Deteksi Kategori Aspek pada Ulasan Restoran dengan Menggunakan Multilabel Logistic Regression
Kata Kunci:
Restoran, Multi-label Classification, Logistic Regression, Binary RelevanceAbstrak
Jurnal ini akan dipublikasikan pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Referensi
HASSAN, S. U., AHAMED, J., & AHMAD, K. (2022). Analytics of machine learning-based algorithms for text classification. Sustainable Operations and Computers, 3, 238–248. https://doi.org/10.1016/j.susoc.2022.03.001
KIM, W. G., LI, J. (JUSTIN), & BRYMER, R. A. (2016). The impact of social media reviews on restaurant performance: The moderating role of excellence certificate. International Journal of Hospitality Management, 55, 41–51. https://doi.org/10.1016/j.ijhm.2016.03.001
MAHARANA, K., MONDAL, S., & NEMADE, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91–99. https://doi.org/10.1016/j.gltp.2022.04.020
PRAMANA, R., DEBORA, SUBROTO, J. J., GUNAWAN, A. A. S., & ANDERIES. (2022). Systematic Literature Review of Stemming and Lemmatization Performance for Sentence Similarity. 2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), 1–6. https://doi.org/10.1109/ICITDA55840.2022.9971451
PUTRA, S. J., GUNAWAN, M. N., & SURYATNO, A. (2018). Tokenization and N-Gram for Indexing Indonesian Translation of the Quran. 2018 6th International Conference on Information and Communication Technology (ICoICT), 158–161. https://doi.org/10.1109/ICoICT.2018.8528762
QAISER, S., & ALI, R. (2018). Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents. International Journal of Computer Applications, 181(1), 25–29. https://doi.org/10.5120/ijca2018917395
RESYANTO, F., SIBARONI, Y., & ROMADHONY, A. (2019). Choosing The Most Optimum Text Preprocessing Method for Sentiment Analysis: Case:iPhone Tweets. 2019 Fourth International Conference on Informatics and Computing (ICIC), 1–5. https://doi.org/10.1109/ICIC47613.2019.8985943
REZAEI, N., & JABBARI, P. (2022). Linear and logistic regressions in R. In Immunoinformatics of Cancers (pp. 87–125). Elsevier. https://doi.org/10.1016/B978-0-12-822400-7.00004-X
WEI, Y., ZHANG, H., FANG, J., WEN, J., MA, J., & ZHANG, G. (2021). Joint aspect terms extraction and aspect categories detection via multi-task learning. Expert Systems with Applications, 174, 114688. https://doi.org/10.1016/j.eswa.2021.114688
ZHANG, M.-L., & ZHOU, Z.-H. (2014). A Review on Multi-Label Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819–1837. https://doi.org/10.1109/TKDE.2013.39
ZHANG, Y., MA, Y., & YANG, X. (2022). Multi-label feature selection based on logistic regression and manifold learning. Applied Intelligence, 52(8), 9256–9273. https://doi.org/10.1007/s10489-021-03008-8
HASSAN, S. U., AHAMED, J., & AHMAD, K. (2022). Analytics of machine learning-based algorithms for text classification. Sustainable Operations and Computers, 3, 238–248. https://doi.org/10.1016/j.susoc.2022.03.001
KIM, W. G., LI, J. (JUSTIN), & BRYMER, R. A. (2016). The impact of social media reviews on restaurant performance: The moderating role of excellence certificate. International Journal of Hospitality Management, 55, 41–51. https://doi.org/10.1016/j.ijhm.2016.03.001
MAHARANA, K., MONDAL, S., & NEMADE, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91–99. https://doi.org/10.1016/j.gltp.2022.04.020
PRAMANA, R., DEBORA, SUBROTO, J. J., GUNAWAN, A. A. S., & ANDERIES. (2022). Systematic Literature Review of Stemming and Lemmatization Performance for Sentence Similarity. 2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), 1–6. https://doi.org/10.1109/ICITDA55840.2022.9971451
PUTRA, S. J., GUNAWAN, M. N., & SURYATNO, A. (2018). Tokenization and N-Gram for Indexing Indonesian Translation of the Quran. 2018 6th International Conference on Information and Communication Technology (ICoICT), 158–161. https://doi.org/10.1109/ICoICT.2018.8528762
QAISER, S., & ALI, R. (2018). Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents. International Journal of Computer Applications, 181(1), 25–29. https://doi.org/10.5120/ijca2018917395
RESYANTO, F., SIBARONI, Y., & ROMADHONY, A. (2019). Choosing The Most Optimum Text Preprocessing Method for Sentiment Analysis: Case:iPhone Tweets. 2019 Fourth International Conference on Informatics and Computing (ICIC), 1–5. https://doi.org/10.1109/ICIC47613.2019.8985943
REZAEI, N., & JABBARI, P. (2022). Linear and logistic regressions in R. In Immunoinformatics of Cancers (pp. 87–125). Elsevier. https://doi.org/10.1016/B978-0-12-822400-7.00004-X
WEI, Y., ZHANG, H., FANG, J., WEN, J., MA, J., & ZHANG, G. (2021). Joint aspect terms extraction and aspect categories detection via multi-task learning. Expert Systems with Applications, 174, 114688. https://doi.org/10.1016/j.eswa.2021.114688
ZHANG, M.-L., & ZHOU, Z.-H. (2014). A Review on Multi-Label Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819–1837. https://doi.org/10.1109/TKDE.2013.39
ZHANG, Y., MA, Y., & YANG, X. (2022). Multi-label feature selection based on logistic regression and manifold learning. Applied Intelligence, 52(8), 9256–9273. https://doi.org/10.1007/s10489-021-03008-8
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