ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA

  • Febrian Wahyu Ramadhan Universitas Islam Negeri Syarif Hidayatullah Jakarta
  • Husni Teja Sukmana Syarif Hidayatullah State Islamic University Jakarta
  • Lee Kyung Oh
  • Luh Kesuma Wardhani UIN Syarif Hidayatullah Jakarta
Keywords: ITSM, ITIL, Sentiment Analysis, Algoritma K-NN

Abstract

Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.

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Published
2019-09-03
How to Cite
Ramadhan, F., Sukmana, H., Oh, L., & Wardhani, L. (2019). ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA. ADI Journal on Recent Innovation (AJRI), 1(1), 14-19. https://doi.org/https://doi.org/10.34306/ajri.v1i1.9