Bayes Classifier Method Research Articles

Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline

Brain atrophy is the degradation of brain cells and tissues to the extent that it is clearly indicative during Mini-Mental State Exam test and other psychological analysis. It is an alarming state of the human brain that progressively results in Alzheimer disease which is not curable. But timely detection of brain atrophy can help millions of people before they reach the state of Alzheimer. In this study we analyzed the longitudinal structural MRI of older adults in the age group of 42 to 96 of OASIS 3 Open Access Database. The nth slice of one subject does not match with the nth slice of another subject because the head position under the magnetic field is not synchronized. As a radiologist analyzes the MRI image data slice wise so our system also compares the MRI images slice wise, we deduced a method of slice by slice registration by driving mid slice location in each MRI image so that slices from different MRI images can be compared with least error. Machine learning is the technique which helps to exploit the information available in abundance of data and it can detect patterns in data which can give indication and detection of particular events and states. Each slice of MRI analyzed using simple statistical determinants and Gray level Co-Occurrence Matrix based statistical texture features from whole brain MRI images. The study explored varied classifiers Support Vector Machine, Random Forest, K-nearest neighbor, Naive Bayes, AdaBoost and Bagging Classifier methods to predict how normal brain atrophy differs from brain atrophy causing cognitive impairment. Different hyper parameters of classifiers tuned to get the best results. The study indicates Support Vector Machine and AdaBoost the most promising classifier to be used for automatic medical image analysis and early detection of brain diseases. The AdaBoost gives accuracy of 96.76% with specificity 95.87% and sensitivity 87.37% and receiving operating curve accuracy 96.3%. The SVM gives accuracy of 96% with 92% specificity and 87% sensitivity and receiving operating curve accuracy 95.05%.

Analisis Sentimen Terhadap Kebijakan Pemerintah Tentang Larangan Mudik Hari Raya Idulfitri di Indonesia Tahun 2021 Menggunkan Metode Naïve Bayes

Social media as a place to access and disseminate information has grown very rapidly, one of which is Twitter. Twitter, as a place for information flow, is a rich source for seeking public opinion and sentiment analysis. Twitter in this study was used as a source to obtain data about the 2021 homecoming in Indonesia. The purpose of this study is to determine public satisfaction with government policies regarding the ban on going home in Indonesia in 2021. The data to be processed is Indonesian-language tweets, the keywords are #mudik and #diarangmudik, the length of data collection is 1 week, with lots of data generated as many as 1000. Sentiment analysis in this study using the Naive Bayes Classification method. The steps in this study are first crawling Twitter data which is then stored in csv format, second preprocessing which consists of tokenizer, case folding, cleansing and stop removal, third Naive Bayes classification which will be carried out after going through the Pre-processing stage, where the results of the classification tweets tend to be positive or negative or neutral. The results of this study obtained an accuracy of 56.52% with each positive sentiment value of 62.28%, negative sentiment as much as 46.72% and neutral sentiment as much as 66.50%.

Prediction of The Level of Public Trust in Government Policies in the 1st Quarter of The Covid 19 Pandemic using Sentiment Analysis

The covid-19 pandemic has made changes in society, including Government policy. The policy changes led to mixing responses from the public, namely netizens. Netizen shares their opinion in social media, including Twitter. Their opinion can represent the public’s trust in the Government. Sentiment analysis analyses others’ opinions and categorises them into positive opinions, negative opinions, or neutral opinions. Sentiment analysis can analyze large numbers of opinions so that public opinion can be analyzed quickly. This paper explains how to analyze public trust using sentiment analysis and to use Naïve Bayes classification method to analyze sentiment. The data research was taken from Twitter in the first quarter of the Covid-19 pandemic, with around 3000 tweets. The tweets were related to Covid-19 and the Government from several countries such as the United States, Australia, Ireland, Switzerland, Italy, Philippines, Sri Lanka, Canada, Netherlands, United Kingdom, Germany, and Lebanon. This study aims to determine the level of public trust in the Government in the first quarter of the Covid-19 pandemic. The research result is expected to be used as a reference for the public policy stakeholders to determine future policies.

