Bayes Classification Algorithm Research Articles

ANALISIS SENTIMEN MASYARAKAT TERHADAP PAYLATER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER

Online shopping is so popular with the public because it is easy and convenient to do. The convenience of online shopping is supported by the payment method via paylater. However, paylater also results in bad behavior such as impulse buying. Various responses from the community made researchers conduct research to find out the public's view of paylater. In this study, researchers tried to do sentiment analysis using the Naive Bayes Classifier and TextBlob methods from the TetxBlob library with the Python programming language. From the dataset collected via Twitter, it produces 405 data. Sentiment analysis using the Naive Bayes Classifier method produces a negative sentiment of 70.62% or 286 data, positive sentiment is 22.72% or 92 data, neutral sentiment is 6.67% or 27 data. Meanwhile, using the TextBlob method also produced more negative sentiment, namely 55.8% or 226 data, positive sentiment collected 33.09% or 134 data, neutral sentiment amounted to 11.11% or 45 data. Thus, it can be concluded that the community feels unfavorable towards the use of paylater. In testing the model with the confussion matrix, it can be seen that the Naive Bayes Classifier algorithm is more accurate by 91% compared to TextBlob which is only 61%.

Implementasi Algoritma Naive Bayes Untuk Klasifikasi Penerima Beasiswa (Studi Kasus Universitas Hamzanwadi)

Every year the University offers various types of scholarships to its students, including Hamzanwadi Selong University, East Lombok. One type of scholarship offered is the Bidikmisi scholarship (KIP/K) which is intended for students who have middle to lower economic levels but have good academic achievement or potential. Every year the number of applicants for this scholarship continues to increase, but the number received each year is limited. The large number of piles of files applying for scholarships and the manual selection process tends to be ineffective and efficient and the results of the selection are inaccurate, so a system is needed that is able to assist in the selection process quickly, easily and on target. The method used is CRIS-DM with the Naive Bayes Classifier algorithm modeling, this method is an approach that refers to the Bayes theorem which combines previous knowledge with new knowledge. The variables consist of 7 attributes, namely: Name, DTKS status, achievement, parents' occupation, total income of parents, home ownership, and number of family dependents[1]. Testing was carried out using k-fold cross validation, and the highest accuracy results were obtained from k-fold 4 of 91.43%, while the AUC was 0.996% with a very good diagnostic classification. Thus it can be interpreted that the Naive Bayes algorithm is very well used in the selection of scholarships for bidikmisi scholarships at Hamzanwadi University

Early Prediction of Heart Diseases using Naive Bayes Classification Algorithm and Laplace Smoothing Technique

Nowadays, medical diseases are one of the primary causes of death, and it is one the major concerns of developed countries. So, the disease identification process needs a lot of attention since if the diseases are idenfied at the early stage, the rate of death can be decreased. Machine learning techniques is one of the popular approaches that is used for identifying the diseases at the early stage. In this paper, two machine learning techniques, namely Naive Bayes classification algorithm and Laplace smoothing technique are used to predict the heart disease. Here, many medical details are used, such as gender, age, fasting blood sugar, blood pressure, cholesterol, etc. to predict the hearth disease of a patient. The proposed decision system supports avoiding unnecessary diagnosis test, which can be highly beneficial to start the treatment quickly. Thus, both time and money can be saved. Both the performance analysis and the experimental results show the efficiency of the proposed scheme over the existing schemes.

Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier

Machine learning is the subset of Artificial Intelligence and it is used for prediction various real time data analytics applications. Health care monitoring is the major area to analyse the result and make effective decisions. We need intelligent and automated process for predicting diseases using medical dataset. Machine learning methods are proposed to handle the dataset. Smart healthcare prediction is proposed to identify the user or patient information or symptoms as an input. Our system has forecasting accuracy index based on likelihood of the disease and health information. We use Naive bayes classifier algorithm for handling classification, prediction and accuracy index of dataset. Our algorithm measures the disease percentage and train the dataset. Once the prediction result will appears based on effective decision to be taken. In our work, we are taken 20000 train dataset and 7500 test data set for evaluation. TensorFlow simulator is used to simulate the system and measure accuracy. In this system achieves 95% accuracy and performance result compared with existing methods.

Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic

The COVID-19 pandemic has an impact on the economy of Padang City. To revive the economy, especially in the tourism sector, the government is trying to improve services to visitors so that the number of tourist visits increases again. As one of the efforts is to find out the opinion of tourists on the beach tourism area visited. This research aims to assist the Padang City government in knowing the positive or negative responses of tourists through the sentiment analysis process to the beach tourism they visit so that The Government of Padang City can determine the policies to be taken in connection with the reviews given by beach tourism visitors. By using reviews on Google Maps on the attractions of Air Manis Beach, Padang Beach, Pasir Jambak Beach, Nirwana Beach, and Pasir Putih Beach, clustering is carried out with the Naive Bayes classification algorithm. Based on the results of the analysis that has been done, 2 of the 5 beaches get negative reviews, namely Pasir Jambak Beach and Pasir Putih Beach which get negative values of 0.550 and 0.650.

Changes in public travel willingness in the post-COVID-19 era: Evidence from social network data

<abstract> <p>Amid the impact of COVID-19, the public's willingness to travel has changed, which has had a fundamental impact on the ridership of urban public transport. Usually, travel willingness is mainly analyzed by questionnaire survey, but it needs to reflect the accurate psychological perception of the public entirely. Based on Weibo text data, this paper used natural language processing technology to quantify the public's willingness to travel in the post-COVID-19 era. First, web crawler technology was used to collect microblog text data, which will discuss COVID-19 and travel at the same time. Then, based on the Naive Bayes classification algorithm, travel sentiment analysis was carried out on the data, and the relationship between public travel willingness and urban public transport ridership was analyzed by Spearman correlation analysis. Finally, the LDA topic model was used to conduct content topic research on microblog text data during and after COVID-19. The results showed that the mean values of compelling travel emotion were -0.8197 and -0.0640 during and after COVID-19, respectively. The willingness of the public to travel directly affects the ridership of urban public transport. Compared with the COVID-19 period, the public's fear of travel infection in the post-COVID-19 era has significantly improved, but it still exists. The public pays more attention to the level of COVID-19 prevention and control and the length of travel time on public transport.</p> </abstract>

Research on Ethical Issues and Coping Strategies of Artificial Intelligence Algorithms Recommending News with the Support of Wireless Sensing Technology

This study shows how well the wireless sensing technology may be used to forecast how people would react to AI‐ (artificial intelligence‐) driven customization in digital news sites. We randomly picked participants to enroll in an online questionnaire. This study determines the ethical issues and coping strategies of AI‐based news using sensor technology. The study proposed an improved naïve Bayes classification algorithm to forecast the acceptance of AI‐driven news sites. Additionally, the technology acceptance framework characteristics continue to be crucial in determining adoption decisions. The findings demonstrate that the observed contingency has a large direct influence and an indirect effect that is moderated by improved user interaction and positivity in forecasting the acceptance of AI‐driven news sites.

Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage is not properly managed it will become a hazard to the environment and humans. Managing medical wastage is a major issue in the city, municipalities in the aspects of the environment, and logistics. An efficient supply chain with edge computing technology is used in managing medical waste. The supply chain operations include processing of waste collection, transportation, and disposal of waste. Many research works have been applied to improve the management of wastage. The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security, trustworthiness, and transparency. To overcome these issues, in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm (NBQ-BBOA). This proposed work is used to track, detect, and manage medical waste. To minimize the transferring cost of medical wastage from various nodes, the Q-Learning algorithm is used. The accuracy obtained for the Naïve Bayes algorithm is 88%, the Q-Learning algorithm is 82% and NBQ-BBOA is 98%. The error rate of Root Mean Square Error (RMSE) and Mean Error (MAE) for the proposed work NBQ-BBOA are 0.012 and 0.045.

Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier

The implementation of health in Indonesia is still marked by problems in managing the workforce, especially among honorary health workers. The State Personnel Agency seeks to improve service quality by selecting good human resources to enhance community services. Several Ministries agreed to change the team member recruitment system from Prospective Civil Servants to Government Employees with Work Agreements. In the field, in the Government Employees with Work Agreements acceptance process, there are still pros and cons, both from the rules and the appointment process. The community hopes that the appointment process can be objective and open so that no group is disadvantaged. To achieve these expectations, researchers used data mining to classify health workers who would become Government Employees with Work Agreements. The data mining process uses probability with the Naive Bayes Classifier algorithm from historical data on Government Employees with Work Agreements receipts for 2021. Data on the history of Government Employees with Work Agreements acceptance of health workers as many as 1078 data have been filtered and cleaned. The results of testing the data are 0.00012 for the worthy assumptions and 0.0032 for the unworthy assumptions. Data processing results will be visually displayed using bubble diagrams and Python programming. The researcher concluded that the process of classifying prospective first-aid team members for medical personnel could be done by data mining using the Naïve Bayes algorithm. The results of this classification can be used as a reference for the following year's Government Employees with Work Agreements revenue classification process.

Optimasi Akurasi Klasifikasi Pada Prediksi Smokte Detection dengan Menggunakan Algoritma Adaboost

The problem of fire is a threat to nature and the environment. To deal with fire incidents, a smoke detector was created and developed in combination with an IoT device so that incident data can be recorded properly where the recorded data will be used as a reference for increasing the accuracy of early detection. Increasing the accuracy of smoke detectors so that they can be combined with artificial intelligence technology. This research proposes prediction optimization using the adaboost algorithm combined with the naïve Bayes classification algorithm with a measurement matrix based on accuracy, recall, and precision. The results showed that using the adaboost algorithm could increase the resulting accuracy value with a value of 0.987. If you look at the evaluation from the precision side, it also shows that the use of the adaboost algorithm can increase the precision value with a value of 0.971. But the recall evaluation showed that without boost it got a better score with a value of 0.995

AUTOMATIC RELATION EXTRACTION BETWEEN ENTITIES FOR AMHARIC TEXT

This research work primarily focused on the automatic relation extraction between entities for Amharic text using supervised machine learning approach.The Walta Information Centre online archive resources were used to create the studys own corpus, which consisted of 2000 sentences and a reasonable quantity of 30,466 words or tokens. The proposed solution has four processes namely preprocessing, Text labeling, feature extraction and feature selection and Recognition. The tokenization and POS are used as preprocessing. After the text is tokenized and giving POS for each of tokens the next step is text labeling system. For text labeling mechanism BIO scheme is used. The tag features are selected for building the model. The Tag feature consists of name entity type and relation type. The name entity type features are represented by Location (LOC), Organization (ORG) and Person (PER) and the relation type features are identified every word which existed between two entities for instance between location-location relation type or location-organization relation type and all the corresponding entities that are appeared it. Vectorizations are done using DictVectorizer and word2features. Support vector machines and conditional random field machine learning are used to for recognizing the entity relation between Amharic texts. SVM with SGD achieved the weighted precision of 49%, recall 10% and f1-score 13% are scored. SVM with Multinomial Naive Bayes Classifier Algorithm achieve precision of 61%, recall 41% and f1-score 48%.SVM with Passive Aggressive classifier achieved weighted average precision of 55%, recall 19% and f1-score 27%. CRF algorithm achieved precision of 87%, recall 87% and f1-score 86%. The CRF model outperform compared with other SVM algorithms.

Analisis Performa Algoritma NBC, DT, SVM dalam Klasifikasi Data Ulasan Pengunjung Candi Borobudur Berbasis CRISP-DM

The approach of visitor sentiment analysis to Borobudur Temple tourist destinations in Indonesia can be classified using various algorithms to get optimal results. Good algorithm performance can be seen from the confusion matrix (accuracy, precision, recall) value, Area Under Curve (AUC) value, and Receiver Operating Characteristic (ROC). This study used the Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM) algorithms against 3850 text data obtained from the Tripadvisor website, especially reviews of Borobudur Temple visitors. The method refers to the Cross-Industry Standard Process for Data Mining (CRISP-DM) for optimizing tourist destination products and services by paying attention to six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results of this study show that the results of NBC's algorithm performance evaluation can be seen to have a change in the confusion matrix value at the accuracy value from 98.73% to 95.6%, the precision value changed from 98.72% to 98.97%, the recall value also changed from 100% to 96.54%. In addition, the Area Under Curve (AUC) of NBC also changed from 0.500 (50%) to 0.693 (69.35%). In addition, the results of the DT algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 97.55% to 94.40%, the precision value increased from 97.63% to 91.86%, the recall value also changed from 99.90% to 99.47%. The Area Under Curve (AUC) of DT value also changed from 0.591 (59.1%) to 0.932 (93.2%). The results of the SVM algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 98.73% to 99.41%; the precision value changed from 98.72% to 100%, and the recall value also changed from 100% to 99.01%. The Area Under Curve (AUC) of the SVM value also changed from 0.961 (96.1%) to 1.00 (100%). In addition, the T-test results show that the SVM algorithm is more dominant compared to other algorithms, where the SVM algorithm T-test value is 0.994 compared to the DT algorithm T-test value of 0.944 and the NBC algorithm T-test value of 0.98. Based on the Receiver Operating Characteristic (ROC) value, it can be seen that the DT algorithm also shows good performance in addition to SVM. It indicates that in analyzing the sentiment of visitors to Borobudur Temple, the best-recommended algorithm is the Support Vector Machine

