SENTIMENT ANALYSIS OF MANAGERIAL EFFECTIVENESS OF MSMEs (EMPIRICAL STUDY OF MSMEs IN CISAAT DISTRICT, SUKABUMI REGENCY)
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Ajiz
Dana Budiman
Slamet Sutrisno
This study aims to analyze the managerial effectiveness of Micro, Small, and Medium Enterprises (MSMEs) through a data mining-based sentiment analysis approach using Orange Data Mining software. This approach is used to examine the perceptions, experiences, and opinions of MSME actors regarding the implementation of managerial functions, which include strategic planning, organizing resources, directing and motivating, controlling and evaluating, and adapting to change. Research data was obtained through in-depth interviews with MSME actors, which were then processed using text mining techniques. The analysis process was carried out in several stages, namely text preprocessing, sentiment analysis using the VADER method, theme mapping with topic modeling based on the Latent Dirichlet Allocation (LDA) algorithm, and data visualization through word clouds, heat maps, and bar plots. The results show that the majority of respondents expressed positive sentiments towards the direction dimension, which reflects the ability of MSME managers to motivate team members. However, negative sentiments were also found regarding the control and adaptation dimensions to change, indicating a lack of consistency in evaluating and responding to market dynamics. This study confirms that the managerial effectiveness of MSMEs is not only determined by technical skills, but also by emotional intelligence, adaptive abilities, and leadership qualities in managing resources efficiently.
Ahmed, A. A. A., Agarwal, S., Kurniawan, Im. G. A., Anantadjaya, S. P. D., & Krishnan, C. (2022). Business boosting through sentiment analysis using Artificial Intelligence approach. International Journal of System Assurance Engineering and Management, 13, 699–709. https://doi.org/10.1007/s13198-021-01594-x
Chiny, M., Chihab, M., Chihab, Y., & Bencharef, O. (2021). LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model. IJACSA) International Journal of Advanced Computer Science and Applications, 12(7), 265–275. www.ijacsa.thesai.org
Feldman, Ronen., & Sanger, James. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press.
Kemenkopukm. (2024). KEMENTERIAN KOPERASI DAN UKM REPUBLIK INDONESIA TERMS OF REFERENCE (TOR) PENGADAAN JASA LAINNYA TENAGA PENDUKUNG PENGEMBANGAN EKOSISTEM BISNIS
Leslie, J. B., Dalton, M., Ernst, C., & Deal, J. (2002). MANAGERIAL EFFECTIVENESS IN A GLOBAL CONTEXT. Center for Creative Leadership.
Lindgren, C. J., Wang, W., Upadhyay, S. K., & Kobayashi, V. B. (2023). SENTIMENT ANALYSIS FOR ORGANIZATIONAL RESEARCH. In Research in Occupational Stress and Well Being (Vol. 21, pp. 95–117). Emerald Publishing. https://doi.org/10.1108/S1479-355520230000021006
Mooney, R. J., & Yong Nahm, U. (2003). Text Mining with Information Extraction. Proceedings of the 4th International MIDP Colloquium, 141–160.
Rahmani, S., Abdillah, F., & Ristanti, V. (2025). Optimizing MSME sustainability through digital marketing, innovation, and financial literacy with financial technology support (Vol. 14, Issue 1). https://doi.
Susan, M. (2020). Financial literacy and growth of micro, small, and medium enterprises in west java, indonesia. In International Symposia in Economic Theory and Econometrics (Vol. 27, pp. 39–48). Emerald Group Publishing Ltd. https://doi.org/10.1108/S1571-038620200000027004
Umar, A. (2018). BUSINESS MODEL CANVAS AS A SOLUTION FOR COMPETING STRATEGY OF SMALL BUSINESS IN INDONESIA. In International Journal of Entrepreneurship (Vol. 22, Issue 1).
