OPTIMASI DEEP NEURAL NETWORK DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI PENYAKIT LIVER

SIDQI, M. NEJATULLAH and Rini, Dian Palupi and Samsuryadi, Samsuryadi (2023) OPTIMASI DEEP NEURAL NETWORK DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI PENYAKIT LIVER. Master thesis, Sriwijaya University.

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Abstract

Liver disease has affected more than one million new patients in the world. which is where the liver organ has an important role function for the body's metabolism in channeling several vital functions. Liver disease has symptoms including jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, enlarged spleen and gallbladder, and abnormalities that are very difficult to detect. Diagnosis of liver disease through Deep Neural Network classification, optimizing the weight values of neural networks with the Particle Swarm Optimization algorithm, to get convergent weights. Layer and epoch parameter settings are used for neural networks, while PSO parameters with the number of particles c1 = 0.4, c2 = 0.6, w = 0.4, and the PSO optimizer function used is Global Best. The results of PSO weight optimization on DNN get the highest accuracy on the HCV 14 dataset of 95.68%, while no optimization results in a lower accuracy of 83.33%.

Item Type:	Thesis (Master)
Uncontrolled Keywords:	Particle Swarm Optimization, Deep Neural Networks, Klasifikasi Penyakit Liver.
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	M. Nejatullah Sidqi
Date Deposited:	01 Mar 2023 04:40
Last Modified:	01 Mar 2023 04:40
URI:	http://repository.unsri.ac.id/id/eprint/90089

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