Employees’ Satisfaction and Sentiment Analysis toward BERSATU Application
DOI:
https://doi.org/10.9744/jti.27.1.21-32Keywords:
Sentiment analysis , digital transformation , Beiersdorf sales activity tracking routine , sentiment classification, topic classificationAbstract
The era of digitalization has boosted companies to adopt innovative technology in seizing competitive superiority. However, adaptation to the change often deals with constraints, affecting operational efficiency and effectiveness. In this regard, PT Beiersdorf Indonesia faces issues regarding the impact of digital transformation of the sales process from manual to digital using Beiersdorf Sales Activity Tracking Routine (BERSATU) application, such as the difficulty of technology adaption by employees, accustomed to manual method, limitations of internet access in some areas, and technical constraints on application as well as insufficient technical support. Therefore, the research aims to analyze sentiment of user review on “BERSATU” application, using various algorithm classification and modeling topic. A total of 600 user reviews had been collected and analyzed to know positive or negative sentiments. Of 600 reviews, 60.33% was positive and 39.67% was negative. Based on the evaluated five algorithms classification, SVM and Naïve Bayes were superior with accuracy above 97% with better F1-Score. Regression Logistics had 96% accuracy, but it had low recall. Random Forest had 94% accuracy with a better F1-Score, but low recall was received. KNN has 93% accuracy, but low recall was obtained. SVM and Naïve Bayes were the recommended for further analysis. Modeling of LDA topics generated three topics with dominant keywords. Topic 1, such as “application,” “bersatu," “data,” “sales,” and “results,” covered 44.5% of the documents. Topic 2, including “application,” “good,” “visit,” and “order,” was relevant at 29.5%. Topic 3, comprising of “application,” “assistance,” “easy,” “sell,” and “store,” was relevant 26.1% of the documents.
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References
M. Del Giudice, V. Scuotto, A. Papa, S. Y. Tarba, S. Bresciani, and M. Warkentin, "A self‐tuning model for smart manufacturing SMEs: Effects on digital innovation," Journal of Product Innovation Management, vol. 38, no. 1, pp. 68-89, 2021, doi: https://doi.org/10.1111/jpim.12560.
M.-Á. Galindo-Martín, M.-S. Castaño-Martínez, and M.-T. Méndez-Picazo, "Digital transformation, digital dividends and entrepreneurship: A quantitative analysis," Journal of business research, vol. 101, pp. 522-527, 2019, doi: https://doi.org/10.1016/j.jbusres.2018.12.014.
S. Bresciani, K.-H. Huarng, A. Malhotra, and A. Ferraris, "Digital transformation as a springboard for product, process and business model innovation," vol. 128, ed: Journal of Business Research, 2021, pp. 204-210, doi: https://doi.org/10.1016/j.jbusres.2021.02.003.
L. Agostini, F. Galati, and L. Gastaldi, "The digitalization of the innovation process: Challenges and opportunities from a management perspective," European journal of innovation management, vol. 23, no. 1, pp. 1-12, 2020, doi: https://doi.org/10.1108/EJIM-11-2019-0330.
S. Bresciani, K.-H. Huarng, A. Malhotra, and A. J. J. o. B. R. Ferraris, "Digital transformation as a springboard for product, process and business model innovation," vol. 128, ed: Elsevier, 2021, pp. 204-210, doi: https://doi.org/10.1016/j.jbusres.2021.02.003.
S. Kraus, P. Jones, N. Kailer, A. Weinmann, N. Chaparro-Banegas, and N. Roig-Tierno, "Digital transformation: An overview of the current state of the art of research," Sage Open, vol. 11, no. 3, p. 21582440211047576, 2021, doi: https://doi.org/10.1177/21582440211047576.
E. Favari, "Sustainability in (mega) project management—a business case for project sustainability," Megaproject Management: A Multidisciplinary Approach to Embrace Complexity Sustainability, pp. 15-26, 2020, doi: https://doi.org/10.1007/978-3-030-39354-0_2.
H. Harini, D. P. Wahyuningtyas, S. Sutrisno, M. I. Wanof, and A. M. A. Ausat, "Marketing strategy for Early Childhood Education (ECE) schools in the digital age," Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, vol. 7, no. 3, pp. 2742-2758, 2023, doi: https://doi.org/10.31004/obsesi.v7i3.4454.
