scholarly journals Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

2020 ◽  
Vol 4 (1) ◽  
pp. 84-88
Author(s):  
D Derisma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Network Algorithms ◽  
Comparison Results ◽  
The World ◽  
Bayes Algorithm ◽  
Human Death

Heart disease is a disease that contributes to a relatively high mortality rate. The rate of human death caused by disease in the heart is a widespread problem in the world. The main objective of this study is to predict people with heart disease using the publicly available dataset in the UCI Repository with the Heart Disease dataset. To obtain the best classification algorithm is by comparing three Algoritma Naive Bayes, Random Forest, Neural Network algorithms, which are frequently used to predict people with heart disease. Comparison results show that Naive Bayes ' algorithm is a precise and accurate algorithm used to predict people with heart disease with a percentage of 83 %.

scholarly journals Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes

2019 ◽  
Vol 1 (01) ◽  
pp. 25-34
Author(s):  
Ade Riani ◽  
Yessy Susianto ◽  
Nur Rahman
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Disease ◽  
Chest Pain ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Mining Method ◽  
The World ◽  
Bayes Algorithm ◽  
Exercise Induced

Heart disease is a disease with a high mortality rate in the world of health. The disease is usually rarely realized the cause. However, there are several parameters that can be used to predict whether a person has a risk of heart disease or not. As for this study, researchers will use several indicators including Age, Sex, Chest pain type, Trestbps, Cholesterol, Fasting blood sugar, Resting ECG, Max heart rate, Exercise-induced angina, Oldpeak, Slope, Number of vessels coloured, and Thal This research will perform calculations using the Data Mining method with the Naive Bayes Algorithm. The results of this study get an accuracy of 86% for the 303 datasets tested. 

scholarly journals KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG

2019 ◽  
Vol 15 (2) ◽  
pp. 189-194 ◽  
Author(s):  
Hendri Mahmud Nawawi ◽  
Jajang Jaya Purnama ◽  
Agung Baitul Hikmah
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Naive Bayes ◽  
Healthy Lifestyle ◽  
Naïve Bayes ◽  
The Body ◽  
Early Age ◽  
Regular Exercise ◽  
The Neural Network ◽  
Bayes Algorithm

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer.  Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body.  By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not.

scholarly journals The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Tech-E ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Rino Rino
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Set ◽  
A Value ◽  
C4.5 Algorithm ◽  
Calculation Results ◽  
Mining Methods ◽  
Bayes Algorithm

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the Naïve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the Naïve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the Naïve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the Naïve Bayes algorithm in predicting heart disease.

scholarly journals PENERAPAN DATA MINING TERHADAP DATA COVID-19 MENGGUNAKAN ALGORITMA KLASIFIKASI

2021 ◽  
Vol 21 (1) ◽  
pp. 44-52
Author(s):  
Rizka Dahlia ◽  
Nanik Wuryani ◽  
Sri Hadianti ◽  
Windu Gata ◽  
Arina Selawati
Keyword(s):  
Data Mining ◽  
South Korea ◽  
Respiratory System ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
The World ◽  
Bayes Algorithm

Coronavirus 2019 or more commonly referred to as COVID-19 is a type of virus that attacks the respiratory system. Until now the number of spread and the number of deaths caused by this virus continues to increase. As of April 21, 2020, based on data from the WHO, the total number of cases infected with this virus reached 2,397,217 with 162 deaths from all over the world. For South Korea itself, as of March 21, 2020, the total number of infected cases was 10,683 with a total of 237 deaths. In this study, researchers conducted data processing on the spread of COVID-19 in South Korea with Rapidminer using a classification algorithm, namely Naïve Bayes, C4.5, and K-Nearest Neighbor by performing the stages of selection, preprocessing, transfotmating, data mining and interpretation or evaluating the quality of the best accuracy of 80.79% with AUC of 0.881 achieved by the Naïve Bayes algorithm. The distribution of the data found that the influential attribute of the isolated class factor from the patient contained in the sex attribute where more women experienced isolation. Keywords— COVID-19, data mining, classification, C4.5, Naïve Bayes, K-NN

scholarly journals KLASIFIKASI NAÏVE BAYES UNTUK MENDIAGNOSIS PENYAKIT PNEUMONIA PADA ANAK BALITA (STUDI KASUS : UPTD PUSKESMAS SUKARAJA SUKABUMI)

2019 ◽  
Vol 6 (3) ◽  
pp. 241
Author(s):  
Ami Rahmawati ◽  
Dede Wintana ◽  
Satia Suhada ◽  
Gunawan Gunawan ◽  
Hamdun Sulaiman
Keyword(s):  
Data Mining ◽  
Indirect Effects ◽  
Naive Bayes ◽  
Developed Countries ◽  
Naïve Bayes ◽  
Physical Damage ◽  
The World ◽  
Difficulty Breathing ◽  
Bayes Algorithm ◽  
Using Data

