scholarly journals Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale

2021 ◽  
Vol 6 (3) ◽  
pp. 236-251
Author(s):  
Novira Azpiranda ◽  
Ahmad Afif Supianto ◽  
Nanang Yudi Setiawan ◽  
Endang Suryawati ◽  
R. Sandra Yuwana ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Business Strategy ◽  
Confusion Matrix ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Customer Reviews ◽  
A Value ◽  
Document Frequency ◽  
System Usability Scale ◽  
System Usability

Al-Ghiff Steak is a restaurant located in Cirebon City that offers quality steaks at affordable prices. For maintaining a competitive Al-Ghiff Steak advantage and reputation, it is important to build a good relationship with customers and have a business strategy that considers customer opinions. However, in its implementation, Al-Ghiff Steak has difficulty when collecting and processing customer review data manually. Therefore, it is necessary to conduct sentiment analysis by utilizing Google Reviews to determine customer perspectives regarding Al-Ghiff Steak products and services. This analysis was conducted on 968 Google Review reviews from 2016 to 2020 using the Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Classification testing is done with a confusion matrix against four parameters: accuracy, precision, recall, and f1-score. SVM with TF-IDF gets accuracy value 83%, precision 64%, recall 60% and f1-score 59%. The sentiment classification result is then visualized in the form of a dashboard. We utilize the System Usability Scale (SUS) for usability testing, which produces a value of 77.5. This result achieve the Acceptable category and an Excellent rating.

scholarly journals Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 229-235
Author(s):  
Syaifulloh Amien Pandega Perdana ◽  
Teguh Bharata Aji ◽  
Ridi Ferdiana
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Term Weighting ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Bahasa Indonesia

Ulasan pelanggan merupakan opini terhadap kualitas barang atau jasa yang dirasakan konsumen. Ulasan pelanggan mengandung informasi yang berguna bagi konsumen maupun penyedia barang atau jasa. Ketersediaan ulasan pelanggan dalam jumlah besar pada website membutuhkan suatu framework untuk mengekstraksi sentimen secara otomatis. Sebuah ulasan pelanggan sering kali mengandung banyak aspek sehingga Aspect Based Sentiment Analysis (ABSA) harus digunakan untuk mengetahui polaritas masing-masing aspek. Salah satu tugas penting dalam ABSA adalah Aspect Category Detection. Metode machine learning untuk Aspect Category Detection sudah banyak dilakukan pada domain berbahasa Inggris, tetapi pada domain bahasa Indonesia masih sedikit. Makalah ini membandingkan kinerja tiga algoritme machine learning, yaitu Naïve Bayes (NB), Support Vector Machine (SVM), dan Random Forest (RF) pada ulasan pelanggan berbahasa Indonesia menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) sebagai term weighting. Hasil menunjukkan bahwa RF memiliki kinerja paling unggul dibandingkan NB dan SVM pada tiga domain yang berbeda, yaitu restoran, hotel, dan e-commerce, dengan nilai f1-score untuk masing-masing domain adalah 84.3%, 85.7%, dan 89,3%.

scholarly journals Analisis Sentimen Opini Pemindahan Ibu Kota Pada Twitter Dengan Metode Support Vector Machine

2021 ◽  
Vol 14 (1) ◽  
pp. 49
Author(s):  
Tezza Fazar Tri Hidayat ◽  
Garno Garno ◽  
Azhari Ali Ridha
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency

