How to cite (IEEE): K. Abu Samah, N. Amirah Misdan, M. Hasrol Jono, & L. Riza "The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification," JOIV : International Journal on Informatics Visualization, vol. 6, no. 1, , pp. 130-137, Mar. 2022. https://doi.org/10.30630/joiv.6.1.879

How to cite (APA): Abu Samah, K., Amirah Misdan, N., Hasrol Jono, M., & Riza, L. (2022). The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification. JOIV : International Journal on Informatics Visualization, 6(1), 130-137. https://doi.org/10.30630/joiv.6.1.879

How to cite (Chicago): Abu Samah, Khyrina Airin Fariza, Amirah Misdan, Nur Farhanah, Hasrol Jono, Mohd Nor Hajar, AND Riza, Lala Septem. "The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification" JOIV : International Journal on Informatics Visualization [Online], Volume 6 Number 1 (31 March 2022) https://doi.org/10.30630/joiv.6.1.879

How to cite (Vancouver): Abu Samah K, Amirah Misdan N, Hasrol Jono M, & Riza L . The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification. JOIV : International Journal on Informatics Visualization [Online]. 2022 Mar; 6(1):130-137. https://doi.org/10.30630/joiv.6.1.879

How to cite (Harvard): Abu Samah, K., Amirah Misdan, N., Hasrol Jono, M., & Riza, L. ,2022. The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification. JOIV : International Journal on Informatics Visualization, [Online] 6(1), pp. 130-137. https://doi.org/10.30630/joiv.6.1.879

How to cite (MLA8): Abu Samah, Khyrina Airin Fariza, Nur Farhanah Amirah Misdan, Mohd Nor Hajar Hasrol Jono, & Lala Septem Riza. "The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification." JOIV : International Journal on Informatics Visualization [Online], 6.1 (2022): 130-137. Web. 2 Oct. 2023 , https://doi.org/10.30630/joiv.6.1.879

BibTex Citation Data :

@article{JOIV879,
    	author = {Khyrina Airin Fariza Abu Samah and Nur Farhanah Amirah Misdan and Mohd Nor Hajar Hasrol Jono and Lala Septem Riza},
    	title = {The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {6},
    	number = {1},
    	year = {2022},
    	keywords = {Bilingual model; classification; Naïve Bayes; Twitter sentiment analysis; web-based dashboard.},
    	abstract = {Online reviews are crucial for business growth and customer satisfaction. There is no exception for the airlines’ company, which places third as the biggest contributor to Malaysia’s Gross Domestic Product. Customer opinions play an important role in maintaining the reputation and improving the quality of service of the airlines. However, there is no specific platform for online review. Most online ratings obtain English, leading to inaccurate results as not all reviews regarding different languages are considered. Airlines currently have no specific platform for online reviews despite being critical for business growth, performance, and customer experience improvement. Hence, this paper proposed implementing a web-based dashboard to visualize the best Malaysian airline companies. The airline companies involved are AirAsia, Malaysia Airlines, and Malindo Air. We designed and developed the proposed study through the bilingual analysis of Twitter sentiment using the Naïve Bayes algorithm. Naïve Bayes algorithm is a machine learning approach to do classification. The tweets extracted were analyzed as metrics that advance airline companies’ online presence. Testing phases have shown that the classifier successfully classified tweets’ sentiment with 93% accuracy for English and 91% for Bahasa. Every feature in the web-based dashboard functions correctly and visualizes a detailed analysis of sentiment. We applied the System Usability Scale to test the study’s usability and managed to get a score of 94.7%. The acceptability score ‘acceptable’ result concluded that the study reflects a good solution and can assist anyone in understanding the public views on airline companies in Malaysia.},
        	issn = {2549-9904},    	pages = {130--137},    	doi = {10.30630/joiv.6.1.879},
        	url = {https://joiv.org/index.php/joiv/article/view/879}
    }
    

Refworks Citation Data :

@article{{JOIV}{879}, author = {Abu Samah, K., Amirah Misdan, N., Hasrol Jono, M., Riza, L.}, title = {The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {6}, number = {1}, year = {2022}, doi = {10.30630/joiv.6.1.879}, url = {} }