PENGGUNAAN METODE FUZZY C-MEANS DAN LEARNING VECTOR QUANTIZATION DALAM MENDETEKSI DINI PENYAKIT KANKER SERVIKS

NURHIDAYAH, NURHIDAYAH and Yusliani, Novi and Rodiah, Desty (2022) PENGGUNAAN METODE FUZZY C-MEANS DAN LEARNING VECTOR QUANTIZATION DALAM MENDETEKSI DINI PENYAKIT KANKER SERVIKS. Undergraduate thesis, Sriwijaya University.

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Abstract

A malignant tumour of the cervix, also known as cervical cancer, is the fourth most common cancer in women. It became top priority in government concern since the high mortality rate in term of cancer that happen in women. To minimize this mortality rate, it needed to do an early detection, so it can be treated while it in the early stage. This research was developed to produce software that can detect cervical cancer early using the Fuzzy C-Means method and Learning Vector Quantization. This test uses 858 data with 15 attributes. Fuzzy C-Means will perform the process to calculate the cluster center, objective function, and partition matrix changes. The Learning Vector Quantization will update the weights using the cluster center that has been obtained from the Fuzzy C-Means method and get the final weight to classify the data used. The results of the use of the Fuzzy C-Means method and Learning Vector Quantization in the early detection of cervical cancer with maximum iteration 100 obtained the average accuracy of 72,56%, precision of 77,9% and Recall of 49,91%. Based on the level of accuracy produced, it can be said that the use of the Fuzzy C-Means Method with Learning Vector Quantization to detect cervical cancer has been successfully developed.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Deteksi Dini, Fuzzy C-Means, Kanker Serviks , Learning Vector Quantization.
Subjects:	T Technology > T Technology (General) > T10.5-11.9 Communication of technical information
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nurhidayah Nurhidayah
Date Deposited:	30 Aug 2022 04:55
Last Modified:	30 Aug 2022 04:55
URI:	http://repository.unsri.ac.id/id/eprint/78087

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