<![CDATA[Generally, timetabling is done by using conventional tables or spreadsheets. As a result, its affect the quality of timetable and it could drain time and energy if data is considered in thousands. Based on these problems, it requires an intelligent system that not only automates its process but also optimizes the result. PSO is proved effective to solve multidimensional and multiparameter optimization problems compared with other methods. DPSO is used in this study because of combinatorics problems. Various strategies are also used in this method such as clustering particle representation, transposition method for particle movement, time-variant approach, guided random strategies, particle's position repair strategies, and multithreading. The strategy is expected to improve timetabling result. With the various strategies that have been used, this study will use “Hybrid Discrete Particle Swarm Optimization†approach. The test results showed the combination of parameters that resulting the best fitness is: bloc_min=0.6, bloc_max=1, bglob_min=0.6, bglob_max=1, brand_min=0, brand_max=0.002, particle total 2 and iteration total 50,000. The resulting fitness is 248,515.76 with the total execution time is 1 hour 46 minutes 14 seconds and 600 milliseconds.]]>
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