Penerapan Metode K-Nearest Neighbor pada Sentimen Analisis Pengguna Twitter terhadap KTT G20 di Indonesia
DOI:
https://doi.org/10.30865/jurikom.v10i1.5427Keywords:
K-Nearest Neighbor, KTT G20, Twitter, Sentiment Analysis, OrangeAbstract
Indonesia will host the KTT (Konferensi Tingkat Tinggi) G20 summit on the island of Bali on November 15, 2022. The G20 was formed with one goal in mind: to boost the global economy, which had just entered a period of crisis. However, Indonesia's participation as a full member of the Group of Twenty (G20) has sparked controversy among the country's general populace and the population of Indonesia itself, necessitating a thoughtful analysis of the group's motives. Sentiment analysis was gleaned from tweets on KTT G20 posted on the social media platform Twitter. Data scraping yielded a total of 2,500 tweets for inclusion in the collection. Methods for classifying tweets into positive, neutral, and negative groups are required because of the large amount of data that has already been collected. The purpose of this study was to analyze public opinion on Twitter during the KTT G20. The data was processed using the Orange neural network using a number of tools and the K-Nearest Neighbor method, yielding a total of 1,107 tweets that were successfully added to the original set, with an average recall and precision of 99%. According to the analysis of sentiment, there were 89 negative tweets, 614 neutral tweets, and 404 positive tweets, with the most common emotions being happiness, surprise, and fearReferences
W. R. D. Astuti and L. M. Fathun, “Diplomasi Ekonomi Indonesia di dalam Rezim Ekonomi G20 pada Masa Pemerintahan Joko Widodo,†Intermestic J. Int. Stud., vol. 5, no. 1, p. 47, 2020, doi: 10.24198/intermestic.v5n1.4.
H. R. Dhega et al., “BALI,†vol. 3, no. 2, pp. 100–115.
I. G. J. Pratama, W. Ardani, and I. A. S. Putri, “Pemanfaatan Presidensi G20 sebagai Sarana Marketing dan Branding Ekonomi Kreatif pada Era Pandemi Covid-19,†Lensa Ilm. J. Manaj. dan Sumberd., vol. 1, no. 1, pp. 28–33, 2022, doi: 10.54371/jms.v1i1.164.
P. N. P. Santi, W. Ardani, and I. A. S. Putri, “Presidensi G20 sebagai Sarana Marketing dan Branding Pariwisata Indonesia serta Pengaruhnya terhadap Peningkatan Kunjungan Wisatawan pada Era Pandemi Covid-19 (Studi Kasus di Hotel Melia Bali),†Lensa Ilm. J. Manaj. dan Sumberd., vol. 1, no. 1, pp. 15–20, 2022, doi: 10.54371/jms.v1i1.167.
J. Indonesia, “AKTUALISASI INDONESIA DALAM G20 : PELUANG ATAU TREN ? Sukma Sushanti Program Studi Hubungan Internasional Fakultas Ilmu Sosial dan Ilmu Politik , Universitas Udayana Email : sukmasushanti@gmail.com PENDAHULUAN Implikasi dari terbentuknya G20 semakin kuat ,†pp. 1–14, 2022.
M. Kartika, S. Saepudin, and D. Gustian, “Analisis Sentimen Dampak Covid-19 Terhadap Pembatalan Keberangkatan Ibadah Haji Pada Tahun 2020,†J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 964–972, 2021.
T. Krisdiyanto, “Analisis Sentimen Opini Masyarakat Indonesia Terhadap Kebijakan PPKM pada Media Sosial Twitter Menggunakan Naïve Bayes Clasifiers,†J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 7, no. 1, p. 32, 2021, doi: 10.24014/coreit.v7i1.12945.
R. Amelia, N. S. Prastiwi, and M. E. Purbaya, “Impementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Masyarakat Indonesia Mengenai Drama Korea Pada Twitter,†J. Ris. Komput., vol. 9, no. 2, pp. 338–343, 2022, doi: 10.30865/jurikom.v9i2.3895.
