Naive Bayes Algorithm Research Articles

Analysis of Product Sales in the Application of Data Mining with Naive Bayes Classification

H&F Shoe Store is a privately owned Micro, Small, and Medium Enterprises retail store that sells merchandise. The owner serves customers directly and also acts as a cashier. In this store, the business owner is less aware of what types or categories of products are most in demand by customers, making sales operations less than optimal. Because of this, special expertise is needed to handle the problems in the retail store, namely data mining or Data Mining with the aim of digging up information related to sales problems, in this case the author will use the Classification method with the Naive Bayes algorithm. In this study, the author uses secondary data obtained from sales notebooks and re-collected into Microsoft Excel according to research needs. The data that has been collected on the software is 121 data which have 10 attributes, namely “Nama Produk”, “Size Produk”, “Kategori Produk”, “Jenis Produk”, “Gender Produk”, “Merek Produk”, “Stok Awal”, “Stok Terjual”, “Stok Sisa”, and “Penjualan”. The Naive Bayes Classifier method has successfully produced good results in classifying sales on a type or category of marketed products, the results obtained are in the form of product sales analysis and Naive Bayes model evaluation values. The results of the model evaluation values on the Confusion Matrix obtained are accuracy of 86.11%, recall of 84.62% and precision of 84.62%.

Implementation of the C4.5 and Naive Bayes Algorithms to Predict Student Graduation

This research aims to determine student graduation using two data mining methods, namely the Naive Bayes Classifier and the C4.5 Algorithm. Research stages include data analysis, data pre-processing, model design in data mining, classification results, method evaluation, and evaluation results. This research uses student data consisting of training data and testing data to evaluate the performance of the two methods in predicting student graduation based on attributes such as attendance scores, behavior scores, Final Semester Examination (UAS) scores, and report card scores. The classification results show significant differences between the two methods. The Naive Bayes Classifier produces predictions that 37 students pass and 17 students do not pass, while the C4.5 Algorithm predicts that 30 students pass and 24 students do not pass. This difference in results indicates that there are differences in the approaches of the two methods to student graduation data, with the Naive Bayes Classifier tending to provide more positive predictions than the C4.5 Algorithm. Evaluation of the performance of the method shows that the Naive Bayes Classifier has an accuracy rate of 100%, which is a perfect result, while the C4.5 Algorithm has an accuracy rate of 89%. This significant difference in evaluation results confirms that the Naive Bayes Classifier is superior in classifying student graduation compared to the C4.5 Algorithm in the context of this research. These findings can help in making decisions regarding student graduation evaluations in the future.

Analisi Prediksi Tingkat Kesejahteraan Masyarakat Nelayan Lombok Timur Dengan Algoritma Naïve Bayes

The water area in Indonesia is wider than the land area, therefore Indonesia is known as a maritime country. Indonesia also has many natural resources that come from the ocean, for example fish. Coastal communities use fish as their livelihood, where most of them become fishermen. Fishing is a daily job to meet the family's economic needs. The Fishermen's Community can be called a group of people who mostly work as fishermen. This group of people is different from people who live in lowland and highland areas, they have different characteristics, natural factors have changed their lifestyle and also their economy. It is because of the uniqueness of their lifestyle that makes the author interested in conducting research. This research uses existing data to obtain new information from that data. Researchers will predict the level of welfare of fishing communities in East Lombok using the Naive Bayes algorithm. To support the results of the decisions that will be evaluated, researchers use five variables, namely: Education, family members, wells, employment and housing. From these five variables, researchers hope to get good final results that can be used to predict the level of welfare of the fishing community in East Lombok. Evaluation is carried out using the Naive Bayes algorithm, this algorithm was chosen because it suits the characteristics of the data we have. Naive Bayes is a simple algorithm. The final results of this research were 99% for accuracy value, 81% for precision value and 100% for recall

Application of Data Mining to Prediction of New Students' Interested Departements With an Approach Naive Bayes Algorithm

Application of Data Mining to Prediction of New Students' Interested Departements With an Approach Naive Bayes Algorithm

Application of Naive Bayes Algorithm for Sentiment Analysis of Service and Facility Satisfaction (Case Study: PKU Muhammadiyah Sukoharjo General Hospital)

