Erisa Rizkyani, . (2022) KLASIFIKASI MULTI-LABEL MENGGUNAKAN METODE MULTI-LABEL K-NEAREST NEIGHBOR (ML-KNN) PADA PENYAKIT KANKER SERVIKS. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
![[img]](http://repository.upnvj.ac.id/style/images/fileicons/text.png) |
Text
ABSTRAK.pdf Download (62kB) |
|
Text
AWAL.pdf Download (966kB) |
|
Text
BAB 1.pdf Download (138kB) |
|
Text
BAB 2.pdf Restricted to Repository UPNVJ Only Download (364kB) |
|
Text
BAB 3.pdf Restricted to Repository UPNVJ Only Download (245kB) |
|
Text
BAB 4.pdf Download (887kB) |
|
Text
BAB 5.pdf Download (192kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (140kB) |
|
Text
RIWAYAT HIDUP.pdf Restricted to Repository UPNVJ Only Download (73kB) |
|
Text
LAMPIRAN.pdf Restricted to Repository UPNVJ Only Download (353kB) |
|
Text
HASIL PLAGIARISME.pdf Restricted to Repository staff only Download (20MB) |
|
Text
ARTIKEL KI.pdf Restricted to Repository staff only Download (347kB) |
Abstract
Based on GLOBOCAN 2020 statistical data, cervical cancer is the 8th most common cancer in women worldwide. Multi-Label K-Nearest Neighbor (ML-KNN) is one of the adaptive algorithms used to solve multi-label classification cases. The dataset used in this study was obtained from the UCI Machine Learning website. The dataset will be preprocessed by eliminating missing values, checking for duplicate data, checking data types, and resampling data by oversampling the Biopsy label due to unbalanced data. After that the data is divided into training data and test data with a ratio of 80:20. The training data is searched for its proximity to the predetermined k value, namely K=1, K=3, K=5, K=7, and K=9. The evaluation results obtained the best performance for the ML-KNN classification, namely when the value of K = 5 which obtained a hamming loss value of 3.59%, accuracy of 93%, precision weighted of 93%, recall weighted of 96%, and f1-score weighted of 94%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1810511001] [Pembimbing 1: Iin Ernawati] [Pembimbing 2: Nurul Chamidah] [Penguji 1: Jayanta] [Penguji 2: Mayanda Mega Santoni] |
| Uncontrolled Keywords: | Classification, Cervical Cancer, Multi-Label K-Nearest Neighbor (ML-KNN), Oversampling. |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Erisa Rizkyani |
| Date Deposited: | 18 Aug 2022 07:10 |
| Last Modified: | 18 Aug 2022 07:10 |
| URI: | http://repository.upnvj.ac.id/id/eprint/19744 |
Actions (login required)
 |
View Item |