RESTAURANT DENSITY PREDICTION SYSTEM USING FEED FORWARD NEURAL NETWORK

Muhammad Kurnia Sandi; Anggunmeka Luhur Prasasti; Marisa W. Paryasto

doi:10.34288/jri.v3i2.58

Authors

Muhammad Kurnia Sandi Telkom University
Anggunmeka Luhur Prasasti Telkom University
Marisa W. Paryasto Telkom University

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v3i2.58

Keywords:

Restaurant Density, Artificial Intelligence, Feedforward Neural Network, Time Series Forecasting

Abstract

In this day and age, information about something is so important. The level of trust of modern society depends on the testing of information. Tested and accurate information will have a good impact on the community. One of the important but often missed information is information about the density of a restaurant. Information about restaurant density is important to know because it can affect the actions of someone who will visit the restaurant. This information is also useful to provide information in advance so that diners avoid full restaurants to avoid the spread of the Covid-19 virus, among other things. With limited operating hours as well as the number of restaurant visitors, information about the density of a restaurant becomes much needed. The lack of information on restaurant density is a major problem in the community. The needs of the community, made this study aims to predict the density of a restaurant an hour later. Based on survey data and existing literature data, with simulation methods and also system analysis built using feedforward neural network artificial intelligence architecture and then trained with Backpropagation algorithms produced an accuracy of 97.8% with literature data.

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References

Akshay Kumar, H., & Suresh, Y. (2016). Multilayer feed-forward neural network to predict the speed of the wind. 2016 International Conference on Computation System and Information Technology for Sustainable Solutions, CSITSS 2016, 285–290. https://doi.org/10.1109/CSITSS.2016.7779372

Auer, P., Burgsteiner, H., & Maass, W. (2008). A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks, 21(5), 786–795. https://doi.org/10.1016/j.neunet.2007.12.036

Benzer, R. (2015). Population dynamics forecasting using artificial neural networks. Fresenius Environmental Bulletin, 24(2), 460–466.

Berno, E., Brambilla, L., Canaparo, R., Casale, F., Costa, M., Della Pepa, C., … Pasero, E. (2003). Application of Probabilistic Neural Networks to Population Pharmacokinetics. Proceedings of the International Joint Conference on Neural Networks, 4(I), 2637–2642. https://doi.org/10.1109/ijcnn.2003.1223983

Chen, M., Hu, M., & Wang, J. (2019). Food Delivery Service and Restaurant: Friend or Foe? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3469971

Christiani, H., Tedjo, P., & Martono, B. (2014). Analisis Dampak Kepadatan Penduduk Terhadap Kualitas Hidup Masyarakat Provinsi Jawa Tengah. Serat Acitya, 3(1), 102–114. Retrieved from http://jurnal.untagsmg.ac.id/index.php/sa/article/view/125

Fadhillah, G. D., Kharisma, A. P., & Afirianto, T. (2020). Pengembangan RestoCrowd: Aplikasi Android Penghitung Jumlah Pengunjung Restoran Berbasis Crowdsourcing dengan Ekstrapolasi. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 4(4), 1042–1047.

Frean, M. (1990). The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks. Neural Computation, 2(2), 198–209. https://doi.org/10.1162/neco.1990.2.2.198

Fuadillah, E. A., & Suliantoro, H. (2016). Restaurant Revenue Management (Studi Kasus Restoran XX Ngaliyan Semarang). Industrial Engineering Online Journal, 5(1), 1–10. Retrieved from https://ejournal3.undip.ac.id/index.php/ieoj/article/view/10654

Grossi, E., & Buscema, M. (2007). Introduction to Artificial Neural Networks (ANN). European Journal of Gastroenterology & Hepatology, 19(12), 1046–1054. https://doi.org/10.1097/MEG.0b013e3282f198a0

Guo, G., Chen, R., Ye, F., Peng, X., Liu, Z., & Pan, Y. (2019). Indoor Smartphone Localization: A Hybrid WiFi RTT-RSS Ranging Approach. IEEE Access, 7, 176767–176781. https://doi.org/10.1109/ACCESS.2019.2957753

