IDENTIFIKASI KEBUTUHAN DASAR DI TEMPAT EVAKUASI SEMENTARA PASCA ERUPSI MERAPI DENGAN SENTIMENT ANALISIS DAN SUPPORT VECTOR MACHINE

AbstractMount Merapi Eruption in 2010 was the biggest after 1872. The impact of this eruption was felt by people who lived around the areas which were affected by this Merapi Eruption. Thus, disaster management was done. One of the disaster management was the fulfillment of basic needs. This research aims to collect public opinion against the fulfillment of basic needs in the shelters after Merapi Eruption based on Twitter data. The algorithm which is used in this research is Support Vector Machine to develop classification model over the data that has been collected. The expected result from this study is to know the basic needs in a shelter. The accuracy gained by performing Cross Validation for 10 folds from Support Vector Machine is 87.96% and Maximum Entropy is 87.45%. Keywords: twitter, sentiment analisis, merapi eruption, support vector machine AbstrakErupsi Gunung Merapi 2010 merupakan yang terbesar setelah tahun 1872. Dampak dari Erupsi Gunung Merapi dirasakan oleh masyarakat yang tinggal di daerah terdampak Erupsi Merapi. Oleh sebab itu dilakukan penanggulangan Bencana. salah satu penanggulangan bencana adalah pemenuhan kebutuhan dasar. Penelitian ini bertujuan untuk mengumpulkan opini publik terhadap pemenuhan kebutuhan dasar di tempat pengungsian pasca erupsi merapi berdasarkan data Twitter. Algoritma yang digunakan dalam penelitian ini adalah Support Vector Machine untuk membangun model klasifikasi atas data yang sudah dikumpulkan. Hasil yang diharapkan dari penelitian ini adalah mengetahui kebutuhan dasar dari suatu tempat pengungsian. Akurasi yang didapatkan dengan melakukan Cross Validation sebanyak 10 fold dari model klasifikasi Support Vector Machine87,96% dan Maximum Entropy 87,45 Kata Kunci: twitter, analisis sentimen, erupsi merapi, support vector machine

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Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.3499 ◽

2014 ◽

Vol 8 (1) ◽

pp. 91 ◽

Cited By ~ 5

Author(s):

Noviah Dwi Putranti ◽

Edi Winarko

Keyword(s):

Support Vector Machine ◽

Maximum Entropy ◽

Social Networking Site ◽

Training Data ◽

Classification Model ◽

Support Vector ◽

Public Sentiment ◽

Pos Tagger ◽

Negative Sentiment ◽

Bahasa Indonesia

AbstrakAnalisis sentimen dalam penelitian ini merupakan proses klasifikasi dokumen tekstual ke dalam dua kelas, yaitu kelas sentimen positif dan negatif. Data opini diperoleh dari jejaring sosial Twitter berdasarkan query dalam Bahasa Indonesia. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap objek tertentu yang disampaikan di Twitter dalam bahasa Indonesia, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data yang sudah terkumpul dilakukan proses preprocessing dan POS tagger untuk menghasilkan model klasifikasi melalui proses pelatihan. Teknik pengumpulan kata yang memiliki sentimen dilakukan dengan pendekatan berdasarkan kamus, yang dihasilkan dalam penelitian ini berjumlah 18.069 kata. Algoritma Maximum Entropy digunakan untuk POS tagger dan algoritma yang digunakan untuk membangun model klasifikasi atas data pelatihan dalam penelitian ini adalah Support Vector Machine. Fitur yang digunakan adalah unigram dengan fitur pembobotan TFIDF. Implementasi klasifikasi diperoleh akurasi 86,81 % pada pengujian 7 fold cross validation untuk tipe kernel Sigmoid. Pelabelan kelas secara manual dengan POS tagger menghasilkan akurasi 81,67%. Kata kunci—analisis sentimen, klasifikasi, maximum entropy POS tagger, support vector machine, twitter. AbstractSentiment analysis in this research classified textual documents into two classes, positive and negative sentiment. Opinion data obtained a query from social networking site Twitter of Indonesian tweet. This research uses Indonesian tweets. This study aims to determine public sentiment toward a particular object presented in Twitter businesses conduct market. Collected data then prepocessed to help POS tagged to generate classification models through the training process. Sentiment word collection has done the dictionary based approach, which is generated in this study consists 18.069 words. Maximum Entropy algorithm is used for POS tagger and the algorithms used to build the classification model on the training data is Support Vector Machine. The unigram features used are the features of TFIDF weighting.Classification implementation 86,81 % accuration at examination of 7 validation cross fold for the type of kernel of Sigmoid. Class labeling manually with POS tagger yield accuration 81,67 %. Keywords—sentiment analysis, classification, maximum entropy POS tagger, support vector machine, twitter.

