Naive Bayes Algorithm Research Articles

Determining the Law of Reading Tajweed (Idgham Qomariyah and Syamsiyah) in the Qur'an Using the Naïve Bayes Algorithm

Learning tajweed to recite the Qur’an is important. Because if there is a mispronunciation, the meaning will be different. This research aims to detect two of the many tajweed, namely idghom qomariyah and syamsiyah using machine learning technology with a classification approach. This research uses the Naive Bayes algorithm to classify idghom qomariyah and syamsiyah in Al-Quran text documents. Based on experimental results using 82,173 text data, Naive Bayes was able to classify idgham qomariyah and syamsiyah with an accuracy rate of 96,80%.

ANALYSIS OF PUBLIC SENTIMENT RELATED TO THE FAILURE OF INDONESIA TO HOST U-20 USING MULTINOMIAL NAÏVE BAYES CLASSIFIER

The case of Indonesia's failure to host the U-20 World Cup in 2023 has become a hot topic of discussion in Indonesia. The rejection of the Israel U-20 national team and security factors by FIFA are considered the main reasons for the cancellation. This raises many issues and controversies from various parties. In this study, sentiment analysis using the Naive Bayes algorithm was conducted. Researchers use the naive bayes algorithm because this algorithm has high accuracy with simple calculations. The data obtained in this study came from 250 tweets of Twitter data with a ratio of training and test data of 7:3. The results showed good data classification with 97.26% accuracy, 93.33% precision, and 100% recall. In conclusion, the classification model developed can describe public sentiment related to Indonesia's failure in the U-20 World Cup well.

IMPROVEMENT OF NAIVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF SHOPEE APPLICATION REVIEWS ON GOOGLE PLAY STORE

Reviews of the shopee application on the google play store are included in useful information if processed properly. Old or new users can analyze app reviews to get information that can be used to evaluate services. The activity of analyzing application reviews is not enough just to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. A sentiment analysis system is a system used to automatically analyze a review to obtain information including sentiment information that is part of an online review. The data is classified using Naive Bayes. A total of 1,000 shopee app user reviews were collected to form the sample dataset. The purpose of this study is to determine the sentiment analysis of shopee application reviews in the Google Play Store using the Naive Bayes algorithm. The stages of this research include, data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Case folding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 81%, this result shows that shopee application reviews tend to be negative.

Klasifikasi Waktu Pada Dokumen Persetujuan Accounting Voucher

This research aims to optimize the approval document management process by implementing time classification techniques using Naive Bayes. Naive Bayes classification is a data classification technique that utilizes probability theory and statistics to predict future probabilities based on Accounting Voucher approval document data from January to April 2023. This study focuses on the application of the Naive Bayes algorithm for time classification, aiming to provide innovative solutions for PT. Kideco Jaya Agung in the mining industry. Attributes used in the Naive Bayes classification method include document type, document number, document status, and time difference. The research results indicate that the probabilities of the 'On Time' and 'Late' classes are approximately 0.9737 and 0.0263, respectively, with an accuracy rate of 97.67441860465115% or 98%.

Prevention and detection of DDOS attack in virtual cloud computing environment using Naive Bayes algorithm of machine learning

The popularity of cloud computing, with its incredible scalability and accessibility, has already welcomed a new era of innovation. Consumers who subscribe to a cloud-based service and use the associated pay-as-you-go features have unlimited access to the applications mentioned above and technologies. In addition to lowering prices, this notion also increased the reliability and accessibility of the offerings. One of the most crucial aspects of cloud technology is the on-demand viewing of personal services, which is also one of its most significant advantages. Apps that are cloud-based are available on demand from anywhere in the world at a reduced cost. Although it causes its users pain with safety concerns, cloud computing can thrive because of its fantastic instantaneous services. There are various violations, but they all accomplish something similar, taking the systems offline. Distributed denial of service attacks are among the most harmful forms of online assault. For fast and accurate DDoS (Distributed Denial of Service, distributed denial of service) attack detection. This research introduced the DDOS attack and a method to defend against it, making the system more resistant to such attacks. In this scenario, numerous hosts are used to carrying out a distributed denial of service assault against cloud-based web pages, sending possibly millions or even trillions of packets. It uses an OS like ParrotSec to pave the way for the attack and make it possible. In the last phase, the most effective algorithms, such as Naive Bayes and Random Forest, are used for detection and mitigation. Another major topic was studying the many cyber attacks that can be launched against cloud computing.

Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients

Heart disease stands as a prominent contributor to global mortality, as indicated by data released by the World Health Organization (WHO). In 2019 alone, an estimated 17.9 million individuals succumbed to cardiovascular disease, accounting for 32% of all worldwide deaths. Of these fatalities, 85% were attributed to heart disease and stroke. Individuals harboring the potential for heart failure often persist in unhealthy lifestyles, regardless of their awareness of underlying heart conditions. To address this issue, the research explores the application of machine learning to identify an optimal method for classifying heart failure patients, employing the Naive Bayes technique. This algorithm has found extensive use in the health sector, demonstrating success in classifying various conditions such as hepatitis, stroke, respiratory infections, and more. The Naive Bayes algorithm, applied in this study, exhibited notable accuracy, precision, sensitivity, and overall classification efficacy. Specifically, the classification accuracy for heart failure patients reached 74.58%, the precision level was 97.67%, sensitivity achieved 75%, and the AUC (Area Under ROC Curve) stood at 0.857, indicating excellent classification within the 0.80 to 0.90 range. These findings can serve as an early warning system for individuals at risk of heart failure.

Simulation Model for Predicting Banking Customers Satisfaction Using Chi-Square Test & Naïve Bayes Classifier

Today, it is necessary for all Public, Private and Government bank to find their banking customers satisfaction ratio depending on feedback provided by the customers. These research paper is useful for finding Personal Characteristics and Banking Behavior of Customers. The study uses primary data which was collected from 550 responders. The responders are taken from different profession and different age group. The bank can offer different kind of services like ATM, Net banking, Mobile banking and so on. The objective of the paper is to find the result of customer satisfaction. The result is found by Non- probabilistic technique chi- Square test and probabilistic technique Naïve Bayes Classifier. The chi- square test (symbolized as χ2) is basically done by analyzing the data of the agreed upon method for the d ifference between variables. It is usually a comparison of two statistical data. Naive Bayes algorithm is based on Bayes theorem and is used to solve classification problems. The result of study indicates both positive and negative influence on banking customer behavior. Keywords Simulation, chi-square test, Naïve Bayes Classifier, demographic segmentation, customer behaviors.

Intelligent System for Internet of Things-Based Building Fire Safety with Naive Bayes Algorithm

Population growth is increasing every year. Population growth causes an increase in population density in a country. The largest population density is in urban areas. Fires in a city with a high population density will potentially cause greater damage. Material and non-material losses due to fire can be caused by not functioning maximally early warning systems, especially fire detection. In addition, other factors, such as system errors in detecting fires, can potentially cause fires. This research aims to build an intelligent system that can minimize building fire detection errors to reduce user material losses. The intelligent system can classify fire potential into four classifications, namely ”very dangerous,” ”dangerous,” ”alert,” and ”safe.” The method used in this research is Research and Development (R&D) with artificial intelligence using the Na¨ıve Bayes method, which has been integrated with the Internet of Things (IoT). This research shows that the Na¨ıve Bayes algorithm can be used to classify fire potential, proven by the overall system testing accuracy of 93.33% with an error of 6.77%.

Data Mining Perbandingan Algoritma K-Nearest Neighbor dan Naïve Bayes dalam Prediksi Penerimaan Beasiswa

One of the goals of the Indonesian State as stated in the Constitution of the Republic of Indonesia is to make the nation's life more intelligent. The government, through the Ministry of Education, Culture, Research and Technology (Kemendikbudristek), is implementing a 12-year compulsory education program. The Indonesia Smart College Card program will start to be implemented in 2021, where initially this program was called bidikmisi. The Indonesia Smart College Card program is intended to help children who experience economic difficulties or are constrained by the costs of continuing their education at tertiary level. Beneficiaries of the Indonesian Smart College Card program will receive full tuition fees from the start of admission to completion of the course within the agreed time period. This aims to ensure that children who have been selected to become students no longer have to worry or feel afraid about their survival when studying. Placing limits on the number of recipients of the Indonesia Smart Tuition Card for Private Universities (PTS) is a problem that must be resolved properly and carefully. The selection process for prospective students who register at the target university aims to find recipients who are worthy of assistance from the Indonesia Smart College Card program. The selection process for prospective students who register at the target university aims to find recipients who are worthy of assistance from the Indonesia Smart College Card program. The results obtained from the application of the research were that from comparing the results of the K-NN and Naive Bayes algorithms, the same results were obtained for the test data, namely Acceptable.

