Firda Aminy Ma'ruf


Position             : Research Assistant

Qualifications   :

                              - M.Sc. in Computer Science (Pusan National University)

                              - B.S.E. in Informatics Engineering (Telkom University)                           

Profile                :


                     Firda specializes on bioinformatics and machine learning application for analyzing omics data. During her Master study in Pusan National University, she joined the Machine Learning and Bioinformatics laboratory. Her graduate research focused on somatic mutation identification in the whole-exome sequencing (WES) data of cancer. It involved developing and applying a deep neural network as the classification model. Currently, she works on computational methods to infer genetic markers for population genetics of Indonesians.                            

Interests            :  

                                 -  Machine learning and Artificial intelligence modeling for analyzing genomics data

                                 - Human genetics and diseases

Awards              :

                                  - Grand ICT Research Center Scholarship (2019)

                                  - Japan Student Services Organization (JASSO) Scholarship for Kumamoto University Spring School (2017)                            

Contact             :  firda.maruf@mrinstitute.org

Publications     :

1. DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.  J. Bioinform. Comput. Biol.        19(6). 2021 Dec 13. doi: 10.1142/S0219720021400175

2. Analysis of the influence of Minimum Redundancy Maximum Relevance as dimensionality reduction method on cancer classification based on microarray data       using Support Vector Machine classifier. 2019. J. Phys.: Conf. Ser. 1192 012011

3. The Comparison Between SVD-DCT and SVD-DWT Digital Image Watermarking. 2018. J. Phys.: Conf. Ser. 971 012006

4. Implementation of Mutual Information and Bayes Theorem for Classification Microarray Data. 2018. J. Phys.: Conf. Ser. 971 012011