RESTAURANT DENSITY PREDICTION SYSTEM USING FEED FORWARD NEURAL NETWORK
| Autor: | Muhammad Kurnia Sandi, Anggunmeka Luhur Prasasti, Marisa W. Paryasto |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Jurnal Riset Informatika, Vol 3, Iss 2, Pp 127-136 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2656-1743 2656-1735 |
| DOI: | 10.34288/jri.v3i2.202 |
| Popis: | 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 accuracy of 97.8% with literature data |
| Databáze: | Directory of Open Access Journals |
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