DOI: https://doi.org/10.21456/vol14iss2pp143-152

Classification of SWOT Statements Employing BERT Pre-Trained Model Embedding

*Husni Thamrin orcid scopus  -  Dept of Informatics, Universitas Muhammadiyah Surakarta, Indonesia
Dewi Oktafiani  -  Dept. of Information Technology, STMIK Amikom Surakarta, Indonesia
Ibrahim Ihsan Rasyid  -  Dept. of Informatics, Institut Teknologi Bandung, Indonesia
Irfan Miftahul Fauzi  -  Dept. of Bio-enterpreneurship, Universitas Muhammadiyah Madiun, Indonesia
Open Access Copyright (c) 2024 JSINBIS (Jurnal Sistem Informasi Bisnis)

Citation Format:
Abstract
SWOT analysis is a highly effective method for organizations to develop strategic planning and gain widespread adoption by various institutions, industries, and businesses. The importance of SWOT analysis lies in its ability to provide a comprehensive assessment of an organization's internal and external factors. Despite its advantages, there are several challenges in its implementation, such as the challenge to identify the four elements of SWOT and to put statements into their correct position as strength, weakness, opportunity, or threat. This study aims to determine the best SWOT statement classification from a combination of using BERT models as feature extraction technique and compare it with traditional method of TF-IDF. The SWOT statement is input to the model to get a vector as a sentence representation. More similar vector representations indicate the closer meaning of the sentences. The similarity is the basis for the classifier to determine whether a sentence falls into the domain S, W, O, or T. We examined two classification algorithms, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). Data consists of 635 SWOT statements from study programs of a higher education institution. Five combinations of feature extraction techniques and classification algorithms were tested. The study finds that SBERT model embedding in conjunction with support vector machine classification yield the best performance with an accuracy of 0.73 and an F1-score of 0.738. It outperforms the more traditional method of feature extraction of TF-IDF and other combinations using the Naive Bayes Classifier.
Fulltext View|Download
Keywords: SWOT analysis; strategic planning; classification; deep learning; model embeddings;
Funding: Universitas Muhammadiyah Surakarta

