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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.

Typhoid is an acute febrile condition caused by infection with Salmonella enterica bacteria, especially the Salmonella typhi variant. Typhoid fever or what we usually know as typhoid fever. However, this disease can also be caused by other types such as Salmonella paratyphi A, Salmonella typhi B, and Salmonella paratyphi C. Typoid fever or typhus abdominalis is an acute infectious disease of the small intestine with symptoms of fever for one week or more accompanied by disorders of the intestinal tract. digestion and with or without impaired consciousness. Bayes' theorem is a theory of probability conditions that takes into account the possibility of an event (hypothesis) depending on other events (evidence). Future events can be predicted if previous events have occurred. Bayes' theorem is a mathematical equation used in probability and statistics to calculate conditional probabilities. In other words, it is used to calculate the probability of an event based on its relationship to other events. Based on the weight value given by the expert to each patient's typhoid symptom data, from the results of the analysis carried out with the diagnosis results from the consultation, the symptoms are High fever (lasting up to two weeks), Headache, Chills, Skin rash, Muscle and joint pain, Extreme fatigue, Dry cough, Confusion or delirium, Nausea and vomiting, Swollen spleen, Abdominal pain with predicted results for Epidemic Typhus with a value of 76.26%.

State regulations state that village funds come from the State Budget (APBN) and are used to support governance, development, development, and social activities as well as community empowerment. It is hoped that the existence of village funds will increase the sources of income for each village, and the addition of village income by the government will improve public service facilities.1. As a result, a sentiment analysis of village officials will be carried out in this study. The Naive Bayes approach will be used to classify public sentiment as part of this investigation. We will evaluate two methods to see which produces more accurate results. In addition, the village government's function as the most important social institution in the community is essential for setting standards, facilitating socialization, and allocating resources. Furthermore, some Eweta community members have not received assistance, which could cause social rivalry among neighbors. Through sentiment categorization, responses will be categorized as either positive or negative. Based on feedback from visitors, this study attempts to assess the validity of the two approaches put to the test and offer insights into the caliber of services rendered by the village administration.

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%.

The North Sumatra Aceh National Sports Week (PON) which will be held in September 2024 in Papu, North Sumatra has drawn many pros and cons from the public. This topic allows the public to provide criticism, suggestions and opinions regarding the 2024 North Sumatra Aceh PON. Instagram is a popular social media for conveying public opinion. The sentiment analysis process can find and resolve problems based on public opinion on social media such as Instagram. The classification method used in this research is the Naïve Bayes Classifier. Datasets can be obtained from the data crawling process using the Google Chrome extension: IGCommentExport. The data is labeled positive, neutral, or negative. The results of the labeling process showed 770 negative data, 256 neutral data and 920 positive data. Then pre-processing is carried out on the data that has been previously labeled, and a word weighting process is also carried out using TF-IDF. After that, modeling was carried out using the Naïve Bayes Classifier and the final process was evaluation-testing. The high accuracy results from the fourth experiment which compared 90% of the training data with 10% of the testing data resulted in an accuracy of 75%. Meanwhile, the sentiment test results show that positive sentiment is more numerous than negative sentiment and neutral sentiment.

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.

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.

Nowadays, advances in information and communication technology have had a major impact on various sectors of life, including in the field of trade and e-commerce. However, as it happens in the scope of social media, TikTok Shop is also dealing with challenges and changes. One of the issues that is sticking out and is currently hot is the permanent closure of the TikTok Shop feature that has occurred in Indonesia. This research will carry out several processes starting with data collection, then data labeling, data preprocessing, data sharing, weighting training data, using the Naive Bayes method, and ending with testing. In this study, the results of the implementation that has been made or built are discussed. The method used is the Naive Bayes Classifier Method to classify training data as much as 800 data, which is 80% of the total data. Then, testing is carried out using 200 testing data, which is 20% of the total data. The evaluation results show an accuracy value of 73%. In addition to the accuracy value, this research also recorded the precision, recall, and F1 score values. The classification that appears most often and contributes the highest in these values is the Positive classification as much as 420 data or 42% of the total data used..    

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.

