KLASIFIKASI PEMBAWA PENYAKIT TALASEMIA ALFA MENGGUNAKAN K-NEAREST NEIGHBOR DENGAN OPTIMASI GENETIC ALGORITHM

HASIBUAN, TAMIR GADING and Arsalan, Osvari and Rodiah, Desty (2024) KLASIFIKASI PEMBAWA PENYAKIT TALASEMIA ALFA MENGGUNAKAN K-NEAREST NEIGHBOR DENGAN OPTIMASI GENETIC ALGORITHM. Undergraduate thesis, Sriwijaya University.

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Abstract

Thalassemia is a genetic disorder that inhibits the production of normal hemoglobin. One variant, alpha thalassemia, results from mutations in the alpha chain gene. Diagnosing thalassemia involves blood tests that include red blood cell morphology analysis, hemoglobin quantification, and genetic testing which are often difficult, expensive, and time-consuming, especially in areas with limited medical resources. Finding solutions to simplify and speed up the diagnosis process is important. One approach is to utilize the K-Nearest Neighbor classification method. K-Nearest Neighbor is a Machine Learning method that is robust to both large and small training data. However, K-Nearest Neighbor is not suitable for high-dimensional data. Therefore, Genetic Algorithm optimization is used to select the best features to reduce the dimensionality of the data. The purpose of this study is to classify alpha thalassemia carriers using the K-Nearest Neighbor algorithm and K-Nearest Neighbor optimized with Genetic Algorithm and see the effect of Genetic Algorithm on K-Nearest Neighbor. The data used is Human Genetics Unit (HGU) data, Sri Lanka. Based on the test results, there is an increase in performance for the optimized K-Nearest Neighbor algorithm. Optimized K-Nearest Neighbor produces the highest average accuracy of 90%, while K-Nearest Neighbor without optimization produces the highest average accuracy of 80%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Talasemia Alfa, Genetik, Hemoglobin, K-Nearest Neighbor, Genetic Algorithm
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data. T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Tamir Gading Hasibuan
Date Deposited:	08 Jul 2024 08:29
Last Modified:	08 Jul 2024 08:29
URI:	http://repository.unsri.ac.id/id/eprint/149197

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