Deteksi Kanker Pada Wanita Berdasarkan Anti-Mullerian Hormone Menggunakan Yeo-Johnson Transformation dan Multi-Layer Perceptron

Firmansyah, Fahrul (2024) Deteksi Kanker Pada Wanita Berdasarkan Anti-Mullerian Hormone Menggunakan Yeo-Johnson Transformation dan Multi-Layer Perceptron. Undergraduate thesis, UPN Veteran Jawa Timur.

	Text (Cover) 20081010099.-cover.pdf Download (947kB)
	Text (Bab 1) 20081010099.-bab1.pdf Download (127kB)
	Text (Bab 2) 20081010099.-bab2.pdf Restricted to Repository staff only until 22 July 2026. Download (491kB)
	Text (Bab 3) 20081010099.-bab3.pdf Restricted to Repository staff only until 22 July 2026. Download (310kB)
	Text (Bab 4) 20081010099.-bab4.pdf Restricted to Repository staff only until 22 July 2026. Download (1MB)
	Text (Bab 5) 20081010099.-bab5.pdf Download (114kB)
	Text (Daftar Pustaka) 20081010099.-daftarpustaka.pdf Download (142kB)

Abstract

Cancer is an urgent health challenge in Indonesia, with fatal impacts and high prevalence, especially among women. WHO data shows a significant increase in cases among women. Deep learning technology shows high potential in the early detection of cancer. This study applies Yeo-Johnson Transformation and Multi-Layer Perceptron (MLP) to detect cancer in women using the Anti-Mullerian Hormone (AMH) variable. The Yeo-Johnson process includes initializing the lambda value and applying the equation based on the type of data, while MLP involves selecting hidden layers, forward and backward propagation, and Adam optimization. Testing results show the MLP model accuracy of 99% on training data and 94% on test data with a structure of 2 hidden layers and 64 perceptrons per layer. AMH itself has a significant contribution to the model's prediction with values of -0.682542 on the training data and -0.715878 on the test data. This method combination has proven effective for cancer detection in women, contributing to early cancer prevention and treatment.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Sari, Anggraini Puspita	NIDN0716088605	anggraini.puspita.if@upnjatim.ac.id
Thesis advisor	Sugiarto, Sugiarto	NIDN0714028703	sugiarto@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Fahrul Firmansyah

Date Deposited:

23 Jul 2024 03:59

Last Modified:

23 Jul 2024 03:59

URI:

https://repository.upnjatim.ac.id/id/eprint/27167