KLASIFIKASI SENTIMEN PERGELARAN MOTOGP DI INDONESIA MENGGUNAKAN ALGORITMA CORRELATED NAÏVE BAYES CLASIFIER
DOI:
https://doi.org/10.31949/infotech.v8i2.3103Abstract
Knowing the public's sentiment towards the international MotoGP event which has been held in Indonesia in 2022 is very necessary because the role of the community is very influential in the implementation and public interest in visiting an international event is still few and difficult because the information is still limited. Tweets, comments, reviews, and opinions of people using social media play an important role in determining whether a particular population is satisfied with products, performances, and services. The method used in this study is the Correlated Naïve Bayes Classifier (CNBC). The Correlated Naive Bayes Classifier (CNBC) method recalculates the correlation value for each attribute of the dataset to that class. There are several processes carried out in this study including data acquisition, data labeling, data preprocessing, feature extraction, classifying data using the Correlated Naive Bayes Classifier (CNBC) method, visualizing data, and finally evaluating the results. This study resulted in an accuracy of 82%.
Keywords:
Klasifikasi sentimen,Correlated Naïve Bayes,Mandalika,MotoGp.Downloads
References
Amrizal, Victor. 2018. “PENERAPAN METODE TERM FREQUENCY INVERSE DOCUMENT FREQUENCY (TF-IDF) DAN COSINE SIMILARITY PADA SISTEM TEMU KEMBALI INFORMASI UNTUK MENGETAHUI SYARAH HADITS BERBASIS WEB (STUDI KASUS: HADITS SHAHIH BUKHARI-MUSLIM).” JURNAL TEKNIK INFORMATIKA 11(2): 149–64.
Chandani, Vinita, and Romi Satria Wahono. 2015. “Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection Pada Analisis Sentimen Review Film.” Journal of Intelligent Systems 1(1). http://journal.ilmukomputer.org.
Dwianto, Enos, and Mujiono Sadikin. Analisis Sentimen Transportasi Online Pada Twitter Menggunakan Metode Klasifikasi Naïve Bayes Dan Support Vector Machine.
Hairani, Data Kesehatan, and Dan Muhammad Innuddin. Kombinasi Metode Correlated Naive Bayes Dan Metode Seleksi Fitur Wrapper Untuk Klasifikasi Data Kesehatan.
https://ekbis.sindonews.com/. 2021. “Sirkuit Mandalika Diyakini Bangkitkan Ekonomi RI.” https://ekbis.sindonews.com/read/460412/34/sirkuit-mandalika-diyakini-bangkitkan-ekonomi-ri-selengkapnya-pride-of-indonesia-sabtu-pukul-1800-wib-1624079209 (January 7, 2022).
Kurniawan, Agung. 2022. “Jadwal MotoGP 2022 - Debut Indonesia, Mandalika Jadi Yang Pertama Di Asia Tenggara.” https://www.bolasport.com/read/313075080/jadwal-motogp-2022-debut-indonesia-mandalika-jadi-yang-pertama-di-asia-tenggara (January 7, 2022).
Nabillah, Asyfah, Syariful Alam, and Mochzen Gito Resmi. 2022. “Twitter User Sentiment Analysis Of TIX ID Applications Using Support Vector Machine Algorithm.” 3(1): 14–27.
Previtali, Francesco, Andres F. Arrieta, and Paolo Ermanni. 2015. “Double-Walled Corrugated Structure for Bending-Stiff Anisotropic Morphing Skins.” Journal of Intelligent Material Systems and Structures 26(5): 599–613.
Rahmasari, Gina, and Rizkiki Andini. “ANALISIS RESPON MASYARAKAT PADA PLATFORM MEDIA SOSIAL TWITTER TERHADAP TOKOH POLITIK, JENDERAL TNI (PURN.) GATOT NURMANTYO.”
Satya Nugraha, Gibran, Mokhammad Nurkholis Abdillah, and Muhammad Innuddin. KOMPARASI AKURASI METODE CORRELATED NAIVE BAYES CLASSIFIER DAN NAIVE BAYES CLASSIFIER UNTUK DIAGNOSIS PENYAKIT DIABETES.
Setyo Nugroho, Kuncahyo & Istiadi, Istiadi & Marisa, Fitri. (2020). Optimasi naive Bayes classifier untuk klasifikasi teks pada e-government menggunakan particle swarm optimization. Jurnal Teknologi dan Sistem Komputer. 8. 21-26. 10.14710/jtsiskom.8.1.2020.21-26
Wongkar, Meylan, and Apriandy Angdresey. 2019. “Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter.” In Proceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019, Institute of Electrical and Electronics Engineers Inc.
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