PKU Muhammadiyah Sukoharjo Hospital is a health facility located in Sukoharjo, Central Java. The role of the hospital in the Sukoharjo community is very important in terms of the level of community satisfaction with its services and facilities. Opinions and assessments from the community also affect the assessment of the quality of service provided by the hospital. A large assessment can be seen on Google Maps review, which will take time for the community. Public complaints on Google Maps review of PKU Muhammadiyah Sukoharjo Hospital became the basic object of this research. This research uses the Naive Bayes algorithm to categorize the assessment based on positive and negative sentiments. Two hundred nine review data from Google Maps were processed with text processing and classified using Naïve Bayes. Test results using 10% test data show a dominant positive sentiment, with 90% accuracy, 94% precision, and 83% recall.

Prioritizing Indicators for Rapid Response in Global Health Security: A Bayesian Network Approach

This study explored a Bayesian belief networks (BBNs) approach, developing two distinct models for prioritizing the seven indicators related to the “rapid response to and mitigation of the spread of an epidemic” category within the context of both the specific category and the Global Health Security Index (GHS index). Utilizing data from the 2021 GHS index, the methodology involves rigorous preprocessing, the application of the augmented naive Bayes algorithm for structural learning, and k-fold cross-validation. Key findings show unique perspectives in both BBN models. In the mutual value of information analysis, “linking public health and security authorities” emerged as the key predictor for the “rapid response to and mitigation of the spread of an epidemic” category, while “emergency preparedness and response planning” assumed precedence for the GHS index. Sensitivity analysis highlighted the critical role of “emergency preparedness and response planning” and “linking public health and security authorities” in extreme performance states, with “access to communications infrastructure” and “trade and travel restrictions” exhibiting varied significance. The BBN models exhibit high predictive accuracy, achieving 83.3% and 82.3% accuracy for extreme states in “rapid response to and mitigation of the spread of an epidemic” and the GHS index, respectively. This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

Analyzing Public Sentiment Towards BSI Service Disruptions Through X: Naïve Bayes Algorithm

Disruptions to banking services can negatively affect customer trust and happiness, thus affecting the bank's reputation in the eyes of the public. Analysis of sentiment expressed on social media is very important because it can provide a direct picture of individual perceptions and responses in real time. This research aims to analyze public sentiment towards disruptions in Bank Syariah Indonesia (BSI) services through social media using the Naive Bayes algorithm. Through this analysis, the research seeks to understand the pattern of public responses and perceptions of BSI disruptions and evaluate the performance of the Naive Bayes algorithm in classifying sentiment on related tweet data. The data used came from specific social media platforms, where sentiment analysis was conducted by categorizing the data into positive, negative, and neutral categories. The research findings show that the sentiment analysis of the community towards BSI service disruptions through X social media platforms shows a diverse pattern of responses and perceptions. This finding recorded 525 data points with negative sentiment, 325 data points with neutral sentiment, and 141 data points with positive sentiment. The research also compared the performance of the Naive Bayes algorithm with the Google Cloud Natural Language API, which showed an accuracy rate of 81.03%. This research provides valuable insights for Bank Syariah Indonesia in understanding public perception of BSI services on social media.

Online payment fraud prediction with machine learning approach using naive bayes algorithm

The increase in e-commerce has provided easy access for the public, but it also opens up opportunities for fraud in online transactions. Payment fraud is also a problem that often arises in transactions through electronic media. This research aims to analyze payment fraud in e-commerce transactions. This research uses a machine learning approach using the Naive Bayes algorithm. This research uses online transaction datasets involving various attributes such as payment and shipping methods. The developed Naive Bayes model achieved an accuracy of 61.03% with K = 7. The evaluation shows a balance between precision (59.46%) and recall (62.05%), although this study is limited by data quality and basic assumptions of Naive Bayes. In future research, it is worth considering the use of additional features or more complex data processing to improve the performance of fraud detection in online transactions. This research provides important insights in the fight against financial crime in the context of electronic commerce.

Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner

Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner

Wind turbine real-time data analysis and monitoring and warning system based on Storm

Abstract This paper introduces a real-time data analysis and monitoring and warning system for wind turbines based on Storm platform. Firstly, the framework and core components of Storm technology are introduced in detail, as well as the related Kafka technology. At the same time, the method of data forecasting is introduced. In state prediction algorithm based on naive Bayes, the basic idea of naive Bayes algorithm is introduced in detail, and the pseudo-code setting meter of the algorithm is given. In addition, the wind turbine condition warning algorithm is designed and implemented. In the chapter of real-time data analysis and early warning system design and implementation for wind turbine, the database is designed, including the design of MySQL database and HBase database, the design and reality of real-time data analysis and visualization system, and the design and reality of wind turbine fault early warning system. Finally, in the chapter of system test and analysis, the system environment and test data set are described, and the prediction algorithm based on naive Bayes is tested and the system is tested.

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

Naive Bayes Algorithm with Feature Selection Using Particle Swarm Optimization

The COVID-19 vaccine in Indonesia has led to the emergence of public opinion which is conveyed on social media such as Twitter. One of the analyses that can be done to produce various information from public opinion is sentiment analysis. Sentiment analysis is used to determine whether an opinion tends to be positive or negative. This study aims to classify the public opinion of the COVID-19 vaccine in Indonesia with sentiment analysis and to visualize the location of the sentiment of the COVID-19 vaccine tweet data in Indonesia. To achieve this aim, the Naïve Bayes algorithm with Particle Swarm Optimization (PSO) feature selection was used. This study uses opinions into positive and negative class sentiments towards 2,547 tweets related to the COVID-19 vaccine in Indonesia from January to June 2021. The results show that the distribution of positive and negative class sentiments is 2,328 and 219, respectively. In addition, the positive sentiment for the COVID-19 vaccine was dominated by people on the island of Java based on a random number matrix initialized by the PSO method. The classification of public opinion on Twitter media provides accurate and optimal performance results using a combination of the Naïve Bayes algorithm with PSO feature selection. The results of the combination of these methods have accuracy and F1 score values of 91.28% and 95.38%, respectively. The visualization of geo-spatial mapping showed that positive sentiments related to the COVID-19 vaccine exist in almost all regions in Indonesia but are dominated by the Jabodetabek area.

Language Identifier

This project presents a language identifier system focused on achieving high accuracy in identifying a specific set of 22 languages: Estonian, Swedish, English, Russian, Romanian, Persian, Pashto, Spanish, Hindi, Korean, Chinese, French, Portuguese, Indonesian, Urdu, Latin, Turkish, Japanese, Dutch, Tamil, Thai, and Arabic. Existing LI systems might struggle with the nuances of these languages, often prioritizing identification of more common languages. Our targeted approach allows for tailored optimization to achieve superior accuracy. We employ the Multinomial Naive Bayes (MNB) algorithm due to its effectiveness in text classification tasks and its ability to handle the high-dimensional, sparse features characteristic of LI based on character and word frequencies. The system achieves a promising accuracy of 95% using an 80/20 split for training and testing data. Keywords: Natural Language Processing (NLP), Language Identification (LI), Multinomial Naive Bayes (MNB), Text Classification, Language Barriers, Accuracy, Text Analysis.

Sentimental Analysis of Online Product Reviews Using Decision Tree Algorithm

Sentiment analysis or opinion classification aims in development of a system which can classify the reviews related to a product. The proposed model, supports to assess the product quality, marketers evaluations regarding the success of the newly launched product, also suggests that versions about the product is liked by the people. It is also helps us to the new features presents in the products and peoples most liked features related to the product. The term product mining also popularly known as opinion mining is using natural language processing – NLP, text mining, text analysis with computational linguistics for identifying and extraction of subjective information of the source materials, In this paper assessment the proposed work is measured with Multinomial Naive Bayes algorithm and Decision Tree algorithm.

Analisis Sentimen Ibu Kota Nusantara menggunakan Algoritma Support Vector Machine dan Naïve Bayes

The Government's policy of moving the Indonesian capital city (IKN) is considered controversial, sparking various responses from the public, especially on the social media platform X. This research aims to analyze tweet sentiment related to IKN and compare two algorithms. In this experiment, we collected 5,128 tweets regarding IKN from the X application. The dataset was classified into 2,598 positive sentiments and 1,659 negative sentiments. To analyze these sentiments, we used Text Mining techniques, comparing the Support Vector Machine (SVM) and Naive Bayes algorithms. To improve the performance of these algorithms in analyzing the data, SMOTE optimization was employed to address data imbalance. Our findings show that the SVM algorithm achieves an accuracy of 84%, while the Naive Bayes algorithm achieves an accuracy of 77%. Thus, it can be concluded that the SVM algorithm is superior to the Naive Bayes algorithm. Furthermore, the use of SMOTE optimization proved to enhance the ability of both algorithms to identify positive sentiment, as evidenced by the precision, recall, and F1-Score values. The SVM algorithm achieved a precision of 82%, recall of 86%, and F1-Score of 84%, while the Naive Bayes algorithm achieved a precision of 71%, recall of 92%, and F1-Score of 80%.