Relokasi ibu kota Indonesia kini telah diresmikan oleh Presiden Joko Widodo pada 26 Agustus 2019 ke Kalimantan, ini adalah sejarah baru dalam sejarah Indonesia karena belum pernah terjadi sebelumnya, sehingga memunculkan banyak pendapat atau tanggapan dari masyarakat. Analisis sentimen adalah kegiatan yang digunakan untuk menganalisis pendapat atau opini seseorang tentang suatu topik. Twitter adalah media sosial yang digunakan untuk mengekspresikan pendapat pengguna dan menyatukannya pada suatu topik. Support Vector Machine adalah metode text mining yang mencakup metode klasifikasi dan Term Frequency - Inverse Document Frequency adalah metode pembobotan karakter. SVM dan TF-IDF dapat digunakan untuk menganalisis sentimen opini publik tentang topik pemindahan ibukota Indonesia. Tujuan dari penelitian ini adalah untuk mengklasifikasikan opini publik tentang topik memindahkan Ibu Kota Indonesia dari ribuan tweet yang telah dikumpulkan dan disaring. Tweet pada dari 22-29 Maret 2020 telah diproses menjadi 992 tweet dan terdiri dari 221 data dengan label positif dan 771 data negatif. Dan menggunakan metode SVM yang memiliki akurasi 77,72% dan dikombinasikan dengan TFIDF yang meningkatkan akurasinya menjadi 78,33%.

scholarly journals Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 138-149
Author(s):  
Ukhti Ikhsani Larasati ◽  
Much Aziz Muslim ◽  
Riza Arifudin ◽  
Alamsyah Alamsyah
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Feature Weighting ◽  
Support Vector ◽  
Chi Square ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency

Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.

scholarly journals Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores

2018 ◽  
Vol 7 (3) ◽  
pp. 479-486 ◽  
Author(s):  
Mastura Md Saad ◽  
Nursuriati Jamil ◽  
Raseeda Hamzah
Keyword(s):  
Support Vector Machine ◽  
Decision Tree ◽  
Emotion Recognition ◽  
Speech Synthesis ◽  
Support Vector ◽  
Text Documents ◽  
Inverse Document Frequency ◽  
Recognition Experiment ◽  
Document Frequency ◽  
Future Work

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification.

Pengujian Usability Website Pondok Pesantren Qodratullah Menggunakan System Usability Scale

2020 ◽  
Vol 6 (1) ◽  
pp. 34-43
Author(s):  
Fitri Purwaningtias ◽  
Usman Ependi
Keyword(s):  
Evaluation Process ◽  
Boarding School ◽  
Educational Institutions ◽  
Prospective Students ◽  
A Value ◽  
System Usability Scale ◽  
System Usability ◽  
Grade Scale ◽  
Final Score ◽  
Group B

Website saat ini telah digunakan diberbagai jenis instansi termasuk instansi pendidikan seperti Pondok Pesantren Qodratullah. Saat ini website Pondok Pesantren Qodratullah menjadi tulang punggung dalam penyebaran informasi terkain pondok pesantren kepada wali santri, alumni, calon santri dan masyarakat luas. Mengingat pentingnya website bagi Pondok Pesantren Qodratullah maka perlu untuk dilakukan evaluasi apakah informasi yang diberikan dan website yang ada telah memiliki nilai kebergunaan bagi pengguna atau tidak. Untuk itu di dalam penelitian ini dilakukan evaluasi untuk melihat perspektif pengguna terhadap website. Prose evaluasi dilakukan dengan system usability scale dengan sepuluh instrumen sebagai pernyataan evaluasi. Hasil evaluasi menunjukkan bahwa website Pondok Pesantren Qodratullah mendapatkan nilai akhir 88. Nilai 88 berarti website Pondok Pesantren Qodratullah mendapatkan adjective rating yang excellence, grade scale tergolong kelompok B dan tingkat acceptability termasuk acceptable. The website is currently used in various types of institutions including educational institutions such as Qodratullah Islamic Boarding School. Currently the Qodratullah Islamic Boarding School website is the backbone in the dissemination of information about Islamic boarding schools to the guardians of students, alumni, prospective students and the wider community. Considering the importance of the website for the Qodratullah Islamic Boarding School, it is necessary to evaluate whether the information provided, and the existing website have a useful value for the user or not. For this reason, in this study an evaluation was conducted to see the user's perspective on the website. The evaluation process is carried out with a system usability scale with ten instruments as evaluation statements. Evaluation results show that the Qodratullah Islamic Boarding School website gets a final score of 88. A value of 88 means that the Qodratullah Islamic Boarding School website gets an adjective rating that excellence, grade scale belongs to group B and the level of acceptability is acceptable

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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