Towards Better Detection of Fraud in Health Insurance Claims in Kenya: Use of Naïve Bayes Classification Algorithm

The extent, possibility, and complexity of the healthcare industry have attracted widespread fraud that has contributed to rising healthcare costs hence affecting patients’ health and negatively impacting the economy of many countries. Despite putting up various technologies and strategies to fight fraud such as planned, targeted audits, random audits, whistle-blowing, and biometric systems, fraud in claims has continued to be a challenge in most of the health insurance providers in Kenya. This paper explored the application of data mining in detecting fraud in health insurance claims in Kenya. Classification algorithms (Naïve Bayes, Decision Tree and K-Nearest Neighbour) were used to build predictive models for the knowledge discovery process. After conducting several experiments, the resulting models showed that the Naïve Bayes works well in detecting fraud in claims with 91.790% classification accuracy and 74.12% testing hit rate. A prototype was developed based on the rules extracted from the Naïve Bayes model, which, if adopted, will save costs by detecting fraud as it is committed. Fraud detection in health insurance claims is much needed in many countries so as to help reduce loss of money and in return improve service delivery to patients.

Gauging Stress, Anxiety, Depression in Student during COVID-19 Pandemic

During the beginning of COVID-19 pandemic, studies came across the world concerning with health issues. Researches began to find the repercussions of the virus. The virus was found to be versatile as it changes its nature and targets the lungs of a person. Later, it was seen an astonishing massacre around the world due to the virus. Many people have lost their life but many more people are still suffering with bad psychological state. Researchers began to research on the nature virus but very few researches were made on the other side-effects of this pandemic. One such crucial subject to attend in contemporary world is the effect of COVID-19 on psychological state in general population. This side-effect may lead to raise an alarming situation in future that could result in more death cases. The proposed paper presents a study on the detection of stress and depression in people caused by the pandemic. The proposed methodology is based on perceived questionnaire method through which people’s responses are recorded in the form of text. COVID victims have been interrogated against a set of questions and their responses are recorded. The methodology performs text mining of their responses that also include the people’s reaction from social networking sites. The text processing of people’s responses is done by natural language processing (NLP). NLP is used to interpret textural facts into meaningful segments that must be understandable to machine. The refined data has been transformed into PSS (perceived stress scale) scaling factor that ranges from 0 to 4 showing various level of stress. The proposed system utilized artificial intelligence in which naive Bayes classifier, K-nearest neighbor (KNN), Decision tree and Random forest algorithms are applied to predict the emotional state of a person. The proposed system also uses data from social networking site for testing purpose. The model successfully shows a comparative study of such three classifiers for the classification of stress level into stress, anxiety and depression.

Generator Fault Classification Method Based on Multi-Source Information Fusion Naive Bayes Classification Algorithm

The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%.

