scholarly journals SENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI’S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD

2021 ◽  
Vol 4 (1) ◽  
pp. 17-22
Author(s):  
Zetta Nillawati Reyka Putri ◽  
Muhammad Muhajir
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Test Results ◽  
Text Data ◽  
Machine Method ◽  
Svm Classification ◽  
The Public ◽  
Positive Class ◽  
Negative Class

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.

Classification of Medical Text Data Using Convolutional Neural Network-Support Vector Machine Method

2020 ◽  
Vol 10 (7) ◽  
pp. 1746-1753
Author(s):  
Lan Liu ◽  
Xiankun Sun ◽  
Chengfan Li ◽  
Yongmei Lei
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Data ◽  
Machine Method ◽  
Medical Text ◽  
Conventional Methods

Conventional methods of medical text data classification, neglect of context among different words and semantic information, has a poor text description, classification effect and generalization capability and robustness. To tackle the inefficiencies and low precision in the classification of medical text data, in this paper, we presented a new classification method with improved convolutional neural network (CNN) and support vector machine (SVM), i.e., CNN-SVM method. In the method, some convolution kernel filters that contribute greatly to the CNN model are first selected by the average response energy (ARE) value, and then used to simplify and reconstruct the CNN model. Next, the SVM classifier was optimized by firefly algorithm (FA) and context information to overcome the disadvantages of over-saturation and over-training in SVM classification. Finally, the presented CNN-SVM method is tested by the simulation experiment and the true classification of medical text data. The experimental results show that the presented CNN-SVM method in this paper can significantly reduce the complexity and amount of computation compared to the conventional methods, and further promote the computational efficiency and classification accuracy of medical text data.

scholarly journals ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM

2020 ◽  
Vol 9 (3) ◽  
pp. 376-390
Author(s):  
Nur Fitriyah ◽  
Budi Warsito ◽  
Di Asih I Maruddani
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Classification Model ◽  
Support Vector ◽  
Test Results ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Rbf Kernel ◽  
Negative Sentiment ◽  
Fold Cross Validation

Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy

scholarly journals A morphological study of galaxies in ZwCl0024+1652, a galaxy cluster at redshift z ∼ 0.4

2019 ◽  
Vol 485 (2) ◽  
pp. 1528-1545
Author(s):  
Zeleke Beyoro Amado ◽  
Mirjana Pović ◽  
Miguel Sánchez-Portal ◽  
S B Tessema ◽  
Ángel Bongiovanni ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Central Region ◽  
Galaxy Cluster ◽  
Support Vector ◽  
Clear Trend ◽  
Visual Classification ◽  
Cluster Galaxies ◽  
The Public ◽  
Emission Line Galaxies

Abstract The well-known cluster of galaxies ZwCl0024+1652 at z ∼ 0.4 lacks an in-depth morphological classification of its central region. While previous studies provide a visual classification of a patched area, we used the public code called galaxy Support Vector Machine (galsvm) and HST/ACS data as well as the WFP2 master catalogue to automatically classify all cluster members up to 1 Mpc. galsvm analyses galaxy morphologies through support vector machine (SVM). From the 231 cluster galaxies, we classified 97 as early types (ETs) and 83 as late types (LTs). The remaining 51 stayed unclassified (or undecided). By cross-matching our results with the existing visual classification, we found an agreement of 81 per cent. In addition to previous Zwcl0024 morphological classifications, 121 of our galaxies were classified for the first time in this work. In addition, we tested the location of classified galaxies on the standard morphological diagrams, colour–colour and colour–magnitude diagrams. Out of all cluster members, ∼20 per cent are emission-line galaxies, taking into account previous GLACE results. We have verified that the ET fraction is slightly higher near the cluster core and decreases with the clustercentric distance, while the opposite trend has been observed for LT galaxies. We found a higher fraction of ETs (54  per cent) than LTs (46  per cent) throughout the analysed central region, as expected. In addition, we analysed the correlation between the five morphological parameters (Abraham concentration, Bershady–Concelice concentration, asymmetry, Gini, and M20 moment of light) and the clustercentric distance, without finding a clear trend. Finally, as a result of our work, the morphological catalogue of 231 galaxies containing all the measured parameters and the final classification is available in the electronic form of this paper.

