Sentiment Analysis Towards the Film Dirty Vote on Twitter Social Media Using the K-Nearest Neighbor Algorithm

Annisa Fadillah; Sriani Sriani

doi:10.24014/ijaidm.v7i2.32471

Sentiment Analysis Towards the Film Dirty Vote on Twitter Social Media Using the K-Nearest Neighbor Algorithm

Annisa Fadillah, Sriani Sriani

Abstract

The appearance of the dirty vote film has received public attention and went viral on social media after being released and watched by millions of people in a short time. The dirty vote film has become a topic of discussion, one of which is on the social media platform Twitter. This research was conducted to determine the views or tendencies of public opinion regarding dirty vote films on Twitter social media using K-Nearest Neighbor which will be classified into positive, neutral and negative sentiment. The sentiment data that was collected in the data crawling process was 4000 pieces of data. Then after preprocessing there were 3978 data. Labeling was carried out using text blob, it was found that the negative sentiment class was 3470 superior to the positive sentiment class of 451 and the neutral sentiment class was 57. The 10-fold cross validation test produced an average accuracy value of 87.5%. Testing was carried out with 80% training data consisting of 3182 data and 20% test data consisting of 796 test data. The results of sentiment analysis show that the K-Nearest Neighbor method can be used for sentiment analysis. The accuracy value obtained was 87%, precision was 87%, recall was 100%, and f1-score was 93%.

Keywords

Dirty Vote; K-Fold Cross Validation; K-Nearest Neighbor; Sentiment Analysis; Social Media

References

B. Huda and B. Priyatna, “Penggunaan Aplikasi Content Management System (CMS) Untuk Pengembangan Bisnis Berbasis E-commerce,” Systematics, vol. 1, no. 2, p. 81, 2019.

D. Sandi, E. Utami, and K. Kusnawi, “Analisis Sentimen Publik Terhadap Elektabilitas Ganjar Pranowo di Tahun Politik 2024 di Twitter dengan Algoritma KNN dan Naïve Bayes,” J. Media Inform. Budidarma, vol. 7, no. 3, pp. 1097–1108, 2023.

P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021.

E. Maria and E. Junirianto, “Sistem Pendukung Keputusan Pemilihan Laptop Menggunakan Metode Topsis,” CAHAYAtech, vol. 7, no. 2, p. 119, 2021, doi: 10.47047/ct.v7i2.99.

M. J. Palepa, N. Pratiwi, and ..., “Analisis Sentimen Masyarakat Tentang Pengaruh Politik Identitas Pada Pemilu 2024 Terhadap Toleransi Beragama Menggunakan Metode K-Nearest Neighbor,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 1, pp. 389–401, 2024.

R. Wijaya and A. Suwandhi, “Sentimen Komentar Universitas Pelita Harapan Pada TikTok Menggunakan Metode K-Nearest Neighbor,” JDMIS J. Data Min. Inf. Syst., vol. 2, no. 1, pp. 26–36, 2024.

Z. Alhaq, A. Mustopa, S. Mulyatun, and J. D. Santoso, “Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter,” J. Inf. Syst. Manag., vol. 3, no. 2, pp. 44–49, 2021.

Betesda, “Universitas Dirgantara Marsekal Suryadarma,” JSI (Jurnal Sist. Informasi), vol. 7, no. 2, pp. 101–114, 2020.

N. S. Marga, “Sentimen Analisis Tentang Kebijakan Pemerintah Terhadap Kasus Corona Menggunakan Metode Naive Bayes,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 2, no. 4, pp. 453–463, 2022.

L. A. Andika, P. A. N. Azizah, and R. Respatiwulan, “Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier,” Indones. J. Appl. Stat., vol. 2, no. 1, p. 34, 2019.

Herwinsyah and A. Witanti, “Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (Svm),” J. Sist. Inf. dan Inform., vol. 5, no. 1, pp. 59–67, 2022.

A. Z. Amrullah, A. Sofyan Anas, and M. A. J. Hidayat, “Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square,” J. Bumigora Inf. Technol., vol. 2, no. 1, pp. 40–44, 2020.

W. Widayat, “Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 1018, 2021.

S. F. Pane and J. Ramdan, “Pemodelan Machine Learning : Analisis Sentimen Masyarakat Terhadap Kebijakan PPKM Menggunakan Data Twitter,” J. Sist. Cerdas, vol. 5, no. 1, pp. 12–20, 2022.

A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, p. 115, 2020.

S. G. Setyorini and Mustakim, “Application of the nearest neighbor algorithm for classification of online taxibike sentiments in indonesia in the google playstore application,” J. Phys. Conf. Ser., vol. 2049, no. 1, pp. 1–7, 2021.

J. Supriyanto, D. Alita, and A. R. Isnain, “Penerapan Algoritma K-Nearest Neighbor ( K-NN ) Untuk Analisis Sentimen Publik Terhadap Pembelajaran Daring,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 4, no. 1, pp. 74–80, 2023.

I. Saputra and D. A. Kristiyanti, Machine Learning Untuk Pemula. Bandung: Informatika Bandung, 2022.

A. Firdaus, “Aplikasi Algoritma K-Nearest Neighbor pada Analisis Sentimen Omicron Covid-19,” J. Ris. Stat., pp. 85–92, 2022.

A. Masruriyah, H. Novita, C. Sukmawati, A. Ramadhan, S. Arif, and B. Dermawan, “Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung,” Comput. Sci., vol. 4, no. 1, pp. 62–70, 2024.

D. Nasien, R. Syahputra, A. A. Marunduri, and R. P. See, “Klasifikasi Penyakit Jantung Menggunakan Decision Tree dan KNN Menggunakan Ektraksi Fitur PCA,” J. Tek. Inform., vol. 4, no. 1, pp. 1–6, 2024.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 697–711, 2021.

DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.32471

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