Naive Bayes Classification Algorithm Research Articles

Determination of Training Participants in Community Work Training Centers Using the Naïve Bayes Classifier Algorithm

Community work training centers are skills training institutions that aim to improve the skills of the surrounding community by providing training programs that align with industry needs. Registration of training participants at the Al-Ikhwan Islamic Boarding School community work training centers often faces obstacles, namely, the selection process is still manual, so it takes a long time, and there is a possibility of errors. This study aims to apply the Naive Bayes Classifier Algorithm to determine whether applicants pass training at the Al-Ikhwan Islamic Boarding School community work training centers. This classification method is used to help optimize the applicant selection process by considering administrative factors, income, and training quotas. RapidMiner software is used as a tool to implement the algorithm. This study found that the Naive Bayes Classifier Algorithm can provide good accuracy results in determining applicants who pass the training selection. The test results show that the resulting model has an accuracy of 90.00% in determining passing training participants with data that has the highest chance of passing, namely data that has the attributes of the female gender, age 20 years, last education Senior High School/Vocational High School, student work/student, income 364,912, father's work as laborer, father's income 3912,280, mother's work as an IRT, and mother's income 885,964. This research increases efficiency and accuracy in determining training applicants at the Al-Ikhwan Islamic Boarding School community work training centers.

Accelerometers-based position and time interval comparisons for predicting the behaviors of young bulls housed in a feedlot system

Animal behavior monitoring is an important tool for animal production. This behavior monitoring strategy can indicate the well-being and health of animals, which can lead to better productive performance. This study aimed to assess the most effective accelerometer attachment position (on either the halter or a neck collar) and data transmission time intervals (ranging from 6 to 600 s) for predicting behavioral patterns, including water and food intake frequencies, as well as other activities in young beef cattle bulls within a feedlot system. A range of machine learning algorithms were applied to satisfy the aims of the study, including the random forest, support vector machine, multilayer perceptron, and naive Bayes classifier algorithms. All studied models produced high performance metrics (above 0.90) when using both attachment positions, except for the models built using the naive Bayes classifier. Therefore, coupling accelerometers with collars is a more viable alternative for use on animals, as doing so is easier than applying accelerometers to halters. Utilizing a dataset with more observations (i.e., shorter time intervals) did not result in considerable improvements in the performance metrics of the trained models. Therefore, using datasets with fewer observations is more advantageous, as it can lead to decreased computational and temporal demands for model training, in addition to saving the battery of the device considered in this study.

Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine

The Indonesian government is attempting to track the spread of the virus by creating an application named “PeduliLindungi” to deal with the coronavirus's exponential increase in cases across the country. Because it has a feature to disclose the user's location immediately, it is envisaged that this program can reduce the transmission of viruses in monitoring. Indonesians have used the PeduliLindungi, and there are user reviews of both positive and negative experiences. Therefore, to enhance these services, an assessment is required. The text mining method can extract information from users' reviews to collect this data. This method's application additionally uses the Naive Bayes Classifier and Support Vector Machine algorithms, which analyze word associations and do a classification evaluation of the data's accuracy. Based on the two methods' calculations, the NBC algorithm's average classification accuracy was 83.81%, and the SVM algorithm was 93.84%. Following that, discoveries on words that frequently exist or are used by people are obtained through word associations in the sentiment analysis of positive or negative reviews.

PERBANDINGAN ANALISIS REGRESI LOGISTIK BINER DAN NAÏVE BAYES CLASSIFIER UNTUK MEMPREDIKSI FAKTOR RESIKO DIABETES

Diabetes is a global health problem that is increasing in prevalence worldwide. This study compares the performance of two data analysis methods, namely binary logistic regression and naïve bayes classifier in predicting diabetes risk. This study aims to identify factors that significantly affect diabetes risk and classify diabetes risk using binary logistic regression, then compare the classification with the naive bayes classifier algorithm. Binary logistic regression models the relationship between independent predictor variables and binary dependent variables, while naïve bayes classifier uses the assumption of independence between variables. In this study, both methods were evaluated based on accuracy, sensitivity, specificity and positive predictive value. The results show that the factors that influence the risk of diabetes are Age, Gender, Polyuria, Polydipsia, Genital thrush, Itching, Irritability, and Partial paresis. Furthermore, the binary logistic regression results have a higher classification accuracy (92.31%) compared to the naïve bayes classifier (84.61%). Therefore, binary logistic regression was identified as the best method to predict diabetes risk in the context of this study

