Bayes Classifier Method Research Articles

Naive Bayes Classifier Method on Sentiment Analysis of Bibit Application Users in Play Store

The increasing public interest in investment and supported by technological advances has begun to appear investment applications in the community which aim to facilitate the public in making investments. One of the investment applications that is widely used today is the Bibit application. This application is widely used by novice investors because of its ease of opening accounts, disbursing funds, purchasing mutual funds and easy-to-understand application design. Because investment applications are still new to the community, there are still many people who doubt and worry about the quality of the Bibit application, marked by the number of reviews in the review column available on the play store. Reviews on the application become a forum for criticism and suggestions to the application and become one of the considerations for potential users. Because reviews can be positive or negative towards the Seedling application. Sentiment analysis is needed to analyze whether the sentiment tends to be positive or negative. Then, classification is carried out to obtain a classification model that can be used to predict user sentiment using the Naive Bayes Classifier method. The results obtained obtained seed application users tend to have positive sentiments with an accuracy value of 79.45%.

ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.

Sistem Pakar Diagnosis Penyakit Kulit Pada Manusia Dengan Metode Naïve Bayes Menggunakan Shiny

Skin is the outermost part of the body's organs which is very susceptible to being influenced by the environment. Besides environmental factors, behavior and lifestyle could also be the cause of skin diseases. An expert system for diagnosing skin diseases can help patients determine the diagnosis of the disease they are suffering from based on the symptoms they feel more efficiently. This system can also provide early detection to patients so that the disease suffered by them can be treated quickly. This research develops a website-based expert system for diagnosing skin diseases in humans using the Naïve Bayes Classifier (NBC) method. The expert system in this research was developed using Shiny Package in R Programming with 10 types of disease and 26 symptoms data. The results of this research show that the expert system has been successful in diagnosing human skin diseases.

Sistem Pendukung Keputusan Gizi dan Penentuan Stunting Berbasis Web di Posyandu Mutiara

Posyandu is the activity base in the field of health for the community, with the Posyandu is expected to community conscious will Healt sice early, because of the activities of Posyandu many involving various parties, especially on helath early is a todder. This Posyandu is regulated and supervised by the Puskesmas in order to maximize activities in the field of Healt between RT and RW. In the case of these many activities are still relying on the recording is manually and a lot of data that is not structured. There is also a wighing activity for toddlers who still use the manual method, namely checking the Card Towards Health (KMS) book. The study is intended to create a system supporting decision that can be used by Kader Posyandu in doing the reporting activities of the weighing toddlers and provides a place virtual namely the facility to upload data that is used to store the data Softcopy which can be downloaded back. This system also provides alternative features for determining nutrition and stunting. The result of the study of this form of system support decisions thet help Kader Posyandu in doing the repoting activities of the weighing toddlers and announcements weighing toddlers. The Nutrition Decision Support System and Stunting Determination at the Posyandu Mutiara was designed using the Naïve Bayes Classification method and was made using the PYTHON programming language by utilizing the Flask Framework, Bootstrap Framework, and MySQL as a database.

Community perceptions classification towards the development of transportation access at Darmo Corridor Surabaya using SVM and naive bayes methods

This research conducts Classification of Public Perceptions of Transportation Access Development in Darmo Corridor Surabaya using SVM and Naive Bayes methods. The increasing mobility demands necessitate adequate public transportation facilities, such as the Surabaya bus, which has been operational since 2018. The Darmo corridor has witnessed significant development in public transportation facilities and infrastructure, serving as a transit point between two Surabaya bus routes. The purpose of this study is to evaluate public perception towards the implementation of Transit Oriented Development (TOD) concept in the corridor. In this study, a qualitative descriptive analysis was conducted on various aspects of TOD implementation, such as pedestrian paths, bicycle lanes, and green open spaces. Furthermore, public perception was analyzed using Naive Bayes Classifier and SVM methods by collecting data from Twitter accounts and giving weights based on the frequency of words in the tweets. From the analysis it could be concluded Naïve Bayes is not suitable for classification because it’s not stable enough, the SVM Methods matchs 95% proven when its repeated for 5 more time it. The results showed that the Darmo corridor successfully implemented the TOD concept well, with 90% of the community giving a positive response to the TOD implementation in the area. This finding indicates the success of TOD initiatives in improving transportation facilities and infrastructure in Darmo corridor.

