Klasifikasi Topik dan Analisa Sentimen Terhadap Kuesioner Umpan Balik Universitas Menggunakan Metode Long Short-Term Memory

Doenny Auddy Lionovan, Leo Willyanto Santoso, Rolly Intan


In evaluating and improving the quality of services and facilities, Petra Christian University (PCU) took feedback questionnaires given to students via online. At this time, reading the comments in the suggestion column is still read manually. So that it becomes less effective and efficient in analyzing sentiments and classifying topics for many comments. This study will apply word2vec and Long Short-Term Memory method to create a program that can help classify topics and sentiment analysis.

Word2vec is used as a method to convert a word into a vector along with mapping the meaning of existing words. The parameter tested on word2vec are the number of iterations and windows size. Whereas the Long Short-Term Memory method is used to classify sentiment and topics. The parameter tested are the number of layers, the number of units, the batch size, and the percentage of dropout.

The result of this study indicates that the word2vec method along with Long Short-Term Memory can be used to analyze sentiment and topic classification. The best configuration results obtained average accuracy in the implementation of the sentiment classification is 89,16 % and for implementation of the topic classification is 92,98 %.


Natural Language Processing; Sentiment Analysis; Text Classification; Long Short-Term Memory; Questionnaire

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