Sentiment Classification of User Reviews for KAI Access Application Using Naive Bayes Method

KAI Access is a train ticket booking application that offers convenience and various features for its users. However, the app has received a low rating of 2.4 out of 5 stars on the Play Store, indicating user dissatisfaction. This study conducts a quantitative sentiment analysis of the KAI Access application based on user sentiments expressed on Twitter. Using the CRISP-DM method, data were collected from Twitter with the Tweepy tool, amassing around 4,000 tweets from June to August 2023. The data underwent a preprocessing stage to ensure the quality and accuracy of the analysis. This stage involved removing duplicate tweets, eliminating retweets, and filtering out emoticons and other non-text elements. In the modeling stage, the Multinomial Naive Bayes Classifier algorithm was employed, achieving an accuracy rate of 84.6%. The model performed better at identifying negative reviews, with a precision of 0.96, recall of 0.86, and an F1-score of 0.91. In contrast, the identification of positive reviews was less effective, with a precision of 0.41, recall of 0.75, and an F1-score of 0.53. These findings shed light on the low ratings for KAI Access, particularly in the context of user reviews. The results of this study provide further understanding regarding the low rating given to KAI Access, particularly in the context of user reviews. By using this classification system, it is hoped that developers can design more specific improvements to enhance the user experience, especially in handling positive reviews which have the potential for performance improvement.

Analysis of Sentiment Based on Opinions from the 2019 Presidential Election

Twitter is a popular social media platform where the public is free to comment and write about anything. It is common for people to post comments containing harsh words and even hate speech. The 2019 presidential election in Indonesia generated a significant amount of comments, with some users praising the candidates, others criticizing them, and some even resorting to insults. To extract meaningful information from these comments and classify the text, sentiment analysis is essential. In this research, sentiment analysis involves the process of categorizing textual documents into two classes: negative and positive sentiment. The opinion data was collected from the Twitter social network in the form of tweets related to the 2019 presidential election. The dataset used in the study consisted of 3,337 tweets, which were divided into 70% training data and 30% test data. The training data comprised tweets whose sentiment was already known, serving as a foundation for the model to learn and make predictions. The primary objective of this research is to determine whether the tweets, written in Indonesian, express positive or negative sentiments. The Naive Bayes Classifier algorithm was employed to classify the tweet data. This algorithm is well-suited for text classification tasks due to its simplicity and efficiency in handling large datasets. The classification results on the test data demonstrated that the Naive Bayes Classifier algorithm achieved an overall accuracy of 71%. Specifically, the accuracy for negativesentiment classification was 71%, while the accuracy for positive sentiment classification was 70%. These results indicate that the Naive Bayes Classifier is effective in distinguishing between positive and negative sentiments in tweets related to the presidential election

Machine Learning Based Spam Comments Detection on Youtube: A Comparison of Regression Models

The rise of spam comments on platforms like YouTube has become a significant concern, as they not only hinder genuine user engagement but also pose serious risks to users' safety and privacy. Machine Learning (ML) offers a powerful solution to combat spam comments by automating the process of detecting and preventing them. With the ability to analyze vast amounts of data and patterns, ML algorithms can effectively distinguish between legitimate comments and those that are spam. One of the commonly employed approaches in ML for spam comment detection is the Naive Bayes classification algorithm. Naive Bayes is a probabilistic algorithm that calculates the likelihood of a comment being spam based on its characteristics and the occurrence of specific keywords or phrases that are typical of spam content. By training the algorithm on a labeled dataset of spam and non-spam comments, it can learn to recognize patterns and generalize its understanding to new, unseen comments. Achieving a detection accuracy of 92.78% is indeed promising, but researchers and developers continue to explore other ML techniques and combinations to further improve the accuracy and robustness of spam comment detection systems. Ensemble methods, deep learning, and Natural Language Processing (NLP) techniques are among the advanced approaches gaining attention in this domain. One crucial aspect of an effective spam detection system is its adaptability and responsiveness to emerging spam tactics.

