文章基本信息

标题：AUTOMATED COMPLAINTS CLASSIFICATION USING MODIFIED NAZIEF-ADRIANI STEMMING ALGORITHM AND NAIVE BAYES CLASSIFIER
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作者：VANNIA FERDINA ; MARCEL BONAR KRISTANDA ; SENG HANSUN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：5
页码：1604-1614
出版社：Journal of Theoretical and Applied
摘要：Complaints provided by customers in the use of products or services is a feedback of the quality of products or services used by customers. In Universitas Multimedia Nusantara (UMN), students can deliver their complaints through an organization, i.e. Dewan Keluarga Besar Mahasiswa (DKBM) UMN. All students� complaints are manually classified into predefined categories by DKBM so that it can be delivered to related division. It costs a lot of time and human resources of DKBM UMN, and also caused misclassification of incoming complaints. In e-complaint system, a method that can be used to support efficient complaint processing is the use of automatic classification system because it can save both time and human resources. Naive Bayes Classifier (NBC) algorithm is one the algorithm that can be used to classify text automatically and for the preprocessing stage, modified Nazief-Adriani stemming algorithm is used. Based on the study conducted, it can be concluded that Naive Bayes Classifier algorithm with modified Nazief-Adriani stemming algorithm is able to do the classification well. This is indicated from the precision value of 91.86%, the recall value of 84.48%, and the f-1 score value of 86.29% for the ratio of training data and test data 90:10, and an average accuracy of 86%.
关键词：e;Complaint; Naive Bayes; Classifier Algorithm; Text Classification; Text Mining