Classification of Graduation Students of the Faculty of Computers and Multimedia, Universitas Islam Kebangsaan Indonesia Using the Naïve Bayes Classifier Algorithm

The purpose of this study to discuss classification of graduation Students of the Faculty of Computers and Multimedia, Universitas Islam Kebangsaan Indonesia using the naïve bayes classifier algorithm. The methodology used in this study uses a unified modeling language. The results show that the data mining system for classifying graduation Students of the Faculty of Computers and Multimedia, Universitas Islam Kebangsaan Indonesia succeeded in processing datasets, namely training data and sample data to form classes in the classification graduation Students of "Graduated on Time" or "Graduated Late". Processing data mining with the naïve bayes classifier algorithm is able to describe and draw conclusions on large amounts of datasets even though the data is complicated to understand. Research with this web-based system is easy for academics or faculties to use and data processing can be done quickly. Keywords: Classification, Naïve Bayes Classifier, Algorithm

Detection of DDoS Attacks Using Machine Learning Classification Algorithms

The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accurate than traditional classification methods. This is a quantitative research applies Logistic Regression, Decision Tree, Random Forest, Ada Boost, Gradient Boost, KNN, and Naive Bayes classification algorithms to detect DDoS attacks on the CIC-DDoS2019 data set, which contains eleven different DDoS attacks each containing 87 features. In addition, evaluated classifiers’ performances in terms of evaluation metrics. Experimental results show that AdaBoost and Gradient Boost algorithms give the best classification results, Logistic Regression, KNN, and Naive Bayes give good classification results, Decision Tree and Random Forest produce poor classification results.

ANALISIS SENTIMEN TWITTER UNTUK MENGETAHUI KESAN MASYARAKAT TENTANG PELAKSANAAN POMPROV JAWA TIMUR TAHUN 2022 DENGAN PERBANDINGAN METODE NAÏVE BAYES CLASSIFIER DAN DECISION TREE BERBASIS SMOTE

Sentiment analysis is a method used to understand, extract, and automatically process text data to get the sentiment contained in an opinion. Sentiment analysis will be used to process comments made by the community or supporters of each participant of POMPROV East Java 2022 through various media, including Twitter, regarding the progress or results of POMPROV East Java 2022. The number of comments, the authors use data mining methods and algorithms to process the comment data to get information about the POMPROV East Java 2022 event. The Naïve Bayes Classifier and Decision Tree classification algorithms are used as tools to classify comments expressed by users. Based on the results of experiments that have been carried out four times according to the number of data splits and twice based on the algorithm used, it can be concluded that the use of the SMOTE algorithm can increase the accuracy of the various data split compositions used. The best results of the Naïve Bayes Classifier method are found in the 7:3 data distribution which increases the accuracy by 14.52% and the Decision Tree method in the 9:1 data division increases the accuracy by 9.45%.

Analysis of Netizen Comments Sentiment on Public Official Statements on Instagram Social Media Accounts

Statements issued by public officials will be pros and cons in the community, there are those who respond positively, negatively or respond neutrally. Likewise on Instagram social media, every statement written on Instagram will get various responses written by netizens in the comment’s column posted. Netizen is an acronym for internet citizens, namely people who are actively using the internet. Due to the large number of comments, it is difficult to see whether the public response is a positive, negative or neutral comment when responding to statements from public officials. Whether the statements issued by public officials through Instagram have a positive, negative or neutral impact, so that if they can be grouped into labels, it can be seen how much public opinion is against these public figures. On social media accounts, not all comments written by netizens have the same writing structure, so we need a mechanism that is able to help analyze comments from netizens by classifying them into positive, negative or neutral response classes. By applying POS Tagging to determine opinion sentences or not and also the Naïve Bayes Classifier method and the tf-idf feature to be able to classify comments into several classes of positive, negative or neutral comments. The classification testing stage uses the cross validation method to test the accuracy of the naive bayes classification algorithm and the tf-idf feature.

Sentiment Analysis on Product Reviews from Shopee Marketplace using the Naïve Bayes Classifier

Online shopping has become a popular shopping method ever since the number of internet users increased. Online shopping activities have become very easy and flexible because they can be completed anywhere and anytime. The products provided are also complete. The products sold often do not always match the actual conditions because the product can only be seen through pictures. Users who have purchased a product can share their opinions using the review feature. However, the products purchased thousands or millions of times have many reviews. To take an overview of the product, it is essential to go through every positive and negative review, which takes a lot of time and effort. Reviews of products from the Shopee marketplace will be classified into positive or negative sentiments towards women's home wear clothing or house dress in this study. The research starts with data crawling, text preprocessing, training data, testing, and evaluation model and then concludes with a general description based on the most frequently discussed topics in the reviews for each sentiment class. Classification is done using the Naïve Bayes Classifier algorithm. The accuracy obtained is 90,03%. The total dataset is 2907 data.