Sentiment Analysis of Netizen's Comments on YouTube about IKN (Capital City) Development in Indonesia

  • Ilmatus Sa’diyah Department of Linguistics, Faculty of Social and Political Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur
  • Aviolla Terza Aviolla Terza Department of Data Science, Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur
  • Cagiva Chaedar Bey Lima Department of Data Science, Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur
  • Mohammad Rafka Mahendra Ariefean Department of Data Science, Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur
  • Ikbar Athallah Department of Data Science, Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur
Abstract views: 30 , PDF downloads: 20
Keywords: sentiment analysis, Indonesian IKN development, python analysis, YouTube comments

Abstract

The National Capital City (IKN) of the Archipelago is located in North Penajam Paser Regency and Kutai Kartanegara Regency, East Kalimantan Province. The transfer and development of IKN does not only have an impact on moving the country's capital from Jakarta to Kalimantan, but also on the future development of IKN which includes social, economic, cultural and environmental aspects. This impact created sentiment in society. For this reason, this research was conducted to identify public sentiment towards IKN development in Indonesia through comments on YouTube. Data in the form of sentiment from public comments on YouTube which comes from data sources in the form of videos selected based on the highest total comments regarding IKN. Data was collected and analyzed using the Python system with stages of Web scraping, pre-processing, sentiment analysis, and classification methods. Based on the data analysis that has been carried out, positive sentiment, neutral sentiment and positive sentiment are distinguished. Meanwhile, positive sentiment can be seen from the words good and prosperous, negative sentiment can be seen from the words debt, corruption and pessimism, while neutral sentiment is related to prices, investors and toll money. This overall sentiment leads to rejection and acceptance of IKN development by the government. The reason is that the impact caused after the construction was a trigger for the emergence of comments on YouTube videos which led to public sentiment. The results of this analysis can be used as hope for the government to anticipate IKN development that will have a negative impact.

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References

Afdhal, I., Kurniawan, R., Iskandar, I., Salambue, R., Budianita, E., & Syafria, F. (2022). Penerapan Algoritma Random Forest untuk Analisis Sentimen Komentar di YouTube Tentang Islamofobia. Jurnal Nasional Komputasi dan Teknologi Informasi, 5(1), 49–54.

Ardhi, D. C., & Sari, D. P. (2022). Sentiment Analysis of YouTube Comments: Potential Indonesian Presidential Election Candidates. International Journal of Computer Applications Technology and Research, 11(12), 451–456.

Aribowo, A. S., Basiron, H., Yusof, N. F. A., & Khomsah, S. (2021). Cross-Domain Sentiment Analysis Model on Indonesian Youtube Comment. International Journal of Advances in Intelligent Informatics, 7(1), 12–25.

Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 di Twitter. INTEGER: Journal of Information Technology, 2(1), 32–41.

Carolina, N. (2022). Healthy City: Pembangunan Kawasan Ibu Kota Negara (IKN) Nusantara Menuju Indonesia Sehat. Prosiding Konferensi Nasional Sosiologi (PKNS), 68–72. Konferensi Nasional Sosiologi IX APSSI.

Farikhah, Firdaus, M. M. Al, & Yuwono, A. (1995). Technological Pedagogical Content Knowledge (TPACK): Sebuah Kerangka Pengetahuan untuk Pembelajaran Keterampilan Menulis.

Ferdiana, R., Jatmiko, F., Purwanti, D. D., Ayu, A. S. T., & Dicka, W. F. (2019). Dataset Indonesia untuk Analisis Sentimen. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI), 8(4), 334.

Giovani, A. P., Ardiansyah, A., Haryanti, T., Kurniawati, L., & Gata, W. (2020). Analisis Sentimen Aplikasi Ruang Guru di Twitter Menggunakan Algoritma Klasifikasi. Jurnal Teknoinfo, 14(2), 115.

Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 4(2), 113.

Halimah Tussa’diah, & Kartika, N. Y. (2022). Critical Discourse Analysis on Linguistic Ideology of The Netizens Comments. ADI Journal on Recent Innovation (AJRI), 4(2), 110–121.

