Pemanfaatan Metode Random Forest Untuk Klasifikasi Leukimia Limfoblast Akut Pada Citra Apusan Darah Tepi ALL-IDB2. (Utilization of the Random Forest Method for Acute Lymphoblastic Leukemia Classification in Peripheral Blood Smear Images from ALL-IDB2).

Chusaini, Muhammad Fachri (2024) Pemanfaatan Metode Random Forest Untuk Klasifikasi Leukimia Limfoblast Akut Pada Citra Apusan Darah Tepi ALL-IDB2. (Utilization of the Random Forest Method for Acute Lymphoblastic Leukemia Classification in Peripheral Blood Smear Images from ALL-IDB2). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Acute Lymphoblastic Leukemia (ALL) is a type of cancer that attacks blood cells and bone marrow. Systematic reviews and meta-analyses show the incidence of ALL in children in Indonesia is 4.32 per 100,000, with a mortality rate ranging from 0.44 to 5.3 per 100,000 children. To assist in leukemia diagnosis, the IoMT research community has investigated various machine learning and deep learning methods that can detect leukemia through image processing. For instance, the SVM method is used to detect ALL images using the ALL-IDB1 and ALL-IDB2 datasets, while the customized KNN method identifies ALL images using the ALL-IDB2 dataset. The ALL-IDB2 dataset, designed by Labati et al., contains a collection of images that facilitate researchers in classifying images using machine learning algorithms. This dataset consists of 260 images, with 130 images of normal patients and 130 images of ALL patients. In recent research, the Random Forest machine learning method was used to classify peripheral blood smear images from the ALL-IDB2 dataset. Several parameter testing scenarios were designed to obtain the highest model evaluation scores. With a combination of G channel parameters in the RGB color space and combined feature extractor parameters, the highest model evaluation results were achieved with a precision score of 96.43% and an F1-score of 96.15%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Acute Lymphoblastic Leukemia (ALL), ALL-IDB2 Dataset, Classification, Machine Learning, Random Forest
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Teknik > Program Studi Teknik Informatika
Depositing User:	1462000206 Muhammad Fachri Chusaini
Date Deposited:	01 Sep 2001 14:33
Last Modified:	05 Oct 2001 18:37
URI:	http://repository.untag-sby.ac.id/id/eprint/35177

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