POI Route Recommendation Model Based on Symmetrical Naive Bayes Classification Spatial Accessibility and Improved Cockroach Swarm Optimization Algorithm

The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommendation model based on symmetrical Naive Bayes classification spatial accessibility (NBCSA) and an improved cockroach swarm optimization algorithm (ICSOA), aiming to recommend POI routes that satisfy tourists’ interests and have the lowest travel costs under tourism transportation geographical conditions. Using the historical POIs visited by tourists as the training set, we construct an improved symmetrical Naive Bayes classification algorithm (NBCA), and the POIs in the destination city are divided into categories by tourists’ preferences. Then we propose an improved NBCSA model to calculate the spatial accessibility field strength (SAFS) for each category’s POIs. Based on the recommended POIs, we propose the ICSOA to recommend optimal POI routes. The experiment verifies that the proposed algorithm can effectively classify the POIs and recommend POIs that best match the tourists’ interests and produce the lowest travel costs. Compared with the TCA and GDA method, the proposed algorithm can output the POI routes with lower travel costs and has higher algorithm execution efficiency. Among the output optimal routes, the proposed algorithm can reduce costs by 5.62% at the lowest and 52.25% at the highest.

Comparison of Naive Bayes Classifier and K-Nearest Neighbor Algorithms with Information Gain and Adaptive Boosting for Sentiment Analysis of Spotify App Reviews

Abstract. At this time, the development of technology are increase rapidly. One of the issue that appear with advance technology is data volume in the world has increase too. With the large data volumes that exist in the world it can be used to some purpose in many field. Entertainment is one of the field that have many interest from user in this world. Spotify is the example of entertainment apps that provided by Google Play Store to give online music streams to their users. Because that apps is provided by Google Play Store, many reviews of the user about the apps it can be classified to know the positive, negative, or neutral. One way to classified the review of user is make sentiment analysis. In this paper, to classify the review we use naïve Bayes classifier and k-nearest neighbors that will be compared with adding Information gain as feature selection and adaptive boosting as boosting algorithm of each classification algorithm that we used. The result of classification using naïve Bayes classifier with adding Information gain and adaptive boosting is 87.28% and k-nearest neighbor with adding information gain and adaptive boosting can perform accuracy of 80.35%. Purpose: Knowing the result each of accuracy from the naïve Bayes classifier and k-nearest neighbor algorithm with adding information gain and adaptive boosting that we used and know how to doing the sentiment analysis step by step with the methods that chosen in this study. Methods/Study design/approach: This study applied data preprocessing, lexicon based labelling with TextBlob, Normalization, Word Vectorization using TF-IDF, and classification with naïve Bayes classifier and k-nearest neighbor, information gain as feature selection, and adaptive boosting as boosting algorithm to boost the accuracy of classification result. Result/Findings: The accuracy of naïve Bayes classifier with adding information gain and adaptive boosting is 87.28%. Meanwhile, by k-nearest neighbor with adding information gain and adaptive boosting reach the accuracy of 80.35%. This result obtained by using 60.000 dataset with data splitting 80% as data training and 20% as data testing. Novelty/Originality/Value: Implementing information gain as feature selection and adaptive boosting as boosting algorithm to naïve Bayes classifier is prove that it can be increase the accuracy of classification, but not same when implementing in k-nearest neighbor. So, for the future research can applied another classification algorithm or feature selection to get better result.