According to the Jakarta Air Quality Index (AQI US) 12 July 2023, 200 indicates unhealthy air quality with an index value between 151 and 200. This figure even shows that Jakarta is currently the second most polluted city in Southeast Asia. (CNN Indonesia., 2023). This incident gave rise to responses from the public which were expressed via social media Twitter. From this incident, sentiment analysis was carried out regarding Jakarta's air quality. The amount of data used for this research was 500 tweet data. The results of the positive and negative sentiment analysis show that negative sentiment appears more frequently than positive sentiment with a percentage of 7% positive sentiment and 14% negative sentiment, by using the Rstudio application. This method uses the naïve Bayes classifier. Data division in the dataset with training data 1:499 and test data 1:476. It was found that the results of the Accuracy, Precision, Recall, and F1-Score values were Accuracy 87.50%, Precision 87.50 Recall 93.33%, and F1-Score 82.35%.       

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.

In the era of rapid digitalization, understanding consumer behavior through data is becoming increasingly important for retail businesses. Shopping trends, such as those contained in this study, provide in-depth insights into various aspects of consumer behavior, from demographics to purchasing preferences and patterns of discount usage. This data is invaluable in formulating effective marketing strategies, improving customer experience, and optimizing business operations. The data used in this study included a variety of relevant variables, such as age, gender, location, product categories purchased, number of purchases, payment methods, and frequency of purchases. This information allows for a comprehensive analysis of how these factors affect consumer spending decisions. For example, analytics can reveal seasonal trends in purchases, product color and size preferences, and the impact of discounts and promo codes on sales volume. In addition, this dataset also reflects the changes in consumer behavior that have occurred over the past few years. Quantitative methodology is a research approach used to collect and analyze numerical data to understand patterns, relationships, and events in a given population. Data is collected from various sources such as online sales transactions, consumer surveys, Naive Bayesian algorithms are applied to the dataset that has been processed. The data was divided into two sets: training (80%) and testing (20%).    

This study employed knowledge discovery in databases (KDD) to extract and discover knowledge from the Benue State Polytechnic (Benpoly) admission database and used a structural causal model (SCM) ontological framework to represent the admission process in the Nigerian polytechnic education system. The SCM ontology identified important causal relations in features needed to model the admission process and was validated using the conditional independence test (CIT) criteria. The SCM ontology was further employed to identify and constrain input features causing bias in the local interpretable model-agnostic explanations (LIME) framework applied to machine learning (ML) black-box predictions. The ablation process produced more stable LIME explanations devoid of fairness bias compared to LIME without ablation, with higher prediction accuracy (91% vs. 89%) and F1 scores (95% vs. 94%). The study also compared the performance of different ML models, including Gaussian Naïve Bayes, Decision Trees, and Logistic Regression, before and after ablation. The limitation is that the SCM ontology is qualitative and context-specific, so the fair-LIME framework can only be extrapolated to similar contexts. Future work could compare other explanation frameworks like Shapley on the same dataset. Overall, this study demonstrates a novel approach to enforcing fairness in ML explanations by integrating qualitative SCM ontologies with quantitative ML/LIME methods.

This research use the Naive Bayes algorithm to classification of user reviews of the Sky Childern Of The Light application from the Google Play Store. The Sky Childern Of The Light application is a popular online game, because it offers a unique and immersive playing experience. This method was chosen because of its simplicity, speed, ease of interpretation, and suitability for high-dimensional data. The advantages of Naive Bayes are the accuracy and efficiency of calculations, fast results and presentation. The data collected was 1500 data with a classification ratio of 8:2 with an accuracy value of 87% using the Naïve Bayes algorithm. This method is very good at analyzing the sentiment of the Sky Children Of The Light application.      

. 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.  

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%.

In the rapidly developing digital era, social media such as Twitter has become part of everyday life and facilitates the rapid dissemination of information, including information about criminals. This research aims to analyze public sentiment towards information about criminals spread on Twitter using the Naive Bayes algorithm. This algorithm was chosen because of its simplicity and effectiveness in text classification. Data was collected through a crawling process from Twitter, followed by a preprocessing stage to remove noise. The research results show that public sentiment towards information about criminals on Twitter is divided into three categories: positive, neutral and negative. After classification, it was found that neutral sentiment increased significantly to 63.4%, while positive and negative sentiment decreased to 10.5% and 26.1%. These findings indicate that people tend to be more careful in reacting to sensitive information. This research provides important insights for related parties in managing information about criminals on social media and can be a reference for developing further policies and strategies.

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.