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CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM

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Mohammad Zoqi Sarwani

Abstract

E-complaint is one of the technologies which is used to collect feedback from customers in the form of criticism and suggestions using electronic systems. For some companies or agencies, ecomplaint is used to provide better services to its customers. This study is aimed to perform sentiment analysis of an e-complaint service, with the case of Brawijaya University. There are three main stages for the proposed system, i.e. Text Preprocessing, Text Weighting, and PNN for
the classification. Tokenization, filtering, and stemming are done in the text preprocessing. Resulted text from the preprocessing stage is weighting using Term Inverse Document Frequent (TFIDF). To classify the negative or positive complaints, PNN are used in the last stage. For the experiments, 70 data are used as the training data, and 20 data are used as the testing data. The experimental results based on the combination of the number of training and testing dataset, showed that the accuracy achieved up to 90%.

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How to Cite
ZOQI SARWANI, Mohammad. CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM. Kursor, [S.l.], v. 8, n. 3, p. 135-140, mar. 2017. ISSN 2301-6914. Available at: <https://kursorjournal.org/index.php/kursor/article/view/88>. Date accessed: 12 dec. 2019. doi: https://doi.org/10.28961/kursor.v8i3.88.
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