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 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 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 Phisher Fighter: Website Phishing Detection System Based on URL and Term Frequency-Inverse Document Frequency Values

2021 ◽  
Author(s):  
E. Sri Vishva ◽  
D. Aju
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Sensitive Information ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Single Piece ◽  
Phishing Detection

Fundamentally, phishing is a common cybercrime that is indulged by the intruders or hackers on naive and credible individuals and make them to reveal their unique and sensitive information through fictitious websites. The primary intension of this kind of cybercrime is to gain access to the ad hominem or classified information from the recipients. The obtained data comprises of information that can very well utilized to recognize an individual. The purloined personal or sensitive information is commonly marketed in the online dark market and subsequently these information will be bought by the personal identity brigands. Depending upon the sensitivity and the importance of the stolen information, the price of a single piece of purloined information would vary from few dollars to thousands of dollars. Machine learning (ML) as well as Deep Learning (DL) are powerful methods to analyse and endeavour against these phishing attacks. A machine learning based phishing detection system is proposed to protect the website and users from such attacks. In order to optimize the results in a better way, the TF-IDF (Term Frequency-Inverse Document Frequency) value of webpages is employed within the system. ML methods such as LR (Logistic Regression), RF (Random Forest), SVM (Support Vector Machine), NB (Naive Bayes) and SGD (Stochastic Gradient Descent) are applied for training and testing the obtained dataset. Henceforth, a robust phishing website detection system is developed with 90.68% accuracy.

scholarly journals ANALISA TESTIMONIAL DENGAN MENGGUNAKAN ALGORITMA TEXT MINING DAN TERM FREQUENCY- INVERSE DOCUMENT FREQUENCE (TF-IDF) PADA TOKO ALLMEEART

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Meylita Putri Simatupang ◽  
Dito Putro Utomo
Keyword(s):  
Text Mining ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Mining Algorithm ◽  
Document Frequency ◽  
Positive Experiences

E-commerce or often referred to as an online shop is the latest trend of the community in carrying out shopping activities, first before the rise of e-commerce companies like today the community to meet their needs still rely on distros around the customer lives, or to a shopping place but now it has switch to shoop online. The advantages offered by online shoop are the relatively low prices, no need to shop locations, and guarantee goods, it has an impact on retail shops that are increasingly lonely. Testimonials are one of the techniques carried out to convince customers to shop at e-commerce they have, testimonials are the responses of buyers for their experience of shopping in an e-commerce application starting from the payment process until the goods are received, the more positive experiences conveyed in the testimonials, the customer who have not shopped on an e-commerce application will be more convinced to shop. Testimonials on an e-commerce application are not always positive, there are times when testimonials are delivered by negative buyers. The customer's problem is the unavailability of percentages or information on the number of buyers with positive and negative shopping experiences because in general testimonials are only delivered in the form of a list.Keywords:  Testimonial Analysis, Text Mining Algorithm, Term Frequency-Inverse Document Frequency (TF-IDF)

scholarly journals Klasifikasi Topik Keluhan Pelanggan Berdasarkan Tweet dengan Menggunakan Penggabungan Feature Hasil Ekstraksi pada Metode Support Vector Machine (SVM)

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Enda Esyudha Pratama ◽  
Bambang Riyanto Trilaksono
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Customer Service ◽  
Information Gain ◽  
Support Vector ◽  
Chi Square ◽  
Term Frequency ◽  
Document Frequency

Pemanfaatan twitter sebagai layanan customer serevice perusahaan sudah mulai banyak digunakan, tak terkecuali Speedy. Mekanisme yang ada saat ini untuk proses klasifikasi bentuk dan jenis keluhan serta informasi tentang jumlah keluhan lewat twitter masih dilakukan secara manual. Belum lagi data twitter yang bersifat tidak terstruktur tentunya akan menyulitkan untuk dilakukan analisa dan penggalian informasi dari data tersebut. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk memproses data teks dari tweet pengguna twitteryang masuk ke akun @TelkomSpeedy untuk diolah menjadi informasi. Informasi tersebut nantinya digunakan untuk klasifikasi bentuk dan jenis keluhan. Merujuk pada beberapa penelitian terkait, salah satu metode klasifikasi yang paling baik untuk digunakan adalah metode Support Vector Machine (SVM). Konsep dari SVM dapat dijelaskan secara sederhana sebagai usaha mencari hyperplane yang dapat memisahkan dataset sesuai dengan kelasnya. Kelas yang digunakan dalam penelitian kali ini berdasarkan topik keluhan pelanggan yaitu billing, pemasangan/instalasi, putus (disconnect), dan lambat. Faktor penting lainnya dalam hal klasifikasi adalah penentuan feature atau atribut kata yang akan digunakan. Metode feature selection yang digunakan pada penlitian ini adalah term frequency (TF), document frequency (DF), information gain, dan chi-square. Pada penelitian ini juga dilakukan metode penggabungan feature yang telah dihasilkan dari beberapa metode feature selection sebelumnya. Dari hasil penelitian menunjukan bahwa SVM mampu melakukan klasifikasi keluhan dengan baik, hal ini dibuktikan dengan akurasi 82,50% untuk klasifikasi bentuk keluhan dan 86,67% untuk klasifikasi jenis keluhan. Sedangkan untuk kombinasi penggunaan feature dapat meningkatkan akurasi menjadi 83,33% untuk bentuk keluhan dan 89,17% untuk jenis keluhan.   Kata Kunci—customer service, klasifikasi topik keluhan, penggabungan feature, support vector machine

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