Naïve Bayes Algorithm for Sentiment Analysis of Blibli.com Review on Google Play Store

Currently, there are approximately 354 million active mobile phones in Indonesia, placing the country fourth globally in terms of the highest number of mobile phone users. E-Commerce, as a form of online transaction, enables the digital exchange of goods and services to meet daily needs. This research aims to implement sentiment analysis using the Naive Bayes classification algorithm as a method to gather user opinions. Thus, the study not only provides insights into customer satisfaction with Blibli.com but also serves as a basis for potential improvements in services or feature development to enhance the online shopping experience. Overall, the Naive Bayes algorithm successfully achieved an accuracy of around 84%, demonstrating its proficiency in categorizing sentiment in reviews. When focusing on negative data, the Naive Bayes algorithm exhibited a precision of approximately 79%, recall of around 95%, and an f1-score of about 86%, indicating its success in identifying and classifying negative reviews with high precision and sensitivity. On the positive side, the Naive Bayes algorithm achieved a precision of about 91%, recall of around 83%, and an f1-score of about 87%.

Sentiment Classification of Over-Tourism Issues in Responsible Tourism Content using Naïve Bayes Classifier

The research problem addressed in this study is the analysis of public sentiment regarding over-tourism issues. Utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the Naive Bayes Classifier (NBC) algorithm, the study navigates through stages of business understanding, data processing, modeling, evaluation, and deployment. The central focus lies in understanding and classifying public sentiments surrounding the challenges associated with over-tourism. The findings reveal that the NBC algorithm, particularly when augmented with Synthetic Minority Over-sampling Technique (SMOTE), demonstrates superior performance metrics, showcasing an accuracy of 84.82%, precision of 91.69%, recall of 76.75%, f-measure of 83.47%, and AUC of 0.838. The comparison with NBC without SMOTE, which registers an accuracy of 78.16%, precision of 87.61%, recall of 74.56%, f-measure of 80.51%, and AUC of 0.745, underscores the significance of addressing class imbalance for improved predictive performance. Integrating CRISP-DM with the NBC algorithm and SMOTE proves instrumental in advancing sentiment analysis methodologies, providing nuanced insights into public perceptions and attitudes concerning the critical issue of over-tourism.

МОДУЛЬ ИНФОРМАЦИОННОЙ СИСТЕМЫ НА ОСНОВЕ НАИВНОГО БАЙЕСОВСКОГО КЛАССИФИКАТОРА ДЛЯ АВТОМАТИЗАЦИИ РАБОТЫ АПТЕКИ

The introduction of the automation and management principles for pharmacists’ activities using information systems is an effective way to increase profi ts and strengthen pharmacy profi tability. Companies use machine learning algorithms to adjust their strategy, study customer reviews about their organization through feedback analysis, and improve the company’s image. However, a signifi cant amount of pharmacists’ time and effort goes into the manual processing of incoming reviews. The article proposes automating the processing using the naive Bayes classifi er algorithm, which is implemented in Python. To train the classifi er, the authors created their own corpus of labeled review texts with two categories, with the total number of reviews being about 500. A parser written in Python was used to search for reviews. The following steps were taken during the preliminary processing of the reviews’ text: lemmatization, elimination of punctuation, the text’s letter conversion to lower case, tokenization, stop words removal, and text vectorization using the bag-of-words model. According to the numerical experiments carried out, the classifi er’s highest accuracy was achieved with an 80/20 ratio of training and test samples that did not include stop words. When using a classifi er, analyzing 100 reviews takes 8 times less time than manual reading. The classifi er itself can be presented as a separate application or as a module within an information system. Thus, the increasing number of positive reviews is an indicator of the company’s effi cient performance as well as the number of satisfi ed customers. The growth of its image will strengthen customers’ loyalty to the company, resulting in greater sales.

A novel method to detect and classify Alzheimer using naive Bayes classification algorithm

A novel method to detect and classify Alzheimer using naive Bayes classification algorithm

Development of a patient health monitoring system based on the internet of things with a module for predicting vital signs

