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 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. 

Metode Naïve Bayes Untuk Penilaian Kinerja Dosen Universitas Islam 45 Bekasi

2018 ◽  
Vol 6 (2) ◽  
pp. 201-210
Author(s):  
Haryono Haryono
Keyword(s):  
Data Mining ◽  
Quality Assurance ◽  
Performance Appraisal ◽  
Naive Bayes ◽  
Value Added ◽  
Naïve Bayes ◽  
Internal Quality ◽  
Appraisal System ◽  
A Value ◽  
Bayes Algorithm

Abstract   Each semester of each lecturer Universitas Islam 45 Bekasi is evaluated by the quality assurance office (KPM) by collecting important documents such as SK, certificates and journals, but the results of lecturer performance assessment are often complained by lecturers. Problems that are complained of them are many lecturers complain of getting minus results there is also a score of 0 or empty even more value obtained lecturer in the previous semester that can be used as a value added for next semester assessment can be lost or not counted for the next semester. By utilizing the data at Quality Assurance Center of Universitas Islam 45 Bekasi is expected to be able to measure internal quality continuously. This study aims to implement naïve Bayes algorithm on lecturer performance appraisal system at Quality Assurance Office of Universitas Islam 45 Bekasi. The method used is qualitative and the algorithm used is Naïve Bayes while the application used is WEKA. Accuracy Information from the implementation of this system can be used by the management of Universitas Islam 45 Bekasi in making decisions.   Keywords: Data Mining, Lecturer's Performance, Naïve Bayes     Abstrak   Setiap akhir semester tiap-tiap dosen Universitas Islam 45 Bekasi dievaluasi kenerjanya oleh kantor penjaminan mutu (KPM) dengan mengumpulkan berkas-berkas penting seperti SK, sertifikat-sertifikat maupun jurnal, namun hasil penilaian kinerja dosen sering dikeluhkan oleh dosen-dosen.  Masalah-masalah yang dikeluhkan tersebut diantaranya adalah banyak dosen mengeluh mendapatkan hasil minus ada pula yang mendapatkan nilai 0 atau kosong bahkan nilai lebih yang didapat dosen pada semester sebelumnya yang dapat digunakan sebagai nilai tambah untuk penilaian disemester selanjutnya bisa hilang atau tidak terhitung untuk semester selanjutnya. Dengan memanfaatkan data pada Pusat Penjaminan Mutu Universitas Islam 45 Bekasi, diharapkan dapat dilakukan pengukur kualitas internal secara berkelanjutan. Penelitian ini bertujuan mengimplementasikan   algoritma naïve Bayes pada sistem penilaian kinerja dosen di Kantor Penjaminan Mutu Universitas Islam 45 Bekasi. Metode yang digunakan adalah kualitatif dan algoritma yang digunakan adalah Naïve Bayes sedangkan aplikasi yang digunakan adalah WEKA. Keakuratan Informasi dari implementasi sistem ini dapat digunakan oleh pihak manajemen Universitas Islam 45 Bekasi dalam mengambil keputusan.   Kata kunci: Data Mining, Kinerja Dosen, Naïve Bayes

Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung

2020 ◽  
Vol 4 (2) ◽  
pp. 437 ◽  
Author(s):  
Dito Putro Utomo ◽  
Mesran Mesran
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Diagnostic Process ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set ◽  
Pca Method

Heart disease is a disease with a high mortality rate, there are 12 million deaths each year worldwide. This is what causes the need for early diagnosis to find out the heart disease. But the process of diagnosis is quite challenging because of the complex relationship between the attributes of heart disease. So it is important to know the main attributes that are used as a decision making process or the classification process in heart disease. In this study the dataset used has 57 types of attributes in it. So that reduction is needed to shorten the diagnostic process, the reduction process can be carried out using the Principal Component Analysis (PCA) method. The PCA method itself can be combined with data mining calcification techniques to measure the accuracy of the dataset. This study compares the accuracy rate using the C5.0 algorithm and the Naïve Bayes Classifier (NBC) algorithm, the results obtained both after and before the reduction are Naïve Bayes Classifier (NBC) algorithms that have better performance than the C5.0 algorithm

IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI PEMESANAN DRIVER GO-JEK ONLINE DENGAN MENGGUNAKAN METODE NAIVE BAYES (STUDI KASUS: PT. GO-JEK INDONESIA)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Delisman Laia ◽  
Efori Buulolo ◽  
Matias Julyus Fika Sirait
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Transportation Industry ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Taxi Service ◽  
Bayes Algorithm ◽  
Using Data

PT. Go-Jek Indonesia is a service company. Go-jek online is a technology-based motorcycle taxi service that leads the transportation industry revolution. Predictions on ordering go-jek drivers using data mining algorithms are used to solve problems faced by the company PT. Go-Jek Indonesia to predict the level of ordering of online go-to drivers. In determining the crowded and lonely time. The proposed method is Naive Bayes. Naive Bayes algorithm aims to classify data in certain classes. The purpose of this study is to look at the prediction patterns of each of the attributes contained in the data set by using the naive algorithm and testing the training data on testing data to see whether the data pattern is good or not. what will be predicted is to collect the data of the previous driver ordering, which is based on the day, time for one month. The Naive Bayes algorithm is used to predict the ordering of online go-to-go drivers that will be experienced every day by seeing each order such as morning, afternoon and evening. The results of this study are to make it easier for the company to analyze the data of each go-jek driver booking in taking policies to ensure that both drivers and consumers or customers.Keywords: Go-jek Driver, Data Mining, Naive Bayes

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 A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Dedi Saputra ◽  
Windi Irmayani ◽  
Deasy Purwaningtias ◽  
Juniato Sidauruk
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Heart Disease ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Swarm Optimization ◽  
C4.5 Algorithm ◽  
Bayes Algorithm

Heart disease is a general term for all of types of the disorders which is affects the heart. This research aims to compare several classification algorithms known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The algorithm is about to optimize of the heart disease predicting by applying Particle Swarm Optimization (PSO). Based on the test results, the accuracy value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm accuracy value is about 85.26% and the last the Support Vector Machine algorithm is about 85.26%. From the three of algorithms above then continue to do an optimization by using Particle Swarm Optimization. The data is shown that Naïve Bayes algorithm with Particle Swarm Optimization has the highest value based on accuracy value of 86.30%, AUC of 0.895 and precision of 87.01%, while the highest recall value is Support Vector Machine algorithm with Particle Swarm Optimization of 96.00%. Based on the results of the research has been done, the algorithm is expected can be applied as an alternative for problem solving, especially in predicting of the heart disease.

NAIVE BAYES ALGORITHM IMPLEMENTATION TO DETECT HUMAN PERSONALITY DISORDERS

2020 ◽  
Vol 17 (1) ◽  
pp. 9-16
Author(s):  
Yoga Aditama Ika Nanda ◽  
Bety Wulan Sari
Keyword(s):  
Personality Disorders ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Data Set ◽  
Human Personality ◽  
A Value ◽  
Bayes Algorithm ◽  
Algorithm Implementation ◽  
Fold Cross Validation

We live in a society that still sees problems regarding one's soul and personality as taboo, even though mental health is as important as physical health. A personality disorder itself is a disorder that can be seen from behavior, mindset, and attitude, which brings difficulties to life. Based on this problem, this study applies the method of Naive Bayes classifier as early detection of human personality disorders. Using a data set of 130 correspondences from the AMIKOM university scope with the age limit of 18-25 years and identified personality disorders is a borderline type disorder. The data obtained was 94 with undiagnosed classes and 36 with undiagnosed classes, with the research variables in the form of questionnaire questions as many as 13 questions. The testing process is done with 10 fold and 5 fold cross-validation, and confusion matrix with the results in the form of accurate 10 folds superior with a value of 88.8% compared to 5 folds that is 88.2%, for precision 10 folds superior with 88.7%, but for 5 fold recall superior with 88.3%, while the final results of these two performances in F1-Score, produce the same value, which is 86.1%.

scholarly journals Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection

2020 ◽  
Vol 1 (3) ◽  
pp. 123-134
Author(s):  
Budiman Budiman ◽  
Reni Nursyanti ◽  
R Yadi Rakhman Alamsyah ◽  
Imannudin Akbar
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Study Program ◽  
Data Set ◽  
Lower Accuracy ◽  
Accuracy Result ◽  
Bayes Algorithm