S. Indah Nurhafida and F. Sembiring, “Analisis Text Clustering Masyarakat Di Twiter Mengenai Mcdonald’Sxbts Menggunakan Orange Data Mining,†SISMATIK (Seminar Nas. Sist. Inf. dan Manaj. Inform., pp. 28–35, 2021.
S. S. Hilabi, H. Prabowo, H. L. H. S. Warnars, and T. N. Mursitama, “Recommendation System Model for Political Communication through Social Media using Content Based Filtering Method,†Int. J. Appl. Eng. Technol., vol. 4, no. 1, pp. 34–39, 2022.
M. F. El Firdaus, N. Nurfaizah, and ..., “Analisis Sentimen Tokopedia Pada Ulasan di Google Playstore Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor,†JURIKOM (Jurnal …, vol. 9, no. 5, pp. 1329–1336, 2022, doi: 10.30865/jurikom.v9i5.4774.
L. R. Dharmawan, I. Arwani, and D. E. Ratnawati, “Analisis Sentimen pada Sosial Media Twitter Terhadap Layanan Sistem Informasi Akademik Mahasiswa Universitas Brawijaya dengan Metode K- Nearest Neighbor,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 3, pp. 959–965, 2020, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/7099.
R. Rasenda, H. Lubis, and R. Ridwan, “Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter,†J. Media Inform. Budidarma, vol. 4, no. 2, p. 369, 2020, doi: 10.30865/mib.v4i2.2051.
F. Rizqi Irawan, “Analisis Sentimen Terhadap Pengguna Gojek Menggunakan Metode K-Nearset Neighbors,†JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 62–68, 2022, doi: 10.33387/jiko.v5i1.4267.
A. Asro’i and H. Februariyanti, “Analisis Sentimen Pengguna Twitter Terhadap Perpanjangan Ppkm Menggunakan Metode K-Nearest Neighbor,†J. Khatulistiwa Inform., vol. 10, no. 1, pp. 17–24, 2022, doi: 10.31294/jki.v10i1.12624.
R. Gunawan, R. Septiadi, F. Apri Wenando, H. Mukhtar, and Syahril, “K-Nearest Neighbor (KNN) untuk Menganalisis Sentimen terhadap Kebijakan Merdeka Belajar Kampus Merdeka pada Komentar Twitter,†J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 2, pp. 152–158, 2022, doi: 10.37859/coscitech.v3i2.3841.
S. Analisis Kegiatan Trading dengan SVM, K. R. Dan, N. Resti Wardani, S. Saepudin, and C. Warman, “Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest,†J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 2, pp. 863–870, 2022.
P. Arsi, I. Prayoga, and M. H. Asyari, “Klasifikasi Sentimen Publik Terhadap Jenis Vaksin Covid-19 yang Tersertifikasi WHO Berbasis NLP dan KNN,†vol. 7, pp. 260–266, 2023, doi: 10.30865/mib.v7i1.5418.
R. Saptari, Rianto, and A. I. Gufroni, “Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Unit Gawat Darurat Rumah Sakit Umum di Indonesia Menggunakan Seleksi Fitur Information Gain dan Support Vector Machine,†J. Oof Informatics Educ., vol. XX, pp. 104–110, 2018, [Online]. Available: http://e-journal.ivet.ac.id/index.php/jiptika/article/view/1925/1369.
J. A. Josen Limbong, I. Sembiring, K. Dwi Hartomo, U. Kristen Satya Wacana, and P. Korespondensi, “Analisis Klasifikasi Sentimen Ulasan Pada E-Commerce Shopee Berbasis Word Cloud Dengan Metode Naive Bayes Dan K-Nearest Neighbor Analysis of Review Sentiment Classification on E-Commerce Shopee Word Cloud Based With Naïve Bayes and K-Nearest Neighbor Meth,†J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 2, pp. 347–356, 2019, doi: 10.25126/jtiik.202294960.
M. R. Firdaus, F. M. Rizki, F. M. Gaus, and I. K. Susanto, “Analisis Sentimen Dan Topic Modelling Dalam Aplikasi Ruangguru,†J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 4, no. 1, p. 66, 2020, doi: 10.30645/j-sakti.v4i1.188.