Application for Selection of Student Final Project Supervisors Based on the Selected Category and Expertise of Lecturers Using the Naive Bayes Classifier Method

At the end of the task the supervisor has an important role for the success achieved and graduation of students. For this reason, ideal supervisors are needed for students. As discussed in the STMIK Hang Tuah Pekanbaru in the process of submitting the title of this thesis, so are some of the problems that arise, namely regarding the matter of coaching because the process is still using conventional methods that is based on personal knowledge of the Head of Study Program, the difficulty of the development process of submitting the Student's final position for difficulties check the final supervisor's assignment. The application of selecting the final project supervisor for students is the solution of the debate. The supervisor lecturer recommendation system that can utilize the naïve bayes classifier algorithm as a determinant of the probability of the lecturer results students can choose. Naive Bayes is a prediction technique based on simple probabilities based on the application of the Bayes theorem (Bayes rule) with a strong assumption of independence. The selection is based on the final criteria for the assignment and expertise of the lecturer. From the application of this recommendation is obtained from the recommendations of supervisors in accordance with the concept of the student's final project. With reference data, training and Bayes rules obtained sufficient results to satisfy students in getting a supervisor who is in accordance with the topic of the student's final project.

Classification of Zakat Fitrah Recipients Using Naïve Bayes Method

Indonesia is a country with a majority Muslim population. In the daily life of the Indonesian population, it is inseparable from the influences of Islamic teachings. In life in this world there are many commands of Allah that must be carried out, including the order to pay zakat. One of them is when Eid al-Fitr is required to pay zakat fitrah for each of its citizens. In grouping the distribution of zakat fitrah using the Naïve Bayes classification method. Naïve Bayes classification itself is a classification method that can be applied in classification. The classification system used to classify categories of zakat fitrah recipients. From each test results using test data and training data randomly, and each test using training data which increased 37 pieces of data in each test. It can be concluded that the more training data the level of accuracy decreases. The determination of the amount of training data and test data is very influential on the final results of calculations using the Naïve Bayes method. Class determination also affects the final results of calculations using this Naïve Bayes method.

Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classifier models built, namely the classifier model with two sentiment classes and four sentiment classes. The accuracy results show that the SVM method has a better accuracy value than the NBC method. SVM has an accuracy value of 63.60% and 82.77% for the two sentiment classes in the four sentiment classifier model. The tweet classification results show that the public's conversation about the Indonesian Social Insurance Administration Organization on Twitter has a negative polarity value tendency.

Model Expert System for Diagnosis of Covid-19 Using Naïve Bayes Classifier

This paper offers an expert system model for COVID-19 diagnosis as an effort to overcome the spread of COVID-19 in Indonesia. The expert system model was built using the Naive Bayes Classifier method. Model development is carried out with preliminary research stages, data collection, analysis, model design, implementation, and testing. The data used to build and test the model comes from the health department and the acceleration of the Covid- 19 countermeasure group in Indonesia. The model was developed with a unified modeling language and a prototyping approach. Tests show that the developed COVID-19 diagnosis system expert model can diagnose COVID-19 based on the symptoms inputted by the user into the system. The application of the model produced in this study helps assist doctors in diagnosing COVID-19.

Cosmetics Customer Segmentation and Profile in Indonesia Using Clustering and Classification Algorithm

The cosmetics business competition in Indonesia is currently increasing so rapidly, cosmetics customers have spread to various brands, and according to taste. The customer for the company is an asset that is very important for business continuity, so that good customer management can increase company revenue. However, it is not easy to manage customers if they cannot read the characteristics of customers, to carry out appropriate business strategies. So that requires a customer analysis method that can provide recommendations for the company. RFM is one of the most widely used analytical methods for analyzing customers through segmentation and profiling of customers. In addition to segmenting, the customer profile is also a very important factor in analyzing customers, ALC is a form of a customer profile that can be used. RFM + ALC method is not easy to do with very large customer history data, so data mining is needed to help conduct the RFM + ALC analysis. Data mining methods using the clustering function with K-Means and the use of the Elbow method to get the most optimal amount of K in the clustering process can be a model used to segment with RFM, as well as the Naïve Bayes and Decision Tree classification methods to determine ALC profile factors the most influential customer. The results of clustering modeling carried out produce two dominant customer segments. While the Naïve Bayes classification model of the ALC factor can provide recommendations for the most influential customer profiles, with the highest level of accuracy with an accuracy value of 65.87% when compared to the Decision Tree.

