Geospatial Sentiment Analysis of Twitter User (X) on Government Performance in Overcoming Floods in Jabodetabek Using IndoBERT and CNN-LSTM Methods
DOI:
https://doi.org/10.52436/1.jpti.1182Kata Kunci:
CNN-LSTM, geospatial mapping, IndoBERT, sentiment analysis, social media, twitter (X)Abstrak
Twitter (X) is one of the most frequently used social media platforms for people to freely express their opinions, including their perceptions of government performance during flood disasters. Among them, the handling of flood disasters in the Jabodetabek region is a highly discussed topic that causes widespread public reaction. This study aims to classify public sentiment using IndoBERT and a hybrid IndoBERT + CNN-LSTM model. A dataset of 3,894 Indonesian-language tweets was collected, pre-processed, and labelled. The sentiment classification was evaluated using 10-fold cross-validation with accuracy, precision, recall, and F1-score as performance metrics. IndoBERT achieved an accuracy of 91.76% and an F1-score of 90.66%, while the IndoBERT + CNN-LSTM model showed better performance with 94.92% accuracy and a 95.41% F1-score. Although this study used raw tweet locations without sentiment labels for geospatial mapping, the results show a significant improvement in sentiment classification from combining semantic and sequential modelling. For future research, the integration of sentiment data into spatial visualization is recommended to provide deeper insights into regional public opinion.
Unduhan
Referensi
Dino, “Banjir: Pengertian, Penyebab, dan Dampaknya,” BPBD Provinsi Jawa Timur, Oct. 19, 2023. https://web.bpbd.jatimprov.go.id/2023/10/19/banjir-pengertian-penyebab-dan-dampaknya/.
M. W. Widjaya, “Penyalahgunaan Kekuatan Sosial Media dalam Membentuk Opini Publik Halaman 1 - Kompasiana.com,” KOMPASIANA, Jan. 10, 2024. [Online]. Available: https://www.kompasiana.com/malikwisnuwidjaya/659e6331c57afb31676c9a52/penyalahgunaan-kekuatan-sosial-media-dalam-membentuk-opini-publik/.
M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P.-M. Cuenca-Jiménez, “A review on sentiment analysis from social media platforms,” Expert Systems With Applications, vol. 223, p. 119862, Mar. 2023, doi: 10.1016/j.eswa.2023.119862.
M. A. Nulhakim, Y. Sibaroni, and K. M. N. K. Khalif, “Geospatial Sentiment Analysis Using Twitter Data on Natural Disasters in Indonesia with Support Vector Machine (SVM) Algorithm,” socjs.telkomuniversity.ac.id, 2024, doi: 10.21108/ijoict.v10i2.1032.
M. F. Mubaraq and W. Maharani, “Sentiment Analysis on Twitter Social Media towards Climate Change on Indonesia Using IndoBERT Model,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 6, no. 4, p. 2426, Oct. 2022, doi: 10.30865/mib.v6i4.4570.
I. Maulana and W. Maharani, "Disaster Tweet Classification Based On Geospatial Data Using the BERT-MLP Method," 2021 9th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia, 2021, pp. 76-81, doi: 10.1109/ICoICT52021.2021.9527513.
K. K. Mohbey, G. Meena, S. Kumar, and K. Lokesh, “A CNN-LSTM-Based hybrid deep learning approach for sentiment analysis on Monkeypox tweets,” New Generation Computing, vol. 42, no. 1, pp. 89–107, Aug. 2023, doi: 10.1007/s00354-023-00227-0.
M. Cho, J. Ha, C. Park, and S. Park, “Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition,” Journal of Biomedical Informatics, vol. 103, p. 103381, Jan. 2020, doi: 10.1016/j.jbi.2020.103381.
A. A. Hafiza and E. B. Setiawan, “Enhancing cyberbullying detection on platform ‘X’ using IndoBERT and hybrid CNN-LSTM model,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 2, pp. 655–672, Apr. 2025, doi: 10.52436/1.jutif.2025.6.2.4321.
B. Ališauskas, “How to Scrape X.com (Twitter) using Python (2025 Update),” ScrapFly Blog, Jan. 21, 2025. https://scrapfly.io/blog/how-to-scrape-twitter/.
H. Satria, “Cara mendapatkan data (Crawl) Twitter X - Maret 2024,” Helmi Satria Writings: Stories and Learnings, Jul. 02, 2024. https://helmisatria.com/blog/updated-crawl-data-twitter-x-maret-2024/.
A. F. Sidabutar, R. Habibi, and W. I. Rahayu, “PERBANDINGAN METODE KLASIFIKASI UNTUK PENGELOMPOKAN RISIKO MAGANG MAHASISWA,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 3, pp. 2071–2076, Dec. 2023, doi: 10.36040/jati.v7i3.7026.
H. Imaduddin, L. A. Kusumaningtias, and F. Y. A’la, “Application of LSTM and GLOVE word embedding for hate speech detection in Indonesian Twitter data,” Ingénierie Des Systèmes D Information, vol. 28, no. 4, pp. 1107–1112, Aug. 2023, doi: 10.18280/isi.280430..
A. L. S. A.-Z. Gunawan, J. Jondri, and K. M. Lhaksamana, “Analisis sentimen pada media sosial Twitter terhadap penanganan bencana banjir di Jawa Barat dengan metode jaringan saraf tiruan,” eProceedings of Engineering, no. Vol. 8 No. 2, Apr. 2021, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/14695/.
