Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung  dengan Teknik Data Mining

Derisma, D

doi:10.30871/jaic.v4i1.2152

Cited by 11 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regarding the measurement of performance models, here are some performance metrics that are commonly and often used [25] and can be obtained through the Naïve Bayes algorithm using the Orange application. 1) Accuracy.…”

Section: Methodsmentioning

confidence: 99%

Economic Classification and Prediction of Southeast Asian Countries During the COVID-19 Pandemic

Suasih¹,

Saskara²,

Wijaya³

et al. 2022

Proceedings of the Tegal International Conference on Applied Social Science &Amp; Humanities (TICASSH 2022)

View full text Add to dashboard Cite

World Bank data shows that during 2020 the economies of countries in the world (including Southeast Asian countries) contracted, and were in a recession. However, there are still countries whose economies are still growing during the COVID-19 pandemic. Economists are still debating the predicate of a recession, because even though it has contracted for two quarters, it doesn't necessarily mean a country is considered a recession. Therefore, this study analyzes the economic classification of Southeast Asian countries during the COVID-19 pandemic (growth or recession), as well as the governance of their economic attributes (export components, agriculture, industry, and manufacturing). This study uses analytical techniques with orange data mining. The results show that based on the Naïve-Bayes algorithm, there are three countries whose economies are classified as experiencing a recession, while the economies of seven other countries are classified as growing. The attributes with the highest rank scores are agriculture, exports, and COVID-19 cases.

show abstract

Section: Methodsmentioning

confidence: 99%

Economic Classification and Prediction of Southeast Asian Countries During the COVID-19 Pandemic

Suasih¹,

Saskara²,

Wijaya³

et al. 2022

Proceedings of the Tegal International Conference on Applied Social Science &Amp; Humanities (TICASSH 2022)

View full text Add to dashboard Cite

show abstract

“…Ini mengarah pada pemeliharaan data pasien setiap hari. Metode penambangan data diterapkan guna mengekstraksi informasi serta memprediksi penyakit di masa depan dari data yang disimpan (Derisma, 2020).…”

Section: Pendahuluanunclassified

Analisa Penyakit Jantung Menggunakan Algoritma Naïve Bayes

Nofiar¹,

Nurkholifah²,

Oktorina³

2023

JSCE

View full text Add to dashboard Cite

Every year more than 2 million Americans die from heart disease which is the number one killer in the world. The results of the Sample Registration System (SRS) survey show that heart disease is the highest cause of death at all ages after stroke, which is 12.9%. The method used in this study uses the Naïve Bayes algorithm. The purpose of this study is to determine if anyone with heart disease has a stroke. From the research results obtained by splitting data using 80:20 to get a prediction accuracy rate of 83% for heart disease prediction cases. In the trial results using the label test data obtained, namely no stroke.

show abstract

“…Dan TN yaitu jumlah data bernilai negatif dan hasil prediksi negati [3]. Confusion matrix terdiri dari beberapa perhitungan [16]:…”

Section: E Confussion Matrixunclassified

“…Presisi merupakan perhitungan untuk mengetahui jumlah data yang benar positif dari semua hasil prediksi benar positif. Presisi dapat dilakukan menggunakan Persamaan (5) [15][3] [16].…”

Section: Presisiunclassified

Komparasi Support Vector Machine, Logistic Regression Dan Artificial Neural Network Dalam Prediksi Penyakit Jantung

Handayani

2021

JEPIN

View full text Add to dashboard Cite

Penyakit jantung adalah salah satu penyakit yang menyebabkan resiko kematian cukup tinggi di dunia. Kolesterol, diabetes, tekanan darah tinggi merupakan faktor-faktor pemicu terjadinya penyakit jantung. Perlu deteksi sejak ini mengenai prediksi penyakit jantung pada setiap individu agar pencegahan dan pengobatan dapat segera dilakukan demi tingkat Kesehatan yang lebih baik. Berbagai metode dapat dilakukan untuk melakukan deteksi penyakit jantung, baik dengan metode tradisional dan metode yang memanfaatkan teknologi. Saat ini mulai banyak bermunculan system pendeteksi penyakit jantung dengan memanfaatkan algoritma machine learning. Algoritma machine learning dianggap mudah untuk diaplikasikan untuk mengklasifikasikan apakah seseorang terkena penyakit jantung. Penelitian ini mencoba melakukan klasifikasi penyakit jantung menggunakan dataset public dari UCI menggunakan tiga algorima machine learning, yaitu Support Vector Machine (SVM), Logistic Regression (LR) dan Artifiacial Neural Network (ANN). Ketiga algorima tersebut diuji menggunakan empat skenario pembagian data training dan testing yang berbeda, yaitu 90:10, 80:20, 70:40 dan 60:40. Dari hasil eksperimen didapatkan hasil akurasi tertinggi pada metode Logistic Regression sebesar 86% menggunakan skenario pembagian data 80:20.

show abstract

Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

Cited by 11 publications

References 5 publications

Economic Classification and Prediction of Southeast Asian Countries During the COVID-19 Pandemic

Economic Classification and Prediction of Southeast Asian Countries During the COVID-19 Pandemic

Analisa Penyakit Jantung Menggunakan Algoritma Naïve Bayes

Komparasi Support Vector Machine, Logistic Regression Dan Artificial Neural Network Dalam Prediksi Penyakit Jantung

Contact Info

Product

Resources

About