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Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.

The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.

JakLingko is a digital-based public transportation integration system developed to facilitate access to various transportation modes in Jakarta. Along with the increasing number of users, reviews on the JakLingko application reflect user experiences and perceptions. This study aims to analyze the sentiment of user reviews on the Google Play Store using the Naïve Bayes method. Data collection was conducted through web scraping, resulting in 3,260 reviews. The data were preprocessed, sentiment-labeled, and classified using Orange Data Mining. The research applied a quantitative experimental approach with a machine learning framework. The classification results showed that neutral sentiment dominated user reviews, followed by negative and positive sentiments. The Naïve Bayes model achieved 100% accuracy based on the confusion matrix and other evaluation metrics such as precision, recall, and F1-score. The findings highlight that Naïve Bayes can be a reliable approach for analyzing public opinion and serve as a reference for evaluating and improving digital service applications.

The Family Hope Program (PKH) is a conditional social assistance program provided by the government to improve the quality of life of underprivileged families through support in the education, health, and social welfare sectors. In its implementation, the process of determining PKH candidate recipients at the West Sumba Regency Social Service often experiences obstacles, especially with regard to objectivity, accuracy of targets, and limitations in complex data management. Thus, a decision support system (SPK) is needed that can assist the agency in selecting prospective recipients more effectively, efficiently, and on target. This study proposes the application of the Naive Bayes method in the development of SPK to determine PKH recipients. The Naive Bayes method was chosen because of its ability to classify data based on probability, and it can handle large volumes of data with a good degree of accuracy. The criteria applied in the classification include the level of household income, the number of members covered, the state of residence, the education of children, and the health of family members. The research process includes needs analysis, system design, data collection, application of Naive Bayes algorithms, and system testing. The findings of the study show that SPK based on Naive Bayes can provide recommendations for PKH recipients with better accuracy compared to manual methods. In addition, the system is able to improve transparency, fairness, and speed in the recipient selection procedure. With this system, it is hoped that the distribution of PKH in West Sumba Regency can be more orderly, balanced, and on target in accordance with the goals of government programs.

Traffic violations are one of the serious problems frequently occurring in various regions, including Binjai City. Various types of violations, such as disobeying road signs and markings, incomplete vehicle documents, and violations that threaten the safety of drivers and other road users, continue to increase despite preventive and repressive efforts carried out by the authorities. This condition indicates that handling traffic violations cannot rely solely on field enforcement but also requires the support of technology capable of analyzing data more comprehensively. This study aims to predict the level of traffic violations by applying the Naïve Bayes method through data mining techniques. The dataset used consists of traffic violation records in 2023 from the Binjai City Police Department, with the main variables including violations of traffic signs and markings, document completeness, and safety-related violations. The Naïve Bayes method was selected because of its ability to perform classification with good accuracy, simplicity, and efficiency in processing large amounts of data. The implementation of this research is realized by developing a web-based application using Visual Studio Code as the development environment and MySQL as the database system. The results of this study are expected to provide structured information regarding traffic violation patterns, support authorities in making more effective decisions, and serve as an alternative solution in the prevention and handling of traffic violations in Binjai City.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.

This study aims to classify the level of satisfaction of Dasawisma cadres with the Carik application in West Semper Village by utilizing the Naive Bayes method. Data was obtained through questionnaires, which were compiled based on three main aspects: ease of use, speed of access, and the usefulness of applications in supporting cadre tasks. After the data is collected, a pre-processing and labeling process is carried out, where the level of satisfaction of respondents is categorized into two classes, namely "satisfied" and "dissatisfied". The Naive Bayes algorithm is applied to predict satisfaction classes based on questionnaire answers. The results of the analysis show that the Naive Bayes method is able to perform classification with sufficient accuracy, so that it can be used as an evaluation tool and decision support in the development of the carik application. This method can also help the management understand user perceptions and improve the system based on objective and routine data in line with the needs of field cadres.

Hypertension is a disease that occurs to arteries that causes the supply of oxygen and nutrition that the body needs to be blocked. Hypertension is often called a silent killer, because it is a kind of disease that is very harmful but comes without awareness to its victim. People with hypertension in average are up to 40 years old and it happened all of his after life . In common hypertension caused by heredity, unhealthy lifestyle, and triggered by the more salty consumption, alcohol and stress. An expert system could be the solution to solve the problem because this system works just like an expert and was created by the naïve Bayes method with the rules and basic system that are the same just like the hyperantion desease. Through this application, users can consult with this system just like usually people consult with the expert to diagnose the sign that happened to the user and find the solution of what happened to themselves.

