns relates to fairness within the students cohort; i.e. a student considers fairness with respect to the examination timetables of their immediate peers, as highly important. Considerations such as providing an equitable distribution of preparation time between all student cohort examinations, not just a majority, are used to form a measure of fairness. In order to satisfy this requirement, we propose an extension to the state-of-the-art examination timetabling problem models widely used in the scientific literature. Fairness is introduced as a new objective in addition to the standard objectives, creating a multi-objective problem. Several real-world examination data models are extended and the benchmarks for each are used in experimentation to determine the effectiveness of a multi-stage multi-objective approach based on weighted Tchebyceff scalarisation in improving fairness along with the other objectives. The results show that the proposed model and methods allow for the production of high quality timetable solutions while also providing a trade-off between the standard soft constraints and a desired fairness for each student

Hyper-heuristics and fairness in examination timetabling problems

Examination timetabling is a challenging optimisation problem in operations research and artificial intelligence. The main aim is to spread exams evenly throughout the overall time period to facilitate student comfort and success; however, existing examination timetabling solvers neglect fairness by optimising the sum or average of the objective function value without considering its distribution among students or other stakeholders. The balance between quality of the overall timetable and fairness (global fairness and within a cohort) is a major concern, thus the latter is added as a new objective function and quality indicator of examination timetables. The objective function is also considered from the perspectives of multiple stakeholders of examination timetabling (i.e. students, invigilators, markers and estates), as opposed to viewing the objective function as an aggregate function. These notions make the problem become a multi-objective optimisation problem. We study sum of power rather than linear summation to enforce fairness and concurrently minimise the objective function, using some perturbation-based hyper- heuristics approaches to optimise the standard objective function. Secondly, multi-stage approach is studied (generating initial feasible solution, improving the standard quality of solution and then improving fairness), to improve the fairness objective function. Given that the standard objective function and fairness objective function conflict, we then studied several multi-objective algorithms employed within the framework of hyper-heuristics. The proposed hyper-heuristic algorithms mainly can be divided into two approaches: classical scalarisation technique-based weighted sum and Tchebyce↵; and population-based non-dominated sorting memetic algorithm II (NSMA-II) and artificial bee colony and strength pareto evolutionary 2 (SPEA2) hybrid (ABC-SPEA2). The experiments were conducted over two multi-objective examination timetabling problem formulations (i.e. with fairness and with multiple stakeholder perspectives), tested over problem instances from four different datasets: Carter, Nottingham, Yeditepe and ITC 2007. The experimental results over multi-objective examination timetabling problem with fairness showed that in terms of the standard objective function the proposed approach could produce results comparable with the best known solutions reported in the literature, whilst in the same time could be forced to be fairer that does or does not compensate on worsening the standard objective function. Fairness within a cohort could be improved much better than global fairness and treating as multi-objective problem could help the search for near-optimal standard objective function escape from local optima trap. The scalarisation technique based hyper-heuristics outperforms the population-based hyper-heuristic. The advantage of treating examination timetabling problem as multi-objective problem is that approximations of the Pareto optimal solutions give the optimal trade-o↵ between standard objective function, fairness among all students, and fairness within a cohort. In addition, the decision maker also can view the solution from multiple stakeholders view. We believe that by giving this more detailed information, the decision maker of examination timetable could make better decisions