Analisis Sentimen Tanggapan Masyarakat Tentang Garuda IKN Menggunakan Metode Naive Bayes

Nyoman Alvia Wirayani; Ni Made Ary Esta Dewi  Wirastuti; Ida Bagus Gede  Manuaba

doi:10.51454/decode.v5i1.860

Authors

Nyoman Alvia Wirayani Teknik Elektro Universitas Udayana
Ni Made Ary Esta Dewi Wirastuti Teknik Elektro Universitas Udayana
Ida Bagus Gede Manuaba Teknik Elektro Universitas Udayana

DOI:

https://doi.org/10.51454/decode.v5i1.860

Keywords:

Analisis Sentimen, Garuda IKN, Naive Bayes

Abstract

Ibu Kota Nusantara (IKN) adalah proyek besar yang bertujuan memindahkan ibu kota negara dari Jakarta ke Kalimantan Timur untuk mengurangi beban Jakarta dan mendorong pertumbuhan ekonomi di Indonesia. Salah satu ikon utama dari IKN adalah Patung Garuda, simbol kekuatan dan kebanggaan nasional. Namun, desain Patung Garuda ini mendapat berbagai kritik dari masyarakat, mulai dari aspek estetika hingga penggunaan anggaran yang dianggap berlebihan. Kritik ini banyak disampaikan melalui media sosial, terutama Twitter, yang menjadi platform utama untuk mengungkapkan pandangan secara terbuka. Penelitian ini bertujuan menganalisis sentimen publik terhadap desain Patung Garuda IKN melalui data dari Twitter dengan menggunakan metode Naive Bayes. Hasilnya menunjukkan bahwa metode Naive Bayes memberikan akurasi sebesar 0,82 dalam klasifikasi sentimen. Penelitian ini juga membuktikan bahwa Naive Bayes lebih akurat dibandingkan pustaka TextBlob. Melalui analisis ini, diharapkan dapat diperoleh pemahaman lebih mendalam mengenai pandangan publik dan isu-isu utama yang menjadi fokus kritik terhadap proyek ini. Hasil penelitian diharapkan dapat menjadi masukan berharga untuk perencanaan dan pengambilan keputusan proyek serupa di masa depan.

References

Abilov, A., Hua, Y., Matatov, H., Amir, O., & Naaman, M. (2021). VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 15, 901–912. https://doi.org/10.1609/icwsm.v15i1.18113

Adwan, O. Y., Al-Tawil, M., Huneiti, A. M., Shahin, R. A., Abu Zayed, A. A., & Al-Dibsi, R. H. (2020). Twitter sentiment analysis approaches: A survey. International Journal of Emerging Technologies in Learning, 15(15), 79–93. https://doi.org/10.3991/ijet.v15i15.14467

Ahuja, R., Chug, A., Kohli, S., Gupta, S., & Ahuja, P. (2019). The impact of features extraction on the sentiment analysis. Procedia Computer Science, 152, 341–348. https://doi.org/10.1016/j.procs.2019.05.008

Alfandi Safira, & Hasan, F. N. (2023). Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier. ZONAsi: Jurnal Sistem Informasi, 5(1), 59–70. https://doi.org/10.31849/zn.v5i1.12856

Almjawel, A. (2019). Books ’ Reviews. 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), 1–6.

Alsaeedi, A., & Khan, M. Z. (2019). A study on sentiment analysis techniques of Twitter data. International Journal of Advanced Computer Science and Applications, 10(2), 361–374. https://doi.org/10.14569/ijacsa.2019.0100248

Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2021). A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks. Expert Systems with Applications, 164(February 2020), 114006. https://doi.org/10.1016/j.eswa.2020.114006

Astari, N. M. A. J., Dewa Gede Hendra Divayana, & Gede Indrawan. (2020). Analisis Sentimen Dokumen Twitter Mengenai Dampak Virus Corona Menggunakan Metode Naive Bayes Classifier. Jurnal Sistem Dan Informatika (JSI), 15(1), 27–29. https://doi.org/10.30864/jsi.v15i1.332

