Afiahayati, S.Kom., M.Cs, Ph.D

  • 26 January 2017, 04.20
  • Oleh:
  • 0

Email : afia@ugm.ac.id, af1a@mail.ugm.ac.id
Education Background :
  • Doctor, Dept. of Biosciences and Informatics/Faculty of Science and Technology, Keio University, Japan, October 2011 – March 2015
    Thesis : Development of de novo assemblers for metagenomic sequencing data
  • Master, Dept. of Computer Science and Electronics/ Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia, September 2008 – April 2010
    Thesis : Multiple Sequence Alignment Using Hidden Markov Model with Augmented Set and Its Influence on Phylogenetic Tree Accuracy-
  • Undergraduate, Dept. of Computer Science and Electronics/Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada , Indonesia, August 2004 – May 2008
    Thesis : Multiple sequence alignment menggunakan hidden markov model
Research Interest :
  • Metagenomic Assembler, Genome Assembly, Comparative Genomic, Machine Learning, Probabilistic, Bioinformatics, Data Mining, Artificial Intelligence
Research Cluster/Group :
  • Bioinformatics, Intelligent System, Computer Science
List of Publications :
  • Afiahayati, Anarossi, E., Yanuarska, R.D., Nuha, F.U., Mulyana, S. (2020). Comet assay classification for buccal Mucossa’s DNA damage measurement with super tiny dataset using transfer learning. Studies in Computational Intelligence 830, pp. 279 – 289.
  • Dwiastuti, M., Afiahayati. (2019). Speech recognition for people with dysarthtia using convolutional neural network. ICIC Express Letters PartB: Applications, 10(9), pp. 845 – 858.
  • Habibie, F., Afiahayati, Herwanto, G.B., Hartati, S., Frisky, A.Z.K. (2019). A Parallel ClustalW Algorithm on Multi-Raspberry Pis for Multiple Sequence Alignment. Proceedings – 2018 1st International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering, BioMIC 2018.
  • Nuha, F.U., Afiahayati. (2018). Training dataset reduction on generative adversarial network. (2018). Procedia Computer Science, 144, pp. 133 – 139.
  • Dewa, C.K., Afiahayati. (2018). Suitable CNN Weight Initialization and Activation Function for javanese Vowels Classification. Procedia Computer Science, 144, pp. 124 – 135.
  • Kurniawan, J., Dewa, C.K., Afiahayati. (2018). Traffic Congestion Detection: Learning from CCTV Monitoring Images using Convolutional Neural Network. Procedia Computer Science, 144, pp. 219 – 297.
  • Rajagede, R.A., Dewa, C.K., Afiahayati. (2017). Recognizing Arabic Letter utterance using convolutional neural network. Proceedings – 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing , SNPD 2017, pp. 181 – 186.
  • Solihah, B., Winarko, E., Afiahayati, Hartati, S., Wibowo,M.E. (2017). A systematic review: B-cell conformational epitope prediction from epitope characteristics view. Proceeding – 2017 3rd International Conference on Science and Technology-Computer, ICST 2016. pp. 93 – 98.
  • Solihah, B., Afiahayati. (2017). Overview on computational-based B-cell epitope prediction: Dataset, method and accuracy. Proceedings – 2016 2nd International Conference on Science and Technology-Computer, ICST 2016. pp. 71 – 76.
  • Afiahayati, A. Mulyana, S. (2015). Multiple Sequence Alignment Menggunakan Hidden Markov Model. Seminar Nasional Informatika (SEMNASIF), 1(1).
  • A. Afiahayati. K Sato. Y Sakakibara. 2015. MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA research, 22 (1), pp.  69-77.
  • A Afiahayati. K Sato. Y Sakakibara. 2013. An extended genovo metagenomic assembler by incorporating paired-end information. PeerJ , 1, p. e196.
  • A Afiahayati. S Mulyana. 2015. Multiple Sequence Alignment Menggunakan Hidden Markov Model. Seminar Nasional Informatika (SEMNASIF), 1 (1).
  • Afiahayati. H Tsuyoshi. S Kengo. S Yasubumi. 2012. A Modified Genovo Metagenome Assembler for Paired End Reads, 研究報告バイオ情報学 (BIO), 2012 (2), pp. 1-6.
  • Afiahayati. S Hartati. 2010. Multiple sequence alignment using Hidden Markov model with augmented set based on BLOSUM 80 and its influence on phylogenetic accuracy. Distributed Framework and Applications (DFmA), pp 1 – 8.
  • more..