scholarly journals ANALISIS SENTIMEN ULASAN APLIKASI TIKTOK DI GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN ASOSIASI

2021 ◽  
Vol 10 (3) ◽  
pp. 346-358
Author(s):  
Sola Fide ◽  
Suparti Suparti ◽  
Sudarno Sudarno
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Kernel Method ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Rbf Kernel ◽  
Parameter Experiment ◽  
The Cost ◽  
Google Play

Corona virus pandemic requires people to do activities from home so the number of internet usage in Indonesia has increased because information is carried out through social media. One of the popular social media in Indonesia is TikTok. However, the Tiktok’s popularity cannot be separated from the footsteps of TikTok in Indonesia which was blocked by government for committing many violations. Each application allows users to provide a review about the application. To find out the users TikTok’s sentiment, sentiment analysis was carried out to classify reviews into positive and negative sentiments. Classification is carried out using the Support Vector Machine (SVM) with kernel Radial Basis Function (RBF) method which is more effective classification algorithm and kernel function, seen from previous studies. The parameters used in the SVM gamma default 0.0004255 and the Cost (C) parameter experiment used is 0,01; 0,1; 1; 10; 100; 1000. The  results can provide information that can be retrieved using the association method. The steps are scrapping data, data preprocessing, sentiment scoring, TF-IDF weighting, classifying using the SVM RBF kernel method and text association. Evaluation of the model using a confusion matrix with the value of accuracy and kappa. The greater the value of accuracy and kappa, the better the performance of the classification model. The review classification resulted in the best accuracy rate of 90.62% and the best kappa of 81.24% which means that it includes an almost perfect classification result. Based on the data association, positive reviews are given because users like and are comfortable with the current version of TikTok which contains funny videos on fyp. Meanwhile, negative reviews were given because the user failed to register and his account was blocked, so the user asked TikTok to continue to make improvements.

scholarly journals KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Lalu Mutawalli ◽  
Mohammad Taufan Asri Zaen ◽  
Wire Bagye
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Big Data ◽  
Mass Communication ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Appearance Time ◽  
Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

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 Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

2021 ◽  
Vol 9 (1) ◽  
pp. 126-136
Author(s):  
Rahmat Robi Waliyansyah ◽  
Umar Hafidz Asy'ari Hasbullah
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Classification Model ◽  
Support Vector ◽  
Coffee Bean ◽  
Coffee Beans ◽  
The Many

Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983.

scholarly journals Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

2020 ◽  
Vol 4 (3) ◽  
pp. 650
Author(s):  
Rian Tineges ◽  
Agung Triayudi ◽  
Ira Diana Sholihati
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Internet Service ◽  
Active User ◽  
The Social ◽  
Twitter Users ◽  
Use Of The Internet

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

scholarly journals Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine

2021 ◽  
Vol 5 (1) ◽  
pp. 17-23
Author(s):  
Oryza Habibie Rahman ◽  
Gunawan Abdillah ◽  
Agus Komarudin
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Hate Speech ◽  
Main Issue ◽  
Support Vector ◽  
Behavior Patterns ◽  
Time Period ◽  
Rbf Kernel

Nowadays social media has become a place for peoples to express their opinions, there are many ways that can be done to express both positive and negative opinions. Hate speech is one of the problems that we find quite a lot in cyberspace, that things can be detrimental to many parties. Twitter as one of social media, can be used as a source of analysis about people's behavior in cyberspace. Many of our society that unconsciously act of hate speech on social media, therefore this study finds out how people's behavior patterns in cyberspace and the main issue of hate speech on a particular topic and time period by classify it into five classes, namely ethnicity, religion, race, inter-groups and neutral using Support Vector Machine. In this study also compares three kernel that common to use and the result is the system can classify hate speech by using RBF kernel and got the highest result with 93% accuracy on 700 data train and 300 data test.

scholarly journals Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7916
Author(s):  
Mingu Kang ◽  
Siho Shin ◽  
Gengjia Zhang ◽  
Jaehyo Jung ◽  
Youn Tae Kim
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Mental Illnesses ◽  
Classification Model ◽  
Classification Error ◽  
Support Vector ◽  
Stress Classification ◽  
Ecg Data

Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive Bayes algorithms of a support vector machine. The stress classification criteria were determined by calculating the average values of the R-S peak, R-R interval, and Q-T interval of the ECG data to improve the stress classification accuracy. For the performance evaluation of the stress classification model, confusion matrix, receiver operating characteristic (ROC) curve, and minimum classification error were used. The average accuracy of the stress classification was 97.6%. The proposed model improved the accuracy by 8.7% compared to the previous stress classification algorithm. Quantifying the stress signals experienced by people can facilitate a more effective management of their mental state.

scholarly journals Machine Learning Based Suspicion of Customer Detention in Banking with Diverse Solver Neighbors and Kernels

2019 ◽  
Vol 8 (4) ◽  
pp. 3244-3249
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Confusion Matrix ◽  
Principal Component ◽  
Support Vector ◽  
Business Sector ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Customer Churn ◽  
Rbf Kernel