ANALYSIS OF THE EXISTENCE OF THE ACCOUNTING PROFESSION IN THE ERA OF SOCIETY 5.0 WITH THE NAIVE BAYES METHOD

Society 5.0 has opened a new phenomenon that allows humans to use modern-based science (Artificial Intelligence) for human needs with the goal of human life more easily. This phenomenon opens up various new possibilities and risks simultaneously. These changes have had a significant impact on the development of the accounting profession, resulting in fewer human resources needed in business, including accounting staff. This research aims to 1) Apply sentiment analysis to the existence of the accounting profession in the era of society 5.0. 2) Application of the naive Bayes method to classify sentiment analysis for the existence of the accounting profession in the era of society 5.0. 3) Testing the naive Bayes algorithm in sentiment analysis for the existence of the accounting profession in the era of society 5.0. This research design uses quantitative research, analysis of student sentiment on the existence of the accounting profession was carried out using data from 432 student opinions from several campuses in the East Java region. Test data using the Naïve Bayes algorithm with Term-Frequency Inverse Document Frequency feature selection and the Lexicon method. The results of this research are divided into five parts consisting of the first part 424 students' opinions on the level of accuracy (78%), the second part 427 students' opinions on the level of accuracy (62%), the third part 429 students' opinions on the level of accuracy (47%), the fourth part 425 students' opinions level of accuracy (56%), fifth part 429 student opinions level of accuracy (52%). It was found that the number of positive sentiments was more dominant than negative or neutral sentiments

Performance Comparison of Decision Tree J48, CART, and Naïve Bayes Algorithms for Predicting Chronic Kidney Disease

Chronic Kidney Disease could be a worldwide issue that proceeds to extend with high treatment costs. Accurate diagnosis is essential for managing this disease. There is a requirement for a technique to anticipate chronic kidney disease, with prevalent use being made of Decision Tree J48, Naive Bayes, and CART algorithms which offer benefits like swift computation, ease of use, and high precision. The researchers aimed to determine the comparison results of Decision Tree J48, CART, and Naive Bayes algorithms for predicting chronic kidney disease. From the research findings, it was concluded that the CART algorithm had the highest accuracy rate of 97.25% in predicting chronic kidney disease, compared to the J48 Decision Tree algorithm and the Naïve Bayes algorithm with accuracy rates of 96.5% and 93.5% respectively. The CART algorithm can be utilized by pathologists to develop a program for predicting chronic kidney disease.

Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm

The influencing factors of building seismic capacity are analyzed, the basic cause events of the assessment target based on fault tree analysis (FTA) are determined, the basic cause events in the FTA model are classified and summarized, and a judgment system of building seismic capacity is built. The weight of each index factor in the Gini index calculation system is used, and the importance of the index is analyzed. On the basis of the Spearman correlation coefficient calculation of the index, the improved naive Bayesian algorithm is combined with the importance of the index to build a judgment model for the seismic capacity of housing buildings. The sample set is constructed based on the judgment system with the basic data of some housing buildings in Huoshan County. In order to improve the generalization ability and avoid overfitting, the K-SMOTE algorithm for mixed sampling was modified to improve sample balance, and random k -fold cross validation method was used for sample division and model optimization, achieving the determination of seismic capacity level of building. The research results indicate the following: (1) the accuracy of model evaluation is 93%, with model accuracy and recall rates of 0.913 and 0.93, respectively, indicating strong generalization ability of the model. (2) Selecting some actual examples of a building, the model judgment results are consistent with the actual results, verifying the correctness of the proposed method for building the model, which can be effectively used for determining the seismic capacity of building structures. (3) Applying the proposed method to the seismic capacity assessment of buildings in the Ta-pieh Mountains of Lu’an, it is concluded that the seismic capacity of urban buildings is common, while that of rural buildings is poor.

Analisis Sharing Data Wemos D1 R32 Menggunakan Web

The advancement of technology has increased the demand for media file storage. One common storage system used today involves the use of cables or devices that need to be connected to other devices. However, this method is not efficient for simultaneous file sharing, especially when many people want to access the same files. This research utilizes the Naive Bayes algorithm and Quality of Service method for testing and SPI (Serial Peripheral Interface) as the serial communication on Wemos D1 R32 with its sensors, in order to address this issue. The research is conducted through stages of literature review, design, testing, implementation, and testing using Wemos D1 R32 as the main platform along with supporting components such as a micro SD card reader module, an LCD OLED display, a micro SD card, and a battery. In the testing phase, conducted three times with each tested file format using the Naive Bayes algorithm in our system, the evaluation results are as follows: Very Good 86.66%, Good 13.33%, Moderate 0%, and Poor 0%.

A Novel Ensemble Model for Complex Entities Identification in Low Resource Language

The fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing.

Predicting Student Performance Through Data Mining: A Case Study in Sultan Ageng Tirtayasa University

Failure in compulsory subjects such as chemistry, calculus, physics, and basic control systems could hamper the graduation process of students. Thus, students must be successful in such obligatory courses. To address this issue, this study aims to predict student performance based on their learning outcomes using data mining techniques. In particular, we utilize decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms to predict student performance. The data for this study were gathered from the learning outcomes of students in the basic control systems course and subsequently modeled using binary and nine-level classifications. The experimental results showed that DT could perform better than KNN, SVM, and NB in the binary and nine-level classifications. Interestingly, the results of DT (i.e., the prediction values) are almost similar to those of the original values of the basic control systems course.

