Human-Computer Interaction in E-Commerce: A Study of Tokopedia’s Usability, Efficiency, and Satisfaction for Elderly Consumers
Keywords:
Accessibility, E-commerce, Elderly, Sentiment Analysis, TokopediaAbstract
Introduction: Elderly individuals are increasingly engaging with e-commerce platforms like Tokopedia. However, cognitive and technological barriers may affect their user experience. This study aims to analyze elderly user perceptions of Tokopedia using sentiment analysis of user reviews.
Method: The data were collected from elderly users’ reviews on Tokopedia. Sentiment classification into positive, negative, and neutral categories was conducted using machine learning algorithms. Model performance was evaluated using precision, recall, and f1-score metrics, presented through heatmaps and pie charts.
Results: The analysis revealed that 58.0% of reviews were negative, 34.8% positive, and 7.2% neutral. The classification model yielded a high f1-score for the negative class (0.91) and perfect precision for the positive class (1.00), but relatively low recall for the positive sentiment (0.71), suggesting challenges in identifying subtle satisfaction expressions from elderly users.
Discussion: These findings indicate persistent barriers in the online shopping experience for elderly users, particularly in terms of interface and usability. Recommendations are proposed to improve age-friendly design and platform accessibility.
References
A. Nafi’, A. Tri, J. Harjanta, B. A. Herlambang, and S. Fahmi, “J-INTECH (Journal of Information and Technology) Analisis Sentimen Review Pelanggan Lazada dengan Sastrawi Stemmer dan SVM-PSO untuk Memahami Respon Pengguna,” 2024.
L. Afuan, M. Khanza, and A. Zahira Hasyati, “ENHANCING SENTIMENT ANALYSIS OF THE 2024 INDONESIAN PRESIDENTIAL INAUGURATION ON X USING SMOTE-OPTIMIZED NAIVE BAYES CLASSIFIER,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 1, pp. 325–333, Feb. 2025, doi: 10.52436/1.jutif.2025.6.1.4290.
A. Dengen and Budiawan, “Integrating Artificial Intelligence in Malaria Mosquitoes: A Bibliometric Analysis for Species Identification and Automated Detection,” Apr. 2025. [Online]. Available: http://ijmurhica.ppj.unp.ac.id/index.php/ijmurhica
J. R. Jim, M. A. R. Talukder, P. Malakar, M. M. Kabir, K. Nur, and M. F. Mridha, “Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review,” Natural Language Processing Journal, vol. 6, p. 100059, 2024, doi: https://doi.org/10.1016/j.nlp.2024.100059.
J. Hemakirthiga K. and Arunadevi, “Comparative Analysis of Classifiers for Chat Classification: A Study on Random Forest, Support Vector Machine, and Multinomial Naive Bayes with Bag of Words and TF-IDF,” in Advances in Artificial Intelligence and Machine Learning in Big Data Processing, N.-N. and K. S. Geetha R. and Dao, Ed., Cham: Springer Nature Switzerland, 2025, pp. 314–326.
H. Huang, A. Asemi, and M. B. Mustafa, “Sentiment Analysis in E-commerce Platforms: A Review of Current Techniques and Future Directions”, doi: 10.1109/ACCESS.2023.0322000.
L. B. -, “Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification,” International Journal For Multidisciplinary Research, vol. 5, no. 5, Sep. 2023, doi: 10.36948/ijfmr.2023.v05i05.6090.
B. Schehl, J. Leukel, and V. Sugumaran, “Understanding differentiated internet use in older adults: A study of informational, social, and instrumental online activities,” Comput Human Behav, vol. 97, pp. 222–230, Aug. 2019, doi: 10.1016/j.chb.2019.03.031.
A. Harits Ramadhani, H. Qahar Djauhari, V. Lius, and A. Nugroho, “Hybrid Naive Bayes TF-IDF Algorithm and Lexicon Approach for Sentiment Analysis of Reviews,” Dec. 2024.
X. Zhang, R. Mao, K. He, and E. Cambria, “Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation,” in Findings of the Association for Computational Linguistics: EMNLP 2023, H. Bouamor, J. Pino, and K. Bali, Eds., Singapore: Association for Computational Linguistics, Dec. 2023, pp. 8772–8783. doi: 10.18653/v1/2023.findings-emnlp.587.
M. Ding Yi and Wang, “Optimization Design of Fresh e-Commerce Platform for the Elderly,” in Human-Computer Interaction, A. Kurosu Masaaki and Hashizume, Ed., Cham: Springer Nature Switzerland, 2024, pp. 147–162.
Q. Jiang, L. Deng, J. Zhang, and Y. Pengbo, “User-Centered Design Strategies for Age-Friendly Mobile News Apps,” Sage Open, vol. 14, no. 4, Oct. 2024, doi: 10.1177/21582440241285393.
T. Hariguna and A. Ruangkanjanases, “Adaptive sentiment analysis using multioutput classification: a performance comparison,” PeerJ Comput Sci, vol. 9, 2023, doi: 10.7717/peerj-cs.1378.
I. Zemaitaityte, J. Pivoriene, R. Bardauskiene, and A. Katkoniene, “Older adults’ digital inclusion: New challenges for Lithuanian social policy,” SHS Web of Conferences, vol. 184, p. 05004, 2024, doi: 10.1051/shsconf/202418405004.
A. Sharma Rahul Kumar and Dagur, “Sentimental Analysis with Various Classification Techniques—A Comprehensive Survey,” in Proceedings of Fifth Doctoral Symposium on Computational Intelligence, V. and F. G. and H. A. E. Swaroop Abhishek and Kansal, Ed., Singapore: Springer Nature Singapore, 2025, pp. 155–170.
