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Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output.
In December 2019, coronavirus (COVID-19) caused by SARS-CoV-2 was first discovered in Wuhan, China. This virus has a high transmission rate and can be transmitted through droplets, airborne, and aerosols. The clinical manifestations are very diverse ranging from mild, moderate, and severe. Therefore, this study aims to conduct a clustering of the spread of the Covid-19 pandemic to facilitate the identification and handling. The method of the K-Means algorithm can be used as a method to obtain the desired clustering. The implementation and evaluation were conducted using RapidMiner tools and Davies Bouldin Index (DBI) respectively. Furthermore, the data sources by Kangdra (2020) were used with a total sample of 110 for the period March-June 2020. The results showed that the optimal cluster is located at k: 2 with a DBI value: 0,094 as the lowest value. Therefore, the cluster is strong since a smaller DBI value gives a better cluster. The clustering obtained is Cluster 1 and 2 with mild and moderate severity. The results are expected to facilitate a better zone identification of the COVID-19 severity level and rising people awareness.
Type II diabetes mellitus is a metabolic syndrome characterized by hyperglycemia. Diabetes is still one of the world's health threats where the number of people with disabilities and mortality rates continue to increase over time. This study aims to analyze the survival of patients with type II diabetes mellitus as well as factors that affect it using survival analysis. This study used medical record data of type II diabetes mellitus patients undergoing treatment for the period 2015-2019 at PKU Muhammadiyah Gamping Hospital and PKU Muhammadiyah Hospital Yogyakarta.
Data mining is an activity to extract the knowledge from large amounts of data as very important information. The type of data in the era of 4.0 is data in the form of text, which is very much derived from social media. Recently, text becomes very important in some applications, such as the processing and the conclusion of a person's review and analysis of political opinion which is very sensitive in almost all countries, including Indonesia. Online text data that circulating on social media has several shortcomings that could potentially hinder the analysis process. One of the drawbacks is the people can post their own content freely, so the quality of their opinions cannot be guaranteed such as spam and irrelevant opinions. The other drawback is the basic truth of the online text data is not always available. Basic truth is more like a particular opinion, indicating whether the opinion is positive, negative and neutral. Therefore, the main objective of this study is to improve the forecasting accuracy of online text data analysis from social media. The method used os Hidden Markov Model (HMM) with Viterbi Algorithm that applied to extract the dataset sentiment at the 2015 elections in Surabaya from the popular site micro blogging called Twitter. The result of the study is Viterbi algorithm has predicted the best route with the candidate Tri Rismaharini gained a prediction of neutral sentiments, whereas ratio candidates gained sentiment negative predictions as well. The proposed Model is accurate to predict candidate features. It also helps political parties to introduce candidates based on reviews so that they can increase candidate performance or they can manage broad publicity to promote candidates.
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