Application Of the Popularity-Based Ranking Model in An Augmented Reality Web-Based Furniture Marketing Information System: Case Study at D Furniture Company

Authors

  • Rais Akbar Sidik STMIK Mardira Indonesia, Bandung, Indonesia. Author
  • Toni Kusnandar STMIK Mardira Indonesia, Bandung, Indonesia. Author
  • Yuda Purnama Putra STMIK Mardira Indonesia, Bandung, Indonesia. Author
  • Egi Badar Sambani STMIK Mardira Indonesia, Bandung, Indonesia. Author

DOI:

https://doi.org/10.55208/jeme.v2i2.172

Keywords:

Augmented Reality, Furniture Marketing, Popularity-Based Ranking Model, Rapid Application Development

Abstract

Application of a Popularity-Based Ranking Model in a Web-Based Augmented Reality Furniture Marketing Information System: A Case Study of D FURNITUR. This marketing information system is essential for delivering pertinent information to consumers and aiding them in purchasing decisions. This research aims to develop a web-based furniture marketing information system incorporating Augmented Reality (AR) and a popularity-based ranking model, employing the Rapid Application Development (RAD) methodology, with D FURNITURE as the case study. The RAD technique was selected to expedite development and facilitate ongoing enhancement based on user feedback. The research encompasses system design and development phases, commencing with needs analysis via surveys and interviews with D FURNITURE management and potential users, culminating in system implementation. The resultant solution enables customers to visualize product designs online via an interactive interface augmented by AR. Prominent features encompass an augmented reality display of products in the user's surroundings, product selection informed by popularity rankings, and a cohesive ordering mechanism facilitated via WhatsApp. The findings yield tangible benefits through a robust and efficient furniture marketing information system, minimizing faults in product display, improving user experience in information retrieval, and augmenting satisfaction via a popularity-based rating system. Implementing the RAD approach facilitates flexible and adaptive development to meet future requirements.

References

Ballouk, H., Ben Jabeur, S., Boubaker, S., & Mefteh-Wali, S. (2024). The effect of social media on bank performance: An fsQCA approach. Electronic Commerce Research, 24(1), 477-495.

Duhari, M. A., Wahyudi, H., & Nugroho, E. A. (2022). Perancangan Sistem Informasi Pengadaan Suku Cadang Kereta Api Berbasis Web. Acman: Accounting and Management Journal, 2(2), 207–2015. https://doi.org/10.55208/aj.v2i2.42

Huang, X., & Liu, X. (2022). Incorporating a topic model into a hypergraph neural network for searching-scenario oriented recommendations. Applied Sciences, 12(15), 7387.

Huang, Y., Li, W., Wu, S., Qiao, X., Guo, M., He, H., & Li, Y. (2023, October). Cloud-Edge-Device Collaborative Image Retrieval and Recognition for Mobile Web. In International Conference on Collaborative Computing: Networking, Applications and Worksharing (pp. 474-494). Cham: Springer Nature Switzerland.

Liu, L., Chen, C., Pei, Q., Maharjan, S., & Zhang, Y. (2021). Vehicular edge computing and networking: A survey. Mobile networks and applications, 26, 1145-1168.

Mahadi, Y., Tiara, R., Abdurrohman, A. F., Rohpandi, D., & Alpiyasin, . F. (2025). Design Of Student Register Application Using Laravel 8 Framework. Acman: Accounting and Management Journal, 4(2), 154–163. https://doi.org/10.55208/aj.v4i2.162

Mutmainah, N., Rahayu, S., Faujan, W., Budiman, D. A., & Ibrahim, R. N. (2025). The Design Of a Web-Based Letter Filing System. Acman: Accounting and Management Journal, 4(2), 145–153. https://doi.org/10.55208/aj.v4i2.161

Shafiee, S. (2024). Unveiling the Latest Trends and Advancements in Machine Learning Algorithms for Recommender Systems: A Literature Review. Procedia CIRP, 121, 115-120.

Sidharta, I., & Rahmahwati, R. (2023). Cross Sectional Study on Information System Facilities on End-User Satisfaction: Study at Retail in Bandung. Electronic, Business, Management and Technology Journal, 1(1), 1–11. https://doi.org/10.55208/ebmtj.v1i1.81

Solano-Barliza, A., Arregocés-Julio, I., Aarón-Gonzalvez, M., Zamora-Musa, R., De-La-Hoz-Franco, E., Escorcia-Gutierrez, J., & Acosta-Coll, M. (2024). Recommender systems applied to the tourism industry: a literature review. Cogent Business & Management, 11(1), 2367088.

Wang, Y., Wang, J., Zhang, W., Zhan, Y., Guo, S., Zheng, Q., & Wang, X. (2022). A survey on deploying mobile deep learning applications: A systemic and technical perspective. Digital Communications and Networks, 8(1), 1-17.

Yeni, N. ., Komara, A. T. ., Suzanto, B. ., & Rusjiana, J. . (2023). The Effect of Cost Accounting Information Systems on Operational Cost Control: Study at A Consulting Company in The City of Bandung. Acman: Accounting and Management Journal, 3(1), 28–34. https://doi.org/10.55208/aj.v3i1.57sss

Wu, K., & Chi, K. (2023). Enhanced e-commerce customer engagement: A comprehensive three-tiered recommendation system. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(3), 348-359.

Xue, Y., Sun, J., Liu, Y., Li, X., & Yuan, K. (2024). Facial expression-enhanced recommendation for virtual fitting rooms. Decision Support Systems, 177, 114082.

Downloads

Additional Files

Published

30-12-2024

How to Cite

Sidik, R. A., Kusnandar, T., Putra, Y. P., & Sambani, E. B. (2024). Application Of the Popularity-Based Ranking Model in An Augmented Reality Web-Based Furniture Marketing Information System: Case Study at D Furniture Company. Journal of Economics, Management, and Entrepreneurship, 2(2), 129-145. https://doi.org/10.55208/jeme.v2i2.172