Asia Pacific Journal of Information Systems (APJIS)

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Current Issue

Date	June 2024
Vol. No.	Vol. 34 No. 2
DOI	https://doi.org/10.14329/apjis.2024.34.2.400
Page	400~420
Title	Sentimental Analysis of Twitter Data Using Machine Learning and Deep Learning: Nickel Ore Export Restrictions to Europe Under Jokowi’s Administration 2022
Author	Sophiana Widiastutie, Dairatul Maarif, Adinda Aulia Hafizha
Keyword	Sentiment Analysis, Nickel Ore Export, Twitter, Machine Learning, Deep Learning
Abstract	Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia’s ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia’s bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.

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