Sentiment Analysis of Online Transportation Service using the Naïve Bayes Methods

Sentiment analysis is a computational study of opinions and emotions expressed textually. Sentiment analysis will group text in sentences or documents to find out the opinions expressed in the sentence or document, whether negative or positive. This sentiment analysis research was conducted on the online taxi transportation service. Gojek uses a lot of social media to communicate with its customers, one of the social media used is Instagram. This research takes 1000 comments from the Instagram of the Gojek page which is used to see the public opinion of the online Gojek transportation services. Comments from the page are processed by doing text preprocessing and then classified using the Naive Bayes Classifier (NBC) method to obtain the value of the public value for online transportation services. The results of this study using the Naive Bayes method resulted in an accuracy value of 81.00%, which means that from all the comments on the Instagram page, the subject of the NBC method could be accurately classified by 81.00% whether the comments were negative or positive comments.

Classification of Graduate Profiles Based on Graduate Tracer Study Using Algorithm Naive Bayes Classifier

The graduate profile is the role of the graduate of the study program or field of expertise / field of work planned after completing education from the study program. The determination of the profile of study program graduates is generally carried out based on the results of the assessment of stakeholder needs. Based on data from the Ministry of Research, Technology and Higher Education, the IT study program is one of the most majors or study programs in Indonesian universities and the highest number of enthusiasts choose this study program each year. Each year, graduates of IT study programs have a large number of graduates, both vocational and non- vocational colleges. The number of IT graduates is large but they have low graduate competencies, even they do not have competencies in the IT field so that their work is not in accordance with the graduate profile that has been designed. Therefore, it is necessary to conduct research to classify the profile of graduates who have worked based on tracer study data using the Naïve Bayes Classifier method. This study uses attributes, namely study program, value criteria, gender and field of work and the labels used are status (Linear and Non-Linear). The results of the study on the classification of the profile of graduates using the Naïve Bayes Classfier method show that alumni work not according to the profile of graduates by 73% and according to the profile of graduates by 23%, with a data accuracy rate of 87% and are included in the good classification category.

SELEKSI FITUR INFORMATION GAIN DAN ALGORITMA NAÏVE BAYES UNTUK REVIEW OPINI KONSUMEN

The growth of internet users in Indonesia is increasing, this is in line with online shopping habits or often referred to as e-commerce which continues to increase. Various things are done by e-commerce companies to maintain customer loyalty, one of which is through product evaluation using consumer opinion reviews. The number of reviews that are too many will be biased, so it is necessary to do a classification method that will help e-commerce companies to find out the extent of their customer loyalty. Consumer review becomes something important because all assessments of the products they buy are all in the review column. In this research, a consumer review is carried out using the Naive Bayes classification method and to improve the accuracy of attributes using the Information Gain feature selection and using the Select by Weight operator which will display the best attributes of the pre processing process. The review data set is taken from consumers' comments on Google Play. The results of this study are classifying consumer reviews into positive reviews and negative reviews with Cross Validation using 10 fold, the accuracy of the Naive Bayes method is 78.4% using the Information Gain feature selection method, the accuracy increases to 81.2%

SENTIMENT ANALYSIS OF DIGITAL WALLET SERVICE USERS USING NAÏVE BAYES CLASSIFIER AND PARTICLE SWARM OPTIMIZATION

Digital wallet services provide many conveniences and benefits to its users. However, not all digital wallet service users have a positive opinion of the service. Sentiment analysis in this study aims to determine the opinions given by Dana and Isaku digital wallet service users whether they contain positive or negative opinions and apply the Naïve Bayes Classifier and Particle Swarm Optimization (PSO) method to the sentiment analysis of digital wallet service users. The Naïve Bayes Classifier method is used because it is simple, fast, high accuracy, and has good enough performance to classify data, but the Naïve Bayes Classifier has the disadvantage that each independent variable is assumed to cause a decrease in the accuracy value. Therefore, this research added an attribute weighting method, namely Particle Swarm Optimization (PSO) to increase the accuracy of the classification of the Naïve Bayes Classifier. This study uses data taken from Twitter as many as 490 tweet data. The test results using the confusion matrix and ROC curve show an increase in accuracy of the Naïve Bayes Classifier Dana digital wallet from 60.00% to 91.67% and I.Saku digital wallet from 53.23% to 85.00%. T-Test and Anova test results show that the two classification methods tested have significant (significant) differences in Accuracy values.

