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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%.

Cyberbullying is an action related to the use of digital technology to intentionally hurt, humiliate or bully other people online. This research focuses on the classification of cyberbullying comments on social media, especially Instagram comments, where many parties who then become a group of people who don't like something will come together to provide negative opinions and comments, which can cause lowered self-confidence and other bad impacts for other users and account owner. Therefore, a classification of Instagram comments regarding cyberbullying was carried out as an effort to prevent this action. The data used in this research is 2000 data, where this data will go through various processes so that it can be executed. In this research, the Naïve Bayes method was used by dividing two classes, namely Bully and Not Bully. Based on the results of the tests that have been carried out, the results obtained are an accuracy value of 84%, a precision value of 84%, a recall of 84%, and an f1-score of 84%.

The remaining food waste in Indonesia reaches around 46.35 million tons, with economic losses reaching 23 million to 48 million tons per year. This condition has led to various campaigns to reduce food waste from people concerned about the problem of food waste. However, the increase in food waste campaigns has yet to be accompanied by a decrease in the volume of food waste in Indonesia. This research aims to determine public sentiment toward food waste campaigns on Instagram social media and determine the accuracy of the methods used in data classification. The method used is the Naïve Bayes Classifier method. The results obtained were from a total of 118 data regarding the food waste campaign; 79% data showed that the public had a positive sentiment, and 21% other data had a negative sentiment. The accuracy results of using sentiment analysis were 78.94%; this shows that the performance of the Naïve Bayes method in classifying data is quite good.

Animal husbandry is the activity of breeding and cultivating farm animals in order to obtain benefits and results from these activities. The most widely cultivated livestock is chicken. One of the companies that utilize chicken is PT Reza Perkasa. The management of data records of laying hens in the company still does not have a system and still uses excel reports every week. Farmers in determining the chicken afkir is still in the form of traditional records. The problem can be solved by making a monitoring application and a system for determining abandoned laying hens using the naïve bayes method. It is expected that with the monitoring application, the general manager can quickly monitor in real-time, so that for the needs of chickens that are useful for improving production quality quickly without the need to wait for manual reports from the head of the cage.

The policy of shutting down TikTok Shop has sparked both pros and cons. On one side, it eliminates jobs for content creators whose income relies on TikTok Shop, while on the other side, it saves UMKM  from predatory pricing wars that harm them. Utilizing the Naive Bayes algorithm, a classification method capable of predicting the likelihood of a class and making decisions based on learning data, the Emotion Recognition research on YouTube comments related to the closure of TikTok Shop is conducted. Data will be classified into five classes: happy, angry, sad, afraid, and surprised. The objective of this research is to find the best emotional model using the Naive Bayes method. The results of user testing with Naive Bayes and Tf-Idf show that the precision values for sad, happy, afraid, and surprised emotions are high, while for anger, the percentage is 59%. The percentages for afraid, happy, sad, and surprised emotions are 91%, 87%, 84%, and 79%, respectively. The overall accuracy is 82%.