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Detection of interviewer falsification in statistics Indonesia’s mobile survey
Rosmansyah Y.a, Santoso I.b, Hardi A.B.a,c, Putri A.a, Sutikno S.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology Bandung, Indonesia
b Polytechnic of Statistics, Jakarta, Indonesia
c BSSN, Jakarta, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019, School of Electrical Engineering and Informatics. All rights reserved.Interviewer falsification is an important issue faced by institutions conducting censuses and surveys around the world, including Statistics Indonesia. This study discusses several methods to systematically detect interviewer falsification and validation using data mining techniques so that human supervisors can take further actions. After analyzing relevant features and conducting experiments, the results showed that unsupervised classification algorithm using simple 2-means clustering achieved 70.5% accuracy, while the supervised classification using logistic regression improved the accuracy to 88.5%. A greater level of accuracy is still needed to be pursued in further research, but the current results are certainly better than the traditional method which has almost no falsification detection method at all.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Data Mining,Interviewer Falsification,Mobile Survey,Statistics[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Yusep Rosmasnyah received a B.S. degree from Bandung Institute of Technology, Indonesia, and both the M.S. and Ph.D. degrees from the University of Surrey, U.K. He has been a researcher and faculty member at the School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia. His current research interest includes mobile learning technologies and cybersecurity. email: yusep@stei.itb.ac.id Ibnu Santoso has completed Diploma studies in Computational Statistics Study Program, College of Statistics in 2007 and Master of Informatics at Bandung Institute of Technology in 2014. Worked in the Integration of Statistical Data Processing, Statistics Indonesia year 2008-2014. He began teaching at the Polytechnic of Statistics STIS in 2014 and since then has studied and taught courses in Algorithm and Programming and Data Mining and Knowledge Management. email: ibnu@stis.ac.id Ariq Bani Hardi is a Master’s Student at the School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia. He received a scholarship from the National Cyber and Crypto Agency (BSSN Republik Indonesia). His main research interests are related to the design and development of security of the mobile application, cybersecurity, and applied cryptography. email: ariq.bani@bssn.go.id[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.15676/ijeei.2019.11.3.2[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]