ANALISIS SENTIMENT CYBERBULLYING PADA SOSIAL MEDIA TWITTER MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

Twitter merupakan salah satu media sosial yang saat ini menjadi trend, karena terdapat banyak sekali berita dan informasi yang yang dapat direspon dengan cepat dan tepat dari berbagai sudut pandang. Hal ini menjadikan Twitter tidak hanya berdampak positif, tetapi juga berdampak negatif bagi pengguna maupun non-pengguna Twitter, salah satunya adalah cyberbullying. Cyberbullying adalah bentuk intimidasi yang pelaku lakukan untuk melecehkan korbannya melalui perangkat teknologi. Korban yang mengalami Cyberbullying akan mengalami gangguan fisik hingga psikologis seperti kesepian, kegelisahan, depresi yang lebih tinggi, dan merasa hargadirinya rendah. Selain itu korban yang mengalami Cyberbullying juga akan merasakan tekanan sehingga menunjukkan keinginan bunuh diri yang lebih tinggi. Pada penelitian ini dilakukan proses analisis sentiment cyberbullying yang disampaikan oleh pengguna pada media sosial twitter dengan mengembangkan sistem berbasis web untuk mengklasifikasikan sentiment tersebut menggunakan metode support vector machine. Data inputan pada sistem ini berupa konten tweet yang diperoleh dari twitter dengan memasukkan keyword hashtag yang berpotensi menimbulkan cyberbullying seperti #cebong atau #kadrun dan tidak melebihi 100 data tweet. Sedangkan outputnya berupa klasifikasi sentiment cyberbullying atau non-cyberbullying dari setiap tweet yang sudah melewati proses text preprocessing dan pembobotan teks dengan TF-IDF. Dari hasil pengujian menunjukan dengan menggunakan 100 data tweet, sistem mampu melakukan proses klasifikasi dengan rata-rata waktu 101100,2 milisecond dan kecepatan pemrosesan 0,000989 data per milisecond. Diperoleh pula hasil pengukuran evaluasi klasifikasi dengan menggunakan metode confusion matrix dengan nilai recall 64%, precision 58% dan tingkat accuracy sebesar 70%.

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

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122114608 ◽

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

Jurnal Informatika dan Rekayasa Elektronik ◽

10.36595/jire.v2i2.117 ◽

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.

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Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine

The Computer Journal ◽

10.1093/comjnl/bxaa125 ◽

2020 ◽

Author(s):

Thamba Meshach W ◽

Hemajothi S ◽

Mary Anita E A

Keyword(s):

Support Vector Machine ◽

Confusion Matrix ◽

Recognition System ◽

Learning Technologies ◽

Support Vector ◽

Affect Recognition ◽

Svm Classifier ◽

Ecg Signal ◽

Hybrid Features ◽

Structured Support Vector Machine

Abstract Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of ‘facial mimicry’ by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.

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Opinion mining on newspaper headlines using SVM and NLP

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i3.pp2152-2163 ◽

2019 ◽

Vol 9 (3) ◽

pp. 2152 ◽

Cited By ~ 1

Author(s):

Chaudhary Jashubhai Rameshbhai ◽

Joy Paulose

Keyword(s):

Support Vector Machine ◽

Natural Language Processing ◽

Language Processing ◽

Opinion Mining ◽

Confusion Matrix ◽

Support Vector ◽

Text Data ◽

Mining Technique ◽

Svm Model ◽

Linear Svm

Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.

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

Journal of Information Technology and Computer Science ◽

10.25126/jitecs.202163330 ◽

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.

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Pemodelan Prediksi Status Keberlanjutan Polis Asuransi Kendaraan dengan Teknik Pemilihan Mayoritas Menggunakan Algoritma-Algoritma Klasifikasi Data Mining

Prosiding Seminar Nasional Teknoka ◽

10.22236/teknoka.v5i.391 ◽

2020 ◽

Vol 5 ◽

pp. 19-24

Author(s):

Dyah Retno Utari ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Confusion Matrix ◽

