Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (Nov 2022)

Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints

  • Keszya Wabang,
  • Oky Dwi Nurhayati,
  • Farikhin

DOI
https://doi.org/10.29207/resti.v6i5.4498
Journal volume & issue
Vol. 6, no. 5
pp. 872 – 876

Abstract

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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%.

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