MAHENDRA, AIRLANGGA OKA (2021) MENU RECOMMENDATION SYSTEM USING DECISION TREE C45 AND K-NEAREST NEIGHBOR ALGORITHM. Other thesis, Universitas Katholik Soegijapranata Semarang.
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Abstract
In this era a lot of coffeshop do some innovation to create new menu. But many of them not categorized their menu effective. Sometimes them create menu without paying attention to the target who will enjoy the menu. Whereas to present a good menu, must pay attention to target who will enjoy the menu. Because for some adult people they are very concerned about sugar level, while for children it’s very important to pay attention about caffeine consumption. To solve this problem, the coffeshop very needed some program to do classification based on age and gender. This is useful to make recommendation where is suitable for target who will enjoy it. With this project the coffeshop can give right treatment on a target. Especially in this project will do comparison to find the best algorithm between Decision Tree C45 and K-Nearest Neighbor. The first step is collecting data using questioner to visitor in coffeshop using google docs. After that the data will imported to Database. To implement the program, we must choose gender, age, commodities and what is algorithm which will be use to do classification. To do comparison in this project will used Confusion Matrix to find accuracy from the result between two algorithm. From this project can be seen that best accuracy is from K-Nearest Neighbor algorithm. The highest accuracy obtained at the end of this project from training data is 100%. While Decision Tree C45 just get highest accuracy around 80%
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 005 Computer programming, programs & data |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | mr AM. Pudja Adjie Sudoso |
| Date Deposited: | 14 Oct 2021 04:23 |
| Last Modified: | 14 Oct 2021 04:23 |
| URI: | http://repository.unika.ac.id/id/eprint/27111 |
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