This study aims to classify the level of satisfaction of Dasawisma cadres with the Carik application in West Semper Village by utilizing the Naive Bayes method. Data was obtained through questionnaires, which were compiled based on three main aspects: ease of use, speed of access, and the usefulness of applications in supporting cadre tasks. After the data is collected, a pre-processing and labeling process is carried out, where the level of satisfaction of respondents is categorized into two classes, namely "satisfied" and "dissatisfied". The Naive Bayes algorithm is applied to predict satisfaction classes based on questionnaire answers. The results of the analysis show that the Naive Bayes method is able to perform classification with sufficient accuracy, so that it can be used as an evaluation tool and decision support in the development of the carik application. This method can also help the management understand user perceptions and improve the system based on objective and routine data in line with the needs of field cadres

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.

Students reading interest is a crucial factor in enhancing the quality of education. However, the lack of structured data makes it challenging to identify specific patterns of reading interest. This study aims to implement a data mining method using the Naive Bayes algorithm to analyze students' reading interest at SMP Negeri 2 Palembang's library. The data used includes book borrowing history, types of books, and library visit frequency over one semester. The analysis results indicate that the Naive Bayes method achieves an accuracy rate of 80% in classifying reading interest based on predetermined categories. These findings are expected to assist the school in designing more effective literacy programs.  

Sentiment Analysis is a technique for extracting text data to obtain information about positive, neutral or negative sentiments. The purpose of sentiment analysis is given by internet users on social media to provide a personal assessment or opinion. Paga Lewu Shop that often gets user sentiment through social media is Paga Lewu Shop. The existence of consumer opinion sentiments about Paga Lewu Shop can be analyzed and utilized to obtain useful information for other customers and the Paga Lewu Shop. By using the Text Mining technique classification method, a sentiment will be known as positive, neutral or negative. One of the algorithms widely used in sentiment analysis is the Naïve Bayes classification method. This study uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotion icon conversion feature (emoticon) to determine the existing sentiment class from tweets about the Paga Lewu Shop. The results of the study show that the Naïve Bayes method without additional features is able to classify sentiment with an accuracy value of 96.44%, while if the tf-idf weighting feature is added along with the conversion of emotion icons, the accuracy value can be increased to 98%.

Mental health is a state of well-being in which a person is aware of his or her abilities, can cope with normal life stresses, can work productively and contribute to his or her community. Mental health encompasses emotional, psychological and social well-being, and affects how a person thinks, feels and acts. It also determines how a person handles stress, relates to others and makes decisions. Prediction methods that can identify the level of mental health of students are important as a preventive measure. One promising method in this regard is the Naïve Bayes Method. This method has the advantage of being able to solve classification problems on complex datasets, such as student mental health data involving many independent variables. An expert system is a system that attempts to adopt human knowledge into computers so that computers can solve problems as is usually done by experts. The purpose of this study was to find out how to predict the level of mental health of students towards the end of school using the Naïve Bayes method. The results of this study are that the prediction of the level of mental health of students towards the end of school using the Naïve Bayes method can be used and the system created works well, without having to consult a doctor or psychologist.

The lungs as the only pump for the respiratory system are very important organs for the continuation of life. Diagnosing or checking lung symptoms early can help people recognize the possibility that they are suffering from lung disease, so that treatment or care can be done earlier to prevent the severity of the disease. The method used in this study is the Naïve Bayes method. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by adding up the frequencies and combinations of values ​​from the given dataset. An expert system is a computer application that can help decision making in more specific fields with methods that have been analyzed in advance by experts or specialists. This study used variables, namely types of lung disease including Pulmonary Tuberculosis (TB), Chronic Obstructive Pulmonary Disease (COPD), Bronchial Asthma and Lung Cancer. The results of this study are that lung disease or types of lungs can be diagnosed using the web-based Naïve Bayes method, and make it easier for sufferers to consult without seeing a doctor by selecting symptoms of lung disease.