Balaraman, S. (2020). Comparison of Classification Models for Breast Cancer Identification using Google Colab. Preprints., May, 1–11. https://doi.org/10.20944/preprints202005.0328.v1

Blank, J., & Deb, K. (2020). Pymoo: Multi-Objective Optimization in Python. IEEE Access, 8, 89497–89509. https://doi.org/10.1109/ACCESS.2020.2990567

Bouazizi, M., & Ohtsuki, T. (2019). Multi-class sentiment analysis on twitter: Classification performance and challenges. Big Data Mining and Analytics, 2(3), 181–194. https://doi.org/10.26599/BDMA.2019.9020002

Chen, E., Deb, A., & Ferrara, E. (2022). #Election2020: the first public Twitter dataset on the 2020 US Presidential election. Journal of Computational Social Science, 5(1), 1–18. https://doi.org/10.1007/s42001-021-00117-9

Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192(xxxx), 105361. https://doi.org/10.1016/j.knosys.2019.105361

Chromiński, K., Benko, Ľ., Hernández-Figueroa, Z. J., González-Domínguez, J. D., & Rodríguez-del-Pino, J. C. (2021). Python Fundamentals. Python Fundamentals, c. https://doi.org/10.17846/fpvai-2021-14

Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional. Jurnal Tekno Kompak, 15(1), 131. https://doi.org/10.33365/jtk.v15i1.744

Elbagir, S., & Yang, J. (2020). Sentiment Analysis on Twitter with Python’s Natural Language Toolkit and VADER Sentiment Analyzer. 0958, 63–80. https://doi.org/10.1142/9789811215094_0005

Fais Sya’ bani, M. R., Enri, U., & Padilah, T. N. (2022). Analisis Sentimen Terhadap Bakal Calon Presiden 2024 Dengan Algoritme Naïve Bayes. JURIKOM (Jurnal Riset Komputer), 9(2), 265. https://doi.org/10.30865/jurikom.v9i2.3989

Firdaus, A. A., Yudhana, A., & Riadi, I. (2023). Analisis Sentimen Pada Proyeksi Pemilihan Presiden 2024 Menggunakan Metode Support Vector Machine. Decode: Jurnal Pendidikan Teknologi Informasi, 3(2), 236-245. https://doi.org/10.51454/decode.v3i2.172

Garcia, K., & Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057. https://doi.org/10.1016/j.asoc.2020.107057

Guo, Y., Das, S., Lakamana, S., & Sarker, A. (2023). An aspect-level sentiment analysis dataset for therapies on Twitter. Data in Brief, 50, 109618. https://doi.org/10.1016/j.dib.2023.109618

Jiuhardi, J., Rosyadi, R., Wijayanti, T. C., & Fitriadi, F. (2023). What Is the Perspective of Millennial Tourists on the Revitalization of Tourism Development in New Ikn? Planning Malaysia, 21(1), 232–246. https://doi.org/10.21837/PM.V21I25.1236

Kaur, C., & Sharma, A. (2020). Social issues sentiment analysis using python. Proceedings of the 2020 International Conference on Computing, Communication and Security, ICCCS 2020, 1–6. https://doi.org/10.1109/ICCCS49678.2020.9277251

Kumar, S. (2024). Sentiment Analysis. Python for Accounting and Finance, 265–300. https://doi.org/10.1007/978-3-031-54680-8_17

Ladani, D. J., & Desai, N. P. (2020). Stopword Identification and Removal Techniques on TC and IR applications: A Survey. 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, 466–472. https://doi.org/10.1109/ICACCS48705.2020.9074166

Lopez, C. E., & Gallemore, C. (2021). An augmented multilingual Twitter dataset for studying the COVID-19 infodemic. Social Network Analysis and Mining, 11(1), 1–14. https://doi.org/10.1007/s13278-021-00825-0

Mandloi, L., & Patel, R. (2020). Twitter sentiments analysis using machine learninig methods. 2020 International Conference for Emerging Technology, INCET 2020, 1–5. https://doi.org/10.1109/INCET49848.2020.9154183

Mas Diyasa, I. G. S., Marini Mandenni, N. M. I., Fachrurrozi, M. I., Pradika, S. I., Nur Manab, K. R., & Sasmita, N. R. (2021). Twitter Sentiment Analysis as an Evaluation and Service Base On Python Textblob. IOP Conference Series: Materials Science and Engineering, 1125(1), 012034. https://doi.org/10.1088/1757-899x/1125/1/012034

Mattia C. (1862). Pr ep rin t n ot pe er re v iew Pr ep t n ot pe er v. The Lancent Pschch, 1–35.