Analysis of the Placement of Disaster Early Warning Facilities Based on Village Data in West Java with a Classification Approach Utilizing Naive Bayes Algorithm

Analysis of the Placement of Disaster Early Warning Facilities Based on Village Data in West Java with a Classification Approach Utilizing Naive Bayes Algorithm

PREDIKSI PENERIMAAN SNMPTN MENGGUNAKAN ALGORITMA C4.5 DAN NAIVE BAYES

School is a formal education unit where teachers teach various disciplines which aim to make students become superior human beings. Education is important for humans. In general, people in Indonesia want their sons and daughters to be able to get an education at State Universities (PTN). There are many paths that can be taken to PTN, one of which is the National Selection for State University Entrance (SNMPTN) which is the PTN entrance route without a test or selection of report cards. However, the quota for acceptance of PTN through the SNMPTN pathway is at least 20% and the passing grade of each PTN cannot be determined so that the possibility of being accepted into PTN through the SNMPTN route is unknown. Therefore, to be able to predict students who can be accepted into PTN through the SNMPTN path, predictions of SNMPTN acceptance are made using classification data mining with the C4.5 and Naive Bayes algorithm. The goal is that with students knowing the possibility of being accepted into PTN through the SNMPTN pathway, it is hoped that more students can be accepted by PTN through the SNMPTN pathway. This study shows that the prediction of PTN acceptance through the SNMPTN path using data mining classification with the C4.5 algorithm produces a much higher accuracy of 85,09% and the AUC value 0,873, while the Naive Bayes algorithm produces an accuracy of 63,01% and the AUC value.

Implementasi Algoritma Naive Bayes Dalam Penentuan Bonus Karyawan

. CV. Aneka Kaca is a company engaged in the supply of the best glass materials, because CV. Aneka Kaca is a direct distributor from Japan. In a company like CV. Aneka Kaca, employees are one of the important assets in helping to improve and stabilize the company. Employees are people who work in a company or agency to carry out an operational task and expect remuneration in the form of a commission or salary. Usually employees will also get bonuses from the company. Bonuses that will be received by employees are one of the ways companies use to increase the motivation of their employees' performance. But giving bonuses to CV. Aneka Kaca still uses the conventional method, so it can take a long time if only managers do the math. The determination of bonuses can sometimes be missed due to one or two problems that can have an impact on awarding bonuses. The Naive Bayes algorithm is a simple probabilistic classifier that computes a probabilistic set by summing the frequencies and value combinations from a given dataset.

Development of an expert-informed rig state classifier using naive bayes algorithm for invisible loss time measurement

Development of an expert-informed rig state classifier using naive bayes algorithm for invisible loss time measurement

Understanding sexual homicide in Korea using machine learning algorithms.

The current study was conducted to confirm the characteristics in sexual homicide and to explore variables that effectively differentiate sexual homicide and nonsexual homicide. Further, newer methods that have received attention in criminology, such as the machine learning method, were used to explore the ideal algorithm for classifying sexual homicide and patterns for sexual homicide in Korea. To do this, 542 homicide cases were analyzed utilizing eight algorithms, and the classification performance of each algorithm was analyzed along with the importance of variables. The results of the analysis revealed that the Naive Bayes, K-Nearest Neighbors, and RF algorithms demonstrate good classification accuracy, and generally, factors such as relationships, marriage, planning, personal weapons, and overkill were identified as crucial variables that distinguish sexual homicide in Korea. In addition, the crime scene information of the crime occurring in the dark (at night) and body disposal were found to have high importance. The current study proposes ways to enhance the efficacy of crime investigation and advance the research on sexual homicides in Korea through a more scientific understanding of sexual homicide that has not been thoroughly explored domestically.