FEBRIAN, ACHMAD and Efendi, Rusdi and Saputra, Danny Matthew (2018) ANALISIS PERBANDINGAN ALGORITMA C4.5 DAN REGRESSION TREE DALAM PENDETEKSIAN PENYAKIT TUBERCULOSIS PARU. Undergraduate thesis, Sriwijaya University.
Text
RAMA_ 55201_ 09111002022.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy |
|
Preview |
Text
RAMA_ 55201_ 09111002022_8826630017_0010058507_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (690kB) | Preview |
Text
RAMA_ 55201_ 09111002022_8826630017_0010058507_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (238kB) | Request a copy |
|
Text
RAMA_ 55201_ 09111002022_8826630017_0010058507_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (569kB) | Request a copy |
|
Text
RAMA_ 55201_ 09111002022_8826630017_0010058507_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (310kB) | Request a copy |
|
Text
RAMA_ 55201_ 09111002022_8826630017_0010058507_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (77kB) | Request a copy |
|
Text
RAMA_ 55201_ 09111002022_8826630017_0010058507_06_ref.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (95kB) | Request a copy |
|
Text
RAMA_ 55201_ 09111002022_8826630017_0010058507_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (147kB) | Request a copy |
Abstract
The classification technique has been widely applied in many areas of the application to perform a prediction. One of the commonly used classification techniques is decision trees. This technique is widely used because it can display the results of the classification that is easily understood by humans in the form of a tree-like. C4.5 and CART are the algorithms of decision trees that are unique in the formation of trees. Where C4.5 will generate a multi-branch tree and CART will generate a tree with a binary branch. The two algorithms will be compared in the detection of Lung Tuberculosis, which according to the WHO is one of the ten most deadly diseases in the world by 2015, where Lung Tuberculosis is responsible for causing more human deaths than HIV and malaria diseases. There are 102 training data used in decision tree formation and 30 data testing to be tested for classification using established trees. The results of the test show that C4.5 has accuracy and processing time faster than CART that is 86,66% with time required 31ms compared to 73,33% and 78ms when not done pruning. When done pruning the result is 93.33% with time required 32ms for C4.5, while CART 80.00% and 172ms. In the test using crossvalidate technique, used data training as much as 132 data and the amount of fold as much as four fold. The results show the accuracy of C4.5 of 93.93% with a time of 15ms, better than the result of CART accuracy of 90.15% with time 109ms.
Item Type: | Thesis (Undergraduate) |
---|---|
Uncontrolled Keywords: | C4.5, CART, Klasifikasi, Pohon Keputusan, Tuberculosis Paru. |
Subjects: | R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control) |
Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
Depositing User: | Mr Halim Sobri |
Date Deposited: | 18 Oct 2019 03:34 |
Last Modified: | 18 Oct 2019 03:34 |
URI: | http://repository.unsri.ac.id/id/eprint/11991 |
Actions (login required)
View Item |