Najiyah, I. (2023). Analisis Sentimen Tanggapan Masyarakat Indonesia Tentang Kenaikan Bbm Menggunakan Metode Artificial Neural Network. Jurnal Responsif : Riset Sains Dan Informatika, 5(1), 92–100. https://doi.org/10.51977/jti.v5i1.1061

Nezhad, Z. B., & Deihimi, M. A. (2022). Diabetes & Metabolic Syndrome : Clinical Research & Reviews Twitter sentiment analysis from Iran about COVID 19 vaccine. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 16(1), 102367. https://doi.org/10.1016/j.dsx.2021.102367

Palomino, M. A. (2022). Evaluating-the-Effectiveness-of-Text-PreProcessing-in-Sentiment-AnalysisApplied-Sciences-Switzerland.pdf.

Pandey, S. V., & Deorankar, A. V. (2019). A Study of Sentiment Analysis Task and It’s Challenges. Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019, 1–5. https://doi.org/10.1109/ICECCT.2019.8869160

Pradha, S., Halgamuge, M. N., & Tran Quoc Vinh, N. (2019). Effective text data preprocessing technique for sentiment analysis in social media data. Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019, 1–8. https://doi.org/10.1109/KSE.2019.8919368

Puspitasari, D. A. (2023). Tracing Trends and Patterns of IKN Words in Media and Twitter: A Linguistic Corpus Study (Menelusuri Tren dan Pola Kata IKN di Media dan Twitter: Kajian Korpus Linguisti). Gramatika STKIP PGRI Sumatera Barat, 9(2), 239–257. https://doi.org/10.22202/jg.2023.v9i2.6947

Saini, S., Punhani, R., Bathla, R., & Shukla, V. K. (2019). Sentiment Analysis on Twitter Data using R. 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019, 68–72. https://doi.org/10.1109/ICACTM.2019.8776685

Sari, F. V., & Wibowo, A. (2019). Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi. Jurnal SIMETRIS, 10(2), 681–686.

Shehu, H. A., Sharif, M. H., Sharif, M. H. U., Datta, R., Tokat, S., Uyaver, S., Kusetogullari, H., & Ramadan, R. A. (2021). Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data. IEEE Access, 9, 56836–56854. https://doi.org/10.1109/ACCESS.2021.3071393

Stephen, J. J., & Prabu, P. (2019). Detecting the magnitude of depression in Twitter users using sentiment analysis. International Journal of Electrical and Computer Engineering, 9(4), 3247–3255. https://doi.org/10.11591/ijece.v9i4.pp3247-3255

Tama, V. O., Sibaroni, Y., & Adiwijaya. (2019). Labeling Analysis in the Classification of Product Review Sentiments by using Multinomial Naive Bayes Algorithm. Journal of Physics: Conference Series, 1192(1). https://doi.org/10.1088/1742-6596/1192/1/012036

Wang, Y., Guo, J., Yuan, C., & Li, B. (2022). Sentiment Analysis of Twitter Data. Applied Sciences (Switzerland), 12(22), 1–14. https://doi.org/10.3390/app122211775

Yasen, M., & Tedmori, S. (2019). Movies reviews sentiment analysis and classification. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019 - Proceedings, 860–865. https://doi.org/10.1109/JEEIT.2019.8717422

Yunus, M., Biddinika, M. K., & Fadlil, A. (2023). Optimasi Algoritma Naïve Bayes Menggunakan Fitur Seleksi Backward Elimination untuk Klasifikasi Prevalensi Stunting. Decode: Jurnal Pendidikan Teknologi Informasi, 3(2), 278-285. https://doi.org/10.51454/decode.v3i2.188