Fuzzy clustering aims to produce clusters that take into account the possible membership of each dataset point in a particular cluster. Fuzzy C-Means Clustering Core and Reduct is a fuzzy clustering method is a Fuzzy C-Means Clustering method that has been optimized using the reduction of Core and Reduct dimensions. The method studied is highly dependent on the distance function used. As a further in-depth study, this study was compiled to see the performance of the Fuzzy C-Means Clustering Core and Reduct using various distance functions. We aim to see how consistent the results of this method are across various distance functions and find the best distance function. The seven distance functions are applied to the same dataset. The seven distances are the Euclidean, Manhattan, Minkowski, Chebyshev, Minkowski-Chebyshev, Canberra, and Averages distances. We use UCI Machine Learning datasets for this research. The quality of the clustering results is compared through several measures. Accuracy, Silhouette score, and Davies Bouldin Index are used as internal measurements. The results of Fuzzy C-Means Core and Reduct clustering on all distance functions have significantly decreased computational load. Accuracy and purity values can be maintained with values above 80%. There was an increase in the value of the Silhouette Coefficient Score and a decrease in the Davies Bouldin Index after the application of dimension reduction. This means the quality of the clustering results can be maintained. The distance with the best evaluation result is the Euclidean distance. This method runs consistently across all tested distance functions.
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.
Sentiment analysis is one part of natural language processing. Sentiment analysis can be done by lexicon based, or machine learning based. Sentiment analysis based on machine learning has advantage of dynamism to meet with new language datasets or new vocabulary. Sentiment analysis seeks to understand the sentiments contained in a sentence. A sentence can be positive, neutral or negative, based on its sentiments. A sentence can have positive, neutral or negative sentiments. However, the fact is each sentence does not always have positive, negative or neutral sentiment clearly. We try to develop a sentiment analysis method that can show the sentiment degree of a sentence. Fuzzy sentiment analysis using convolutional neural network are introduced in this paper to produce more accurate sentiment analysis results. Convolutional neural networks are a popular machine learning method for sentiment analysis. The concept of fuzzy sets is used to express the sentiment degree of a sentence. Euclidean distance analysis to determine the proximity of two vectors is used to show that this method is better than the standard method. The method we propose successfully produces a value that indicates the degree of sentiment of a sentence. Comparison of the euclid distance between the results of the standard sentiment analysis and our method shows that the results of the fuzzy sentiment analysis using convolutional neural network have a distance that is relatively close to the true sentiment value. Fuzzy convolutional neural network analysis sentiment is proven to be able to produce better and smoother sentiment analysis results than standard methods.
Fuzzy membership function was introduced intothe Support Vector Machine (SVM) resulting in modifications. Selecting the correct membership function is an important step in the Fuzzy Support Vector Machine (FSVM) method. One of the general criteria for selecting fuzzy membership is determined by the distance between a point and its fixed center category. This study aims to develop the SVM method into Fuzzy SVM (FSVM) with several distance functions that are applied to the Early Stage Diabetes data which collects 520 data. The distance functions used include Euclid, Canberra distance, Minkowski distance, Chebyshev distance, Minkowski Chebyshev distance, and Bray-Curtis distance where this distance function is used to determine the best distance that can be seen from the results of accuracy, specificity, g-means which is best for viewing diabetes risk. The results of this comparison show that the FSVM method with several distance functions is more than the SVM method. Where the FSVM method at the Canberra distance with a penalty value of = is the best distance to see the risk of diabetes, based on the results of specificity = 100%, g-means = 86.91%, and accuracy = 85.26% is superior to the SVM method at the penalty value = with specificity = 69.36%, g-means = 77.31%, and accuracy = 79.49%. Although the FSVM method produces an evaluation value at sensitivity = 75.53%, it is lower than the SVM method with a sensitivity value = 86.17%.
Fuzzy Subtractive Clustering (FSC) is a technique of fuzzy clustering where the cluster to be formed is unknown. The distance function in the FSC method has an important role in determining the number of points that have the most neighbors. Therefore, this study uses several distance functions. The results obtained indicate that the DBI results indicate that the Euclidean distance has a good cluster evaluation result in the number of clusters 4. Meanwhile, for the PC value the combination of the Minkowski Chebysev distance produces a good PC value in the number of clusters 2.
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