PENDEKATAN DATA MINING UNTUK MEMILIH PRODUK TERLARIS MENGGUNAKAN ALGORITMA NAIVE BAYES

Technological advances today can be exploited to process data into more useful information. In data collection, information collection is especially useful to maximize profits and develop marketing strategies. One way to increase profits is by using data mining techniques to help business actors in making decisions about stocks, increased profits and more. The Matahari Department Store is the largest retail platform in Indonesia, one of the retail stores located in Batam is the Matahari Department store Nagoya Hill Batam. The transaction data on the store that is still processed does not use a method that causes the processing of product sales data to be less effective and less efficient. Seeing from the number of transactions, a system is needed to predict the sale of the best-selling product as long as it can determine the correct stock for the products sold and can increase the profit, sale and purchase of the product. This research was conducted with the aim of applying data mining methods using the Naive Bayes Classifier algorithm to select the best-selling products in the outlet of the Matahari Nagoya Hill Batam Department Store. By using the collected sales data, the system is expected to increase profits steadily and avoid shortages of product stocks. Through analysis using the Naive Bayes Classifier method, the study achieved an accuracy of 67% and obtained a bag sales result to be the best-selling sale during January 2023 through March 2023 with a sales percentage of 20%.

COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE

The last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source of data for understanding public opinion and the perceived risk of an issue. In recent decades, when discussing climate change, there are those who agree and those who oppose it. Sentiment analysis is a branch of learning in the realm of text mining that is used as a solution to see opinions on a problem, one of which is climate change. In this study, we will try to analyze opinions on climate change issues using the Random Forest and Naïve Bayes classifier methods. Data were obtained from Twitter for the period January 2022-June 2022. The training data used in this research is 80%:20%. There are slightly more positive statements than negative ones. The results obtained with the Naïve Bayes classifier method are an accuracy of 76.25%, an F-1 score of 78%, and a recall of 80%. While the results of the random forest method are 70.6% accuracy, 69% F-1 score, and 63% recall. The Nive Bayes method is better than the Random Forest method for classifying climate change opinions with an accuracy of 76.25%.

DETERMINATION OF BANK INDONESIA SCHOLARSHIP RECIPIENTS USING NAÏVE BAYES CLASSIFIER

A scholarship is a grant given to students as financial aid for education. One of the most sought-after scholarships is the scholarship from Bank Indonesia. Currently, the selection process for Bank Indonesia scholarship recipients still involves verifying the completeness of the administrative documents of the prospective recipients. Manual administrative verification requires a long time for data processing and re-verification. Therefore, there is a need for a data classification system to assist in the decision-making process for Bank Indonesia scholarship recipients. This study aims to implement the naïve Bayes classifier method to classify Bank Indonesia scholarships accurately. The variables used include gender, semester, parental income, grade point average (GPA), achievement, organizational activity, and the number of dependents. This research found that the naïve Bayes classifier method for classifying Bank Indonesia scholarship recipients can be done accurately with an accuracy rate of 86,84%.

Implementation of Naive Bayes Classifier (NBC) for Sentiment Analysis on Twitter in Mobile Legends

