scholarly journals IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR

2020 ◽  
Vol 3 (2) ◽  
pp. 110-117
Author(s):  
Irayori Loelianto ◽  
Moh. Sofyan S Thayf ◽  
Husni Angriani
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Design Stage ◽  
Prospective Students ◽  
Classifier Design ◽  
Python Programming Language ◽  
Bayes Theory ◽  
Python Programming

STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

scholarly journals Application of the Naïve Bayes Algorithm for Student Graduation Analysis

2018 ◽  
Vol 7 (4.15) ◽  
pp. 421
Author(s):  
Erick Akhmad Fahmi Alfa’izy ◽  
Khairil Anam ◽  
Naidah Naing ◽  
Rosanita Tritias Utami ◽  
Nur Anim Jauhariyah ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
College System ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Using Data ◽  
Analysis System ◽  
Law Student

Design an analysis system to find out graduation by comparing previous data and existing data to overcome errors in a college system. By taking data records that are already available to be processed using the naïve Bayes algorithm. This research was conducted at Universitas Maarif Hasyim Latif. In this case, the object of research is to analyze the data of students with naïve Bayes algorithms to find out their graduation. For sampling the data taken is the previous Faculty of Law Student data to be used as training data, to retrieve the entire data using data records that are already available in the Directorate of Information Systems. That the naïve Bayes algorithm can be used in the classification of data in the form of a string or textual. This is based on researchers' trials in taking examples of calculations that have been done before. To compare the results of the classification of graduation analysis using the naïve Bayes algorithm testing is done with a sample of data in the form of training data compared to data testing. From the calculations that have been made, the accuracy is 77.78%. 

ANALISIS MODEL NAIVE BAYES UNTUK IDENTIFIKASI PENGGOLONGAN DAYA LISTRIK DI KOTA LHOKSUMAWE

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Muhammad Saidi ◽  
Fajriana Fajriana ◽  
Wahyu Fuadi ◽  
Ermatita Ermatita ◽  
Iwan Pahendra
Keyword(s):  
Naive Bayes ◽  
Electrical Power ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Customer Data ◽  
Poor Households ◽  
House Area ◽  
Naive Bayes Method

Electricity subsidy is provided for all 450 VA power household customers and 900 VA power household customers who are poor and disadvantaged. However, there are many facts that household customers with 450 VA power are capable and 900 VA power household customers consist of capable households, boarding houses or luxury rented. Households are able to use more electricity than poor households. This paper describe to the identification of household customers' electrical power in the Lhokseumawe city to facilitate PLN in classifying customer power by using the Naive Bayes method. Naive bayes value variables used in this study are: monthly income, highest diploma, last job, house area, subscription fee and government registered household. The classification of household customer power is grouped into three categories, namely low (450 VA down), medium (900 VA) and high (above 1300 VA).. Based on household customer data that is used as training data, the Naive Bayes method is able to classify the customer data tested. So the Naive Bayes method successfully predicts the magnitude of the probability of household electrical power with an accuracy percentage of 80%.Keywords: Electricity, Naive Bayes,  CBS, low birth weight, subsidy

scholarly journals Diagnosis of Diabetes Using Naïve Bayes Classifier Method

2021 ◽  
Vol 1 (1) ◽  
pp. 22-29
Author(s):  
Tasya Ardhian Nisaa ◽  
Shavira Maya Ningrum ◽  
Berlianda Adha Haque
Keyword(s):  
Data Mining ◽  
Programming Language ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Method ◽  
Bayes Classifier ◽  
Genetic Inheritance ◽  
Data Mining Techniques ◽  
Python Programming Language ◽  
Python Programming

Not a few people suffer from diabetes, diabetes is usually caused by genetic inheritance from parents and grandparents. Not only from heredity but many criteria or characteristics can determine a person has diabetes. This research was conducted by looking for a dataset on Kaggle that contains criteria for someone diagnosed or undiagnosed with diabetes such as age, gender, weakness, polyuria, polydipsia, and others. Furthermore, from these criteria, predictions are calculated using the Naive Bayes classification method where this method is one of the data mining techniques. This prediction calculation uses the Python programming language. From these criteria, each criterion is grouped with similarities and the results of the program that have been made can diagnose someone with diabetes. The prediction calculations that have been carried out have resulted in 90% accuracy, 93% precision, 89% recall, 92% specificity, and 91% F1-Score.

scholarly journals Naïve Bayes Algorithm for Classification of Student Major’s Specialization

2020 ◽  
Vol 1 (1) ◽  
pp. 17
Author(s):  
Astia Weni Syaputri ◽  
Erno Irwandi ◽  
Mustakim Mustakim
Keyword(s):  
Social Sciences ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Natural Sciences ◽  
Training Data ◽  
Average Value ◽  
Bayes Algorithm

Majors are important in determining student specialization. If there is an error in the direction of the student, it will certainly affect the education of subsequent students. In SMA Negeri 1 Kampar Timur, there are two majors, namely Natural Sciences and Social Sciences. To determine these majors, it is necessary to reference the average value of student grades from semester 3 to semester 5 which includes the average value of Islamic religious education, Indonesian, Citizenship Education, English, Natural Sciences, Social Sciences, and Mathematics. Naive Beyes algorithm is an algorithm that can be used in classifying majors found in SMA Negeri 1 Kampar Timur. To determine the classification of majors in SMA Negeri 1 Kampar Timur, training data and test data are used, respectively at 70% and 30%. This data will be tested for accuracy using a confusion matrix and produces a fairly high accuracy of 96.19%. With this high accuracy, the Naive Bayes algorithm is very suitable to be used in determining the direction of students in SMA Negeri 1 Kampar Timur.

scholarly journals Structure Extension of Tree-Augmented Naive Bayes

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 721 ◽  
Author(s):  
YuGuang Long ◽  
LiMin Wang ◽  
MingHui Sun
Keyword(s):  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Training Data ◽  
Conditional Probability Distribution ◽  
Independence Assumption ◽  
Bayesian Network Classifiers ◽  
Leibler Divergence ◽  
The Difference ◽  
Structure Extension

Due to the simplicity and competitive classification performance of the naive Bayes (NB), researchers have proposed many approaches to improve NB by weakening its attribute independence assumption. Through the theoretical analysis of Kullback–Leibler divergence, the difference between NB and its variations lies in different orders of conditional mutual information represented by these augmenting edges in the tree-shaped network structure. In this paper, we propose to relax the independence assumption by further generalizing tree-augmented naive Bayes (TAN) from 1-dependence Bayesian network classifiers (BNC) to arbitrary k-dependence. Sub-models of TAN that are built to respectively represent specific conditional dependence relationships may “best match” the conditional probability distribution over the training data. Extensive experimental results reveal that the proposed algorithm achieves bias-variance trade-off and substantially better generalization performance than state-of-the-art classifiers such as logistic regression.

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

2021 ◽  
Vol 5 (3) ◽  
pp. 527-533
Author(s):  
Yoga Religia ◽  
Amali Amali
Keyword(s):  
Feature Selection ◽  
Customer Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Point Of View ◽  
Classification Model ◽  
Passenger Satisfaction ◽  
Airline Passenger ◽  
Bayes Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

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