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
Perbandingan Algoritma Winnowing dan Algoritma Rabin-Karp pada Aplikasi Pendeteksi Kesamaan Dokumen Skripsi Analisis Kesuksesan Layanan Digital Perguruan Tinggi Menggunakan Model Delone & McLean untuk Meningkatkan Kepuasan Pengguna Implementasi Metode MAUT pada Sistem Penunjang Keputusan dalam Perancangan Sistem E-Voting Pemilihan Calon Ketua OSIS Front matter JSINBIS 14 (2) 2024 Preface JSINBIS 14 (2) 2024 Back matter JSINBIS 14 (2) 2024 More recent articles
Most cited articles
Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk Penerapan Model Promosi Berbasis Web Captive Portal Hotspot dengan Manajemen Terpusat Penerapan Metode AHP dan Fuzzy Topsis Untuk Sistem Pendukung Keputusan Promosi Jabatan Sistem Informasi Pengukuran Kinerja Pada Perkebunan Kelapa Sawit Dengan Menggunakan Metode Balanced Scorecard Implementasi Metode Promethee Dan Borda Dalam Sistem Pendukung Keputusan Pemilihan Lokasi Pembukaan Cabang Baru Bank More cited articles
  1. Abu-Alaish, A., Jaradat, G., Al-Shugran, M., Alodat, I., 2021. Automating SWOT Analysis Using Machine Learning Methods. International Journal of Advances in Soft Computing and its Applications, 13(2), 140-161
  2. Achmad, N., Sholahuddin, M., Murwanti, S., Nugroho, S.P., 2021. Determining SWOT of Culinary Tourism in South Square of Surakarta Palace. International Journal of Business, Economics and Management, 4(2), 427–432. https://doi.org/10.31295/ijbem.v4n2.1729
  3. Akella, S., 2021. Applying SWOT for B2B Decisions, Extension to larger data with Machine Learning Regression. Journal of Physics: Conference Series, 1998. 10.1088/1742-6596/1998/1/012005
  4. Alalie, H.M., Harada, Y., Noor, I.M., 2019. Impact of Strength, Weakness, Opportunities, Threats (SWOT) Analysis on Realizing Sustainable Competitive Advantage in Banking Industry Sector in Iraq. International Journal of Scientific and Research Publications, 9(3), 49–52. http://dx.doi.org/10.29322/IJSRP.9.03.2019.p8708
  5. Aurachman, R., Putri, E.M., 2020. University Strategic System Engineering based on BAN PT Accreditation Criteria One using SysML and Semantic Approach. Journal of Physics: Conference Series, 1477, 1-6. 10.1088/1742-6596/1477/5/052022
  6. Azizah, S.F.N., Cahyono, H.D., Sihwi, S.W., Widiarto, W., 2023. Performance Analysis of Transformer Based Models (BERT, ALBERT and RoBERTa) in Fake News Detection. ArXiv Preprint ArXiv:2308.04950. https://doi.org/10.48550/arXiv.2308.04950
  7. Bilal, M., Almazroi, A.A., 2023. Effectiveness of fine-tuned BERT model in classification of helpful and unhelpful online customer reviews. Electronic Commerce Research, 23(4), 2737–2757. http://dx.doi.org/10.1007/s10660-022-09560-w
  8. Cahyawijaya, S., Lovenia, H., Aji, A.F., Winata, G.I., Wilie, B., Mahendra, R., … others, 2022. NusaCrowd: Open Source Initiative for Indonesian NLP Resources. ArXiv Preprint ArXiv:2212.09648. https://doi.org/10.48550/arXiv.2212.09648
  9. Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., Lopez, A., 2020. A Comprehensive Survey on Support Vector Machine Classification: Applications, Challenges and Trends. Neurocomputing, 408, 189–215. https://doi.org/10.1016/j.neucom.2019.10.118
  10. Denaya, D., 2023. IndoSBERT: Indonesian SBERT for Semantic Textual Similarity tasks. Retrieved November 9, 2023. https://huggingface.co/denaya/indoSBERT-large
  11. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K., 2018. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. ArXiv Preprint ArXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805
  12. Etokidem, A.J., Nkpoyen, F., Ekanem, C., Mpama, E., Isika, A., 2020. Strengths, Weaknesses, Opportunities and Threats (SWOT) Analysis of Civil Society Organisations for Immunisation in a Nigerian Rural Community. West African Journal of Medicine, 37(6), 650–655
  13. Fafurida, Oktavilia, S., Karsinah, Fauziah, S., 2020. Development of Potential Culinary and Shopping Tourism in Central Java Province. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan, 21(2), 107–117. https://doi.org/10.23917/jep.v21i2.10590
  14. Firqa, A., 2022. Indo-Sentence-BERT: Indonesian Sentence BERT for Semantic Similarity. Retrieved November 9, 2023. https://huggingface.co/firqaaa/indo-sentence-bert-base
  15. Fuadi, D., Suwandi, J., Sutama, Maryadi, 2019. Developing Learning Model of Life Skills-Based Courses in University. In Proceedings of the 4th Progressive and Fun Education International Conference, Profunedu 2019, 6-8 August 2019, Makassar, Indonesia. http://dx.doi.org/10.4108/eai.7-8-2019.2288438
  16. Gomes, L., da Silva Torres, R., Côrtes, M.L., 2023. BERT-and TF-IDF-Based Feature Extraction for Long-Lived Bug Prediction in FLOSS: a Comparative Study. Information and Software Technology, 160, 107217. https://doi.org/10.1016/j.infsof.2023.107217
  17. González-Carvajal, S., Garrido-Merchán, E.C., 2020. Comparing BERT Against Traditional Machine Learning Text Classification. ArXiv Preprint ArXiv:2005.13012. https://doi.org/10.48550/arXiv.2005.13012