On July 11, 2016, the mobile legends game was first released on the Indonesian server, and became one of the first MOBA games to enter the e-sports branch in Indonesia. The popularity of this game is growing rapidly, reaching more than 500 million downloads in the play store, and reaping a lot of controversy from mobile legends players. So that people create content and express their concerns through social media Twitter in the form of uploads and tweets. This makes the writer want to know how the public sentiment towards mobile legends online games on Twitter social media. The purpose of this research was to analyze how people's opinion about the Mobile Legends Online Game uses the Naïve Bayes Classifier method and to find out the level of accuracy that is obtained automatically by the system. As well as doing manual testing of the Confusion matrix from the results obtained. in classifying tweets. The results of community tweets taken using the data scraping method totaled 217 tweets and became a dataset, after the preprocessing process the tweets totaled 199 and became data testing, after being labeled it showed 100 positive tweets, 25 negative and 74 neutral. The level of accuracy obtained using the Naïve Bayes Classifier method is 80% with 104 positive tweets, 7 negative tweets and 88 neutral tweets. With a precision value of 83% positive, 100% negative and 76% neutral. The recall values obtained were 86% positive, 28% negative and 91% neutral. As for the F1-Score values obtained 84% positive, 44% negative and 83% neutral.

Sentiment Analysis of Telemedicine Applications on Twitter Using Lexicon-Based and Naive Bayes Classifier Methods

Since the onset of the COVID-19 pandemic in Indonesia, many people have turned to telemedicine programs as an alternative to minimize social interactions, opting for consultations from the safety of their homes using smartphones and internet connectivity. Given the necessity for physical distancing and avoiding crowded places, these applications have become indispensable substitutes for in-person medical consultations. Numerous apps facilitating access to healthcare services have been introduced in Indonesia, ranging from business startups to initiatives by the Ministry of Health. Telemedicine can potentially revolutionize healthcare in Indonesia, addressing critical health challenges. A significant issue within Indonesia's healthcare system is the scarcity of doctors and their uneven distribution. With only four doctors per 10,000 people, this figure falls far below the WHO guideline of 10 doctors per 1,000. Sentiment analysis of these applications was conducted to evaluate how telemedicine applications meet public needs and offer an alternative solution. Lexicon-based and naive Bayes methods were employed to classify tweet data into positive, neutral, and negative sentiments. The results revealed 908 positive tweets, 172 negative tweets, and 168 neutral tweets, indicating predominantly positive public perceptions of telemedicine applications. The naive Bayes classifier exhibited a 74% accuracy rate, with a precision of 98% and a recall of 86%. These findings underscore the positive impact and acceptance of telemedicine applications among the Indonesian populace, emphasizing their significance in augmenting the nation's healthcare landscape.

Wood Type Identification System using Naive Bayes Classification

Wood, a forest product and natural resource, is a raw material used to make household goods. Some types of wood have almost the same pattern or structure. Wood quality varies greatly depending on the tree species and the environmental conditions in which it grows. This makes it challenging to identify the type of wood, especially for wooden furniture users. Therefore, wood classification is essential to ensure that the wood used meets the required quality standards and requirements. Automatic classification of wood using image processing has several advantages and can make it easier for humans. One of the image processing methods for wood classification is the Naïve Bayes method. Feature extraction technique using GLCM using contrast, correlation, energy, and homogeneity attributes. The GLCM methods can be combined to create a system design to distinguish five wood species using an image-based wood type identification system. The study results have successfully designed a system to determine five types of wood using the framework of an image-based wood type identification system. An application system has been produced to distinguish five types of wood using the framework of an image-based wood type identification system with the GLCM feature extraction method and the Naive Bayes classification method. The application system successfully identified wood species with a test accuracy rate of 88%.

IMPLEMENTASI KLASIFIKASI KEHAMILAN BERESIKO DENGAN METODE NAIVE BAYES PADA PUSKESMAS KELURAHAN MALAKA JAYA

Pregnancy risk is classified into three categories: Low Risk Pregnancy (KRR), High Risk Pregnancy (KRT), and Very High Risk Pregnancy (KRST). Examination data recorded in the Maternal and Child Health (MCH) book at Puskesmas is used to categorize pregnancy risk. Although the maternal mortality rate has decreased, recent surveys show a significant increase. The causes include lack of counseling on obstetric and gynecological health, lack of emergency obstetric and neonatal care, and the use of ineffective traditional medicine. To overcome this problem, it is necessary to improve quality maternal health services. Efforts to accelerate the reduction of maternal mortality rate (MMR) include access to appropriate health services, postpartum care, and family planning services. Data processing uses data mining techniques with the Naïve Bayes classification method, which has high accuracy and short execution time. This study aims to develop a Naïve Bayes classification model in early detection of pregnancy risk, so that it can help diagnose pregnant women with low or high risk.