Naïve Bayes ◽

Majority Voting ◽

Support Vector ◽

F Measure

Asuransi kendaraan bermotor merupakan jenis usaha pertanggungan terhadap kerugian atau risiko kerusakan yang dapat timbul dari berbagai macam potensi kejadian yang menimpa kendaraan. Persaingan dalam bisnis asuransi khususnya untuk kendaraan bermotor menuntut inovasi dan strategi agar keberlangsungan bisnis tetap terjamin. Salah satu upaya yang dapat dilakukan perusahaan adalah memprediksi status keberlanjutan polis asuransi kendaraan dengan menganalisis data-data profil dan transaksi nasabah. Prediksi terhadap keputusan pemegang polis menjadi sangat penting bagi perusahaan, karena dapat menentukan strategi pemasaran yang mempengaruhi keputusan pelanggan untuk pembaharuan polis asuransi. Penelitian ini telah mengusulkan suatu model prediksi status keberlanjutan polis asuransi kendaraan dengan teknik pemilihan mayoritas dari hasil klasifikasi menggunakan algoritma- algoritma data mining seperti Naive Bayes, Support Vector Machine dan Decision Tree. Hasil pengujian menggunakan confusion matrix menunjukkan nilai akurasi terbaik diperoleh sebesar 93,57%, apapun untuk nilai precision mencapai 97,20%, dan nilai recall sebesar 95,20% serta nilai F-Measure sebesar 95,30%. Nilai evaluasi model terbaik dihasilkan menggunakan pendekatan pemilihan mayoritas (majority voting), mengungguli kinerja model prediksi berbasis pengklasifikasi tunggal.

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Bayes Classifier dan Support Vector Machine dalam Klasifikasi Judul Karya Akhir Mahasiswa Program Studi PTIK UNJ

PINTER Jurnal Pendidikan Teknik Informatika dan Komputer ◽

10.21009/pinter.3.1.9 ◽

2019 ◽

Vol 3 (1) ◽

pp. 54-62

Author(s):

Razi Aziz Syahputro ◽

Widodo ◽

Hamidillah Ajie

Keyword(s):

Support Vector Machine ◽

Cross Validation ◽

Nearest Neighbor ◽

Confusion Matrix ◽

Vector Space Model ◽

Support Vector ◽

Bayes Classifier ◽

K Nearest Neighbor ◽

Space Model ◽

Fold Cross Validation

Penelitian ini dilatarbelakangi dengan dibutuhkannya sistem pengklasifikasian untuk memudahkan pihak Jurusan Teknik Elektro khususnya Program Studi PTIK untuk mengklasifikasikan judul skripsi berdasarkan peminatan. Sebelum sistem dibuat diperlukan pertimbangan dari beberapa algoritma klasifikasi yang ada, maka dari itu penelitian ini memilih 3 algoritma dari 10 algoritma terbaik menurut ICDM tahun 2006. Klasifikasi terhadap dokumen teks pendek seperti judul skripsi mahasiswa memiliki kesulitan tersendiri daripada dokumen teks panjang karena semakin sedikit kata semakin sulit diklasifikasi. Sehingga tujuan dari penelitian ini adalah untuk mengetahui algoritma yang paling efektif untuk mengklasifikasi judul skripsi. Penelitian ini terdiri dari beberapa tahap yaitu pengumpulan data, pengelompokan data melalui angket oleh dosen ahli, pre-processing text, pembobotan kata menggunakan vector space model dan tf-idf, evaluasi dengan k-fold cross validation, klasifikasi menggunakan k-nearest neighbor, naïve bayes classifier, dan support vector machine, dan analisis dengan confusion matrix. Percobaan dilakukan dengan menggunakan 266 data judul skripsi mahasiswa PTIK UNJ dari angkatan 2010-2013, dengan data terakhir berasal dari sidang skripsi pada semester 105(semester ganjil 2016/2017). Hasil dari klasifikasi menggunakan algoritma tersebut didapatkan algoritma yang paling efisien yaitu support vector machine dengan akurasi 82% dari 10 kali percobaan.