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.

scholarly journals Comparison of Data Mining Methods Using the Naïve Bayes Algorithm and K-Nearest Neighbor in Predicting Immunotherapy Success

Tech-E ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 30
Author(s):  
Budi Harto ◽  
Rino Rino
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Rapid Development ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Risk Of Death ◽  
K Nearest Neighbor Algorithm ◽  
Mining Methods ◽  
Bayes Algorithm

tumor or cancer is a disease that is a problem for people who are increasing every year. This disease in both the early and final stages requires attention because in this disease sufferers have a large risk of death. along with the rapid development of technology, we can use the technology to facilitate in all fields one of which is to predict success in a therapy. Data mining is one of the techniques used by the author in testing the dataset used in this study to get the best algorithm between Naïve Bayes and the K-Nearest Neighbor algorithm by using the Rapid Miner S tudio application and applying the best algorithm into the expected application or expert system. can help users predict the success of a therapy.

scholarly journals Rekayasa Klasifikasi Pencarian Abstrak Tentang Mikrokontroler e-Journal Instek dengan Algoritma Naive Bayes

2020 ◽  
Vol 1 (1) ◽  
pp. 34-45
Author(s):  
Faisal ◽  
A.MUHAMMAD SYAFAR ◽  
UMMI AZIZAH MUKADDIM
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Design Method ◽  
Naïve Bayes ◽  
Unified Modeling ◽  
Running System ◽  
Sample Data ◽  
Calculation Results ◽  
Bayes Algorithm

Abstrak Objektif. Jurnal Instek merupakan jurnal elektronik yang ada di Teknik Informatika UIN Alauddin Makassar. E-Journal merupakan representasi elektronik sederhana dari jurnal. Dalam kebanyakan kasus peningkatan volume informasi yang berbentuk E-Journal menimbulkan kesulitan untuk mengelompokkan E-Journal sesuai dengan kategorinya. Berdasarkan hal tersebut maka dirancang sebuah website untuk mengelompokkan E-Journal agar sesuai dengan kateogrinya, pengelompokkan E-Journal terdiri dari empat kategori yaitu, Data Mining, Game, Multimedia, dan Sistem Informasi, sehingga mempermudah seseorang untuk mengelompokkan E-Journal sesuai dengan kategorinya. Material and Metode.. Dalam penelitian ini, jenis penelitian yang digunakan adalah penelitian deskriptif kualitatif adapun metode kualitatif. Adapun metode perancangannya menggunakan unified modeling (UML). Analisis yang dilakukan mencakup analisis sistem yang berjalan dan analisi sistem yang diusulkan. Hasil. Hasil penelitian ini berupa website yang dapat mengelompokkan E-Journal berdasarkan klasifikasi. Sistem yang dibangun menggunakan algoritma Naïve Bayes untuk mengelompokkan atau mengklasifikasikan E-Journal. Kesimpulan.. Berdasarkan hasil perhitungan Klasifikasi sampel data 1 diperoleh 0,0666 sebagai jumlah tertinggi dengan kategori Mikrokontroller. Abstrak Objektive. Instek Journal is an electronic journal in the Informatics Engineering UIN Alauddin Makassar. E-Journal is a simple electronic representation of a journal. In most cases an increase in the volume of information in the form of an E-Journal makes it difficult to group the E-Journal according to its category. Based on this, a website is designed to classify E-Journal to fit its category, the grouping of E-Journal consists of four categories, namely, Data Mining, Games, Multimedia, and Information Systems, making it easier for someone to group E-Journal according to its category. Materials and Methode. In this research, the type of research used is descriptive qualitative research as for the qualitative method. The design method uses unified modeling (UML). The analysis carried out includes analysis of the running system and analysis of the proposed system. Results. The results of this study in the form of a website that can classify E-Journal based on classification. The system is built using the Naïve Bayes algorithm to classify or classify E-Journal. Conclusion Based on the calculation results of the classification of sample data 1 obtained 0.0666 as the highest number with the category of microcontroller

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