Contextual Text Mining on Social Media of Political Leaders Using Machine Learning Algorithms

Now a day’s most of the political leaders use social media to easily communicate with the people, such as sharing their ideas, promoting their policies etc. Contextual text mining is used to acknowledge the opinions of the political leaders as well as attitude on different subjects such as opinions, discussions and microblogs. Natural language processing (NLP) is included for performing the contextual text mining in order to provide the communication between the human and the machine with natural language. A new model has been implemented to compare the dataset of the political leaders which is extracted from facebook and twitter with different machine learning algorithms like Support Vector Machine (SVM), Naive Bayes Classifier (NBC) and Ensemble Learning Methods (ELM) to provide better and accurate results than other machine learning algorithms.

Sentiment Analysis of Electric Cars Using Naive Bayes Classifier Method

In recent years, electric cars have become increasingly popular as an alternative to environmentally friendly vehicles in the automotive industry. These vehicles use electric power as an energy source that can reduce dependence on fossil fuels so as to contribute to efforts to reduce greenhouse gas emissions and air pollution. However, the presence of electric cars raises pro and con opinions from the public. Where, the conversation about electric cars has become one of the hot conversations on social media twitter. Twitter is a microblogging-based social media that facilitates its users to write short messages and share them easily and quickly. These opinions require sentiment analysis. The purpose of conducting sentiment analysis is to find out how people's perceptions and opinions on electric cars are leading in a positive direction or in a negative direction. Thus, sentiment analysis can help companies in designing marketing strategies, product development, and making better business decisions. Then the opinions will be classified based on positive and negative categories. This research uses the naive bayes classifier method to generate positive and negative sentiment towards electric cars on Twitter. The accuracy results of naive bayes obtained by using a confusion matrix in this research are 78.57% with a dataset split composition of 80%:20%.

SENTIMENT ANALYSIS OF MYPERTAMINA APPLICATION USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS

In line with the needs of the community and the progress of the times in the advanced field of fintech, cash payments are currently considered insecure as well as ineffective and efficient. To run a non-cash or cashless transaction program currently run by the government, PT. Pertamina invites the public to use E-Payment from the My Pertamina application in collaboration with LinkAja. In this study, the sentiments of MyPertamina application users will be analyzed based on reviews on the Google Play Store. Review data will be analyzed to determine whether the review has positive, negative, or neutral sentiments. The data analysis stage is text preprocessing to change uppercase to lowercase, clearing text, separating text, taking important words, changing essential words, and labeling data into positive, negative, and neutral classes. As well as the classification and evaluation of results. This study used the Support Vector Machine (SVM) and Naïve Bayes classification methods. To evaluate the results, the confusion matrix was used to test the accuracy, precision, recall, and F1 score value. The classification results obtained the highest accuracy value for the Support Vector Machine (SVM) method, which had accuracy (68.50%), precision (70.00%), recall (69.70%), and F1 score (68.46%). Meanwhile, the Naïve Bayes method has performance with accuracy (63.00%), precision (63.90%), recall (61.34%), and F1 score (59.55%).

APPLICATION OF DATA MINING USING THE NAÏVE BAYES CLASSIFIER METHOD TO ANALYZE THE LEVEL OF CUSTOMER SATISFACTION IN ICE CREAM MIXUE

Ice Cream Mixue is a company engaged in the production of beverages and food. They provide a wide range of ice cream flavors with premium quality and natural ingredients, attracting many customers' attention. This study uses a quantitative method because the data sources are apparent. The research method is a scientific way to obtain data with specific purposes and uses. The technique used is the Naïve Bayes Classifier Algorithm with rapidminer results. 356 customers answered the questionnaire results satisfied, 64 customers who answered the questionnaire results were neutral, and 80 customers responded to the questionnaire results and were not happy. In this case, the Naïve Bayes Classifier Method can predict the level of customer satisfaction for ice cream mixes using the Rapidminer Application with Satisfied Words. This analysis obtained Class Precision satisfaction results of 94.72%, pred. neutral 2.0%, and pred. They are dissatisfied by 4%.