<span>Recent issues related to human health in the world have shown the importance of telemedicine considering necessities to perform the remote monitoring of patients. In this study, using a patient smart monitoring system (PSMS), we collected 5,000 samples of heart rate and blood saturation vital signs from 4 volunteers and tried to find better correlation algorithms to develop a module to predict what these vital signs will be in the next 60 seconds. The following regression algorithms recurrent neural network (long short-term memory) (RNN(LSTM)), autorregresive integrated moving average (ARIMA), value-added reseller vector autoregression (VAR) were used to forecast the patient's state of health in the next 60 seconds. Further, the support vector machine (SVM) and Naive Bayes classification algorithms use the data forecasted by the regression algorithms as input to predict the health status of the patients. When comparing algorithms, we focused on the F measure, a metric used to evaluate the performance of machine learning algorithms. As a result, RNN(LSTM) and SVM showed the performance score value of machine learning algorithms F 0.84, RNN(LSTM) and Naive Bayes 0.81, VAR and SVM 0.82, and VAR and Naive Bayes 0.75. Compared to them, the correlation of ARIMA regression algorithms and SVM classification showed a better F score of 0.86 for machine learning algorithms than the others.</span>

Use of the Naive Bayes Classifier Algorithm in Machine Learning for Student Performance Prediction

This study focused on the development and analysis of a methodological platform grounded in machine learning principles for evaluating learning processes and enhancing student outcomes. The aim of this research was to develop and test a method for evaluating students’ academic performance based on the Naive Bayes classifier. Also, an objective of this study was to create an efficient tool capable of automating and optimize the assessment of educational performance using contemporary machine learning methods and technologies. The study employed the Naive Bayes analysis technique to predict student achievements, with the algorithm being implemented in Python. Despite an emphasis on the development of a software product, the research primarily focused on the development and analysis of the method. Our findings underscore the novelty of this approach, which can serve as a valuable tool for educational institutions and educators.

Penerapan Data Mining Menggunakan Metode Algoritma Naive Bayes Classifier untuk Mendukung Strategi Promosi

The purpose of this study is to make it easier to find out the prediction results with the classification method from PPDB data for 2021-2022 to support promotion strategies at the Skye Foundation with the Naive Bayes Classifier Algorithm and to find out the level of accuracy in classification in determining promotion strategies. In this study the authors used 210 data as training data and 53 data as testing data for manual testing in excel and for testing using RapidMiner tools. Based on the results of the analysis in the research conducted, the results of predicting registration and non-registration quickly and accurately from tests carried out by comparing training data with data testing using RapidMiner tools obtained an accuracy rate of 92.45%.

Construction of a radiomics-based model for predicting the efficacy of radiotherapy and chemotherapy for non-small cell lung cancer

ObjectivePre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB). MethodsIn this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance. ResultsA total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve. ConclusionCT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed.

Classification of six banana ripeness levels based on statistical features on machine learning approach

<span>Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.</span>

Sentiment Analysis of the Popularity of Parties Supporting the 2024 Presidential Candidates on Twitter Using the Naive Bayes Classifier Algorithm

In accordance with the democratic system of government, Indonesia allows each political party or combination of parties running in the general election to nominate its own presidential and vice presidential candidates, as long as these candidates meet legal requirements. Presidential elections are scheduled for 2024. A political figure who wants to run for president at that time will have to rely heavily on public opinion for support. As the 2024 election approaches, political parties are increasingly turning to social media to spread their messages and increase their support. The aim of this research is to compare the level of support for the two leading candidates for president and vice president in 2024 on Twitter to determine the proportion of tweets that are positive, negative, or neutral.The aim of this research is to compare the level of support for the two main candidates for president and vice president in 2024 on Twitter to determine the proportion of tweets that are positive, negative, or neutral.

SENTIMEN ANALISIS PENGGUNA TWITTER TERHADAP SEA GAMES 2023 DENGAN METODE NAIVE BAYES

The development of computers and telecommunications has made people often express their opinions through social media Twitter. As recently, after the 2023 SEA Games were held in Cambodia, various kinds of tweets appeared from various sports. From this opinion, it can be seen that the country's success in holding the Sea Games by conducting a sentiment analysis. So this research was conducted to find out how the public opinion regarding the holding of the 2023 SEA Games using the Naive Bayes Classifier algorithm. The data used was 1888 data obtained by crawling Twitter data using the keywords SEA Games and RapidMiner tools. The division of training data and testing data is carried out into two comparisons, namely 50:50 and 60:40. The best accuracy results are obtained with a data comparison of 50:50, which is 70.15%, while a data comparison of 60:40 has a smaller result, namely 70.07%.