Respiratory tract disease is a common condition that can affect anyone regardless of age. Starting from relatively mild symptoms to alarming symptoms. Although some respiratory diseases are not life-threatening, they should not be taken lightly as they can cause serious complications. What often happens is that it is difficult for a patient to see a specialist doctor because of the limited number of respiratory specialists who cannot fully serve patients, so people often have difficulty if they want to consult directly. This triggers the habit of the community to treat complaints on their own with simple drugs bought freely at drugstores or pharmacies without knowing for sure the disease they suffer, as well as the length of waiting for queues, consultation fees that are quite expensive and not everyone has a short distance to the hospital prefer not to go to a specialist. Like other organs of the human body, breathing is also prone to various diseases. Respiratory organs will be disrupted and can even cause death. By using the Naïve Bayes method above, it is known that the diagnosis of respiratory disease is that the young female patient is diagnosed with a type of respiratory disease called Farangitis (P05) with a percentage of 47.44%.

This research aims to classify news headlines into clickbait and non-clickbait using the Multinomial Naive Bayes method. The data used comes from the dataset CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines. The research process involves stages of data collection, preprocessing, feature extraction, model training, model evaluation, and result analysis. The test results show that the Multinomial Naive Bayes algorithm consistently produces an accuracy rate of around 78%. Optimization using Grid Search did not result in an accuracy improvement. However, there was an improvement in the recall value for the non-clickbait class from 76% to 80%. The best parameter found was an alpha of 0.15. Therefore, the Multinomial Naive Bayes algorithm can be effectively used to address the problem of classifying clickbait news headlines, with the potential to contribute to clickbait prevention efforts in the future.

The insurance business within an insurance company offers insurance products owned by the insurance company. In every insurance product there is a premium payment and the premium is the income of an insurance company at the rate of the amount insured. The problem that PT BNI Life Insurance has is that there are many stops in premium payments such as policy redemptions due to errors in the benefits received or incorrect selection of the insurance product, this can reduce the achievement of targets for an insurance company. The aim of this research is to find out the best classification algorithm compared between K-Nearest Neighbor and Naive Bayes to predict the type of insurance product that customers will choose. In this research, data mining methods are applied to compare two different methods, namely the K-Nearest Neighbor method and the Naïve Bayes method. The level of accuracy results for the K-Nearest Neighbor method is 80% and the Naïve Bayes method is 70.53%, which means that the K-Nearest Neighbor method is the best method to apply to an insurance product classification system based on the demographics of prospective customers.

Political dynasty is a political power exercised by a group of people who are related by family, with the aim of obtaining power and ensuring that this power remains within the group by passing it on to other family members. This study conducts a sentiment analysis on comments related to the Supreme Court decision which is believed to pave the way for Kaesang Pangarep in support of Jokowi's political dynasty. Sentiment analysis is carried out using the Naive Bayes method, a commonly used algorithm for text classification based on probability. The data used consists of comments from videos taken from social media platforms. These comments are then categorized into positive, negative, and neutral sentiments. The results of the study show the distribution of public sentiment towards this issue, providing an overview of how the public responds to the decision. The Naive Bayes method is chosen for its simplicity and its ability to provide reasonably accurate results in text analysis.

The Family Hope Program (PKH) is a program that provides attention to the community, especially the health category, education category and social welfare category for poor families. The Family Hope Program (PKH) aims to reduce poverty and improve the welfare of the Indonesian population. Due to the large number of residents who want to register themselves as PKH recipients, there are residents who manipulate data or claim to be poor people in order to get PKH. If this continues to happen, and there is no preventive action, it is not impossible that many residents are not right in receiving PKH provided by the Government. One of the efforts that can be made is to test the classification of prospective PKH recipients in Bah Sorma Village. This study aims to classify prospective recipients of the Family Hope Program in Bah Sorma Village. The dataset used is data on prospective PKH recipients in Bah Sorma Village, Pematang Siantar City. This research is a comparative study of previous research using the Naïve Bayes method. The method used in this research is Data Mining with the C4.5 method which is used to see the accuracy of the best method than previous research. The accuracy result obtained by this research is 98.18%. Based on the results obtained, research with the case of classification of prospective PKH recipients in Bah Sorma Village using the C4.5 Algorithm gets better accuracy than previous research using Naïve Bayes obtaining an accuracy of 80%.