Comparison of K-Nearest Neighbor, Naive Bayes Classifier, Decision Tree, and Logistic Regression in Classification of Non-Performing Financing

The Non-Performing Financing (NPF) indicator of one of the Islamic Banks in Indonesia in the 1st to 3rd quarter of 2021 in a row of 9.69%; 9.97%; 9.46%. The NPF movement tends to improve slightly from time to time but still exceeds the maximum limit stipulated in Bank Indonesia’s Regulation Number 23/2/PBI/2021, which is no more than 5%. This shows that the Islamic bank has a financing performance that can be said to be less good. Preventive steps that can be taken to reduce the NPF ratio in order to improve the health of the bank is to classify prospective financing customers. This research was conducted using the K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree, and Logistics Regression classification methods to predict potential financing customers. The dataset is divided into 80% training and 20% testing. It was found that the best classification result was the Naive Bayes Classifier in the proportion of distribution of 80% training data and 20% testing data with an accuracy value of 84.69%, sensitivity of 58.25%, and specificity of 90.16%.

Analisis Sentimen Pencitraan Perguruan Tinggi di Yogyakarta Menggunakan Metode Naїve Bayes Classifier

This research utilizes data from Twitter to analyze sentiment in Yogyakarta's universities using the Naïve Bayes Classifier method. The Naive Bayes Classifier method is one of the text classification methods based on the probability of keywords in comparing training and testing documents. The data used consists of tweets in Indonesian language with keywords from the top 10 universities in Yogyakarta based on webometrics, as well as four other relevant keywords about Yogyakarta that are frequently searched through Google. From the conducted research, there are 1710 data collected from Twitter, which are used for classification and categorized into 3 labels: positive, negative, and neutral. The data is divided into 70% for training and 30% for testing randomly. The result of sentiment analysis classification from the test data shows that 82.1% of the data is categorized as neutral, 14.8% as positive, and 3.1% as negative, with an accuracy value of 73%.

Comparative Analysis of Community Sentiment Against the Implementation of Booster Vaccination in Indonesia Using the K-Nearest Neighbor and Naïve Bayes Classifier Methods

Comparative Analysis of Community Sentiment Against the Implementation of Booster Vaccination in Indonesia Using the K-Nearest Neighbor and Naïve Bayes Classifier Methods

Penerapan Metode Naïve Bayes Classifier Pada Klasifikasi Sentimen Terhadap Anies Baswedan Sebagai Bakal Calon Presiden 2024

Anies Baswedan is a political figure who has been declared as a 2024 presidential candidate. Public opinion is a valuable source of information to analyze sentiment towards Anies Baswedan as a 2024 presidential candidate. Limited human power, emotional instability, and the length of time required are difficulties in analyzing sentiment on large amounts of data manually. Machine learning is utilized to provide convenience in sentiment classification. This research applies the Naïve Bayes Classifier method in the classification of sentiment towards Anies Baswedan as a 2024 presidential candidate. This study aims to determine the performance of the Naïve Bayes Classifier method in the classification of sentiment towards Anies Baswedan as a 2024 presidential candidate. The dataset used was 3,400 which were labeled by crowdsourcing resulting in 2,130 positive (62.65%) and 1,270 negative (37.35%). Tests were conducted using the 10-fold cross-validation and 5-fold cross-validation methods, each consisting of two experimental scenarios, namely using an unbalanced dataset and using a balanced dataset.The Naive Bayes Classifier method produces the best model in the 10-fold cross-validation test with an accuracy of 89.76%, precision of 89.92%, recall of 89.76%, and f1-score of 89.75% on the sixth fold by determining a threshold value of 13 in an experiment using a balanced dataset consisting of 1,270 positives and 1,270 negatives with an average accuracy rate of 79.88%.