Hariati, & Saputri, A. S. (2022). Best Practice Kebijakan Pembangunan Ibu Kota Negara (Ikn) Di Kalimantan Timur, Indonesia. Journal of Government and Politics, 4(1), 16–28.

Hudha, M., Supriyati, E., & Listyorini, T. (2022). Analisis Sentimen Pengguna Youtube Terhadap Tayangan #Matanajwamenantiterawan dengan Metode Naïve Bayes Classifier. JIKO (Jurnal Informatika dan Komputer), 5(1), 1–6.

Kadewandana, D., & Cahyadiputra, A. (2023). Public Opinion Analysis on Social Media About the Establishment of Indonesia’s New Capital City. Islamic Communication Journal, 8(2), 229–250.

Khomsah, S. (2021). Sentiment Analysis on YouTube Comments Using Word2Vec and Random Forest Sentimen Analisis pada Opini YouTube Menggunakan Word2Vec dan Random Forest. Jurnal Informatika dan Teknologi Informasi, 18(1), 61–72.

Liu, S., & Young, S. D. (2019). Surveillance. J Forensic Leg Med, 33–36.

Mailoa, F. F., & Lazuardi, L. (2021). Analisis Sentimen Data Twitter Menggunakan Metode Text Mining tentang Masalah Obesitas di Indonesia. Journal of Information Systems for Public Health, 6(1), 44.

Mohammad, S. M. (2016). Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text. Emotion Measurement, 201–237.

Mulyani, S., & Novita, R. (2022). Implementation of the Naive Bayes Classifier Algorithm for Classification of Community Sentiment About Depression on Youtube. Jurnal Teknik Informatika (Jutif), 3(5), 1355–1361.

Nurrun Muchammad Shiddieqy, H., Paulus Insap, S., & Wing Wahyu, W. (2016). Studi Literatur tentang Perbandingan Metode untuk Proses Analisis Sentimen di Twitter. Seminar Nasional Teknologi Informasi dan Komunikasi, 7(2), 57–64.

Permana, K. A. B., Sudarma, M., & Ariastina, W. G. (2019). Analisis Rating Sentimen pada Video di Media Sosial Youtube Menggunakan STRUCT-SVM. Majalah Ilmiah Teknologi Elektro, 18(1), 113.

Rahman, A., Rahmat, F., Fariqi, M. Y., & Adi, S. (2020). Metode Naive Bayes untuk Menganalisis Akurasi Sentimen Komentar di Youtube. Jurnal EECCIS, 14(1), 31–34.

Saadillah, A., Haryudi, A., Reskiawan, M., & Amanah, A. I. (2023). Penggunaan Bahasa Sarkasme Netizen di Media Sosial. Jurnal Onoma: Pendidikan, Bahasa, dan Sastra, 9(2), 1437–1447.

Saputra, P. Y., Subhi, D. H., & Winatama, F. Z. A. (2019). Implementasi Sentimen Analisis Komentar Channel Video Pelayanan Pemerintah di Youtube Menggunakan Algoritma Naïve Bayes. Jurnal Informatika Polinema, 5(4), 209–213.

Sarli, Nurhadi, & Sari, E. S. (2023). Analisis Penggunaan Gaya Bahasa Sarkasme Netizen di Media Sosial Tiktok. KNOWLEDGE: Jurnal Inovasi Hasil Penelitian dan Pengembangan, 3(1), 84–92.

Whelan, L. (2024). Web crawling vs. web scraping - What’s the difference?

Published
2025-01-15
How to Cite
Sa’diyah, I., Aviolla Terza, A. T., Lima, C. C. B., Ariefean, M. R. M., & Athallah, I. (2025). Sentiment Analysis of Netizen’s Comments on YouTube about IKN (Capital City) Development in Indonesia. GHANCARAN: Jurnal Pendidikan Bahasa Dan Sastra Indonesia, 6(2), 347—361. https://doi.org/10.19105/ghancaran.v6i2.15432
Section
Articles