Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Keszya Wabang
Universitas Diponegoro
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints Keszya Wabang; Oky Dwi Nurhayati; Farikhin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4498

Abstract

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%.
Co-Authors Farikhin Oky Dwi Nurhayati