S. Rajendran, S. Srinivas, and E. Pagel, "Mining voice of customers and employees in insurance companies from online reviews: A text analytics approach," Benchmarking: An International Journal, vol. 30, no. 1, pp. 1-22, 2023, doi: https://doi.org/10.1108/BIJ-12-2020-0650.
S. Smetanin, "The applications of sentiment analysis for Russian language texts: Current challenges and future perspectives," IEEE Access, vol. 8, pp. 110693-110719, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3002215
S. L. Lo, E. Cambria, R. Chiong, and D. Cornforth, "Multilingual sentiment analysis: from formal to informal and scarce resource languages," Artificial Intelligence Review, vol. 48, pp. 499-527, 2017, doi: https://doi.org/10.1007/s10462-016-9508-4.
S. Fransiska, R. Rianto, and A. I. Gufroni, "Sentiment analysis provider by. U on Google Play Store reviews with TF-IDF and support vector machine (SVM) method," Scientific Journal of Informatics, vol. 7, no. 2, pp. 203-212, 2020, doi: https://doi.org/10.15294/sji.v7i2.25596.
S. Styawati, A. Nurkholis, A. A. Aldino, S. Samsugi, E. Suryati, and R. P. Cahyono, "Sentiment analysis on online transportation reviews using Word2Vec text embedding model feature extraction and support vector machine (SVM) algorithm," in 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 2022, pp. 163-167: IEEE, doi: https://doi.org/10.1109/ISMODE53584.2022.9742906.
D. Pratmanto, R. Rousyati, F. F. Wati, A. E. Widodo, S. Suleman, and R. Wijianto, "App review sentiment analysis shopee application in Google Play Store using naive Bayes algorithm," in Journal of Physics: Conference Series, vol. 1641, no. 1, p. 012043, 2020, doi: https://doi.org/10.1088/1742-6596/1641/1/012043
V. Oktaviani, B. Warsito, H. Yasin, and R. Santoso, "Sentiment analysis of e-commerce application in Traveloka data review on Google Play site using naïve Bayes classifier and association method," in journal of physics: conference series, vol. 1943, no. 1, p. 012147, 2021, doi: https://doi.org/10.1088/1742-6596/1943/1/012147.
A. R. Isnain, J. Supriyanto, and M. P. Kharisma, "Implementation of k-nearest neighbor (K-NN) algorithm for public sentiment analysis of online learning," IJCCS, vol. 15, no. 2, pp. 121-130, 2021, doi: https://doi.org/10.22146/ijccs.65176.
S. Masturoh, R. L. Pratiwi, M. R. R. Saelan, and U. Radiyah, "Application of the k-nearest neighbor (KNN) algorithm in sentiment analysis of the Ovo e-wallet application," Jurnal Ilmu Pengetahuan dan Teknologi Komputer, vol. 8, no. 2, pp. 84-89, 2023, doi: https://doi.org/10.33480/jitk.v8i2.3997.
B. Warsito and A. Prahutama, "Sentiment analysis on tokopedia product online reviews using random forest method," in E3S Web of Conferences, 2020, vol. 202, p. 16006: EDP Sciences, doi: https://doi.org/10.1051/e3sconf/202020216006.
V. C. Mawardi and E. Darmaja, "Logistic regression method for sentiment analysis application on Google Playstore," International Journal of Application on Sciences, Technology and Engineering, vol. 1, no. 1, pp. 241-247, 2023, doi: https://doi.org/10.24912/ijaste.v1.i1.241-247.
G. J. Sagala and Y. T. Samuel, "Sentiment analysis on ChatGPT app reviews on Google Play Store using random forest algorithm, support vector machine and naïve Bayes," International Journal of Engineering Business and Social Science, vol. 2, no. 04, pp. 1194-1204, 2024, doi: https://doi.org/10.58451/ijebss.v2i04.148.
A. Juneja and N. N. Das, "Big data quality framework: Pre-processing data in weather monitoring application," in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 559-563: IEEE, doi: https://doi.org/10.1109/COMITCon.2019.8862267.
O. F. Ayilara, L. Zhang, T. T. Sajobi, R. Sawatzky, E. Bohm, and L. M. Lix, "Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry," Health quality of life outcomes, vol. 17, pp. 1-9, 2019, doi: https://doi.org/10.1007/978-981-15-1289-6_25.
