Prediction System for stock ordering by using Naïve Bayes technique / Muhammad Hafizuddin Rozi

This project is about a decision-making system that can help a restaurant named Mama Chop Papa Grill at Kota Bharu to decide ordering of the products. Decision making system has been a tool for business to help them to grow. As we know, good decision-making system can help companies to make a better decision for their business process. Currently, there is not much decision-making system that is built for a fast food restaurant or any small restaurants.it is because not many small or medium restaurants have the urge to have a decision-making system. In order to make a good decision-making system for the restaurant, this project was proposed. To develop the system, first this research has identified the problem. After the problems were identifies, objectives were created. A technique has been chosen to analyse the relation between stocks and sales as to make prediction for the restaurant. To develop the system, a methodology has been created as to make sure that this project can be done smoothly. The data was gathered from the owner of the restaurant and the data was from the month of January until December of 2014. After gathering the data, the data then was cleaned using techniques from data mining. After that, Naïve Bayes was implied as to extract the rules from the data. That rule that was extracted was then embedded into a prototype that was developed. The prototype was developed to show how the rules worked. Lastly, conclusion and recommendations of the project was provided in the last chapter of this thesis. Other researchers that want to continue this project can have an insight of what are the limitations that this project has faced before.