M. R. Haziq, Y. Sibaroni, and S. S. Prasetyowati, “WORD EMBEDDING OPTIMIZATION IN SENTIMENT ANALYSIS OF REVIEWS ON MYTELKOMSEL APP USING LONG SHORT-TERM MEMORY AND SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 6, pp. 1581–1589, Dec. 2024, doi: 10.52436/1.jutif.2024.5.6.2498.
C. Toraman, E. H. Yilmaz, F. ?ahi?Nuç, and O. Ozcelik, “Impact of tokenization on language Models: An analysis for Turkish,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 4, pp. 1–21, Mar. 2023, doi: 10.1145/3578707.
“indobenchmark/indobert-base-p1 · Hugging Face,” Hugging Face. https://huggingface.co/indobenchmark/indobert-base-p1/.
K. Kusnawi and K. Anam, “Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 2, pp. 803–818, Apr. 2025, doi: 10.52436/1.jutif.2025.6.2.4015.
E. Yulianti and N. K. Nissa, “ABSA of Indonesian customer reviews using IndoBERT: single- sentence and sentence-pair classification approaches,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 5, pp. 3579–3589, Jul. 2024, doi: 10.11591/eei.v13i5.8032.
I. Tri Julianto, D. Kurniadi, M. Rikza Nashrulloh, and A. Mulyani, “Comparison of data mining Algorithm for forecasting Bitcoin Crypto currency trends,” Jurnal Teknik Informatika (Jutif), vol. Vol. 3 No. 3, Jun. 2022, [Online]. Available: https://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/343.
J. J. Andrade, L. G. Da Fonseca, M. Farage, and G. L. De Oliveira Marques, “PREDICTION OF THE PERFORMANCE OF BITUMINOUS MIXES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS,” Revista Mundi Engenharia Tecnologia E Gestão (ISSN 2525-4782), vol. 5, no. 6, Aug. 2020, doi: 10.21575/25254782rmetg2020vol5n61367.
M. Jedrzejowska, “What is geospatial mapping, and how does it work?,” Spyrosoft, Oct. 15, 2024. https://spyro-soft.com/blog/geospatial/what-is-geospatial-mapping-and-how-does-it-work/.
I. Mwanja, “Research Guides: An Introduction to Geospatial Mapping: Understanding Geospatial Mapping,” Virginia Tech, May 16, 2025. https://guides.lib.vt.edu/an-introduction-to-geospatial-mapping/.
I. E. Livieris, E. Pintelas, and P. Pintelas, “A CNN–LSTM model for gold price time-series forecasting,” Neural Computing and Applications, vol. 32, no. 23, pp. 17351–17360, Apr. 2020, doi: 10.1007/s00521-020-04867-x.
D. T. Hermanto, A. Setyanto, and E. T. Luthfi, “Algoritma LSTM-CNN untuk Binary Klasifikasi dengan Word2vec pada Media Online,” Creative Information Technology Journal, vol. 8, no. 1, p. 64, Mar. 2021, doi: 10.24076/citec.2021v8i1.264.
W. Anggraeni, M. F. Arrizal Kusuma, E. Riksakomara, R. P. Wibowo, Pujiadi, and S. Sumpeno, “Combination of BERT and hybrid CNN-LSTM models for Indonesia Dengue tweets classification,” International Journal of Intelligent Engineering and Systems, vol. 17, no. 1, pp. 813–826, Dec. 2023, doi: 10.22266/ijies2024.0229.68.
B. Juarto and Yulianto, “Indonesian news classification using IndoBert,” International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. Vol 11 No.2, Feb. 2023, [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/2654/.
M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, “Evaluating trust prediction and confusion matrix measures for web services ranking,” IEEE Access, vol. 8, pp. 90847–90861, Jan. 2020, doi: 10.1109/access.2020.2994222.
“Tokenizer,” Hugging Face. https://huggingface.co/docs/transformers/main_classes/tokenizer/.
A. Musa, F. M. Adam, U. Ibrahim, and A. Y. Zandam, “HauBERT: A Transformer Model for Aspect-Based Sentiment Analysis of Hausa-Language Movie Reviews,” MDPI, p. 43, Apr. 2025, doi: 10.3390/engproc2025087043.
G. Afham Asnawi, “Perbandingan Model IndoBERT dan Model Hybrid pada Analisis Sentimen Opini Masyarakat Terhadap Judi Online,” UIN Ar-Raniry Banda Aceh, 2025, [Online]. Available: https://repository.ar-raniry.ac.id/id/eprint/41795.
A. Zhdanovskaya, D. Baidakova, and D. Ustalov, “Data labeling for Machine learning Engineers: Project-Based curriculum and Data-Centric Competitions,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 13, pp. 15886–15893, Jun. 2023, doi: 10.1609/aaai.v37i13.26886.
“Fine-tuning a model with the Trainer API - Hugging Face LLM Course,” Hugging Face. https://huggingface.co/learn/llm-course/chapter3/3.
E. Helmud, E. Helmud, F. Fitriyani, and P. Romadiana, “Classification comparison performance of supervised machine learning random forest and decision tree algorithms using confusion matrix,” Jurnal Sisfokom (Sistem Informasi Dan Komputer), vol. 13, no. 1, pp. 92–97, Feb. 2024, doi: 10.32736/sisfokom.v13i1.1985.
“classification_report,” Scikit-learn. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html.
