Staff Profile

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Sim Kwan Hua

Discipline Leader – Computer Science and Software Engineering, Lecturer 
BITCompSci(Hons)(UMS), MSc(IT)(UNIMAS)

Faculty of Engineering, Computing and Science

Office No:+60 82 260 694
Fax No:+60 82 260 813
Room No: E607, Building E
Email: khsim@swinburne.edu.my
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Kwan Hua has been a lecturer in the field of programming and IT related units since 2001. He joined Faculty of Engineering, Computing & Science, Swinburne University of Technology Sarawak Campus (SUTS) as a lecturer in 2007. Subsequently, he was appointed as the Discipline Leader for Bachelor of Computer Science Program in 2011. He has keen interest in interdisciplinary research, his key research area includes time series analysis, data mining, data analysis and predictive analytic. He has gained both internal and external research grants in those research areas.

Research Interests

  • Time Series Analysis
  • Time Series Pattern Recognition
  • Data Mining
  • Data and statistical analysis
  • Predictive Analytic

Professional Memberships

  • Senior Member, Australian Computer Society
  • Certified Professional, Australian Computer Society
  • Member, Institute of Electrical and Electronic Engineers
  • Member, Association for Computing Machinery
  • Member, International Academy of Computer Technology

PhD/Master by Research Opportunities

Potential research higher degree candidates are welcome to enquire about postgraduate opportunities in the following areas/ projects:

  • Financial Time Series Analysis
  • Data Mining through Statistical Analysis and Modeling
  • Time Series Pattern Recognition

Please contact Sim Kwan Hua for more information on the above topics.

Current RHD Supervision

Name Degree Area of Research Start year Role
Saad Tariq MSC Information Engineering and Theory 2021 Coordinating Supervisor
James Sheldon Cameron MSC Testing/Blockchain 2019 Associate Supervisor
Nicholas Bong MSc (Completed) Time Series Analysis 2016 Coordinating Supervisor
Isaac Goh MSC (Completed) Time Series Analysis 2014 Associate Supervisor
  • Publications


  • Book Chapter Sim, K. H. and Sim, K. Y. (2019). Dynamic Data Sampling Approach by Using Price Distribution of Crude Palm Oil to Forecast High Magnitude Price Movement, Applied Mechanics and Materials, vol. 829, 106-113
  • Journal paper Sim, K. H., Sim, K. Y. and Raman, V. (2020). Scalable Pattern Recognition in Financial Time Series Data with Double-Cycled Value Based Data Representation, International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 416-422
  • Journal paper Kwan-Yong Sim and Kwan-Hua Sim, Short-term and Long-term Day-of-the-Week Effects in Crude Oil Market: A Study for the Past 35 Years, Information – An International Interdisciplinary Journal, vol. 19 no. 8(A), pp.3289-3294, 2016.
  • Journal paper Sim, K. H., Bong, N.C.-Y. and Sim, K. Y. (2017). Analyzing Price Movement of Crude Oil through Historical Price Data Distribution by Using Apriori Data Mining Algorithm, International Journal of Computer Theory and Engineering, 9(5), 334-338.
  • Journal paper Kwan-Hua Sim, Isaac Goh, and Kwan-Yong Sim, Has Crude Oil Market Become More Efficient: A Study from the Day-of-the-Week Effect Perspective, An International Interdisciplinary Journal, vol. 19 no. 8(A), pp.3289-3294, 2016.
  • Journal paper Kwan-Hua Sim, Isaac Goh, and Kwan-Yong Sim, Plantation Index Forecasting Using Historical Price Distribution, Information – An International Interdisciplinary Journal, vol. 19 no. 8(A), pp.3289-3294, 2016.
  • Journal paper Kwan-Hua Sim, Isaac Goh, and Kwan-Yong Sim, Analysing Price Movements of Crude Oil Futures by Mining of Dynamic Sample Size through Price Distribution of the Historical Data, International Journal of Modeling and Optimization vol. 5, no. 6, pp. 393-397, doi: 10.7763/IJMO, 2015.
  • Conference paper Sim, K. Y. and Sim, K. H. (2018), Has Crude Oil Market Become More Efficient : A Study from the Day-of-the-Week Effect Perspective, in the Proceedings of the Ninth International Conference on Information (Information’2018), 7-9 Dec 2018, Tokyo, Japan
  • Conference paper Sim, K. H., Sim, K. Y., and Bong N. (2018), Dynamic Time Interval Data Representation in Scalable Financial Time Series Pattern Recognition, In the Proceedings of 2018 2nd International Conference on Computer Science and Artificial Intelligence 8-10 December, 2018, Shenzhen, China
  • Conference paper Kwan-Hua Sim, Kwan-Yong Sim, Isaac Goh, Forecasting High Magnitude Price Movement of Crude Palm Oil Futures by Identifying the Breaching of Price Equilibrium through Price Distribution Mining, The Fifth International Conference on Digital Information Processing and Communications, ISBN: 978-1-4673-6831-5, IEEE, Sierre, Switzerland, 2015
  • Conference paper Kwan-Hua Sim, Kwan-Yong Sim, Isaac Goh, Y. C. Tan, Forecasting Price Volatility Range of Crude Palm Oil by Mining the Historical Data Using Hybrid Range Mode, Intelligent Systems and Applications, Proceedings of the International Computer Symposium (ICS), ISBN: 978-1-61499-483-1, IOS Press, Taichung, Taiwan, 2015
  • Conference paper Kwan-Hua Sim, Kwan-Yong Sim, P. Then, Forecasting Price Volatility Cluster of Commodity Futures Index by Using Standard Deviation With Dynamic Data Sampling Based on Significant Interval Mined From Historical Data, 2014 International Conference on Control, Decision and Information Technologies (CoDIT), pp. 758 – 763, ISBN:978-1-4799-6773-5, IEEE, Metz, France, 2014
  • Conference paper K.H. Sim, A. Nyuak, N.G. Chang, “Mining the Contextualized Length Parameter of Relative Strength Index to Retrieve the Information of Extreme Levels from Historical data”, IET International Conference on Information and Communications Technologies (IETICT), pp. 186-191, Beijing, China, 2013
  • Conference paper K.H. Sim, “Information Mining Over Significant Interval on Historical Data: A Study on World Major Indexes”, The Second International Conference on Advances in Information Mining and Management”, ISBN 978-1-61208-227-1, Venice, Italy, 2012
  • Conference paper K.H. Sim, “Recognizing the Formation of Trend: A Standard Deviation Approach”, The 4th International Conference on Interaction Sciences: IT, Human and Digital Content (ICIS 2011), ISBN 978-89-88678-44-2, pp. 136-141, IEEE Press, Busan, Korea, 2011