<p><em>Pneumonia is a contagious infectious disease that is the leading cause of death in toddlers in the world. In developed countries, there are 4 million cases each year, totaling 156 million cases of pneumonia every year worldwide. Pneumonia is caused by, among others, bacteria, viruses, fungi, exposure to chemicals or physical damage from the lungs, as well as indirect effects from other diseases. Pneumonia is characterized by symptoms of coughing and / or difficulty breathing such as rapid breathing, and pulling the lower chest wall inward. Therefore, early detection of pneumonia in children under five is very necessary in order to be able to prevent and cope with the disease into a serious stage as the purpose of this study is to diagnose pneumonia in toddlers using data mining classification, the naïve Bayes algorithm. Of the 118 cases consisting of 113 cases of patients diagnosed with pneumonia and 5 cases of patients who were not diagnosed with pneumonia, an accuracy value of 98% was obtained, so it can be interpreted that the naïve bayes algorithm has a good correlation with the attributes contained in the dataset.</em></p><p><em><strong>Keywords: </strong></em><em>Naïve Bayes Algorithm, Pneumonia.</em></p><p><em>Pneumonia adalah penyakit infeksi menular yang merupakan penyebab utama kematian pada balita di dunia. Di negara maju terdapat 4 juta kasus setiap tahun hingga  total di seluruh dunia ada 156 juta kasus pneumonia anak balita  setiap tahun. Pneumonia antara lain disebabkan oleh bakteri, virus, jamur, pajanan bahan kimia atau kerusakan fisik dari paru-paru, maupun pengaruh tidak langsung dari penyakit lain. Pneumonia ditandai dengan gejala batuk dan atau kesulitan bernapas seperti napas cepat, dan tarikan dinding dada bagian bawah ke dalam. Oleh Karena itu, deteksi dini penyakit pneumonia pada anak balita sangat diperlukan</em><em> </em><em>agar dapat mencegah dan menanggulangi penyakit tersebut kedalam tahap yang serius</em><em> seperti tujuan p</em><em>enelitian ini </em><em>yaitu</em><em> untuk mendiagnosis penyakit pneumonia pada anak balita menggunakan klasifikasi data mining yaitu algoritma naïve bayes. Dari 118 kasus yang terdiri dari 113 kasus pasien yang terdiagnosis pneumonia dan 5 kasus pasien yang tidak terdiagnosis pneumonia maka diperoleh nilai akurasi sebesar 98%, sehingga dapat diartikan bahwa algoritma naïve bayes memiliki korelasi yang baik dengan atribut yang terdapat pada dataset.</em></p><p><em><strong>Keywords: </strong></em><em>Naïve Bayes Algorithm, Pneumonia.<strong></strong></em></p><p> </p><p><em><br /></em></p>

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2018 ◽  
Vol 7 (2.32) ◽  
pp. 363 ◽  
Author(s):  
N Rajesh ◽  
Maneesha T ◽  
Shaik Hafeez ◽  
Hari Krishna
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Decision Trees ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Bayes Algorithm ◽  
The One

Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.  

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

Analisa Topik Pendidikan Dalam Al-Quran dengan Pendekatan Text Mining

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bustami Yusuf ◽  
Muhammad Zaeki ◽  
Hendri Ahmadian ◽  
Khairan Ar ◽  
Sri Wahyuni
Keyword(s):  
Evaluation Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Sources Of Knowledge ◽  
Scientific Disciplines ◽  
Study Results ◽  
The World ◽  
The Subject ◽  
Bayes Algorithm

Education is one of the sciences that makes humans much better by learning various scientific disciplines. Al-Quran is one of the sources of knowledge that is believed by Muslims around the world. Because technology has penetrated almost every domain of our lives , including the world of education. Thus, the authors make technology as tool  for researching educational topics in Al-Quran by implementing text exploration .The research was carried out by making some basic words that were related to the subject of education as the keywords in this study. The keywords are “Ajar”, “Bicara”, “Cipta”, “Dengar”, “Ingat” and “Lihat”. Then, the authors implemented the Naïve Bayes Classifier algorithm. To test and evaluate the results, the author used two methods, i.e. recall and precision. The study results are the keyword “cipta” by 3.05 %, “Ingat” 2.25 %, “Ajar” 1.96 %,“Lihat” 0.82 %, finally “Dengar” 0.62% and “Bicara” 0.34% with  total  weight of 3,516 words that  have been filtered. The overall percentage of the results is 9.04% of the total number of words 38,761 in the Al-Quran. For the Naïve Bayes algorithm evaluation method,  the recall and precision scores are 0.605 and 0.366, respectively.

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

scholarly journals Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara

2020 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Derisma Derisma ◽  
Fajri Febrian
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Accuracy Rate ◽  
Cancer Disease ◽  
Network Support

Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar  98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %.   Kata kunci: kanker payudara, neural network, support vector machine, naive bayes   Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate.   Keywords: breast cancer, neural network, support vector machine, naive bayes

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