In a restaurant that is running well, of course, it requires the best quality human resources. One of the human resources that is needed by a restaurant is a stock keeper. Stock keeper restoration is needed to ensure that supplies are maintained properly. The selection process for receiving stock keepers is still carried out conventionally so that the selection process is sometimes not appropriate in accepting the best stock keepers. In determining the selection of restaurant stock keepers, there are several criteria, namely work experience, education, ability to operate Microsoft, attitude, communication, psychological test, interview, personal vehicle and age. Because of these problems, a system is needed that can assist in obtaining reliable and targeted recommendations. The application of a decision support system in this study is used by implementing the Multi-Attribute Utility Theory (MAUT) and ROC methods where these methods are very helpful in producing the best weight and preference values from alternative data and criteria so as to get the final result, namely the best restaurant stock keeper recommendations the Alternative A5 with a score of 1,005 on behalf of Lollymoci
Pujasera Hangout Salihara is a company engaged in the business of serving food and beverages for the public. During the Covid 19 pandemic, there were many impacts, one of which was in the restaurant business sector. With the Fuzzy database method Tahani model and Simple Additive Weighting are used in determining the destination of the favorite food menu at the food court according to customer desires accurately, quickly, and easily understandable, helping the food court owner and tenant of the food court at the salihara hangout food court in providing food menu recommendations, the most popular drink menu and the highest and lowest rating of each food court in a week or a month. The method of Fuzzy database model Tahani and Simple Additive Weighting are applied in making a decision support system with stages determined by the researcher. The result of the Decision Support System is a system that can assist in making decisions that are carried out accurately and according to the desired goals. In applications that have been built, the results are based on the value of the degree of membership and the truth value of the calculation process in the application. Testing is done by means of the BlackBox testing.Keywords:Decision support system, Fuzzy database method, Tahani model, Simple Additive Weighting, Pujasera.
The Sinovac vaccine is an example of a type of inactivated vaccine. The government bought Sinovac, Novavax, AstraZeneca, and Pfizer vaccines. This vaccine is used to treat the Covid-19 pandemic. This vaccine is used to treat the Covid-19 pandemic. The role of the Indonesian people in expressing and stating the pros and cons often involves public services that are easily accessible by many people, namely social media, one of which is Twitter. This can be used as material to analyze who produces data in support of decisions. The technique that can be used is sentiment analysis. The method used in this study is the Naïve bayes Classification. The purpose of this study was to determine the value of sentiment analysis on the Sinovac vaccine using the Naive Bayes Classification method on Twitter social media using Indonesian. The result of this research is the final probability value based on the condition 0.000002765 for positive and 0.000000359 for negative. A response with a positive comment has a greater probability of a response with a negative comment.
Inorganic waste is waste that cannot be decomposed, in the laboratory a trash can has been provided, but there are still many laboratory users who still put, leave waste such as used paper and plastic bottles in any place. can help in facilitating laboratory users in disposing of waste only by using sensors, so a tool is designed that can be easily affordable and easy to use to prevent the amount of waste and piles of waste paper and plastic bottles. The author designed the Design of Automatic Inorganic Garbage Disposal Using Ultrasonic Sensors and Arduino Uno Case Study: Unas Artificial Intelligence Laboratory by using our hand proximity sensor the trash can opens automatically, we don't need to hold or touch the trash can, just use the proximity sensor, it opens automatically, and the trash can will close automatically if the proximity sensor moves away. So the author wants to design and make this tool that can be very helpful for use in the laboratory in order to minimize the amount of inorganic waste left behind.
There are still many people who are less concerned with environmental cleanliness, where this can lead to the accumulation of bad bacteria that cause disease. One of them is Leptospirosis, a disease caused by a bacterial infection, namely the Leptospira strain. Leptospirosis is a disease caused by the laying of bacteria in animals that is transmitted to humans. This disease is often ignored by the public due to lack of understanding about this disease and the high cost of conducting examinations and consulting a doctor or hospital. So that in overcoming this we need a way that is able to help the community in knowing and diagnosing Leptospirosis, one of which is by using an expert system. The expert system used in solving the problem of Leptospirosis is by using the Naïve Bayes method. The application of the Naïve Bayes method in diagnosing Leptospirosis is carried out by collecting data about Leptospirosis where this process aims to find out what symptoms are caused by Leptospirosis. The process of collecting data on this disease is done by interviewing an expert or doctor who handles the problem of Leptospirosis. The results of the diagnosis of Leptospirosis based on the calculation of the Naïve Bayes method with new user data samples get results with a definite level of accuracy where the user experiences Leptospirosis disease with mild symptoms of 63% and the results of the user experiencing Leptospirosis disease with severe symptoms of 37%. Naïve Bayes is able to diagnose with 100% accuracy seen from the total severe and mild symptoms
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.