A predictive risk level classification of diabetic patients using deep learning modified neural network

In health care firm, data mining (DM) has an effectual role in predicting the diseases. Today, diabetes is the chief global health issue. Several algorithms are introduced for predicting the diabetes disease and its accuracy estimation. Yet, there is no effectual algorithm for providing the severity of diabetes in respect of ratio which interprets the impact of diabetes on different organs of the human body. To overcome such drawbacks, predictive and risk level classification of diabetes patients using DLMNN and Naive Bayes (NB) classification methods is system model. This system model system comprises 2 phases namely, phase-1: diabetic disease prediction model, and phase-2: risk analysis. In phase-1, the patient data are taken as of the dataset. Then, from this patient dataset repeated data are removed using HDFS Map Reduce (). Next, as the preprocessing stage, the missing attributes are replaced by averaging the considered data. After that, from the preprocessed data the disease is predicted using DLMNN classification method which results in obtaining the diabetic patient data. Then, the diabetic patient data are sent to phase-2. In phase 2, the missing attributes are replaced using the same average method. Next, the patient data is sorted centered on age utilizing recursive K-means clustering algorithm. Finally, the clustered patient data is classified using the NB classifier algorithm. Experiential results contrasted the system model modified deep learning algorithm with the existing IKMC algorithm in rapports of precision, accuracy, F-measure, and recall. The outcomes confirmed that the system model diabetes prediction and analysis model shows better results on considering the existent methods.

Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store

In this research, the performance of SVM classification method will be compared with other classification methods, by using the Naïve Bayes classification method. Naïve Bayes classification method is a light classification method and has a high accuracy if applied to the text classification according to some previous studies. The accuracy of the classifier is measured using the K-fold cross validation method whose results will be tabulated in a confusion matrix table, with a value of K = 3. In this study, the data processed are textual reviews of applications in the Indonesian language Google Play Store obtained from previous research. The test results obtained from the 3-fold cross-validation method produce that SVM Classifier has a higher value of accuracy when compared with the accuracy of the Naïve Bayes classifier, the SVM classifier gets an accuracy of 81.46% and Naïve Bayes classifier by 75.41%.

Classification of genetic expression in prostate cancer using support vector machine method

Prostate cancer has long been a concern of expert’s human genetics in health research. However, an explanation of the main causes of prostate cancer cannot be obtained metabolically-biologic, except the most common one of which is heredity. Explanation of the risk of contracting prostate cancer is sought through genetic explanation of prostate cancer cells and healthy prostate cells from DNA sequencing in the form of micro arrays data or in the form of Gleason values. Cancer cell genetic data is high dimensional where the number of variables observed were far more than the individual observed. It’s make ordinary multivariate classification techniques fail to handle this data because of the singularity matrix. In addition, the observations number of cancer patients are small since they are rarely found. With these two facts, then in this paper we will use a machine learning approach to study the classification, namely SVM. SVM will be compared with the Naive Bayes Classifier and Discriminant Analysis method to determine the accurate division in distinguishing prostate cancer cells from healthy prostate cells. The sample data used consisted of 102 people with 2135 genetic variables which were then divided into training data and testing data. Based on the results of the study, the classification by the SVM method has an accuracy value of 96% with a precision error in the tumor class of 7%. The Naive Bayes classification has a precision error of 23.5% with a classification accuracy of 84%. While the Discriminant Analysis method produces an accuracy of 92% with a precision error of 13.33%.

Diabetes Mellitus Prediction using Ensemble Machine Learning Techniques

The healthcare industry is inflicted with the plethora of patient data which is being supplemented each day manifold. Researchers have been continually using this data to help the healthcare industry improve upon the way major diseases could be handled. They are even working upon the way the patients could be informed timely of the symptoms that could avoid the major hazards related to them. Diabetes is one such disease that is growing at an alarming rate today. In fact, it can inflict numerous severe damages; blurred vision, myopia, burning extremities, kidney and heart failure. It occurs when sugar levels reach a certain threshold, or the human body cannot contain enough insulin to regulate the threshold. Therefore, patients affected by Diabetes must be informed so that proper treatments can be taken to control Diabetes. For this reason, early prediction and classification of Diabetes are significant. This work makes use of Machine Learning algorithms to improve the accuracy of prediction of the Diabetes. A dataset obtained as an output of K-Mean Clustering Algorithm was fed to an ensemble model with principal component analysis and K-means clustering. Our ensemble method produced only eight incorrectly classified instances, which was lowest compared to other methods. The experiments also showed that ensemble classifier models performed better than the base classifiers alone. Its result was compared with the same Dataset being applied on specific methods like random forest, Support Vector Machine, Decision Tree, Multilayer perceptron, and Naïve Bayes classification methods. All methods were run using 10k fold cross-validation.