A. S. Talaat, “Sentiment analysis classification system using hybrid BERT models,” J Big Data, vol. 10, no. 1, Dec. 2023, doi: 10.1186/s40537-023-00781-w.
M. Osman, R. Cooper, A. A. Sayer, and M. D. Witham, “The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review,” Age Ageing, vol. 53, no. 7, Jul. 2024, doi: 10.1093/ageing/afae135.
M. Olumekor, S. N. Polbitsyn, M. S. Khan, H. P. Singh, and I. A. Alhamad, “Ageing and digital shopping: Measurement and validation of an innovative framework,” PLoS One, vol. 20, no. 3 March, Mar. 2025, doi: 10.1371/journal.pone.0315125.
M. Aitsam, A. Jimenez Rodriguez, and A. Di Nuovo, “Efficient data processing pipeline for event-based vision datasets: techniques and insights,” Engineering Research Express, vol. 6, no. 4, Dec. 2024, doi: 10.1088/2631-8695/ad9235.
Q. Jiang, L. Deng, J. Zhang, and Y. Pengbo, “User-Centered Design Strategies for Age-Friendly Mobile News Apps,” Sage Open, vol. 14, no. 4, Oct. 2024, doi: 10.1177/21582440241285393.
Y. Mamani-Coaquira and E. Villanueva, “A Review on Text Sentiment Analysis with Machine Learning and Deep Learning Techniques,” 2024, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2024.3513321.
S. Agarwal, S. Godbole, S. Roy, and D. Punjani, “How much noise is too much: A study in automatic text classification,” in Proceedings - IEEE International Conference on Data Mining, ICDM, 2007, pp. 3–12. doi: 10.1109/ICDM.2007.21.
L. Xu and W. Wang, “Aspect-based sentiment classification with BERT and AI feedback,” Natural Language Processing Journal, vol. 10, p. 100136, 2025, doi: https://doi.org/10.1016/j.nlp.2025.100136.
A. W. Pradana and M. Hayaty, “The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 375–380, Oct. 2019, doi: 10.22219/kinetik.v4i4.912.
B. Setiawan, “A Review of Sentiment Analysis Applications in Indonesia Between 2023-2024.”
R. Chavan, S. Latthe, M. Dhorepati, A. Suryawanshi, N. Sharma, and A. Salge, “Sentiment analysis using vader & word cloud techniques,” AIP Conf Proc, vol. 3217, no. 1, p. 020012, Dec. 2024, doi: 10.1063/5.0234543.
J. O. Krugmann and J. Hartmann, “Sentiment Analysis in the Age of Generative AI,” Customer Needs and Solutions, vol. 11, no. 1, Dec. 2024, doi: 10.1007/s40547-024-00143-4.
Y. Mao, Q. Liu, and Y. Zhang, “Sentiment analysis methods, applications, and challenges: A systematic literature review,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 4, p. 102048, 2024, doi: https://doi.org/10.1016/j.jksuci.2024.102048.
G. L. and V. C. S. and S. K. and M. S. Saripilli Prashanth and Aruna Kumari, “A Sentiment Analysis of Tweets by Using TF-IDF Vectorizer and Lemmatization with POS Tagging,” in Accelerating Discoveries in Data Science and Artificial Intelligence I, A. and K. N. and S. B. Lin Frank M. and Patel, Ed., Cham: Springer Nature Switzerland, 2024, pp. 377–386.
H. Bichri, A. Chergui, and M. Hain, “Investigating the Impact of Train / Test Split Ratio on the Performance of Pre-Trained Models with Custom Datasets,” 2024. [Online]. Available: www.ijacsa.thesai.org
M. Abbas, K. Ali, A. Jamali, K. Ali Memon, and A. Aleem Jamali, “Multinomial Naive Bayes Classification Model for Sentiment Analysis,” IJCSNS International Journal of Computer Science and Network Security, vol. 19, no. 3, p. 62, 2019, doi: 10.13140/RG.2.2.30021.40169.
U. K. B. Saravanan, M. Vijay, T. Shreedhar, G. Rajasekar, Y. R, and P. Shakthipriya, “Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment,” in 2023 2nd International Conference on Edge Computing and Applications (ICECAA), 2023, pp. 429–434. doi: 10.1109/ICECAA58104.2023.10212150.
C. Dewi, R. C. Chen, H. J. Christanto, and F. Cauteruccio, “Multinomial Naïve Bayes Classifier for Sentiment Analysis of Internet Movie Database,” Vietnam Journal of Computer Science, vol. 10, no. 4, pp. 485–498, Nov. 2023, doi: 10.1142/S2196888823500100.
B. Ramsay, A. Ralescu, E. Van Der Knaap, and S. Visa, “Confusion Matrix-based Feature Selection. Confusion Matrix-based Feature Selection,” 2011. [Online]. Available: https://www.researchgate.net/publication/220833270
E. Tjoa, H. J. Khok, T. Chouhan, and C. Guan, “Enhancing the confidence of deep learning classifiers via interpretable saliency maps,” Neurocomputing, vol. 562, p. 126825, 2023, doi: https://doi.org/10.1016/j.neucom.2023.126825.
A. Chatterjee, M. W. Gerdes, and S. G. Martinez, “Identification of risk factors associated with obesity and overweight—a machine learning overview,” Sensors (Switzerland), vol. 20, no. 9, May 2020, doi: 10.3390/s20092734.
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