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Sistem Pengenalan Wajah 3D Menggunakan ICP dan SVM

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2019661609 ◽

2019 ◽

Vol 6 (6) ◽

pp. 601

Author(s):

Ledya Novamizanti ◽

Nadya Viana De Lima ◽

Eko Susatio

Keyword(s):

Support Vector Machine ◽

Face Recognition ◽

Template Matching ◽

Confusion Matrix ◽

Iterative Closest Point ◽

Reference Image ◽

Support Vector ◽

Identification System ◽

3D Face ◽

3 Dimensional

Pengenalan wajah merupakan salah satu teknologi biometrik yang banyak diaplikasikan terutama pada sistem keamanan. Sistem absensi dengan wajah, mengenali pelaku tindak kriminal dengan CCTV adalah beberapa aplikasi dari pengenalan wajah. Efisiensi dan akurasi menjadi faktor utama pengenalan wajah banyak diaplikasikan. Pada penelitian ini, sistem identifikasi diimplementasikan dalam bentuk pengenalan wajah 3 dimensi berbasis template matching menggunakan metode Iterative Closest Point (ICP) dan klasifikasi Support Vector Machine (SVM). Iterative Closest Point (ICP) memberikan informasi dimensi dengan meminimalisasi kesalahan antara titik-titik dalam satu tampilan dan titik terdekatnya agar template wajah 3D yang dibuat sesuai dengan citra referensi. Sedangkan SVM adalah adalah metode klasifikasi dengan menentukan kelas citra berdasarkan informasi yang diperoleh dari proses ektraksi ciri. Hasil akhir dari penelitian ini adalah suatu aplikasi yang mampu melakukan identifikasi pengenalan pola wajah 3D. Berdasarkan confusion matrix, diperoleh bahwa sistem ini bekerja dengan precision 97,30%, recall 100,00%, accuracy 97,56% pada pengambilan frame citra sebanyak 48, iterasi ke 49, partisi 12, dan menggunakan SVM tipe OAA.AbstractFace recognition is a biometric technology that is widely applied especially in the security system. Attendance systems with faces, recognizing criminals with CCTV are some of the applications of face recognition. Efficiency and accuracy are the main factors that face recognition is widely applied. In this study, the identification system was implemented in the form of 3-dimensional face recognition based on template matching using the Iterative Closest Point (ICP) method and Support Vector Machine (SVM) classification. Iterative Closest Point (ICP) provides dimensional information by minimizing errors between points in one view and the closest point so that 3D face templates are made in accordance with the reference image. Whereas SVM is a classification method by determining the image class based on information obtained from the extraction of features. The final result of this study is an application that is able to identify 3D face pattern recognition. Based on the confusion matrix, found that this system works with 97.30% precision, recall 100.00%, 97.56% accuracy in image frame capture as much as 48 iterations to 49, the partition 12, and using the SVM-type OAA.

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KOMPARASI METODE KLASIFIKASI PADA ANALISIS SENTIMEN USAHA WARALABA BERDASARKAN DATA TWITTER

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.752 ◽

2019 ◽

Vol 15 (2) ◽

pp. 267-274

Author(s):

Tati Mardiana ◽

Hafiz Syahreva ◽

Tuslaela Tuslaela

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Decision Tree ◽

Nearest Neighbor ◽

Naive Bayes ◽

Confusion Matrix ◽

Naïve Bayes ◽

Support Vector ◽

K Nearest Neighbor

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif. Penelitian ini menggunakan data realtime dari tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil pengujian dengan confusion matrix diperoleh nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.

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Prediction of student’s performance through educational data mining techniques

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v22.i3.pp1708-1715 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1708

Author(s):

Nibras Z. Salih ◽

Walaa Khalaf

Keyword(s):

Support Vector Machine ◽

Control Systems ◽

Student Performance ◽

Undergraduate Students ◽

Confusion Matrix ◽

Educational Data Mining ◽

General Information ◽

Support Vector ◽

Academic Education ◽

Academic Information

Many educators have worried about the failures of students through academic education. Thus, a variety of predictions have been applied to general information including culture, social, and economic information which wasn’t related to student performance. We have gathered an actual dataset from three years of academic stages of Mustansiriyah University in Iraq. The dataset consists of academic information without any socioeconomic data, it includes forty-four undergraduate students with thirteen attributes. We have proposed a model that explains the correlation between two main subjects which are, mathematics, and control systems. This study aimed to identify student failure of the control systems subject in the third year depending on the academic features of the mathematics subjects in the first and second years. Three algorithms were applied to the dataset including Naïve Bayes, support vector machine, and multilayer perceptron. Since the dataset was imbalanced, this leads to appear overfitting problem in the results so the synthetic minority oversampling technique was utilized to solve this problem. Our results show that the support vector machine algorithm proves an efficient classification after applied synthetic minority oversampling technique. The accuracy of the classifiers was measured from the confusion matrix using the Waikato environment for knowledge analysis (WEKA) tool and its related metrics.

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