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All Journal ILKOM Jurnal Ilmiah Indonesian Journal of Data and Science
Putri, Inggrianti Pratiwi
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Performa Klasifikasi K-NN dan Cross Validation pada Data Pasien Pengidap Penyakit Jantung Azis, Huzain; Purnawansyah, Purnawansyah; Fattah, Farniwati; Putri, Inggrianti Pratiwi
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.507.81-86

Abstract

Globally, the number one cause of death each year is cardiovascular disease. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels, such as coronary heart disease, heart failure or heart failure, hypertension and stroke. The purpose of this study was to measure the performance of accuracy, precision, recall and f-measure of the K-NN and Crossvalidation methods on a dataset of cardiovascular patients. The dataset used was 1000 records consisting of 11 attributes (age, gender, height, etc.) cardiovascular and non cardiovascular patient data, the dataset was obtained from the UCI Machine Learning Repository managed by the Hungarian Institute of Cardiology Budapest: Andras Janosi, MD, University Hospital, Zurich, Switzerland. The steps taken are: dividing the simulation ratio of the dataset to 20:80, 50:50 and 80:20, applying crossvalidation (k-fold = 10) and classification using the K-NN method (k = 2 to K = 900). The research results from the simulation of the dataset ratio 50:50 obtained an accuracy value of 82%, 82% precision, 82% recall and 80% f-measure at a value of K = 13, then the research results from the simulation of the dataset ratio 20:80 obtained an accuracy value of 87%, 87% precision, 97% recall and 92% f-measure at the value of K = 3, and the results of research from the simulation of the dataset ratio 80:20 obtained an accuracy value of 91%, 92% precision, 60% recall and 72% f-measure at the value K = 5.
Analisis Performa Metode K- Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular Putri, Inggrianti Pratiwi
Indonesian Journal of Data and Science Vol. 2 No. 1 (2021): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ijodas.v2i1.25

Abstract

Secara global, penyebab kematian nomor satu setiap tahunnya adalah penyakit Cardiovascular. Penyakit cardiovascular adalah penyakit yang disebabkan gangguan fungsi jantung dan pembuluh darah, seperti Penyakit Jantung Koroner, Penyakit Gagal jantung atau Payah Jantung, Hipertensi dan Stroke. (Kemenkes RI, 2014). Tujuan dari penelitian ini adalah mengukur performa (akurasi, presisi, recall dan f-measure) metode knn dan crossvalidation pada dataset cardiovascular. dataset yang digunakan sebanyak 1000 record terdiri dari 11 atribut (age, gender, height, dsb) data pasien cardiovascular dan non cardiovascular, dataset tersebut diperoleh dari UCI Machine Learning Repository yang dikelola oleh Hungarian Institute of Cardiology Budapest: Andras Janosi, M.D., University Hospital, Zurich, Switzerland. Tahapan yang dilakukan yaitu: membagi rasio simulasi dataset (20:80, 50:50, 80:20), penerapan crossvalidation (k-fold=10) dan klasifikasi menggunakan metode K-NN (k=2 hingga K=900), Hasil penelitian dari simulasi rasio dataset 50:50 memperoleh nilai akurasi 82%, presisi 82%, recall 82% dan f-measure 80% pada nilai K=13. Kemudian hasil penelitian dari simulasi rasio dataset 20:80 memperoleh nilai akurasi 87%, presisi 87%, recall 97% dan f-measure 92% pada nilai K=3. Dan hasil penelitian dari simulasi rasio dataset 80:20 memperoleh nilai akurasi 91%, presisi 92%, recall 60% dan f-measure 72% pada nilai K=5.
Performa Klasifikasi K-NN dan Cross Validation pada Data Pasien Pengidap Penyakit Jantung Azis, Huzain; Purnawansyah, Purnawansyah; Fattah, Farniwati; Putri, Inggrianti Pratiwi
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.507.81-86

Abstract

Globally, the number one cause of death each year is cardiovascular disease. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels, such as coronary heart disease, heart failure or heart failure, hypertension and stroke. The purpose of this study was to measure the performance of accuracy, precision, recall and f-measure of the K-NN and Crossvalidation methods on a dataset of cardiovascular patients. The dataset used was 1000 records consisting of 11 attributes (age, gender, height, etc.) cardiovascular and non cardiovascular patient data, the dataset was obtained from the UCI Machine Learning Repository managed by the Hungarian Institute of Cardiology Budapest: Andras Janosi, MD, University Hospital, Zurich, Switzerland. The steps taken are: dividing the simulation ratio of the dataset to 20:80, 50:50 and 80:20, applying crossvalidation (k-fold = 10) and classification using the K-NN method (k = 2 to K = 900). The research results from the simulation of the dataset ratio 50:50 obtained an accuracy value of 82%, 82% precision, 82% recall and 80% f-measure at a value of K = 13, then the research results from the simulation of the dataset ratio 20:80 obtained an accuracy value of 87%, 87% precision, 97% recall and 92% f-measure at the value of K = 3, and the results of research from the simulation of the dataset ratio 80:20 obtained an accuracy value of 91%, 92% precision, 60% recall and 72% f-measure at the value K = 5.
Co-Authors Fattah, Farniwati Huzain Azis Purnawansyah Purnawansyah