Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning
This research was conducted to apply the KNN (K-Nearest Neighbor) algorithm in conducting sentiment analysis of Twitter users on issues related to government policies regarding Online Learning. Research using Tweet data as much as 1825 Indonesian tweet data data were collected from February 1, 2020...
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| Main Authors: | , , |
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| Format: | EJournal Article |
| Published: |
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2021-04-30.
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| Subjects: | |
| Online Access: | Get Fulltext Get Fulltext |
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| Summary: | This research was conducted to apply the KNN (K-Nearest Neighbor) algorithm in conducting sentiment analysis of Twitter users on issues related to government policies regarding Online Learning. Research using Tweet data as much as 1825 Indonesian tweet data data were collected from February 1, 2020 to September 30, 2020. Using the python library, Tweepy. word weighting using TF-IDF, will be classified into two classes of sentiment values, positive and negative. After testing with K of 20, the highest accuracy results were obtained when K = 10 with an accuracy value of 84.65% with a precision of 87%, a recall of 86% f measure 87% and an error rate of 0.12% and a tendency was also obtained. public opinion on online learning tends to be positive. |
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| Item Description: | https://jurnal.ugm.ac.id/ijccs/article/view/65176 |