In the current moving technological business sector, the amount spent for attaching the new customer is highly expensive and time consuming process than adopting some methods to hold and retain the existing customers. So the business sector is in need to make a research on with holding the existing customers by using the current technology. The methods to make the retention of the existing customers with high reliablility are a challenging task. With this view, we focus on predicting the customer churn for the banking application. This paper uses the customer churn bank modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data preprocessing is done and the relationship between the attributes are identified. Second, the data set is reduced with the principal component analysis to form the 2 component feature reduced dataset. Third, the raw dataset and 2 component PCA reduced dataset is fitted to various solvers of logistic regression classifiers and the performance is analyzed with the confusion matrix. Fourth, the raw dataset and 2 component PCA reduced dataset is fitted to various neighboring algorithms of K-Nearest Neighbors classifiers and the performance is analyzed with the confusion matrix. Fifth, the raw dataset and 2 component PCA reduced dataset is fitted to various kernels of Support Vector Machine classifiers and the performance is analyzed with the confusion matrix. The implementation is carried out with python code using Anaconda Navigator. Experimental results shows that, the rbf kernel of Support vector machine classifier is effective with the accuracy of 85.8% before applying PCA and accuracy of 80.9% after applying PCA compared to other classifiers.

scholarly journals Veil and Hijab: Twitter Sentiment Analysis Perspective

2020 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
Lusiana Lestari ◽  
M Didik R Wahyudi ◽  
Usfita Kiftiyani
Keyword(s):  
Support Vector Machine ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Accuracy Score ◽  
Base Function ◽  
Rbf Kernel ◽  
The Veil

Controversies about veil and hijab are often occur in society. Especially in today’s digital era, public opinion expressed through social media can greatly influence the others opinions, regardless of whether it is positive or negative. Therefore, this research was aiming to conduct an approach through analysis sentiment of public opinion about the veil and hijab to know how much accurate the sentiment analysis predict the positive, negative, or other sentiments with using Twitter data as the research object. The algorithm used in this study is Support Vector Machine (SVM) because of its fairly good classification model though it trained using small set of data. The SVM on this research was combined with Radial Base Function (RBF) kernel because of its numerical difficulties that are fewer than linear and polynomial kernel and also because this research doesn’t have a large feature.  The amount of data used is 3556 tweets data. Tweets data, which is numbered 1056, is classified manually for the learning process. The remaining 2500 data will be classified automatically with the classifier model that has been created. A total of 1056 tweets data that have been classified manually is separated into training and testing data with a ratio of 8: 2. The result of the sentiment analysis process using Support Vector Machine algorithm RBF kernel with C=1 and γ=1  has an accuracy score of 73.6% with precision to negative opinions are 62%, positive opinions are 83%, neutral opinions reach 53% and irrelevant opinions that talk about hijab and veil reach 98%. It shows that sentiment analysis can be used for predicting the negative, positive or other sentiments of a sentence based on a certain topic, in this case veil and hijab.

scholarly journals Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes

2021 ◽  
Vol 5 (2) ◽  
pp. 393-399
Author(s):  
Mahendra Dwifebri Purbolaksono ◽  
Muhammad Irvan Tantowi ◽  
Adnan Imam Hidayat ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Confusion Matrix ◽  
Diabetes Patient ◽  
High Rate ◽  
Classification Model ◽  
Support Vector ◽  
Machine Learning Approach ◽  
The Republic ◽  
Performance Results

Diabetes (diabetes) was a metabolic disorder caused by high levels of sugar in the blood caused by disorders of the pancreas and insulin. According to data from the Ministry of Health of the Republic of Indonesia, Diabetes was the third-largest cause of death in Indonesia with a percentage of 6.7%. The high rate of death from diabetes encouraged this study, with the aim of early detection. This research used a Machine Learning approach to classify the data. In this paper, a comparison of Support Vector Machine (SVM) and Modified Balanced Random Forest (MBRF) was discussed for classifying diabetes patient data. Both methods were chosen because it was proven in previous studies to get high accuracy, so that the two methods are compared to find the best classification model. Several preprocessing methods were used to prepare the data for the classification process. The entire combination of preprocessing steps will be carried out on the two classification methods to produce the same dataset. The evaluation was carried out using the Confusion Matrix method. Based on the experimental results in the process of testing the system being built, the maximum performance results were 87.94% using SVM and 97.8% using MBRF.

scholarly journals Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine

2019 ◽  
Vol 3 (2) ◽  
pp. 243-250
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Bobby Suryo Prakoso ◽  
Dedi Ariadarma
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Opinion Mining ◽  
Confusion Matrix ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Toll Road ◽  
Svm Algorithm ◽  
Svm Model ◽  
Textual Data

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Support Vector Machine algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying SVM Algorithm model. The results obtained from the study using the SVM model are obtained Confusion Matrix result, namely accuracyof 78.18%, Precision of 74.03%, and Sensitivity or Recall of 86.82%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.  

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