APLIKASI PEMANTAUAN MEJA RESTORAN UNTUK EFISIENSI OPERASIONAL DAN PENGALAMAN PELANGGAN

Authors

  • Arriffah Ainurrohmah Azhahroh STMIK Amikom Surakarta
  • Winanda Qoulan Syadida Syadida STMIK Amikom Surakarta
  • Dabith Hafithuddin STMIK Amikom Surakarta

DOI:

https://doi.org/10.70248/jismdb.v3i1.2490

Abstract

Efisiensi operasional merupakan tantangan krusial dalam industri restoran, terutama dalam manajemen meja saat jam sibuk. Penelitian ini bertujuan untuk merancang dan membangun sebuah aplikasi pemantauan meja restoran yang inovatif untuk meningkatkan efisiensi operasional dan pengalaman pelanggan. Metode pengembangan sistem yang digunakan adalah System Development Life Cycle (SDLC) model waterfall, yang meliputi tahap identifikasi masalah, desain sistem, implementasi kode, dan pengujian. Hasil utama dari penelitian ini adalah aplikasi fungsional yang mampu memvisualisasikan status meja secara real-time melalui kode warna: hijau (kosong), merah (terpakai), dan kuning (perlu dibersihkan). Aplikasi ini berhasil diintegrasikan dengan sistem kasir, di mana pembayaran pelanggan memicu notifikasi otomatis ke staf kebersihan. Hasil uji coba menunjukkan aplikasi dapat mengoptimalkan alokasi meja, mengurangi waktu tunggu pelanggan, dan meningkatkan responsivitas pelayan, serta mendapatkan umpan balik kepuasan yang tinggi dari pengguna. Aplikasi ini terbukti menjadi solusi efektif untuk memperbaiki manajemen meja di restoran.

References

Alfarisi, M. F. (2021). Sistem prediksi permintaan restoran menggunakan metode regresi linier. Jurnal Teknologi Informasi dan Ilmu Komputer, 8(4), 765–773. https://doi.org/10.25126/jtiik.2021841212

AL.Housni, H., Al Rawahi, H., & Al Maqbali, M. (2022). Smart restaurant table booking and management system. International Journal of Advanced Computer Science and Applications, 13(1), 123–129. https://doi.org/10.14569/IJACSA.2022.0130114

Ahrens, J. (2020). Review of Foodservice Service Quality Scales to Evaluate Customers’ Perception and Satisfaction. Proceedings of the International Conference on Hospitality and Tourism Management, 5(2), 34–41. https://doi.org/10.13140/RG.2.2.28958.38728

Deksne, I., Kozlovskis, S., & Barzdins, J. (2021). Integrating POS and reservation systems in restaurants. Procedia Computer Science, 192, 4205– 4213. https://doi.org/10.1016/j.procs.2021.09.287

Galabi, T., Nugraha, F., & Wibowo, S. (2023). Analisis integrasi POS dan RMS pada restoran menengah. Jurnal Sistem Informasi Bisnis, 15(2), 89– 97. https://doi.org/10.31933/jsib.v15i2.456

Hwang, J., & Seo, S. (2021). Menu engineering and restaurant performance: Evidence from digital ordering systems. International Journal of Hospitality Management, 94, 102862. https://doi.org/10.1016/j.ijhm.2020.102862

Jingga, P., & Limantara, N. (2014). Pengembangan sistem pemesanan meja restoran berbasis web. Jurnal Teknologi Informasi, 5(1), 45–52. https://doi.org/10.1234/jti.v5i1.234

Kumari, A., Jain, R., & Sharma, S. (2023). The Study of Customer Perception on Contactless Menus at Restaurants. European Chemical Bulletin, 12(7), 5113–5120. https://doi.org/10.48047/ecb/2023.12.7.508

Lesiak, M., Nowak, P., & Kowalski, R. (2024). Digital assistants in restaurant POS systems. Journal of Retail Technology, 19(3), 201–212. https://doi.org/10.1016/j.jrettec.2024.04.002

Monitoring Revenue Management Practices. (2023). Hospitality Revenue Journal, 12(2), 55–67. https://doi.org/10.1080/hrj.2023.78945

Motowilowa, K., Jensen, L., & Schmidt, R. (2024). Augmented reality lighting and table setup in restaurants. International Journal of Hospitality Technology, 8(1), 33–49. https://doi.org/10.1016/j.ijht.2024.01.003

Mussa, M., Li, Q., & Chen, Y. (2023). Customer behavior analytics for restaurants. Journal of Data Analytics in Hospitality, 4(2), 102–118. https://doi.org/10.1016/j.jdah.2023.05.004

Pawar, R., Patil, S., & Deshmukh, P. (2017). Android based restaurant table booking system. International Journal of Computer Applications, 165(4), 22–27. https://doi.org/10.5120/ijca2017914130

Rahman, M., Li, Y., & Chen, P. (2025). Smart QR-based Restaurant Dine- in System with Sales Analysis. International Journal of Advanced Computer Science and Applications, 16(2), 55–63. https://doi.org/10.14569/IJACSA.2025.0160207

Roy, P., Sharma, A., & Das, K. (2022). Enhancing customer experience in F&B through operational efficiency. Journal of Hospitality Operations, 14(3), 144–157. https://doi.org/10.1016/j.jhop.2022.03.005

Small Queuing Restaurant Sustainable Revenue Management. (2020). Journal of Hospitality Financial Management, 28(1), 88–101. https://doi.org/10.1080/jhfm.2020.45521

Suleman, D., Aqib, M., & Nugroho, H. (2024). The Impact of SERVQUAL on Consumers' Satisfaction, Loyalty, and Intention to Use Food Delivery Services. Journal of Foodservice Business Research, 27(4), 445–462. https://doi.org/10.1080/10454446.2024.2372858

Xie, H. (2024). Real-time table allocation optimization in restaurants.

Computers & Industrial Engineering, 188, 109911. https://doi.org/10.1016/j.cie.2024.109911

Yun, S., Park, H., & Choi, J. (2024). Adaptive table service models in dynamic environments. Service Science Journal, 16(2), 112–124. https://doi.org/10.1287/servsci.2024.0098

Downloads

Published

2025-10-08

Issue

Section

Articles