SENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA

This research aims to delve into the sentiment analysis of the public towards the Jatigede Reservoir ecotourism in Sumedang Regency through Twitter and YouTube social media platforms. The sentiment analysis method employed is the Naïve Bayes Classifier. Data was collected through crawling from bo...

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Main Author: Wididharma, Kelvin
Format: Final Project
Language:Indonesia
Subjects:
Online Access:https://digilib.itb.ac.id/gdl/view/78679
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:78679
spelling id-itb.:786792023-11-09T09:33:57ZSENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA Wididharma, Kelvin Perencanaan wilayah Indonesia Final Project Ecotourism, Sentiment Analysis, Naïve Bayes Classifier, Social Media, Sustainable Tourism. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/78679 This research aims to delve into the sentiment analysis of the public towards the Jatigede Reservoir ecotourism in Sumedang Regency through Twitter and YouTube social media platforms. The sentiment analysis method employed is the Naïve Bayes Classifier. Data was collected through crawling from both social media platforms, followed by a series of preprocessing steps, including data cleansing, text preprocessing, and translation, before ultimately being classified into three sentiment categories: positive, neutral, and negative. The analysis results indicate that the majority of sentiments associated with the Jatigede Reservoir are positive (approximately 70%), while neutral sentiments make up around 20%, and negative sentiments are only around 10%. The Naïve Bayes Classifier method yielded an accuracy rate of 69.29%, demonstrating its effectiveness in classifying public sentiment towards this ecotourism destination. Furthermore, this study integrates the findings of sentiment analysis with ecotourism development strategies. These strategies encompass enhancing service quality, public education efforts, and more effective promotion. The implementation of these strategies is aimed at creating sustainable ecotourism, preserving the environment, and empowering the local community to gain greater economic benefits from the Jatigede Reservoir ecotourism. Recommendations from this research are directed towards tourism managers, the Sumedang Regency government, and potential investors, with a focus on enhancing positive interactions with visitors, deepening understanding of ecotourism, and more effective destination promotion. This research provides valuable insights into how sentiment analysis can be a powerful tool in ecotourism development and sustainable decision-making in the tourism sector. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
topic Perencanaan wilayah
spellingShingle Perencanaan wilayah
Wididharma, Kelvin
SENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA
description This research aims to delve into the sentiment analysis of the public towards the Jatigede Reservoir ecotourism in Sumedang Regency through Twitter and YouTube social media platforms. The sentiment analysis method employed is the Naïve Bayes Classifier. Data was collected through crawling from both social media platforms, followed by a series of preprocessing steps, including data cleansing, text preprocessing, and translation, before ultimately being classified into three sentiment categories: positive, neutral, and negative. The analysis results indicate that the majority of sentiments associated with the Jatigede Reservoir are positive (approximately 70%), while neutral sentiments make up around 20%, and negative sentiments are only around 10%. The Naïve Bayes Classifier method yielded an accuracy rate of 69.29%, demonstrating its effectiveness in classifying public sentiment towards this ecotourism destination. Furthermore, this study integrates the findings of sentiment analysis with ecotourism development strategies. These strategies encompass enhancing service quality, public education efforts, and more effective promotion. The implementation of these strategies is aimed at creating sustainable ecotourism, preserving the environment, and empowering the local community to gain greater economic benefits from the Jatigede Reservoir ecotourism. Recommendations from this research are directed towards tourism managers, the Sumedang Regency government, and potential investors, with a focus on enhancing positive interactions with visitors, deepening understanding of ecotourism, and more effective destination promotion. This research provides valuable insights into how sentiment analysis can be a powerful tool in ecotourism development and sustainable decision-making in the tourism sector.
format Final Project
author Wididharma, Kelvin
author_facet Wididharma, Kelvin
author_sort Wididharma, Kelvin
title SENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA
title_short SENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA
title_full SENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA
title_fullStr SENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA
title_full_unstemmed SENTIMENT ANALYSIS OF JATIGEDE RESERVOIR ECOTOURISM, SUMEDANG REGENCY USING THE NAIVE BAYES CLASSIFIER METHOD ON TWITTER AND YOUTUBE MEDIA
title_sort sentiment analysis of jatigede reservoir ecotourism, sumedang regency using the naive bayes classifier method on twitter and youtube media
url https://digilib.itb.ac.id/gdl/view/78679
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