Abstrak
Pariwisata memiliki peran penting dalam mendorong perekonomian daerah dan meningkatkan kesejahteraan masyarakat. Objek wisata Baturraden, yang terletak di Kabupaten Banyumas, Jawa Tengah, merupakan salah satu destinasi yang memiliki daya tarik utama adalah wisata alam. Data perkembangan pengunjung objek wisata Baturraden selama beberapa tahun terakhir menunjukkan tren yang baik. Namun, untuk memastikan keberlanjutan dan peningkatan kualitas layanan, penting untuk memahami persepsi dan pengalaman pengunjung. Penelitian ini menggunakan pendekatan Aspect-Based Sentiment Analysis (ABSA) untuk menganalisis ulasan pengunjung terhadap Baturraden dengan memanfaatkan model IndoBERT, sebuah model deep learning berbasis Bidirectional Encoder Representations froms Transformers (BERT) yang dirancang khusus untuk bahasa Indonesia. Analisis difokuskan pada empat aspek utama: Attraction, Accessibility, Amenities, dan Ancillary Services. Selanjutnya dilakukan proses pre-processing yang mencakup Case Folding, Cleansing, Tokenizing, Normalisasi, Stemming dan Stopword. TahapEvaluasi model dilakukan menggunakan confusion matrix untuk menghitung nilai akurasi, precision, recall, dan F1-score. Hasil penelitian ini menunjukan nilai akurasi senilai 94,61%, precision 83,22%, recall 96% dan F1-score 88,11%. Hal tersebut menunjukan bahwa model dapat melakukan klasifikasi ulasan kedalam aspek – aspek yang diperlukan. Tantangan utama terletak pada analisis ulasan yang beragam dari segi bahasa dan dialek, serta jumlah data yang tidak imbang pada masing-masing aspek.
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Referensi
Badan Pusat Statistik, “Pemulihan Pariwisata Domestik Indonesia 2022.” Accessed: Sep. 10, 2024. [Online]. Available: https://www.bps.go.id/id/pressrelease/2023/05/02/2041/pemulihan-pariwisata-domestik-indonesia-2022-.html
Ni Luh Putu Merawati, Amrullah Ahmad Zuli, and Ismarmiaty, “Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 123–131, 2021, doi: 10.29207/resti.v5i1.2587.
N. Kresna Diwangkara1, S. R. Sari2, and R. S. Rukayah3, “PENGEMBANGAN PARIWISATA KAWASAN BATURRADEN,” Jurnal Arsitektur ARCADE, vol. 4, no. 2, pp. 120–128, Jul. 2020, doi: 10.31848/arcade.v4i2.431.
B. Mathayomchan and K. Sripanidkulchai, “Utilizing Google Translated Reviews from Google Maps in Sentiment Analysis for Phuket Tourist Attractions,” in 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, Jul. 2019, pp. 260–265. doi: 10.1109/JCSSE.2019.8864150.
M. Afrad, C. Febrianto, S. Wijayanto, and Y. Fathoni, “Analysis of Visitor Reviews on Baturaden Tourist Attraction Using Machine Learning Methods,” Edu Komputika, vol. 11, no. 1, pp. 57–64, 2024, doi: doi.org/10.15294/edukom.v11i1.10561.
M. A. Khadija, I. S. D. Jayanti, and F. U. Nimah, “Towards Smart City: Aspect Based Sentiment Analysis of Indonesian Public Aspiration Complaints Data Using Machine Learning,” in Proceedings - International Conference on Informatics and Computational Sciences, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 215–220. doi: 10.1109/ICICoS62600.2024.10636859.
D. Arianto and I. Budi, “Aspect-based Sentiment Analysis on Indonesia’s Tourism Destinations Based on Google Maps User Code-Mixed Reviews (Study Case: Borobudur and Prambanan Temples),” in Proceedings of the 34th Pacific Asia Conference A Language, Information and Computation, Oct. 2020, pp. 359–367. [Online]. Available: https://aclanthology.org/2020.paclic-1.41.pdf
U. Nuha and C. H. Lin, “Aspect-Based Sentiment Analysis with Semi-Supervised Approach on Taiwan Social Distancing App User Reviews,” in 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 444–447. doi: 10.1109/ICAIIC57133.2023.10067048.
D. A. K. Khotimah and R. Sarno, “Sentiment analysis of hotel aspect using probabilistic latent semantic analysis, word embedding and LSTM,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 4, pp. 275–290, 2019, doi: 10.22266/ijies2019.0831.26.
J. Devlin, M.-W. Chang, K. Lee, K. T. Google, and A. I. Language, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” [Online]. Available: https://github.com/tensorflow/tensor2tensor
X. Li, L. Bing, W. Zhang, and W. Lam, “Exploiting BERT for End-to-End Aspect-based Sentiment Analysis,” Oct. 2019, [Online]. Available: http://arxiv.org/abs/1910.00883
D. S. Bimaputra and E. Sutoyo, “Aspect-Based Sentiment Analysis of Hotels in Bali on Tripadvisor Using BERT Algorithm,” International Journal of Advances in Data and Information Systems, vol. 4, no. 2, Apr. 2023, doi: 10.25008/ijadis.v4i2.1284.
H. Santosa, F. Rachman, S. A. Austen, Christianto, and A. S. Girsang, “IndoBERT for classifying hate speech in Twitter,” in AIP Conference Proceedings, American Institute of Physics, Mar. 2024. doi: 10.1063/5.0199750.
B. Wilie et al., “IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding.” [Online]. Available: https://github.com/annisanurulazhar/absa-playground
F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” Nov. 2020, [Online]. Available: http://arxiv.org/abs/2011.00677
I. R. Hidayat and W. Maharani, “General Depression Detection Analysis Using IndoBERT Method,” International Journal on Information and Communication Technology (IJoICT), vol. 8, no. 1, pp. 41–51, Aug. 2022, doi: 10.21108/ijoict.v8i1.634.
N. Yusliani, R. Primartha, and M. Diana, “Multiprocessing Stemming: A Case Study of Indonesian Stemming,” Int J Comput Appl, vol. 182, pp. 15–19, Dec. 2019, doi: 10.5120/ijca2019918476.
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, Oct. 2024, doi: 10.11591/eei.v13i5.8032.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Dec. 2014, [Online]. Available: http://arxiv.org/abs/1412.6980
J. H. Computer, S. M. Honova, V. P. Computer, C. A. Setiawan, I. H. Parmonangan, and Diana, “Sentiment Analysis of Skincare Product Reviews in Indonesian Language using IndoBERT and LSTM,” in Proceeding - IEEE 9th Information Technology International Seminar, ITIS 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ITIS59651.2023.10420222.
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