Design and Implementation of a Web-Based Visual Search System for MSME E-Commerce Using the Flask Framework
DOI:
https://doi.org/10.70609/g-tech.v10i1.8942Keywords:
Deep learning, E-commerce, Framework Flask, Visual Search, Web AplicationAbstract
This research presents the design and implementation of an end-to-end web-based visual search system for MSME e-commerce using the Flask framework and a VGG16-based convolutional neural network. The system addresses two critical challenges commonly faced by MSME digital platforms: product tagging errors during product uploads by sellers and limitations of text-based search for customers. A dual-model architecture is implemented, consisting of a visual search module for similarity-based image retrieval and a backend classification module for automatic product categorization. The system is evaluated using a locally collected MSME product image dataset from the Tapal Kuda region, achieving a classification accuracy of 89.17% and visual search performance with a macro precision of 0.85, macro recall of 1.0, and macro F1-score of 0.91. To support real-time deployment, visual features are pre-extracted and stored, enabling efficient query processing with response times under 2 seconds during concurrent usage testing. The results demonstrate that the proposed system provides effective and practical visual search functionality within a localized MSME context while maintaining feasible computational requirements, making it suitable for deployment in resource-constrained MSME environments.
References
Ahmad, S. Z., Abu Bakar, A. R., & Ahmad, N. (2019). Social media adoption and its impact on firm performance: The case of the UAE. International Journal of Entrepreneurial Behavior & Research, 25(1), 84-111. DOI: https://doi.org/10.1108/IJEBR-08-2017-0299
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 1-74 DOI: https://doi.org/10.1186/s40537-021-00444-8
Bell, S., & Bala, K. (2015). Learning visual similarity for product design with convolutional neural networks. ACM Transactions on Graphics, 34(4), 1-10. DOI: https://doi.org/10.1145/2766959
Dagan, A., Guy, I., & Novgorodov, S. (2021). An image is worth a thousand terms? Analysis of visual e-commerce search. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 102-112. DOI: https://doi.org/10.1145/3404835.3462950
Diartono, D. A., Suhari, Y., & Supriyanto, A. (2015). Pengembangan Model Cyber Cluster E-commerce Berbasis CMS dan SEO Produk UMKM. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 9(2), 145-156. DOI: https://doi.org/10.22146/ijccs.7543
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python (2nd ed.). O'Reilly Media.
Handayani, E., Sari, P. P., & Islami, M. J. (2021). Pemanfaatan Teknologi Informasi dan Komunikasi (TIK) oleh UMKM pada Masa Pandemi COVID-19. Jurnal Komunika: Jurnal Komunikasi, Media Dan Informatika, 10(2), 113-119. DOI: https://doi.org/10.31504/komunika.v10i2.4622
Hatcher, W. G., & Yu, W. (2018). A survey of deep learning: Platforms, applications and emerging research trends. IEEE Access, 6, 24411-24432. DOI: https://doi.org/10.1109/ACCESS.2018.2830661
Jing, Y., Liu, D., Kislyuk, D., Zhai, A., Xu, J., Donahue, J., & Tavel, S. (2015). Visual search at Pinterest. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1889-1898. DOI: https://doi.org/10.1145/2783258.2788621
Kiapour, M. H., Han, X., Lazebnik, S., Berg, A. C., & Berg, T. L. (2015). Where to buy it: Matching street clothing photos in online shops. IEEE International Conference on Computer Vision, 3343-3351. DOI: https://doi.org/10.1109/ICCV.2015.382
Li, F., Kant, S., Araki, S., Bangera, S., & Shukla, S. S. (2020). Neural networks for fashion image classification and visual search. arXiv preprint arXiv:2005.08170.
Li, J., Liu, H., Gui, C., Chen, J., Ni, Z., Wang, N., & Chen, Y. (2018). The design and implementation of a real time visual search system on JD E-commerce platform. Proceedings of the 19th International Middleware Conference Industry, 9-16. DOI: https://doi.org/10.1145/3284028.3284030
Liu, Z., Luo, P., Qiu, S., Wang, X., & Tang, X. (2016). DeepFashion: Powering robust clothes recognition and retrieval with rich annotations. IEEE Conference on Computer Vision and Pattern Recognition, 1096-1104. DOI: https://doi.org/10.1109/CVPR.2016.124
Nurhidayati, N., Sugiyah, S., Syahyuni, D., & Wianti, W. (2021). Merubah Tantangan Sebagai Peluang di Masa Pandemi Bagi Komunitas UMKM Naik Kelas Kota Bekasi. Jurnal Abdimas Ekonomi dan Bisnis, 1(1), 6-11. DOI: https://doi.org/10.31294/abdiekbis.v1i1.240
Rahayu, R., & Day, J. (2017). E-commerce adoption by SMEs in developing countries: Evidence from Indonesia. Eurasian Business Review, 7(1), 25-41. DOI: https://doi.org/10.1007/s40821-016-0044-6
Saprianto, F. (2020). Dilematis UMKM di Era Digital. Scientific Journal of Reflection: Economic, Accounting, Management and Business, 3(1), 21-30. DOI: https://doi.org/10.37481/sjr.v3i1.122
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Sugiono, S. (2021). Tantangan dan peluang pemanfaatan augmented reality di perangkat mobile dalam komunikasi pemasaran. Jurnal Komunika: Jurnal Komunikasi, Media Dan Informatika, 10(1), 1-12. DOI: https://doi.org/10.31504/komunika.v10i1.3715
Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., & Li, J. (2014). Deep learning for content-based image retrieval: A comprehensive study. Proceedings of the 22nd ACM International Conference on Multimedia, 157-166. DOI: https://doi.org/10.1145/2647868.2654948
Zhang, Y., Pan, P., Zheng, Y., Zhao, K., Zhang, Y., Ren, X., & Jin, R. (2018). Visual search at Alibaba. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 993-1001. DOI: https://doi.org/10.1145/3219819.3219820
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kevin Harlis Oktaviano, Kevin Ilham Apriandy, M. Sholahudin Sunardiyanta
This work is licensed under a Creative Commons Attribution 4.0 International License.
