Multi-class Cancer Classification with OVR-Support Vector Machines Selected by Naïve Bayes Classifier

2006 ◽  
pp. 155-164 ◽  
Author(s):  
Jin-Hyuk Hong ◽  
Sung-Bae Cho
Keyword(s):  
Support Vector Machines ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Cancer Classification ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Vector Machines

scholarly journals IMPLEMENTASI ALGORITMA NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE PADA KLASIFIKASI SENTIMEN REVIEW LAYANAN TELEMEDICINE HALODOC

2021 ◽  
Vol 2 (2) ◽  
pp. 96-104
Author(s):  
REYNALDA NABILA CIKANIA
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Telemedicine Service ◽  
Application Service ◽  
Auc Value

Halodoc is a telemedicine-based healthcare application that connects patients with health practitioners such as doctors, pharmacies, and laboratories. There are some comments from halodoc users, both positive and negative comments. This indicates the public's concern for the Halodoc application so it is necessary to analyze the sentiment or comments that appear on the Halodoc application service, especially during the COVID-19 pandemic in order for Halodoc application services to be better. The Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms are used to analyze the public sentiment of Halodoc's telemedicine service application users. The negative category sentiment classification result was 12.33%, while the positive category sentiment was 87.67% from 5,687 reviews which means that the positive review sentiment is more than the negative review sentiment. The accuracy performance of the Naive Bayes Classifier Algorithm resulted in an accuracy rate of 87.77% with an AUC value of 57.11% and a G-Mean of 40.08%, while svm algorithm with KERNEL RBF had an accuracy value of 86.1% with an AUC value of 60.149% and a G-Mean value of 49.311%. Based on the accuracy value of the model can be known SVM Kernel RBF model better than NBC on classifying the review of user sentiment of halodoc telemedicine service

scholarly journals NAIVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE SEBAGAI ALTERNATIF SOLUSI UNTUK TEXT MINING

2019 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Iin Ernawati
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
The Relationship

This study was conducted to text-based data mining or often called text mining, classification methods commonly used method Naïve bayes classifier (NBC) and support vector machine (SVM). This classification is emphasized for Indonesian language documents, while the relationship between documents is measured by the probability that can be proven with other classification algorithms. This evident from the conclusion that the probability result Naïve Bayes Classifier (NBC) word “party” at least in the economic document and political. Then the result of the algorithm support vector machine (svm) with the word “price” and “kpk” contains in both economic and politic document.  

scholarly journals Survei Tentang Analisis Sentimen Pada Media Sosial

2021 ◽  
Vol 20 (2) ◽  
pp. 177
Author(s):  
Putri Agung Permatasari ◽  
Linawati Linawati ◽  
Lie Jasa
Keyword(s):  
Support Vector Machine ◽  
Topic Modeling ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Media sosial saat ini telah menjadi bagian penting dalam kehidupan sehari-hari tidak hanya untuk kebutuhan pribadi melainkan bisa di gunakan dalam bisnis, serta banyak hal yang bisa dilakukan. Media sosial yang digunakan seperti Facebook, Twitter, Youtube, Instagram, Likenid, dan Whatsapp. Dengan adanya media sosial tersebut banyaknya data yang ada berupa gambar, comment berupa text atau emoticon, video, dan lainnya, sehingga masyarakat bebas beropini. Dengan adanya analisis sentimen opini yang berkembang dan banyak di media sosial tersebut dapat menghasilkan data dan informasi yang bermanfaat. Dalam analisis sentimen diperlukannya algoritma klasifikasi data diantaranya Naive Bayes Classifier, Support Vector Machine, K-NN, RNN, C4.5, Lexicon Based, LDA Based Topic Modeling, dan beberapa algoritma lainnya. Artikel ini menelaah beberapa literature analisis sentimen pada media sosial. Saat ini media sosial yang sering digunakan dalam analisis adalah Twitter dan pengguna algoritma yang dapat meningkatkan tingkat akurasi adalah algoritma Naive Bayes Classifier dan Support Vector Machine.  Hasil perhitungan akurasi klasifikasi data berbeda-beda terlihat pada data uji pada penelitian tersebut.

scholarly journals Analysis of Emoticon and Sarcasm Effect on Sentiment Analysis of Indonesian Language on Twitter

2019 ◽  
Vol 5 (2) ◽  
pp. 100
Author(s):  
Debby Alita ◽  
Sigit Priyanta ◽  
Nur Rokhman
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Analysis Process

Background: Indonesia is an active Twitter user that is the largest ranked in the world. Tweets written by Twitter users vary, from tweets containing positive to negative responses. This agreement will be utilized by the parties concerned for evaluation.Objective: On public comments there are emoticons and sarcasm which have an influence on the process of sentiment analysis. Emoticons are considered to make it easier for someone to express their feelings but not a few are also other opinion researchers, namely by ignoring emoticons, the reason being that it can interfere with the sentiment analysis process, while sarcasm is considered to be produced from the results of the sarcasm sentiment analysis in it.Methods: The emoticon and no emoticon categories will be tested with the same testing data using classification method are Naïve Bayes Classifier and Support Vector Machine. Sarcasm data will be proposed using the Random Forest Classifier, Naïve Bayes Classifier and Support Vector Machine method.Results: The use of emoticon with sarcasm detection can increase the accuracy value in the sentiment analysis process using Naïve Bayes Classifier method.Conclusion: Based on the results, the amount of data greatly affects the value of accuracy. The use of emoticons is excellent in the sentiment analysis process. The detection of superior sarcasm only by using the Naïve Bayes Classifier method due to differences in the amount of sarcasm data and not sarcasm in the research process.Keywords:  Emoticon, Naïve Bayes Classifier, Random Forest Classifier, Sarcasm, Support Vector Machine

scholarly journals Perbandingan Klassifikasi SMS Berbasis Support Vector Machine, Naive Bayes Classifier, Random Forest dan Bagging Classifier

2021 ◽  
Vol 10 (3) ◽  
pp. 432-437
Author(s):  
Devi Irawan ◽  
Eza Budi Perkasa ◽  
Yurindra Yurindra ◽  
Delpiah Wahyuningsih ◽  
Ellya Helmud
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Short Message ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Message Service

Short message service (SMS) adalah salah satu media komunikasi yang penting untuk mendukung kecepatan pengunaan ponsel oleh pengguna. Sistem hibrid klasifikasi SMS digunakan untuk mendeteksi sms yang dianggap sampah dan benar. Dalam penelitian ini yang diperlukan adalah mengumpulan dataset SMS, pemilihan fitur, prapemrosesan, pembuatan vektor, melakukan penyaringan dan pembaharuan sistem. Dua jenis klasifikasi SMS pada ponsel saat ini ada yang terdaftar sebagai daftar hitam (ditolak) dan daftar putih (diterima). Penelitian ini menggunakan beberapa algoritma seperti support vector machine, Naïve Bayes classifier, Random Forest dan Bagging Classifier. Tujuan dari penelitian ini adalah untuk menyelesaikan semua masalah SMS yang teridentifikasi spam yang banyak terjadi pada saat ini sehingga dapat memberikan masukan dalam perbandingan metode yang mampu menyaring dan memisahkan sms spam dan sms non spam.  Pada penelitian ini menghasilkan bahwa Bagging classifier algorithm ini mendapatkan ferformance score tertinggi dari algoritma yang lain yang dapat dipergunakan sebagai sarana untuk memfiltrasi SMS yang masuk ke dalam inbox pengguna dan Bagging classifier algorithm dapat memberikan hasil filtrasi yang akurat untuk menyaring SMS yang masuk.

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