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All Journal Jurnal Informasi dan Komputer
Putri, Larasati Nabila
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics Other

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ANALISIS SENTIMEN KEPUASAN PEMANGKU KEPENTINGAN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN K-NEAREST NEIGHBOUR Ratniasih, Ni Luh; Putri, Larasati Nabila
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.378

Abstract

Stakeholders are individuals or groups who have interests and can exert influence on an object. It is very important to measure the satisfaction of stakeholders (stakeholders) to get feedback and input for the purposes of developing and implementing strategies to increase stakeholder satisfaction, so it is necessary to know the opinions of stakeholders. The research method consists of several stages including the first stage is problem identification and literature study, the second stage is collecting data on stakeholder satisfaction (Students), the third stage is data preprocessing, the fourth stage is feature extraction in order to facilitate classification using the Naïve Bayes Classifier (NBC) method and K-Nearest Neighbor (KNN). The fourth stage is the testing and evaluation stage of the model. The fifth stage is testing the accuracy of the method. The purpose of this study is to compare the level of accuracy between the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) methods on sentiment analysis from comments on the results of measuring stakeholder satisfaction. The results of the accuracy rate using the Naïve Bayes Classifier (NBC) method of 91.13% and K-Nearest Neighbor (KNN) of 83.06% so that the performance of the Naïve Bayes Classifier (NBC) method is higher in the analysis of stakeholder satisfaction sentiment.
Co-Authors Ni Luh Ratniasih, Ni Luh