Classification of dental lesions fluorescence spectra using support vector machine method

2005 ◽  
Author(s):  
I. S. Atanasov ◽  
Ekaterina G. Borisova ◽  
O. I. Yordanov ◽  
Tzonko T. Uzunov ◽  
Lachezar A. Avramov
Keyword(s):  
Support Vector Machine ◽  
Fluorescence Spectra ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Dental Lesions

scholarly journals Comparison of Machine Learning Algorithms Using WEKA and Sci-Kit Learn in Classifying Online Shopper Intention

2019 ◽  
Vol 3 (1) ◽  
pp. 58
Author(s):  
Yefta Christian
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Kappa Statistic ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Results ◽  
Online Shoppers ◽  
Online Stores

<p class="8AbstrakBahasaIndonesia"><span>The growth of online stores nowadays is very rapid. This is supported by faster and better internet infrastructure. The increasing growth of online stores makes the competition more difficult in this business field. It is necessary for online stores to have a website or an application that is able to measure and classify consumers’ spending intentions, so that the consumers will have eyes on things on the sites and applications to make purchases eventually. Classification of online shoppers’ intentions can be done by using several algorithms, such as Naïve Bayes, Multi-Layer Perceptron, Support Vector Machine, Random Forest and J48 Decision Trees. In this case, the comparison of algorithms is done with two tools, WEKA and Sci-Kit Learn by comparing the values of F1-Score, accuracy, Kappa Statistic and mean absolute error. There is a difference between the test results using WEKA and Sci-Kit Learn on the Support Vector Machine algorithm. Based on this research, the Random Forest algorithm is the most appropriate algorithm to be used as an algorithm for classifying online shoppers’ intentions.</span></p>

scholarly journals ALGORITMA KLASIFIKASI NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM LAYANAN KOMPLAIN MAHASISWA

2020 ◽  
Vol 5 (2) ◽  
pp. 211-220 ◽  
Author(s):  
Hermanto Hermanto ◽  
Ali Mustopa ◽  
Antonius Yadi Kuntoro
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Machine Method ◽  
The Status ◽  
Auc Value ◽  
Bayes Algorithm

Service in the world of education is an important element for the creation of an academic atmosphere that is conducive to the implementation of a successful teaching and learning process. The process of service to students there is a tendency to be implemented not following the minimum service standards that must be provided to students so that students tend to complain about the services provided. Submission of criticism, complaints, input, or suggestions for dissatisfaction and problems that exist in the university environment is still very limited. Complaints can be constructive if submitted to the right place and party. In this research the data processing of email complaints from students conducted at the academic student body (students.bsi.ac.id). Student complaint data that will be processed is data in the form of * .xls complaint file. Before text data is analyzed using text mining methods, the pre-processing text needs to be done including tokenizing, case folding, stopwords, and stemming. After pre-processing, the classification method is then performed in classifying each complaint category and dividing the status into two parts, namely complaint and not complaint so that the status becomes a normal condition in text mining research. The purpose of this study is to obtain the most accurate algorithm in the classification of student complaints and can find out the results of the classification of the Naïve Bayes algorithm method and Support vector Machine used and compared. In this study, the results of testing by measuring the performance of these two algorithms using Cross-Validation, Confusion Matrix, and ROC Curves. The obtained Support vector Machine algorithm has the highest accuracy value compared to Naïve Bayes. AUC value = 0.922. for the Support vector machine method using the student academic data collection dataset (students.bsi.ac.id) has 84.45%, from the Naïve Bayes algorithm has an accuracy rate of about 69.75% and AUC value = 0.679.

scholarly journals Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method

2016 ◽  
Vol 2 (1) ◽  
pp. 57 ◽  
Author(s):  
Veronikha Effendy
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Public Transport ◽  
Public Transportation ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Traffic Jam ◽  
The Public ◽  
Traffic Jams

<p>Traffic jams that occur in big cities in Indonesia due to the increased use of private vehicles. One solution to overcome this problem is to increase the use of public transport. But, the existing public transport is still not much in demand by the community. Some people express their opinions regarding the use of city public transportation via Twitter.  The opinions can be processed as a sentiment analysis to determine the positive opinions and negative opinions. The opinion will then be analyzed to determine factors that are the main cause of the ineligibility use of public transport as well as the factors that make the public choose to use this type of transport. By upgrading of facilities and services based on the results of sentiment analysis, it is expected that people will switch to use city public transportation, which would reduce the traffic jam.  This research used SVM method to process sentiment analysis. The result has shown SVM accuracy reaches 78.12%, which indicates that the results of this reserach deserve to be considered.</p>

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