Analysis of User Comment Sentiment on the Siwaslu Application Using the Naive Bayes Method

Diva Rizky Azzami; Baenil Huda; Agustia Hananto; Tukino

doi:10.59934/jaiea.v4i3.1132

Authors

Diva Rizky Azzami Universitas Buana Perjuangan Karawang
Baenil Huda Universitas Buana Perjuangan Karawang
Agustia Hananto Universitas Buana Perjuangan Karawang
Tukino Universitas Buana Perjuangan Karawang

DOI:

https://doi.org/10.59934/jaiea.v4i3.1132

Keywords:

sentiment analysis; siwaslu application; naive bayes; text preprocessing; tf-idf

Abstract

This study aims to identify sentiment in user comments on the Siwaslu application by utilizing the Naive Bayes model. The Siwaslu application itself is a digital platform developed to support election supervision, with the aim that the public can provide input that can be used to improve the quality of the application's services. The data analyzed consisted of 2,926 comments that had gone through the pre-processing stage, such as converting text to lowercase, removing punctuation and stopwords, and implementing stemming using the Literary algorithm. After that, the text features are extracted using the method (TF-IDF) and then fed into the Naive Bayes classification model. The results of the evaluation showed that from the overall data, as many as 1,642 comments were classified as negative and another 1,284 as positive. The Naive Bayes classification model used succeeded in providing an accuracy of 88%, with a precision of 0.84 in the negative class and 0.94 in the positive class. The resulting F1-score is 0.90 for the negative class and 0.85 for the positive class, respectively. Overall, these results show that the Naive Bayes model is quite effective in analyzing sentiment and can make a real contribution to efforts to improve the quality of Siwaslu application services in the future.

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