TY - GEN
T1 - Classification of public complaint data in sms complaint using naive bayes multinomial method
AU - Yance Nanlohy, Lucan
AU - Mulyanto Yuniarno, Eko
AU - Mardi Susiki Nugroho, Supeno
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/19
Y1 - 2020/9/19
N2 - SMS Complaint is an electronic public complaint tool for reporting issues on government performance. Text mining classification utilized to determine the value of each complaint category. The SMS data in this study sourced from the SMS Complaint Service of Ambon City Government. There were 6 categories of classification, namely Public Service, Infrastructure, Bureaucracy, Health, Education, and Social. The classification performed to measure levels of accuracy of the Stemming process and non-Stemming process represented in Matrix with values of recall, precision, and f1 score. The methods used in the measurement were Naive Bayes Multinomial. With the naive Bayes method, an accuracy level with stemming of 91.38% obtained, and while the accuracy level without stemming was 90.73%. The result showed that the naive Bayes method could be used effectively to predict complaint data through stemming.
AB - SMS Complaint is an electronic public complaint tool for reporting issues on government performance. Text mining classification utilized to determine the value of each complaint category. The SMS data in this study sourced from the SMS Complaint Service of Ambon City Government. There were 6 categories of classification, namely Public Service, Infrastructure, Bureaucracy, Health, Education, and Social. The classification performed to measure levels of accuracy of the Stemming process and non-Stemming process represented in Matrix with values of recall, precision, and f1 score. The methods used in the measurement were Naive Bayes Multinomial. With the naive Bayes method, an accuracy level with stemming of 91.38% obtained, and while the accuracy level without stemming was 90.73%. The result showed that the naive Bayes method could be used effectively to predict complaint data through stemming.
KW - Classification
KW - Multinomial
KW - Naive Bayes
KW - SMS Complaint
KW - Text Mining
UR - https://www.scopus.com/pages/publications/85096618155
U2 - 10.1109/ICOVET50258.2020.9229941
DO - 10.1109/ICOVET50258.2020.9229941
M3 - Conference contribution
AN - SCOPUS:85096618155
T3 - 4th International Conference on Vocational Education and Training, ICOVET 2020
SP - 241
EP - 246
BT - 4th International Conference on Vocational Education and Training, ICOVET 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Vocational Education and Training, ICOVET 2020
Y2 - 19 September 2020
ER -