CARDIAC DISEASE PREDICTION USING RANDOM FOREST WITH LINEAR MODEL

Making forecasts and diagnosing ailments has never been simple for medical professionals when it comes to heart conditions. Due to this, people can take the necessary action to treat heart disease before it gets worse if it is discovered in its early stages anywhere in the world. The three main causes of heart disease-drinking alcohol, smoking cigarettes, and not exercising-have become serious issues in recent years. The health care industry has produced a substantial amount of data over time, which has made machine learning capable of providing effective outcomes in prediction and decision-making. Only male patients risk level for heart disease is predicted by the Heart Disease Prediction (HDP) in the current system, which is created using the Naive Bayes and Decision Tree algorithms. For prediction, the algorithm makes use of medical parameters such age, sex, smoking status, BMI, and physical health, etc. A patients likelihood of developing heart disease is predicted by the HDP. Fishers Discriminant Ratio is one of the methods for feature selection based on random forest (RF), which is recommended to identify the best features for heart disease prediction and enhance the precision of RF-based classification. Our goal in this study is to identify the best factors that can improve the prediction accuracy of heart disease and finding the most effective variables to raise the accuracy of heart disease prediction. Evaluation criteria, namely accuracy, specificity, sensitivity, and area under the ROC curve, are employed to verify the efficacy of the proposed approach on a public dataset comprising patients of both genders. The primary benefits of applying machine learning for heart disease prediction are that it reduces the complexity of the doctors time, is patient- and cost-friendly, and manages the largest (enormous) amount of data through feature selection and the random forest algorithm. Early diagnosis of cardiovascular disease can help with lifestyle modifications for high-risk patients, which can lower complications and be a significant medical milestone. KEYWORDS: Random Forest, Decision Tree, Naive Bayes, Feature Selection, and Evaluation Metrics.

A Comparative Study of Genetic Algorithm-Based Ensemble Models and Knowledge-Based Models for Wildfire Susceptibility Mapping

Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies.

Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology.

Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.

SMS Spam Detection using Machine Learning

SMS spam detection using Naive Bayes algorithm is a widely used technique in the field of text classification. The main aim of this approach is to classify the incoming messages into spam or ham categories. The Naive Bayes algorithm works by calculating the probability of a message belonging to a particular class, based on the occurrence of different words in the message. In this paper, we present an efficient and accurate approach for SMS spam detection using the Naive Bayes algorithm. The proposed approach utilizes a pre- processing step for feature extraction, which includes tokenization, stop-word removal, and stemming. The Naive Bayes algorithm is then trained on a dataset of labeled messages to learn the probability distributions of different words in spam and ham messages. Finally, the trained model is used to classify incoming messages into spam or ham categories. The results of our experiments show that the proposed approach achieves high accuracy in detecting SMS spam messages. Keywords: Naive Bayes Algorithm, Text Classification, Probability, Feature Extraction, Tokenization, StopWord Removal, Stemming, Labeled Dataset, Probability Distribution, Accuracy

Exploring the application of teaching evaluation models incorporating association rules and weighted naive Bayesian algorithms

Teaching evaluation is a judgment of the value of teachers’ teaching and students’ learning, and has become an important part of teaching management and teaching processes in universities. However, the workflow of implementing teaching evaluation is relatively cumbersome, often requiring the completion of a large amount of data calculation tasks. Therefore, how to apply modern science and technology to establish a comprehensive, objective and feasible teaching evaluation system and optimize the evaluation process is an important issue that urgently needs to be solved. The study first uses the Apriori algorithm to explore the correlation between evaluation indicators and results, and then optimizes the teaching evaluation indicators. On this basis, incremental learning is used to improve the classification training ability of the weighted naive Bayesian algorithm, and it is combined with the Apriori algorithm for teaching evaluation. The results show that the fused algorithm takes only 50 seconds to process 500 transactions, and the running speed improves rapidly. As the minimum support threshold decreases, the increase in the time required by the algorithm gradually decreases, resulting in a higher running speed. In the self-built university teaching evaluation database, compared with the BP (Error Back Propagation) algorithm, the combined algorithm has a relatively small fluctuation of accuracy in classifying teaching data, stable at 80 % to 95 %. Meanwhile, in the efficiency comparison, the algorithm requires a slow increase in time as the amount of data increases, resulting in higher efficiency. The application of the algorithm after the fusion of the two in teaching evaluation has not only high accuracy, but also higher efficiency, providing reference technical support for the optimization of university teaching evaluation.