  18. Granulo, A., Tanović, A., 2020. The advantage of using SWOT analysis for companies with implemented ITIL framework processes. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 1656–1661. https://doi.org/10.23919/MIPRO48935.2020.9245393
  19. Hao, Y., Dong, L., Wei, F., Xu, K., 2019. Visualizing and understanding the effectiveness of BERT. ArXiv Preprint ArXiv:1908.05620. https://doi.org/10.48550/arXiv.1908.05620
  20. Imaduddin, H., A’la, F.Y., Nugroho, Y.S., 2023. Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach. International Journal of Advanced Computer Science and Applications (IJACSA), 14(8), 113-117. https://dx.doi.org/10.14569/IJACSA.2023.0140813
  21. Kale, A.S., Pandya, V., Troia, F.D., Stamp, M., 2023. Malware Classification with Word2Vec, Hmm2Vec, BERT, and ELMo. Journal of Computer Virology and Hacking Techniques, 19(1), 1–16. http://dx.doi.org/10.1007/s11416-022-00424-3
  22. Lima, E., Nascimento, M.H.R., Alencar, D.B.D., Nascimento, M.R., Júnior, J.R.L.P., Silva, A.L.F.D., 2021. Swot Analysis Implemented With Fuzzy Inference to Support Decision Making. International Journal for Innovation Education and Research. https://doi.org/10.31686/ijier.vol9.iss9.3368
  23. Lohrke, F.T., Mazzei, M.J., & Frownfelter-Lohrke, C., 2022. Should it Stay or Should it Go? Developing an Enhanced SWOT Framework for Teaching Strategy Formulation. Journal of Management Education, 46(2), 345–382. https://doi.org/10.1177/10525629211021143
  24. Makmun, A., Thamrin, H., 2018. Performance of Similarity Algorithms for Statement Mapping in a SWOT Analysis Application. AIP Conference Proceedings, 1977. https://doi.org/10.1063/1.5042902
  25. Mateos, M.C., 2020. Hitting the Target. Quality Progress, 53(4), 34–41
  26. Milios, A., BehnamGhader, P., 2022. An Analysis of Social Biases Present in BERT Variants Across Multiple Languages. ArXiv Preprint ArXiv:2211.14402. https://doi.org/10.48550/arXiv.2211.14402
  27. Munikar, M., Shakya, S., Shrestha, A., 2019. Fine-Grained Sentiment Classification using BERT. 2019 Artificial Intelligence for Transforming Business and Society (AITB), 1, 1-5. https://doi.org/10.1109/AITB48515.2019.8947435
  28. Namugenyi, C., Nimmagadda, S.L., Reiners, T., 2019. Design of a SWOT Analysis Model and Its Evaluation in Diverse Digital Business Ecosystem Contexts. Procedia Computer Science, 159, 1145-1154. https://doi.org/10.1016/j.procs.2019.09.283
  29. Pamungkas, E.W., Putri, D.G.P., Fatmawati, A., 2023. Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities. International Journal of Advanced Computer Science and Applications, 14(6). https://dx.doi.org/10.14569/IJACSA.2023.01406125
  30. Pérez-Mayos, L., Ballesteros, M., Wanner, L., 2021. How Much Pretraining Data do Language Models Need to Learn Syntax?. ArXiv Preprint ArXiv:2109.03160. https://doi.org/10.48550/arXiv.2109.03160
  31. Pisner, D.A., Schnyer, D.M., 2020. Support Vector Machine. Machine learning, 101-121. https://doi.org/10.1016/B978-0-12-815739-8.00006-7
  32. Prabhu, S., Mohamed, M., Misra, H., 2021. Multi-Class Text Classification using BERT-Based Active Learning. ArXiv Preprint ArXiv:2104.14289. https://doi.org/10.48550/arXiv.2104.14289
  33. Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 3982-3992. https://doi.org/10.18653/v1/D19-1410
  34. Shen, Y., Liu, J., 2021. Comparison of Text Sentiment Analysis Based on BERT and word2vec. 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC), 144-147. https://doi.org/10.1109/ICFTIC54370.2021.9647258
  35. Taherdoost, H., Madanchian, M., 2021. Determination of Business Strategies Using SWOT Analysis; Planning and Managing the Organizational Resources to Enhance Growth and Profitability. Macro Management & Public Policies, 3(1), 19-22. https://doi.org/10.30564/mmpp.v3i1.2748
  36. Teimoori, D., Alinezhad, A., 2019. Organizational Sustainable Competitive Advantage using ORESTE, TRIZ, SWOT Approaches in Gray Conditions. Iranian Journal of Optimization, 11(2), 85–96. https://dorl.net/dor/20.1001.1.25885723.2019.11.2.2.3
  37. Thamrin, H., & Pamungkas, E.W., 2017. A Rule Based SWOT Analysis Application: A Case Study for Indonesian Higher Education Institution. Procedia Computer Science, 116, 144–150. https://doi.org/10.1016/j.procs.2017.10.056
  38. Togayev, S., Zayniddinova, D., 2023. SWOT-The Essence and Methodological Problems of Analysis. Educational Research in Universal Sciences, 2(4), 1003–1008. https://doi.org/10.5281/zenodo.7922139
  39. Tweheyo, G., Mugarura, A., 2020. Relevance of SWOT Analysis in Universal Secondary Education. International Journal of Education and Social Science Research, 3(1), 242–257. http://dx.doi.org/10.37500/IJESSR.2020.30121
  40. Vlados, C., Chatzinikolaou, D., 2019. Towards a restructuration of the conventional SWOT analysis. Business and Management Studies, 5(2), 76-84. https://doi.org/10.11114/bms.v5i2.4233
  41. Wang, Z., Ng, P., Ma, X., Nallapati, R., Xiang, B., 2019. Multi-Passage BERT: A Globally Normalized BERT Model for Open-Domain Question Answering. ArXiv Preprint ArXiv:1908.08167. https://doi.org/10.48550/arXiv.1908.08167

Last update:

No citation recorded.

Last update: 2024-04-30 23:58:05

No citation recorded.