Hagan, M. T., & Menhaj, M. B. (1994). Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993. https://doi.org/10.1109/72.329697

Hegarty, C. J. (2017). The Global Positioning System (GPS). In P. J. G. Teunissen & O. Montenbruck (Eds.), Springer Handbook of Global Navigation Satellite Systems (Vol. 50, pp. 197–218). Switzerland: Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-319-42928-1

Hlavacs, H., & Hummel, K. A. (2013). Cooperative positioning when using local position information: Theoretical framework and error analysis. IEEE Transactions on Mobile Computing, 12(10), 2091–2104. https://doi.org/10.1109/TMC.2012.197

Qian, H., Miao, T., Liu, L., Zheng, X., Luo, D., & Li, Y. (2020). Indoor transmission of SARS-CoV-2. Indoor Air. https://doi.org/10.1111/ina.12766

Razavi, S., & Tolson, B. A. (2011). A new formulation for feedforward neural networks. IEEE Transactions on Neural Networks, 22(10), 1588–1598. https://doi.org/10.1109/TNN.2011.2163169

Richard, M. (2019). Bisnis Restoran Cepat Saji Berpeluang Tumbuh 15% Tahun Ini. Retrieved from Bisnis Indonesia website: https://ekonomi.bisnis.com/read/20190211/12/887488/bisnis-restoran-cepat-saji-berpeluang-tumbuh-15-tahun-ini

Schmidt, W. F., Kraaijveld, M. A., & Duin, R. P. W. (1992). Feed Forward Neural Networks With Random Weights. International Conference on Pattern Recognition, 1–4. IEEE Computer Society Press.

Sexton, J., & Seaman, J. (2021). Despite reopening, restaurants still “high risk” for spreading COVID-19, experts say. Retrieved from denverpost.com website: https://www.denverpost.com/2021/02/12/colorado-restaurants-covid-transmission/

Thalib, M. T. N. (2018). Analisis Hubungan Volume, Kecepatan, Dan Kepadatan Arus Lalu Lintas Pada Ruas Jalan Prof. Dr. H.B. Jassin Dengan Membandingkan Metode Greenshielddan Metode Greenberg. RADIAL –JuRnal PerADaban SaIns, RekayAsa Dan TeknoLogi, 6(1), 59–68. Retrieved from https://stitek-binataruna.e-journal.id/radial/article/view/165

Vatansever, S., & Butun, I. (2017). A broad overview of GPS fundamentals: Now and future. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017. https://doi.org/10.1109/CCWC.2017.7868373

Wardani, I., Jumain, J., & Mufarihin, M. (2020). Pengaruh Harga, Free Wifi Dan Fasilitas Terhadap Kepuasan Pelanggan Pada Kedai Coffee JMP Pahlawan Lamongan. Jurnal Media Komunikasi Ilmu Ekonomi, 35(2), 1–12. Retrieved from https://ejournal.stiekhad.id/index.php/melati/article/view/15

Whitley, D., & Karunanithi, N. (1992). Generalization in feed forward neural networks. Proceedings. IJCNN - International Joint Conference on Neural Networks, 77–82. https://doi.org/10.1109/ijcnn.1991.155316

Widjaya, O. H., Suryawan, I. N., & Stefani, S. (2014). Analisis Pengaruh Waktu Tunggu, Harga, Kualitas Terhadap Kepuasan Pelanggan Dan Loyalitas Pelanggan “R” Seafood. SNIT 2014, B18–B25. Jakarta: LPPM Bina Sarana Inforatika. Retrieved from http://seminar.bsi.ac.id/snit/index.php/snit-2014/article/view/355

Wilamowski, B. M. (2011). How to not get frustrated with neural networks. Proceedings of the IEEE International Conference on Industrial Technology, (December), 5–11. https://doi.org/10.1109/ICIT.2011.5754336