scholarly journals Optimasi Textblob Menggunakan Support Vector Machine Untuk Analisis Sentimen (Studi Kasus Layanan Telkomsel)

2021 ◽  
Vol 10 (1) ◽  
pp. 13-15
Author(s):  
Kevin Perdana ◽  
Titania Pricillia ◽  
Zulfachmi
Keyword(s):  
Support Vector Machine ◽  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
A Value ◽  
Processing Techniques

Sentiment analysis refers to Natural Language Processing techniques that are classified as Unsupervised Learning to identify positive, negative, or neutral opinions. Many of these opinions come through Twitter, because social media is quite effective and efficient in commenting because it can only write a maximum of 140 characters. From previous research, the value of the accuracy of the sentiment analysis carried out by one of the NLP libraries, namely TextBlob, has shown that Unsupervised Learning does not produce such good scores. With the Telkomsel service case study the writer took the dataset from Twitter and the results of the analysis with TextBlob only showed a value of 58.59%. Optimization is done by adding the Support Vector Machine method which is included in the Supervised Learning category. The best results obtained from this study are values that show 75%.

scholarly journals Particle Swarm Optimization-based Support Vector Machine Method for Sentiment Analysis in OVO Digital Payment Applications

2021 ◽  
Vol 4 (2) ◽  
pp. 232-239
Author(s):  
Retno Sari ◽  
Ratih Yulia Hayuningtyas
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Particle Swarm ◽  
Support Vector ◽  
In Ovo ◽  
Machine Method ◽  
Swarm Optimization ◽  
Support Vector Machine Method ◽  
The Right

Sentiment analysis is used to analyze reviews of a place or item from an application or website that then classified the review into positive reviews or negative reviews. reviews from users are considered very important because it contains information that can make it easier for new users who want to choose the right digital payment. Reviews about digital payment ovo are so much that it is difficult for prospective users of ovo digital payment applications to draw conclusions about ovo digital payment information. For this reason, a classification method is needed in this study using support vector machine and PSO methods. In this study, we used 400 data that were reduced to 200 positive reviews and 200 negative reviews. The accuracy obtained by using the support vector machine method of 76.50% is in the fair classification, while the accuracy obtained by using the support vector machine and Particle Swarm Optimization (PSO) method is 82.75% which is in good classification.

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>

scholarly journals EKSTRAKSI FITUR MENGGUNAKAN MODEL WORD2VEC PADA SENTIMENT ANALYSIS KOLOM KOMENTAR KUISIONER EVALUASI DOSEN OLEH MAHASISWA

2020 ◽  
Vol 7 (1) ◽  
pp. 35
Author(s):  
Muhammad Rusli
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Related Research

<p><em>This research is about Sentiment Analysis using the Word2vec model. this research was conducted by Fauzi (2019). But in his research the use of the Word2vec model produces an accuracy of 70%, because the data used is small. In little data Word2vec cannot grasp the similarity of meaning well. So that related research was conducted which used lecturer evaluation comment data and also Wikipedia article data in Indonesian language as Word2vec model. In this study a comparison of average extraction features of Word2vec and Bag of Centroid base Word2vec was done and a combination of the two was then performed using the Support Vector Machine method. The application of Word2vec Average base feature extraction in the lecturer evaluation commentary data resulted in an accuracy of 84,8%. Then using the Bag of Centroid base feature extraction using Word2vec Hierarchy Clustering produces the best accuracy of 81,6% with a total of 75 features. The result of merging the two feature extractions produces an accuracy of 85,3%.</em></p><p><strong><em>Keywords</em></strong><em>: Sentiment Analysis, Word2vec, Feature extraction</em></p><p><em>Penelitian ini mengenai Sentiment Analysis menggunakan model Word2vec. penelitian ini pernah dilakukan oleh  Fauzi (2019). Namun pada penelitiannya penggunaan model Word2vec menghasilkan akurasi 70%, karena data yang digunakan sedikit. Dalam data yang sedikit Word2vec tidak dapat menangkap kemiripan makna dengan baik. Sehingga dilakukan penelitian terkait yang mana menggunakan data komentar evaluasi dosen dan juga data artikel Wikipedia berbahasa Indonesia  sebagai model Word2vec. Pada penelitian ini dilakukan perbandingan ekstraksi fitur Average base Word2vec dan Bag of Centroid base Word2vec dan juga dilakukan penggabungan keduanya kemudian dilakukan klasifikasi menggunakan metode Support Vector Machine. Penerapan ekstraksi fitur Average base Word2vec pada data komentar evaluasi dosen menghasilkan akurasi sebesar 84,8%. Kemudian menggunakan ekstraksi fitur Bag of Centroid base Word2vec menggunakan Hirarki Clustering menghasilkan akurasi terbaik sebesar 81,6% dengan jumlah 75 fitur. Hasil penggabungan kedua ekstraksi fitur menghasilkan akurasi sebesar 85,3%.</em></p><p><em><strong>Kata kunci</strong></em><em>: </em><em>Sentiment Analysis</em>, <em>Word2vec</em><em>, </em>Ekstraksi fitur<em>.</em></p><p><em><br /></em></p>

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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