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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%.

Technological advancements have brought significant transformations across various fields, including the application of machine learning in recommendation and classification systems. Machine learning leverages data processing, utilizes algorithms, and efficiently identifies patterns to produce accurate recommendations and predictions. This study aims to review machine learning-based recommendation system approaches, analyze model performance, and compare the algorithms used. A literature review was conducted by examining journals published in the past five years, focusing on algorithm implementation. The findings indicate that the Naïve Bayes algorithm delivers the best performance, achieving an accuracy of up to 97%. This algorithm is particularly well-suited for processing small to medium-sized datasets with high efficiency. The research provides comprehensive insights into the performance and limitations of various algorithms, serving as a valuable guide for future developments in the field.

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.  

KPRI KOKARNABA Baturraden faces challenges in managing increasingly complex sales data, particularly in identifying the most in-demand products to maximize profit. This study aims to analyze sales patterns using the Naïve Bayes algorithm as a probability-based classification method. The collected sales data were analyzed to identify categories of best-selling and less popular products within the cooperative. The results indicate that the Naïve Bayes algorithm has an accuracy rate of 77.56% in predicting product categories. This research is expected to assist the cooperative in optimizing stock management and improving member satisfaction.

Social assistance is an important aspect of government and non-government programs that can help on a large scale for the community so that the impact is to lighten life in the short term, but social assistance has several criteria such as income, social conditions, family status and the impact of the economic situation. . Knowing the criteria for social assistance is done by applying data mining to social assistance using the Naive Bayes algorithm procedure which produces accuracy calculations from 100 testing data, obtained good values, namely accuracy of 95.00%, precision of 92.31%, and recall of 97.95%.

Medical record data can be used as a benchmark and comparison in the health business to ascertain the rate at which a disease is developing in a given area. It would be beneficial, though, if this data could be transformed into useful information, like illness forecasts. Infectious diseases like malaria are common in tropical and subtropical regions. West Sumba Regency is the region with the highest number of malaria cases, and this figure rises year. Of the different Puskesmas labor locations, Lolo Wano Health Center has the largest number of positive cases of malaria. In order to apply information system technology and prevent malaria early, research was done at the Lolo Wano Community Health Center to predict malaria using the Naïve Bayes approach. This is because the Community Health Center does not currently have a malaria prediction system. Six of the 16 features in the patient dataset—a total of 27 patient data—were malaria symptoms. When there are suitable illness indicators, positive predictions are produced using the outcomes of Naïve Bayes computations. Before the patient proceeds with a direct medical evaluation, these anticipated results may be utilized as a provisional approximation. Naïve Bayes, Center, Prediction, Malaria

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.

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.      

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.

The use of social media is already powerful and difficult to avoid. Social media users are not only limited to the general public, but also public figures and even economic actors who use social media as a means of marketing. In every post from the account owner, there will always be followers who can give likes and comments. Unfortunately, not all comments are related to the uploaded post. One of the most annoying comments is spam comments. Spam comments are comments that are not clear and contain about business (promos / selling), links or various other things that are promoting something. Using the Naive Bayes algorithm, this study wants to identify spam comments, especially on Instagram social media. Where the data is retrieved using the tools provided by Google. Which is then processed with the Rapidminer application to get the Naive Bayes calculation results.

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%.