Application of The Naïve Bayes Classifier Algorithm to Classify
Community Complaints

Application of The Naïve Bayes Classifier Algorithm to Classify
Community Complaints

classification, complaints/ community reports, Naïve Bayes Classifier

Unsatisfactory public services encourage the public to submit complaints/ reports to public service providers to improve their
services. However, each complaint/ report submitted varies. Therefore, the first step of the community complaint resolution
process is to classify every incoming community complaint. The Ombudsman of The Republic of Indonesia annually receives a
minimum of 10,000 complaints with an average of 300-500 reports per province per year, classifies complaints/ community
reports to divide them into three classes, namely simple reports, medium reports, and heavy reports. The classification process
is carried out using a weight assessment of each complaint/ report using 5 (five) attributes. It becomes a big job if done manually.
This impacts the inefficiency of the performance time of complaint management officers. As an alternative solution, in this study,
a machine learning method with the Naïve Bayes Classifier algorithm was applied to facilitate the process of automatically
classifying complaints/ community reports to be more effective and efficient. The results showed that the classification of
complaints/ community reports by applying the Naïve Bayes Classifier algorithm gives a high accuracy value of 92%. In addition,
the average precision, recall, and f1-score values, respectively, are 91%, 93%, and 92%

Keszya Wabang1
, Oky Dwi Nurhayati2
, Farikhin3

Diponegoro University

31-04-2022

Fajar bagus W

PDF

Indonesia

Text