KOMBINASI METODE CASE BASED REASONING (CBR) DAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK KLASIFIKASI PENYAKIT HEPATOCELLULAR CARSINOMA BERDASARKAN FAKTOR RISIKO

FAUZAN, MUHAMMAD and Utami, Alvi Syahrini and Oktadini, Nabila Rizky (2021) KOMBINASI METODE CASE BASED REASONING (CBR) DAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK KLASIFIKASI PENYAKIT HEPATOCELLULAR CARSINOMA BERDASARKAN FAKTOR RISIKO. Undergraduate thesis, Sriwijaya University.

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Abstract

This research was developed to produce software to detect Hepatocellular Carcinoma disease using Case-Based Reasoning (CBR) and Particle Swarm Optimization (PSO). First, Case-Based Reasoning is applied to preprocess the data set, so that the weight vector for each attribute will be used in Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) is used for decision-making based on selected features and recognized diseases. This test is done by looking at iterations and particles. Based on the test, the highest accuracy is 76.96% using 100 iterations and 20 particles. It can be concluded from these results that the calculation of case-based reasoning and particle swarm optimization is said to be quite accurate in the early detection of disease.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Hepatocellular Carsinoma, Case Based Reasoning (CBR), dan Particle Swarm Optimization (PSO)
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Fauzan
Date Deposited:	03 Aug 2021 07:03
Last Modified:	03 Aug 2021 07:03
URI:	http://repository.unsri.ac.id/id/eprint/51255

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