TEXT MINING DALAM MEMBANDINGKAN METODE NAÏVE BAYES DENGAN C.45 DALAM MENGIDENTIFIKASI BERITA HOAX PADA MEDIA SOSIAL

Hoax news (hocus to trick) has a very big influence in disseminating information, especially in the world of social media. News has an important impact on social and political conditions, and news can move the economy of a country. For this reason, it is necessary to have an analysis to classify hoax news and not hoaxes, and have high accuracy in classifying the news. In this study, two methods were used as a comparison in achieving high accuracy, namely the Naïve Bayes method which is famous for having high accuracy in classification with little data, and the C.45 method which can minimize noise in the data. The data used are 300 articles with 10 topics which contain hoax and non-hoax news. The data is obtained from the internet through social media, such as Twitter, Instagram and Facebook. Testing using the Naïve Bayes method has a higher accuracy than the C.45 method. The amount of data used has a major influence on the test results, if more data enters the training stage, then this study will have higher accuracy. However, the results of this test can be recommended to increase accuracy in the construction of a hoax news detection system.

Chemical and sensory properties of analogue rice based on kimpul flour (Xanthosoma sagitifolium)

Analog rice is artificial rice shaped like rice grains made from non-rice carbohydrate-rich flour with water, which can overcome food security in Indonesia. Taro kimpul is a local food rich in carbohydrates that cannot be widely used. Therefore, kimpul thread has the potential to be used as raw material in the manufacture of analog rice. This study aimed to determine the chemical characteristics of kimpul taro analog rice with dyes and binders. In addition, it is expected to increase consumer acceptance based on sensory testing. This research method uses an experimental laboratory method by making analog rice with 4 formulations. The analysis was water content, ash content, protein content, fat content, carbohydrate content, and sensory (hedonic) analysis, including colour, taste, texture, and overall aroma. The results showed that analog rice A was the best formula selected using the Bayes method based on the results of chemical and hedonic tests. Chemical and sensory characteristics of analog rice A with the use of 4% CMC and 32% beet are as follows moisture 2.88%; ash 2.3%; fat 1.1%; 5.7% protein; carbohydrate 87.94% and a preference value with an average range of neutral-good.

Application of Nave Bayes Algorithm for SMS Spam Classification Using Orange

Mobile phones have become a necessity for everyone. SMS is a communication service that is used to send and receive short messages in the form of text on mobile phones. Among all the advantages of SMS, there is a very annoying activity called spam (unsolicited commercial advertisements). Spam is the continuous use of electronic devices to send messages. called spammers. Spam messages are sent by advertisers with the lowest operating costs. Therefore, there are a lot of spammers and the number of messages requested is huge. Therefore, many aspects are harmed and disturbed. When SMS enters the user's mobile device, this study aims to classify spam and ham SMS. SMS classification adopts naive Bayes method. By looking at the contents of the SMS, the application of the naive Bayes method in data mining can distinguish unwanted SMS from non-spam. Results The classification accuracy rate is 0.999%. Based on the research that I have done, the Naive Bayes method can classify 1000 SMS spam data contained in the SMS spam data set file correctly.

Sentiment Analysis of Face-To-Face Learning Based on Social Media

The use of restricted face-to-face learning during the epidemic in Indonesia was discussed not just by education and health professionals, but also on social media. The study used the Twitter dataset with the keywords 'school' and 'face-to-face' to examine public opinion about face-to-face learning. The research data was obtained from Twitter utilizing Drone Emprit Academic, and it was then processed using the Naive Bayes method to create sentiment analysis. During that time, research revealed that 32% of people were positive, 54% were negative, and 14% were indifferent. Because of worries about the dangers associated with the use of face-to-face learning, negative attitudes predominate.

A STUDY OF GENERALIZED LINEAR MIXED MODEL FOR COUNT DATA USING HIERARCHICAL BAYES METHOD

Poisson Log-Normal Model is one of the hierarchical mixed models that can be used for count data. Several estimation methods can be used to estimate the model parameters. The first objective of this study was to examine the performance of the parameter estimator and model built using the Hierarchical Bayes method via Markov Chain Monte Carlo (MCMC) with simulation. The second objective was applied the Poisson Log-Normal model to the West Java illiteracy Cases data which is sourced from the Susenas data on March 2019. In 2019, the incidence of illiteracy is a very rare occurrence in West Java Province. So that, it is suitable as an application case in this study. The simulation results showed that the Hierarchical Bayes parameter estimator through MCMC has the smallest Root Mean Squared Error of Prediction (RMSEP) value and the absolute bias is relatively mostly similar when compared to the Maximum Likelihood (ML) and Penalized Quasi-Likelihood (PQL) methods. Meanwhile, the empirical results showed that the fixed variable is the number of respondents who have a maximum education of elementary school have the greatest risk of illiteracy. Also, the diversity of census blocks significantly affects illiteracy cases in West Java 2019.

Electronic health record to predict a heart attack used data mining with Naïve Bayes method

<span>A heart attack is a medical emergency. A heart attack usually occurs when a blood clot blocks the flow of blood to the heart. Cardiovascular disease is a variety of diseases that attack the body's cardiovascular system including the heart and blood vessels. Cardiovascular diseases (CVD) include angina, arrhythmia, heart attack, heart failure, atherosclerosis, stroke, and so on. To resolving (CVD) is to evaluate large scores of datasets, to compare for any information that can be used to forecast, to take care of organize. The method used Naïve Bayes classification because that method can determine target which can be used to answer some questions like whether the patient has the potential for heart disease. After data analyst, authors can use data to electronic health records (EHR).</span>

Prediction Of Medical Actions For Covid Patients Using Naïve Bayes Method

Management of medical actions carried out in handling patients who are ODP (people under monitoring), OTG (asymptomatic people), PDP (patient under monitoring) and positive Covid-19 patients is carried out based on assumptions, such as self-isolation, hospitalization, or special treatments in the ICU (Intensive Care Unit) room. The condition of the body in each patient is different, a patient may have same symptoms but the treatment is different, especially in elderly patients. Many problems occur in determining medical action because the patient's body condition is different. Therefore, it needs to be appointed as a research. The research method used in this study was Nive Bayes algorithm with supporting application Rapid Miner. It was applied to carry out the process of testing on patient data as much as 500 data, 25 variables or patient symptoms and 3 outputs as a form of medical action. Based on the results of the analysis carried out in this study, prediction of medical actions for ODP, PDP, OTG and positive Covid-19 patients were obtained by comparing training data with testing data using Rapid Miner application. It resulted that an accuracy rate of 76.00% was obtained

Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine

Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. Keywords: