Penerapan Pendekatan Adjustment Factor dalam Analisis Sentimen Kepuasan Konsumen Menggunakan Indo-BERT
Astrid Noviana Paradhita(1*); Myrtana Pusparisti(2); Putra Pamungkas(3);
(1) Manajemen Bisnis, Universitas Sebelas Maret, Surakarta, Indonesia
(2) Manajemen Bisnis, Universitas Sebelas Maret, Surakarta, Indonesia
(3) Bisnis Digital, Universitas Sebelas Maret, Surakarta, Indonesia
(*) Corresponding Author
AbstractPengukuran tingkat kepuasan konusmen menjadi komponen penting dalam meningkatkan kualitas layanan dan pengambilan keputusan dalam suatu jasa manufaktur yang memiliki kewajiban untuk terus meningkatkan kualitas layanan dan fungsi terhadap kualitas jasa yang diberikan. Data umpan balik konsumen yang umumnya berbentuk teks dan tidak terstruktur membuat analisis sulit dilakukan. Oleh karena itu, diperlukan pendekatan analisis khusus yang mampu memahami konteks bahasa Indonesia secara komputasional. Penelitian ini bertujuan untuk menganalisis sentimen kepuasan konsumen terhadap kualitas layanan jasa yang diberikan. Analisis sentimen juga dilakukan untuk menangkap suasana teks yang disesuaikan dengan frasa tertentu dalam kualitas jasa yang memiliki tingkat sensitivitas lebih kuat. Dengan mengintegrasikan TF-IDF, SVM, Indo-BERT dan rule-based adjustment factor, penelitian ini berhasil melakukan analisis sentimen terhadap 10.421 data umpan balik konsumen. Hasil penelitian menunjukkan bahwa integrasi metode dapat meningkatkan konsistensi dalam mendeteksi sentimen konsumen dan menghasilkan klasifikasi yang lebih representatif terhadap konteks kualitas layanan jasa. Analisis sentimen menghasilkan performa yang baik dengan tingkat akurasi mencapai 90% dan tingkat presisi mencapai 85.7%. Selain itu, juga diperoleh nilai recall sebesar 100% dan F1-score 92.3%. KeywordsAnalisis Sentimen; TF-IDF; SVM; Indo-BERT; Adjusment Factor
|
Full Text:PDF |
Article MetricsAbstract view: 26 timesPDF view: 10 times |
Digital Object Identifier https://doi.org/10.33096/busiti.v7i3.3377
|
Cite |
References
N. L. Ratniasih and N. W. Ninik Jayanti, “Sentiment Analysis of Stakeholder Satisfaction Measurement,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 2, p. 217, Apr. 2022, doi: 10.22146/ijccs.72245.
D. Nurmalasari, D. H. Qudsi, N. Chairani, and H. R. Yuliantoro, “Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 3, pp. 311–317, Nov. 2024, doi: 10.32736/sisfokom.v13i3.2217.
Y. Mao, Q. Liu, and Y. Zhang, “Sentiment analysis methods, applications, and challenges: A systematic literature review,” Apr. 01, 2024, King Saud bin Abdulaziz University. doi: 10.1016/j.jksuci.2024.102048.
S. J. Pipin and H. Kurniawan, “Analisis Sentimen Kebijakan MBKM Berdasarkan Opini Masyarakat di Twitter Menggunakan LSTM,” SIFO Mikroskil, vol. 23, no. 2, pp. 197–208, Oct. 2022.
A. S. Talaat, “Sentiment analysis classification system using hybrid BERT models,” J. Big Data, vol. 10, no. 1, pp. 1–18, Dec. 2023, doi: 10.1186/s40537-023-00781-w.
M. Amien and G. F. Gunawan, “ELANG: Journal of Interdisciplinary Research BERT dan Bahasa Indonesia: Studi tentang Efektivitas Model NLP Berbasis Transformer,” ELANG: Journal ofInterdisciplinary Research, vol. 1, no. 2, Jan. 2024, doi: https://doi.org/10.32664/elang.v1i02.
D. I. Putri, A. N. Alfian, M. Y. Putra, and P. D. Mulyo, “IndoBERT Model Analysis: Twitter Sentiments on Indonesia’s 2024 Presidential Election,” 2024. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
H. Jayadianti, W. Kaswidjanti, A. T. Utomo, S. Saifullah, F. A. Dwiyanto, and R. Drezewski, “Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN,” ILKOM Jurnal Ilmiah, vol. 14, no. 3, pp. 348–354, Dec. 2022, doi: 10.33096/ilkom.v14i3.1505.348-354.
