Dr. Ferda Ernawan

Associate Professor
Faculty of Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

Highest Degree
Ph.D. in Image Processing from Universiti Teknikal Malaysia Melaka, Malaysia

My URL
https://livedna.org/62.9955

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Biography

Ferda Ernawan is currently an Associate Professor at the Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang. He received his Master in Software Engineering and Intelligent and Ph.D in image processing from the Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka in 2011 and 2014 respectively.

Area of Interest:

Computer Sciences
Watermarking
Image Compression
Image Processing
Speech Processing

Selected Publications

  1. Yahaya, C.A.C., A. Firdaus, F. Ernawan and W.I.S.W. Din, 2022. The relevance of bibliometric analysis to discover the areas research efforts: Root exploit evolution. Int. J. Inform. Visualization, 6: 489-497.
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  2. Ernawan, F., P.W. Adi, S.C. Liew, E.A. Sarwoko and E. Winarno, 2022. Fast image watermarking based on signum of cosine matrix. Indonesian J. Electr. Eng. Comput. Sci., 25: 1383-1391.
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  3. Ernawan, F., K. Handayani, M. Fakhreldin and Y. Abbker, 2022. Light gradient boosting with hyper parameter tuning optimization for COVID-19 prediction. Int. J. Adv. Comput. Sci. Appl., 13: 514-523.
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  4. Ernawan, F., A. Aminuddin, D. Nincarean, M.F. Ab Razak and A. Firdaus, 2022. Three layer authentications with a spiral block mapping to prove authenticity in medical images. Int. J. Adv. Comput. Sci. Appl., 13: 211-223.
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  5. Ernawan, F. and D. Ariatmanto, 2022. An efficient adaptive scaling factor for 4×4 DCT image watermarking. Multimed. Tools Appl., 10.1007/s11042-022-14220-0.
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  6. Ariatmanto,D. and F. Ernawan, 2022. Adaptive scaling factors based on the impact of selected DCT coefficients for image watermarking. J. King Saud Univ. Comput. Inf. Sci., 34: 605-614.
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  7. Aminuddin, A. and F. Ernawan, 2022. AuSR2: Image watermarking technique for authentication and self-recovery with image texture preservation. Comput. Electr. Eng., 10.1016/j.compeleceng.2022.108207.
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  8. Aminuddin, A. and F. Ernawan, 2022. AuSR1: Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking. J. King Saud Univ. - Comput. Inform. Sci., 34: 5822-5840.
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  9. Prasetyo, M.L., A.T. Wibowo, M. Ridwan, M.K. Milad and S. Arifin et al., 2021. Face recognition using the convolutional neural network for barrier gate system. Int. J. Interact. Mobile Technol., 15: 138-153.
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  10. Ernawan, F., D. Ariatmanto and A. Firdaus, 2021. An improved image watermarking by modifying selected DWT-DCT coefficients. IEEE Access, 9: 45474-45485.
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  11. Yahaya, C.A.C., A. Firdaus, S. Mohamad, F. Ernawan and M.F. Ab Raza, 2020. Automated feature selection using boruta algorithm to detect mobile Malware. Int. J. Adv. Trends Comput. Sci. Eng., 9: 9029-9036.
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  12. Osman, S, 2020. The effect of augmented reality application toward student's learning performance in PC assembly. Int. J. Adv. Trends Comput. Sci. Eng., 9: 401-407.
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  13. Ng, K.H., S.C. Liew and F. Ernawan, 2020. An improved RDWT-based image steganography scheme with QR decomposition and double entropy. Int. J. Adv. Comput. Sci. Appl., 11: 222-231.
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  14. Mustaffa, Z., M.H. Sulaiman, M.F.M. Mohsin, Y. Yusof, F. Ernawan, B. Yusob and N.M. Noor, 2020. An application of barnacle mating optimizer in infectious disease prediction: A dengue outbreak cases. Iraqi J. Sci., 61: 2132-2141.
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  15. Fuad, M. and F. Ernawan, 2020. Video steganography based on DCT psychovisual and object motion. Bull. Electr. Eng. Inf., 9: 1015-1023.
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  16. Ernawan, F. and M.F. Abdullah, 2020. A New Embedding Technique Based On Psychovisual Threshold for Robust and Secure Compressed Video Steganography. Proceeding of XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 29 August 2020-05 September 2020, IEEE, 1-5.
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  17. Ariatmanto, D. and F. Ernawan, 2020. An improved robust image watermarking by using different embedding strengths. Multimedia Tools Appl., 79: 12041-12067.
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  18. Ariatmanto, D. and F. Ernawan, 2020. An Adaptive Scaling Factor for Multiple Watermarking Scheme. Proceeding of 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 20-21 November 2019, IEEE, 175-178.
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  19. Alias, N. and F. Ernawan, 2020. Multiple watermarking technique using optimal threshold. Indones. J. Electr. Eng. Comput. Sci., 18: 368-376.
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  20. Abdul Halim, N.A., F. Ernawan, D.N. Eh Phon and K. Moorthy, 2020. Fragile watermarking scheme based on sha-256 hash function and mersenne twister for medical image authentication. Int. J. Adv. Trends Comput. Sci. Eng., 9: 8569-8574.
