Analisa Perbandingan Data Mining Pada Klasifikasi Penyakit Jantung Menggunakan Algoritma Extreme Learning Machine (Elm) Dan K-Nearest Neighbor (K-NN)

LARASATI, INTAN and , Azizah Fatmawati, ST., M.Cs (2021) Analisa Perbandingan Data Mining Pada Klasifikasi Penyakit Jantung Menggunakan Algoritma Extreme Learning Machine (Elm) Dan K-Nearest Neighbor (K-NN). Skripsi thesis, Universitas Muhammadiyah Surakarta.

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Abstract

Heart”disease”is”type”of”non communicable disease which results in.a”high’mortality” rate. Heart disease is caused by.several.risk”factors.including”smoking, anunhealth.lifestyle,” high holesterol, ”hypertension,”and”diabetes. Based on’these facts, an”appropriate”algorithm& is needed”to”classify”heart disease as an effort to prevent n’increase’in”the”death”rate from heart”disease..The”algorithm.used is.expected to work accurately.in”the’classification”method. among”them,.there”aren two algorithm used, namely.the& Extreme.Learning”Machine&(ELM) algorithm and.the K-Nearest Neighbour (K-NN)/algorithm.7The1aim5is1to”compare”the”two algorithms, in.order5t determine.which”algorithm has the higher percentage”of accuracy in classifying heart1disease5data.%To3achieve2the1objectives of the study,7several%research methods were”carried”out,”namely data preprocessing with the”data collection stage,&data splitting”and data normalization followed by%the%ELM and K-NN”algorithm” methods at”the.data”processing stage..From”the”steps”that”have”bee” carried”out,.the”final’result”of the”Extreme Learning Machine2(ELM) algorithm obtained a7greater&accuracy& value&of 93.33%,”while”the K-Nearest”Neighbour (K-NN) algorithm1obtained1 an1accuracy value1of 83.52%. This’shows that”in”this study the Extreme Learning Machine (ELM) algorithm;works more”optimally2than7the”K-Nearest4Neighbour3(K-NN) algorithm in the”classification%of heart%disease data.

Item Type:	Karya ilmiah (Skripsi)
Uncontrolled Keywords:	Data.Mining,.Extreme.Learning.Machine,,K-Nearest Neighbor, Penyakit.Jantung
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Ilmu Komunikasi dan Informatika > Teknik Informatika
Depositing User:	INTAN LARASATI
Date Deposited:	08 Aug 2021 13:57
Last Modified:	08 Aug 2021 13:57
URI:	http://eprints.ums.ac.id/id/eprint/92923

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