Roy Binsar Sinaga, . (2022) DETEKSI DINI PENYAKIT KANKER PARU DENGAN KOMBINASI METODE KLASIFIKASI ADABOOST DAN RANDOM FOREST. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
Text
ABSTRAK.pdf Download (10kB) |
|
Text
AWAL.pdf Download (506kB) |
|
Text
BAB 1.pdf Download (25kB) |
|
Text
BAB 2.pdf Restricted to Repository UPNVJ Only Download (372kB) |
|
Text
BAB 3.pdf Restricted to Repository UPNVJ Only Download (169kB) |
|
Text
BAB 4.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
BAB 5.pdf Download (11kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (83kB) |
|
Text
RIWAYAT HIDUP.pdf Restricted to Repository UPNVJ Only Download (20kB) |
|
Text
LAMPIRAN.pdf Restricted to Repository UPNVJ Only Download (2MB) |
|
Text
HASIL PLAGIARISME.pdf Restricted to Repository staff only Download (15MB) |
|
Text
ARTIKEL KI.pdf Restricted to Repository staff only Download (770kB) |
Abstract
Lung cancer is the most common type of cancer in men and the fifth most common among other cancers in women in Indonesia. To find out whether a person has lung cancer or not, it is necessary to carry out a series of diagnostic stages by quite a lot of medical personnel and the results cannot be obtained quickly. Therefore, in this study, an experiment will be conducted to use a combination of the Adaboost and Random Forest classification algorithms in early detection of lung cancer cases based on a series of indicators or attributes related to lung cancer. The data used in this study is secondary data from the kaggle.com website. The data consists of 309 data records with 10 features and 1 class which is then preprocessed. Followed by the formation of a model consisting of two models, namely the Random Forest model and the Adaboost model which applies Random Forest as a weak learner. After the model is formed, testing is carried out with test data, where data is divided using hold-out validation twice, namely a scale of 70: 30 and 80: 20. The evaluation results show that the combination of the Adaboost and Random Forest methods produces accuracy, precision, recall, and specificity. the highest were 95.40%, 96%, 96.30%, 96%, respectively. These results are better than the application of the Random Forest method without Adaboost which produces the highest accuracy, precision, recall, and specificity of 93.20%, 92.70%, 96.00%, 95.90%, respectively.
Item Type: | Thesis (Skripsi) |
---|---|
Additional Information: | [No.Panggil: 1810511012] [Pembimbing 1: Didit Widiyanto] [Pembimbing 2: Bambang Tri Wahyono] [Penguji 1: Yuni Widiastiwi] [Penguji 2: Nurul Chamidah] |
Uncontrolled Keywords: | Lung Cancer, Random Forest, Adaptive Boosting |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
Depositing User: | Roy Binsar Sinaga |
Date Deposited: | 10 Aug 2022 06:32 |
Last Modified: | 10 Aug 2022 06:32 |
URI: | http://repository.upnvj.ac.id/id/eprint/19703 |
Actions (login required)
View Item |