TY - GEN
T1 - Application of text mining for classification of textual reports
T2 - 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
AU - Prajitno, Isti Surjandari
AU - Megawati, Chyntia
AU - Dhini, Arian
AU - Sanditya Hardaya, I. B.N.
N1 - Publisher Copyright:
© IEOM Society International.
PY - 2016
Y1 - 2016
N2 - The rapid development of Information and Communication Technology (ICT) has made ICT an important part in the daily life of society. In that connection, the Indonesian government also tried to take advantage of ICT to be able to establish two-way communication with the public or commonly known as e-Government. One way is to create a website called LAPOR! (Layanan Aspirasi dan Pengaduan Online Rakyat or National Complaint Handling System). All kind of reports that conveyed by public through LAPOR! could be important inputs for the government to develop and improve public services. The high number of reports makes manual analysis becomes ineffective so that big data analysis becomes important. This study uses Text Mining methods for analyzing textual data in the form of opinions or complaints submitted by the public through LAPOR! by classifying those reports into classes. Then the data set in each class was clustered into specific topics. The results of this study show that the majority of public report is associated with poverty, particularly regarding social assistance, such as KPS (Kartu Perlindungan Sosial or Social Security Card) and BLSM (Bantuan Langsung Sementara Masyarakat or Temporary Direct Cash Assistance), which were not well distributed or not on target.
AB - The rapid development of Information and Communication Technology (ICT) has made ICT an important part in the daily life of society. In that connection, the Indonesian government also tried to take advantage of ICT to be able to establish two-way communication with the public or commonly known as e-Government. One way is to create a website called LAPOR! (Layanan Aspirasi dan Pengaduan Online Rakyat or National Complaint Handling System). All kind of reports that conveyed by public through LAPOR! could be important inputs for the government to develop and improve public services. The high number of reports makes manual analysis becomes ineffective so that big data analysis becomes important. This study uses Text Mining methods for analyzing textual data in the form of opinions or complaints submitted by the public through LAPOR! by classifying those reports into classes. Then the data set in each class was clustered into specific topics. The results of this study show that the majority of public report is associated with poverty, particularly regarding social assistance, such as KPS (Kartu Perlindungan Sosial or Social Security Card) and BLSM (Bantuan Langsung Sementara Masyarakat or Temporary Direct Cash Assistance), which were not well distributed or not on target.
KW - Classification
KW - Clustering
KW - Public's Reports
KW - Self-Organizing Maps
KW - Support Vector Machine
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=85018398771&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85018398771
SN - 9780985549749
T3 - Proceedings of the International Conference on Industrial Engineering and Operations Management
SP - 1147
EP - 1156
BT - 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
PB - IEOM Society
Y2 - 8 March 2016 through 10 March 2016
ER -