Y. A. Singgalen, “Performance Analysis of IndoBERT for Sentiment Classification in Indonesian Hotel Review Data,” Journal of Information System Research (JOSH), vol. 6, no. 2, pp. 976–986, Jan. 2025, doi: 10.47065/josh.v6i2.6505.
Y. Badulescu, F. Cañas, and N. Cheikhrouhou, “Judgmental adjustment of demand forecasting models using social media data and sentiment analysis within industry 5.0 ecosystems,” International Journal of Information Management Data Insights, vol. 4, no. 2, Nov. 2024, doi: 10.1016/j.jjimei.2024.100272.
C. Sun, J. Mai, W. He, H. Zhu, and Q. Liu, “Complex system modeling using deviation-smoothing belief rule base with training and optimization,” Eng. Appl. Artif. Intell., vol. 158, Oct. 2025, doi: 10.1016/j.engappai.2025.111539.
Y. Yu, B. Li, L. Zhao, C. Ma, and G. Jiao, “A novel optimal control strategy for active suspension systems with control weighting coefficient dynamically adjusted based on operating conditions,” Mech. Syst. Signal Process., vol. 244, Jan. 2026, doi: 10.1016/j.ymssp.2025.113752.
C. Apriyadi and Styawati, “Sentiment Analysis of Cyber Attacks in Bank Syariah Indonesia Using SVM and Indobert Method,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 2, pp. 819–838, Apr. 2025, doi: 10.52436/1.jutif.2025.6.2.2636.
R. I. Perwira, V. A. Permadi, D. I. Purnamasari, and R. P. Agusdin, “Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content,” Journal of Information Systems Engineering and Business Intelligence, vol. 11, no. 1, pp. 30–40, Feb. 2025, doi: 10.20473/jisebi.11.1.30-40.
L. P. F. Larasanty, M. K. A. Jaya, K. W. Astuti, and I. W. M. Santika, “Pengembangan Kuisioner Udayana untuk Penilaian Kepuasan Pasien terhadap Pelayanan Kefarmasian oleh Apoteker di Pusat Kesehatan Masyarakat,” JURNAL MANAJEMEN DAN PELAYANAN FARMASI (Journal of Management and Pharmacy Practice), vol. 11, no. 1, p. 62, Mar. 2021, doi: 10.22146/jmpf.60998.
L. Murphy, “The Questionnaire Surveying Research Method: Pros, Cons and Best Practices,” Science Open, vol. 2, Mar. 2023, doi: 10.14293/S2199-1006.1.SOR-.PP3WYS8.v1.
M. Siino, I. Tinnirello, and M. La Cascia, “Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers,” Inf. Syst., vol. 121, Mar. 2024, doi: 10.1016/j.is.2023.102342.
H. T. Kesgin and M. F. Amasyali, “Advancing NLP models with strategic text augmentation: A comprehensive study of augmentation methods and curriculum strategies,” Natural Language Processing Journal, vol. 7, Jun. 2024, doi: 10.1016/j.nlp.2024.100071.
H. H. Hamid, S. Bangare, A. Nirmal, and S. Bangal, “Sentiment Analysis: A Comprehensive Reviewof Technique and Applications,” Journal of Emerging Technologies and Innovative Research (JETIR), vol. 10, no. 8, Aug. 2023, [Online]. Available: www.jetir.org
K. P. Harmandini and K. Muslim, “Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment,” Jurnal dan Penelitian Teknik Informatika, vol. 8, no. 2, 2024, doi: 10.33395/v8i2.13376.
A. Simanjuntak et al., “Research and Analysis of IndoBERT Hyperparameter Tuning in Fake News Detection,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 13, no. 1, pp. 60–67, Feb. 2024, doi: 10.22146/jnteti.v13i1.8532.
K. Hasanah, “Comparison of Sentiment Analysis Model for Shopee Comments on Google Play Store,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 1, pp. 21–30, Feb. 2024, doi: 10.32736/sisfokom.v13i1.1916.
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Buletin Sistem Informasi dan Teknologi Islam (BUSITI)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.