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All Journal Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Hartono Hartono
Magister of Computer Science, Potensi Utama University
Computer Science & IT

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Sentiment Analysis of IDAHOBIT Celebrations using Naïve Bayes and Decision Tree Algorithms Jaka Kusuma; Hartono Hartono; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.33

Abstract

The development of LGBTIQ in Indonesia reflects the shift in culture and the emergence of this phenomenon has attracted the attention of the Indonesian people. The use of NLP, ML, and statistics technology in tweet analysis can be used to identify sentiments contained in tweets. This study compares Naïve Bayes algorithm and Decision Tree in sentiment analysis classification, in which the multilingual sentiment analysis method is used in the labeling process of training data. Naïve Bayes results give the best classification with 100% accuracy, precision, and recall, and the number of positive sentiments is 385, negative sentiments are 3117, and neutral sentiments are 899. It looks that the negative class is the most superior compared to other classes. This proves that the Indonesian people have an unfavorable response to the IDAHOBIT celebration.
Sentiment Classification on Mandalika MotoGP Event Using K-Means Clustering and Random Forest Khairul Fadhli Margolang; Muhammad Zarlis; Hartono Hartono
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.35

Abstract

As one the most famous world-class motorcycle racing competition, MotoGP is an event broadcast live on television with millions of viewers on each race. Indonesia, especially the Pertamina Mandalika Circuit, will hold this prestigious racing event in the 19th series of 2022. This event sparks Indonesian netizens' reactions on social media, especially on Twitter. This research aims to analyze the public sentiment and emotional value regarding this event, with the data collected from Twitter social media. With the features of sentiment and emotion values extracted from the contents of this tweet, we use K-means clustering to generate sentiment clusters as targets for the classification using the Random Forest (RF) algorithm. From the evaluation using the 5-fold and 10-fold cross-validation, we get the highest accuracy of 0.99, the highest precision of 0.990175, and the highest recall of 0.99 from the RF model with ten trees configuration. We also get the lowest accuracy, precision, and recall values of 0.96, 0.960934, and 0.96 from the RF models with 15 and 20 trees configuration, with the 10-fold evaluation
Sentiment Classification on Twitter Social Media Using K-Means Clustering, C4.5 and Naive Bayes (Case Study: Blocking Paypal by Kominfo) Muhammad Zulkarnain Lubis; Hartono Hartono; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.37

Abstract

Kominfo (Ministry of Communication and Information) requires all PSEs (Electronic System Providers) to register themselves so that their access is not blocked, as shown in the case of Paypal and several other PSEs. The blocking case reaps mixed opinions from netizens, especially Twitter social media users. We use the sentiment values obtained from the content of tweets collected through the crawling process and employ the K-Means Clustering to group them into clusters. Finally, we use these clusters as the target in a dataset and classify them using the C4.5 and Naive Bayes algorithms. Of the 1000 netizen tweets studied, we found that 6.5% of netizens supported the blocking action, 75.4% did not care or felt that the blocking action had no effect on them, and 15.4% did not support the blocking by Kominfo. The classification results in this study resulted in a 98.2% accuracy value, a 95% precision value, and a 95.5% recall value.
Co-Authors B. Herawan Hayadi Jaka Kusuma Khairul Fadhli Margolang Muhammad Zarlis Muhammad Zulkarnain Lubis