YULIANTO, MARTINUS MARIO (2023) CLASSIFICATION OF DIABETES MELLITUS USING NAIVE BAYES METHOD. Other thesis, Universitas Katolik Soegijapranata Semarang.
![[img]](http://repository.unika.ac.id/style/images/fileicons/text.png) |
Text (COVER)
19.K1.0025-MARTINUS MARIO YULIANTO-COVER_a.pdf Download (609kB) |
|
Text (BAB I)
19.K1.0025-MARTINUS MARIO YULIANTO-BAB I_a.pdf Download (287kB) |
|
Text (BAB II)
19.K1.0025-MARTINUS MARIO YULIANTO-BAB II_a.pdf Restricted to Registered users only Download (127kB) |
|
Text (BAB III)
19.K1.0025-MARTINUS MARIO YULIANTO-BAB III_a.pdf Download (130kB) |
|
Text (BAB IV)
19.K1.0025-MARTINUS MARIO YULIANTO-BAB IV_a.pdf Download (916kB) |
|
Text (BAB V)
19.K1.0025-MARTINUS MARIO YULIANTO-BAB V_a.pdf Download (127kB) |
|
Text (DAFTAR PUSTAKA)
19.K1.0025-MARTINUS MARIO YULIANTO-DAPUS_a.pdf Download (342kB) |
|
Text (LAMPIRAN)
19.K1.0025-MARTINUS MARIO YULIANTO-LAMP_a.pdf Download (720kB) |
Abstract
Diabetes mellitus is an epidemic of chronic disease caused by blood sugar or glucose levels in the body that are too high or above normal. There are 3 types of diabetes mellitus, namely type 1 diabetes mellitus, type 2 diabetes mellitus and gestational diabetes mellitus. Indonesia is the highest contributor to diabetes mellitus in the world. Diabetes Mellitus itself has several symptoms such as easy hunger, easy sleepiness, high thirst, and many more of them. Diabetes Mellitus itself can give all humans without exception and regardless of their age. This disease is generally caused by a lack of awareness of maintaining a healthy body, and the general public's ignorance of the disease it causes. Therefore, the authors created a system that is used to be able to detect whether a person has diabetes mellitus or not. The purpose of making this system is to assist the wider community in helping to detect diabetes mellitus at an early stage. This system uses the Naïve Bayes algorithm to detect Diabetes Mellitus. In this paper the authors detect the performance of the Naïve Bayes method in detecting diabetes mellitus. This analysis uses Accuracy, Precision, and Recall. The results obtained in this study are that Naïve Bayes is very good for analyzing diabetes mellitus suffered with % accuracy, % precision, and % recall.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | 600 Technology (Applied sciences) > 610 Medicine and health > 616 Diseases |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | Mrs Christiana Sundari |
| Date Deposited: | 31 May 2023 06:41 |
| Last Modified: | 31 May 2023 06:41 |
| URI: | http://repository.unika.ac.id/id/eprint/31882 |
Actions (login required)
 |
View Item |