Comparative Analysis of Support Vector Machine-Recursive Feature Elimination and Chi-Square on Microarray Classification for Cancer Detection with Naïve Bayes

Cancer is a world-famous deadly disease. According to the World Health Organization (WHO), cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. One well-known technique for cancer detection is the DNA microarray technique. DNA microarray technology provides an opportunity for researchers to analyze thousands of gene expression profiles at the same time to determine whether a person has cancer or not. However, one of the problems in DNA microarray data is the large number of features that require feature selection. In overcoming these problems, this study will use the feature selection Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Chi-Square and use the Naive Bayes classification method. The accuracy results from using feature selection with those that are not will be compared. The accuracy between using the two feature selection methods will also be compared to find which feature selection method is better when combined with the Naive Bayes classification method. To get an overall picture of the performance comparison, this study also considers precision, recall, and F1-score. The best accuracy results obtained were 100% lung cancer data with SVM-RFE and Chi-Square, 99.6% ovarian cancer with SVM-RFE, 93.7% breast cancer with SVM-RFE, and 90% colon cancer with SVM- RFE.

Diagnosa Penyakit Ginjal Kronis Menggunakan Metode Klasifikasi Naïve

Chronic Kidney Disease is a very dangerous disease that is often not seriously considered with the effects of this disease which leads to death. More than 26 million people in the United States are not aware of their kidney disease, only 8% of them begin to realize the disease, each body must be known early whether or not the body condition / / by diagnosing it, in this study classifications will be carried out on the diagnostic data attributes Chronic kidney disease aims to simplify the process of classifying symptoms and making decisions on the diagnosis of kidney disease, this process is carried out using a data mining classification approach using the Naïve Bayes Classifier method

Supervised Machine Learning Segmentation and Quantification of Gastric Pacemaker Cells.

Interstitial Cells of Cajal (ICC) are specialized pacemaker cells that generate and actively propagate electrophysiological events called slow waves. Slow waves regulate the motility of the gastrointestinal tract necessary for digesting food. Degradation in the ICC network structure has been qualitatively associated to several gastrointestinal motility disorders. ICC network structure can be obtained using confocal microscopy, but the current limitations in imaging and segmentation techniques have hindered an accurate representation of the networks. In this study, supervised machine learning techniques were applied to extract the ICC networks from 3D confocal microscopy images. The results showed that the Fast Random Forest classification method using Trainable WEKA Segmentation outperformed the Decision Table and Naïve Bayes classification methods in sensitivity, accuracy, and F-measure. Using the Fast Random Forest classifier, 12 gastric antrum tissue blocks were segmented and variations in ICC network thickness, density and process width were quantified for the myenteric plexus ICC network (the primary pacemakers). Our findings demonstrated regional variation in ICC network density and thickness along the circumferential and longitudinal axis of the mouse antrum. An inverse relationship was observed in the distal and proximal antrum for density (proximal: 9.8±4.0% vs distal: 7.6±4.6%) and thickness (proximal: 15±3 μm vs distal: 24±10 μm). Limited variation in ICC process width was observed throughout the antrum (5±1 μm).Clinical Relevance- Detailed quantification of regional ICC structural properties will provide insights into the relationship between ICC structure, slow waves and resultant gut motility. This will improve techniques for the diagnosis and treatment of functional GI motility disorders.

Implementation of Decision Tree and Naïve Bayes Classification Method for Predicting Study Period

student learning performance influences the quality of a university. One indicator of assessment of student learning performance is the student’s study period. By predicting the student’s study period, universities can evaluate performance to strive to improve the quality of universities. Data mining is one of the choice technique in terms of predictions. But, each algorithm in data mining has the advantages of each, including Decision Tree and Naïve Bayes Algorithm which were tested in this research. This research shows Decision Tree is better than Naïve Bayes Algorithm to be the best choice to predicting study period with accuracy level tested for several ratios of training data and test data are 60:40,65:35,70:30,75:25 and 80:20 produce the highest accuracy values obtaines by decision tree with accuracy values are 90%, 89,14%, 89,3%, 88,8% and 88%. But, the amount of training data in this research does not affect the value of accuracy. This is shown from the value of accuracy in a certain ratio is very small but sometimes also the value of accuracy is high