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  21. Mustaffa, Z., M.H. Sulaiman, M.F.M. Mohsin, Y. Yusof, F. Ernawan and K.A.M. Rosli, 2019. An Application of Hybrid Swarm Intelligence Algorithms for Dengue Outbreak Prediction. Proceeding of IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 09-11 April 2019, IEEE, 731-735.
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  22. Ferda Ernawan, Dhani Ariatmanto 2019. Image watermarking based on integer wavelet transform-singular value decomposition with variance pixels Int. J. Electr. Comput. Eng., 9: 2185-2195.
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  23. Ernawan, F., S.C. Liew, Z. Mustaffa and K. Moorthy, 2019. A blind multiple watermarks based on human visual characteristics. Int. J. Electr. Comput. Eng., 8: 2578-2587.
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  24. Ernawan, F., 2019. Tchebichef image watermarking along the edge using YCoCg-R color space for copyright protection. Int. J. Electr. Comput. Eng., 9: 1850-1860.
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  25. Ernawan, F. and M.N. Kabir 2019. An improved watermarking technique for copyright protection based on tchebichef moments. IEEE Access 7: 151985-152003.
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  26. Mustaffa, Z., M.H. Sulaiman, F. Ernawan and N.K.M. Noor, 2018. Short term forecasting based on hybrid least squares support vector machines. Adv. Sci. Lett., 24: 7455-7460.
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  27. Mustaffa, Z., M.H. Sulaiman, D. Rohidin, F. Ernawan and S. Kasim, 2018. Time series predictive analysis based on hybridization of meta-heuristic algorithms. Int. J. Adv. Sci. Eng. Info. Technol., 8: 1919-1925.
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  28. Ernawan, F., M.N. Kabir and J.M. Zain, 2018. Bit allocation strategy based on Psychovisual threshold in image compression. Multimed. Tools Appl., 77: 13923-13946.
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  29. Ernawan, F. and M.N. Kabir, 2018. A robust image watermarking technique with an optimal DCT-psychovisual threshold. IEEE Access, 6: 20464-20480.
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  30. Ernawan, F. and M.N. Kabir, 2018. A block-based RDWT-SVD image watermarking method using human visual system characteristics. Vis. Comput., 36: 19-37.
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  31. Chandran, S., K. Moorthy, M.A. Ismail, M.Z. Osman, M.A.M. Hamza and F. Ernawan, 2018. An improved integrative random forest for gene regulatory network inference for breast cancer. Adv. Sci. Lett., 24: 7566-7571.
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  32. Ernawan, F., N. Kabir and K.Z. Zamli, 2017. An efficient image compression technique using Tchebichef bit allocation. Optik, 148: 106-119.
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  33. Ernawan, F., 2016. Robust image watermarking based on psychovisual threshold. J. ICT Res. Appl., 10: 228-242.
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  34. Abu, N.A., F. Ernawan and F. Salim, 2015. A smooth formant peak via discrete tchebichef transform. J. Comput. Sci., 11: 351-360.
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  35. Abu, N.A. and F. Ernawan, 2015. A novel psychovisual threshold on large DCT for image compression. Scient. World J. 10.1155/2015/821497.
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  36. Ernawan, F., N.A. Abu and N. Suryana, 2014. Psychovisual threshold for generating quantization process in tchebichef moment image compression. J. Comput., 9: 702-710.
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  37. Ernawan, F., N.A. Abu and N. Suryana, 2014. Integrating a smooth psychovisual threshold into an adaptive JPEG image compression. J. Comput., 9: 644-653.
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  38. Ernawan, F., N.A. Abu and N. Suryana, 2014. An optimal tchebichef moment quantization using psychovisual threshold for image compression. Adv. Sci. Lett., 20: 70-74.
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  39. Ernawan, F., N.A. Abu and N. Suryana, 2014. An adaptive JPEG image compression using psychovisual model. Adv. Sci. Lett., 20: 26-31.
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  40. Ernawan, F. and S.H. Nugraini, 2014. The optimal quantization matrices for JPEG image compression from psychovisual threshold. J. Theoret. Applied Inform. Technol., 70: 566-572.
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  41. Abu, N.A. and F. Ernawan, 2014. Psychovisual threshold on large tchebichef moment for image compression. Applied Mathemat. Sci., 8: 6951-6961.
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  42. Ernawan, F., N.A. Abu and N. Suryana, 2013. Vowel recognition using discrete tchebichef transform. J. Applied Sci., 13: 465-471.
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  43. Ernawan, F., N.A. Abu and N. Suryana, 2013. Adaptive tchebichef moment transform image compression using psychovisual model. J. Comput. Sci., 9: 716-725.
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  44. Abu, N.A., F. Ernawan and N. Suryana, 2013. An image dithering via tchebichef moment transform. J. Comput. Sci., 9: 811-820.
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  45. Ernawan, F. and N.A. Abu, 2011. Efficient discrete tchebichef on spectrum analysis of speech recognition. Int. J. Mach. Learn. Comput., 1: 1-6.
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Member since October, 2016