ANALISIS SENTIMENT PUBLIC TERHADAP KEPUTUSAN MK MENGENAI BATAS USIA KANDIDAT CAPRES DAN CAWAPRES BERDASARKAN MEDIA SOSIAL TWITTER MENGGUNAKAN METODE VADER (VALENCE AWARE DICTIONARY AND SENTIMENT REASONER)

Aulia Alqusyah Fitri(1), Andreas Perdana(2),


(1) Prodi Sistem Informasi, STMIK Dharma Wacana Metro, Lampung
(2) Prodi Tekhnik Infomatika, STMIK Dharma Wacana Metro, Lampung
Corresponding Author

Abstract


There is a lot of discussion on Twitter about the decision of the Constitutional Court (MK) regarding the age limit for presidential and vice presidential candidates. The abundance of tweets on Twitter has made it difficult to determine the intended meaning of each tweet, whether it is negative, positive, or neutral. Therefore, sentiment analysis is needed to easily interpret the meaning of each tweet, whether it is negative, positive, or neutral. In this research, sentiment analysis techniques will be used by labeling tweets using VADER (Valence Aware Dictionary and Sentiment Reasoner) to find out how the public responded to the Constitutional Court's decision regarding the age limit for presidential and vice presidential candidates.  A total of 2621 tweet data were obtained through data crawling using web scraping with the Google Colab application and utilizing the Python programming language, then analyzed and visualized using VADER (Valence Aware Dictionary and Sentiment Reasoner). The research results indicate that the majority of the public response, accounting for 90.35%, is neutral sentiment, 6.02% negative sentiment, and 3.63% positive sentiment. The predominance of neutral sentiment may occur because many tweets only convey facts without including opinions or evaluations, or texts that are descriptive in nature without expressing emotions or evaluations, thereby tending to be considered neutral by the VADER algorithm.


Keywords


Sentiment Analysis, Age Limit, VADER

References


M. T. Anwar, D. Riandhita, and A. Permana, “Analisis Sentimen Masyarakat Indonesia Terhadap Produk Kendaraan Listrik Menggunakan VADER,” J. Tek. Inform. dan Sist. Inf., vol. 10, no. 1, pp. 783–792, 2023.

M. I. Maulana, E. Budianita, M. Fikry, and F. Yanto, “Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier,” J. Sist. Komput. dan Inform., vol. 4, no. 3, pp. 485–492, 2023.

Yahya, Data Mining. CV Jejak, anggota IKAPI, 2020.

E. Camizuli and E. J. Carranza, “Exploratory Data Analysis (EDA),” in The Encyclopedia of Archaeological Sciences, American Cancer Society, 2018, pp. 1–7.

E. T. L. Kusrini, Algoritma Data Mining. Yogyakarta: Penerbit Andi Yogyakarta, 2009.

M. H. Aghdaie, S. H. Zolfani, and E. K. Zavadskas, “Synergies of Data Mining and Multiple Attribute Decision Making,” in Procedia - Social and Behavioral Sciences, 2014, vol. 110, pp. 767–776.

T. Mustaqim, “Sentiment Analysis Opini Pelantikan Kabinet Pemerintah Indonesia Tahun 2019 Menggunakan Vader Dan Random Forest,” Universitas Negeri Semaran, 2020.

I. S. K. Idris, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine ( SVM ),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023.

E. A. Marwa and A. B. Kristanto, “Analisis Sentimen Pengungkapan Informasi Manajemen : Text Mining Berbasis Metode VADER,” Own. Ris. J. Akunt., vol. 6, no. 3, pp. 2973–2984, 2022.

dan S. I. Rulin Swastika, Siti Mukodimah, Ferry Susanto, Muhamad Muslihudin, Implementasi Data Mining (Clastering, Association, Prediction, Estimation, Classification), 1st ed. Indramayu: CV. Adanu Abimata, 2023.

F. S. Fauzi, Rita Irviani, Andino Maseleno, “Revolutionizing Education through Technology : Big Data and Online Learning,” in CICCSE, 2017, vol. 1, no. 1, p. 44.

I. Junaedi, N. Nuswantari, and V. Yasin, “Perancangan Dan Implementasi Algoritma C4 . 5 Untuk Data Mining,” J. Inf. Syst. Informatics Comput., vol. 3, no. 1, pp. 29–44, 2019.

S. Khademolqorani and A. Z. Hamadani, “An Adjusted Decision Support System through Data Mining and Multiple Criteria Decision Making,” in Procedia - Social and Behavioral Sciences, 2013, vol. 73, pp. 388–395.


Full Text: PDF

Article Metrics

Abstract View : 403 times
PDF Download : 187 times

DOI: 10.56327/jurnaltam.v15i1.1667

Refbacks

  • There are currently no refbacks.