Sentiment Analysis on Public Perception of the Nusantara Capital on Social Media X Using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) Methods
DOI:
https://doi.org/10.30871/jaic.v9i3.9318Keywords:
Sentiment Analysis, Confusion Matrix, IKN, K-Nearest Neighbor, Support Vector MachineAbstract
The relocation and development of the National Capital City (IKN) as the center of government activities has become a hot topic, sparking diverse opinions among the public. The proposal to move the capital from DKI Jakarta to East Kalimantan has drawn significant attention from online communities, particularly on social media platform X (Twitter). This study aims to explore public sentiment regarding the development of IKN by applying artificial intelligence-based classification algorithms, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). Sentiments are categorized as positive or negative to provide deeper insights into public perceptions. Through web crawling techniques, a total of 4,000 data points were collected. After the preprocessing stage, 3,608 data points remained, which were then translated into English to facilitate labeling using the Vader Sentiment method. The analysis results indicate that negative sentiment (1,873) is more dominant than positive sentiment (1,735). The data was then split into two sets: 80% for training (2,886 data points) and 20% for testing (722 data points). Based on the evaluation results, SVM and K-NN proved to be effective for sentiment analysis. SVM achieved an accuracy of 76%, precision of 78%, recall of 81%, and an f1-score of 79%, while K-NN attained an accuracy of 65%, precision of 62%, recall of 98%, and an f1-score of 76%. With superior performance, SVM emerges as a more reliable method for classifying public sentiment regarding the IKN development policy.
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[1] Munawaroh, A., Ridhoi, R., & Rudiman, R. (2024). Sentiment Analysis Dengan Naïve Bayes Berbasis Orange Terhadap Resiko Pembangunan IKN. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 587-592.
[2] Adi, S. I. R., Bakkara, B., Zega, K. A., Vielita, F. N., & Rakhmawati, N. A. (2024). Analisis Sentimen Masyarakat Terhadap Progres IKN Menggunakan Model Decision Tree. JIKA (Jurnal Informatika), 8(1), 57-65.
[3] Puspita, R., & Suciati, T. N. (2020). Mobile phone dan media sosial: Penggunaan dan tantangannya pada jurnalisme online Indonesia. Ekspresi Dan Persepsi: Jurnal Ilmu Komunikasi, 3(2), 132-146.M. Wegmuller, J. P. von der Weid, P. Oberson, & N. Gisin, “High resolution fiber distributed measurements with coherent OFDR,” Prosiding ECOC’00, 2000, paper 11.3.4, p. 109.
[4] Syah, H., & Witanti, A. (2022). Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (Svm). Jurnal Sistem Informasi Dan Informatika (Simika), 5(1), 59-67.
[5] Lestandy, M., Abdurrahim, A., & Syafa’ah, L. (2021). Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 802-808.
[6] Ardiansyah, D., Saepudin, A., Aryanti, R., & Fitriani, E. (2023). Analisis Sentimen Review Pada Aplikasi Media Sosial Tiktok Menggunakan Algoritma K-Nn Dan Svm Berbasis Pso. Jurnal Informatika Kaputama (JIK), 7(2), 233-241.
[7] Legito, L., Riau, N. P., Putro, A. N. S., Mardiani, E., Arifin, N. Y., Sepriano, S., & Erkamim, M. (2023). Penerapan Algoritma K-Nearest Neighbor untuk Analisis Sentimen Terhadap Isu Khilafah dan Radikalisme di Indonesia: Implementation K-Nearest Neighbor Algorithm for Sentiment Analysis on Khilafah and Radicalism Issues in Indonesia. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 324-330.
[8] Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan metode naïve bayes dan support vector machine pada analisis sentimen twitter. SMATIKA Jurnal: STIKI Informatika Jurnal, 10(02), 71-76.
[9] Khairunnisa, S., Adiwijaya, A., & Al Faraby, S. (2021). Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19). J. Media Inform. Budidarma, 5(2), 406.
[10] Irham, L. G., Adiwijaya, A., & Wisesty, U. N. (2019). Klasifikasi Berita Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine. J. Media Inform. Budidarma, 3(4), 284.
[11] Anwar, M. T. (2023). Analisis Sentimen Masyarakat Indonesia Terhadap Produk Kendaraan Listrik Menggunakan VADER. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1), 783-792.
[12] Septiani, D., & Isabela, I. (2022). Analisis term frequency inverse document frequency (tf-idf) dalam temu kembali informasi pada dokumen teks. SINTESIA J. Sist. dan Teknol. Inf. Indones, 1(2), 81-88.
[13] Ramlan, R., Satyahadewi, N., & Andani, W. (2023). Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM. Jambura Journal of Mathematics, 5(2), 431-445.
[14] Fitriyah, N., Warsito, B., & Di Asih, I. M. (2020). Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM). Jurnal Gaussian, 9(3), 376-390.
[15] Mara, A. T., Sediyono, E., & Purnomo, H. (2021). Penerapan Algoritma K-Nearest Neighbors Pada Analisis Sentimen Metode Pembelajaran Dalam Jaringan (Daring) Di Universitas Kristen Wira Wacana Sumba. JOINTER: Journal of Informatics Engineering, 2(01), 24-31.
[16] Husada, H. C., & Paramita, A. S. (2021). Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM). Teknika, 10(1), 18-26.
[17] Nasution, M. R. A., & Hayaty, M. (2019). Perbandingan akurasi dan waktu proses algoritma K-NN dan SVM dalam analisis sentimen twitter. Jurnal Informatika, 6(2), 226-235.
[18] Bria, N. A. R., & Witanti, A. (2023). Analisis Sentimen Masyarakat Indonesia Menggunakan Algoritma Support Vector Machine Tentang Pilpres 2024. JATI (Jurnal Mahasiswa Teknik Informatika), 7(6), 3328-3333.
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