O. F. Ayilara, L. Zhang, T. T. Sajobi, R. Sawatzky, E. Bohm, and L. M. Lix, "Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry," Health quality of life outcomes vol. 17, pp. 1-9, 2019, doi: https://doi.org/10.1186/s12955-019-1181-2.
R. Novendri, A. S. Callista, D. N. Pratama, and C. E. Puspita, "Sentiment analysis of YouTube movie trailer comments using naïve Bayes," Bulletin of Computer Science Electrical Engineering vol. 1, no. 1, pp. 26-32, 2020, doi: https://doi.org/10.25008/bcsee.v1i1.5.
D. Yogish, T. Manjunath, and R. S. Hegadi, "Review on natural language processing trends and techniques using NLTK," in Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part III 2, pp. 589-606, 2019, Springer, doi: https://doi.org/10.1007/978-981-13-9187-3_53.
J. Jefriyanto, N. Ainun, and M. A. Al Ardha, "Application of naïve Bayes classification to analyze performance using stopwords," Journal of Information System, Technology Engineering, vol. 1, no. 2, pp. 49-53, 2023, doi: https://doi.org/10.61487/jiste.v1i2.15.
J. Jumadi, D. Maylawati, L. Pratiwi, and M. Ramdhani, "Comparison of Nazief-Adriani and Paice-Husk algorithm for Indonesian text stemming process," in IOP Conference Series: Materials Science and Engineering, vol. 1098, no. 3, p. 032044, 2021, doi: https://doi.org/10.1088/1757-899X/1098/3/032044.
G. S. Budhi, R. Chiong, I. Pranata, and Z. Hu, "Using machine learning to predict the sentiment of online reviews: A new framework for comparative analysis," Archives of Computational Methods in Engineering, vol. 28, pp. 2543-2566, 2021, doi: https://doi.org/10.1007/s11831-020-09464-8.
A. G. Budianto, A. T. E. Suryo, A. F. Zulkarnain, G. R. Cahyono, and S. F. Az-Zahra, "A text mining approach to analyzing the omnichannel retail business performance of the KlikIndomaret app," Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri, vol. 26, no. 2, 2024, doi: https://doi.org/10.9744/jti.26.2.131-144.
R. A. Kulevicz, G. E. d. O. Porfirio, O. S. de Oliveira, A. A. Zavala Zavala, B. A. d. Silva, and M. Constantino, "Influence of sustainability reports on social and environmental issues: Bibliometric analysis and the word cloud approach," Environmental Reviews, vol. 28, no. 4, pp. 380-386, 2020, doi: https://doi.org/10.1139/er-2019-0075.
Z. Jiang, B. Gao, Y. He, Y. Han, P. Doyle, and Q. Zhu, "Text classification using novel term weighting scheme-based improved TF-IDF for internet media reports," Mathematical Problems in Engineering, vol. 2021, pp. 1-30, 2021, doi: https://doi.org/10.1155/2021/6619088.
K. L. Tan, C. P. Lee, K. S. M. Anbananthen, and K. M. Lim, "RoBERTa-LSTM: A hybrid model for sentiment analysis with transformer and recurrent neural network," IEEE Access, vol. 10, pp. 21517-21525, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3152828.
Z. Jalil et al., "COVID-19 related sentiment analysis using state-of-the-art machine learning and deep learning techniques," Frontiers in public health, vol.9, p.812735, 2022,doi: https://doi.org/10.3389/fpubh.2021.812735.
B. Gaye and A. Wulamu, "Sentiment analysis of text classification algorithms using confusion matrix," in Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Beijing, China, December 16–18, 2019, Proceedings, Part I 3, 2019, pp. 231-241: Springer, doi: https://doi.org/10.1007/978-981-15-1922-2_16.
A. Osmani, J. B. Mohasefi, and F. S. Gharehchopogh, "Enriched latent dirichlet allocation for sentiment analysis," Expert Systems, vol. 37, no. 4, p. e12527, 2020, doi: https://doi.org/10.1111/exsy.12527.
H. Yin, X. Song, S. Yang, and J. Li, "Sentiment analysis and topic modeling for COVID-19 vaccine discussions," World Wide Web, vol. 25, no. 3, pp. 1067-1083, 2022, doi: https://doi.org/10.1007/s11280-022-01029-y.
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