Rafli Dika Pramudya, . (2024) KLASIFIKASI TUMOR OTAK BERDASARKAN CITRA MRI DENGAN METODE RANDOM FOREST CLASSIFIER MENGGUNAKAN EKSTRAKSI FITUR GLCM. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Brain tumor is a dangerous disease, as it is one of the causes of death in women, men, and even children. Based on research by Suta et al. in 2019, it is said that the number of deaths due to brain tumors reached 4.25 per 100,000 population per year. So that by creating a machine learning model that can classify the type of brain tumor will help in handling this disease. In this study, the Brain Tumor MRI Dataset was used to create a Random Forest model based on the results of Gray Level Co-Occurrence Matrix feature extraction. The result of this research is an evaluation of the Random Forest model in classifying brain tumor types based on Gray Level Co-Occurrence Matrix feature extraction. The best results were obtained in the Random Forest model with the n_estimators parameter of 140 with an accuracy value of 91%, precision of 91%, and recall of 91%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2010511048] [Pembimbing: Neny Rosmawarni] [Penguji 1: Widya Cholil] [Penguji 2: Hamonangan Kinantan Prabu] |
| Uncontrolled Keywords: | Tumor Otak, MRI, Random Forest, GLCM, Pengolahan Citra |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | RAFLI DIKA PRAMUDYA |
| Date Deposited: | 28 Jun 2024 05:38 |
| Last Modified: | 05 Sep 2024 04:11 |
| URI: | http://repository.upnvj.ac.id/id/eprint/30234 |
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