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Study about Recognition of Digital Meter Dial Reading Based on SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.615.194 ◽

2014 ◽

Vol 615 ◽

pp. 194-197

Author(s):

Zhen Yuan Tu ◽

Fang Hua Ning ◽

Wu Jia Yu

Keyword(s):

Support Vector Machine ◽

Cross Validation ◽

Recognition Rate ◽

Classification Model ◽

Support Vector ◽

Feature Points ◽

Svm Classification ◽

Performance Factors ◽

Model Combining ◽

Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

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Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Entropy ◽

10.3390/e23101247 ◽

2021 ◽

Vol 23 (10) ◽

pp. 1247

Author(s):

Mingyang Liu ◽

Jin Yang ◽

Wei Zheng

Keyword(s):

Support Vector Machine ◽

Maximum Entropy ◽

Leak Detection ◽

Least Square ◽

Support Vector ◽

Maxent Model ◽

Water Pipelines ◽

The Impact ◽

Multi Classification ◽

Improved Support Vector Machine

Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.

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

Jurnal Gaussian ◽

10.14710/j.gauss.v9i3.28932 ◽

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

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Veil and Hijab: Twitter Sentiment Analysis Perspective

IJID (International Journal on Informatics for Development) ◽

10.14421/ijid.2020.09108 ◽

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.

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Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

International Journal of Computational Physics Series ◽

10.29167/a1i1p120-130 ◽

2018 ◽

Vol 1 (1) ◽

pp. 120-130 ◽

Cited By ~ 1

Author(s):

Chunxiang Qian ◽

Wence Kang ◽

Hao Ling ◽

Hua Dong ◽

Chengyao Liang ◽

...

Keyword(s):

Support Vector Machine ◽

Environmental Factors ◽

Cross Validation ◽

Concrete Strength ◽

Simulation Method ◽

Support Vector ◽

Svm Model ◽

Artificial Neural Network Ann ◽

Influence Degree ◽

Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i3.1942 ◽

2020 ◽

Vol 4 (3) ◽

pp. 504-512

Author(s):

Faried Zamachsari ◽

Gabriel Vangeran Saragih ◽

Susafa'ati ◽

Windu Gata

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Feature Selection ◽

Public Opinion ◽

Naive Bayes ◽

Naïve Bayes ◽

Capital City ◽

Support Vector ◽

National Capital ◽

Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

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Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

Current Bioinformatics ◽

10.2174/1574893615999200819162140 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chun Qiu ◽

Sai Li ◽

Shenghui Yang ◽

Lin Wang ◽

Aihui Zeng ◽

...

Keyword(s):

Support Vector Machine ◽

Expression Profiles ◽

Independent Set ◽

Classification Model ◽

Support Vector ◽

Feature Subset ◽

Hub Genes ◽

Effective Prevention ◽

Key Genes ◽

Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Journal of the Operations Research Society of China ◽

10.1007/s40305-021-00354-9 ◽

2021 ◽

Author(s):

Jia-Bin Zhou ◽

Yan-Qin Bai ◽

Yan-Ru Guo ◽

Hai-Xiang Lin

Keyword(s):

Support Vector Machine ◽

Negative Impact ◽

Twin Support Vector Machine ◽

Fuzzy Membership ◽

Support Vector ◽

Membership Functions ◽

Fuzzy Membership Functions ◽

Intuitionistic Fuzzy ◽

Benchmark Datasets ◽

The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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