scholarly journals Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification

2013 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Sandi Fajar Rodiyansyah ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
High Accuracy ◽  
Bayesian Classification ◽  
Support Vector ◽  
Bayes Classifier ◽  
Traffic Jam ◽  
Naive Bayesian ◽  
Naïve Bayesian

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92%  dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine  AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58%  by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine

scholarly journals Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Sandi Fajar Rodiyansyah ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
High Accuracy ◽  
Bayesian Classification ◽  
Support Vector ◽  
Bayes Classifier ◽  
Traffic Jam ◽  
Naive Bayesian ◽  
Naïve Bayesian

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92%  dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58%  by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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

Naïve Bayesian Classification of Uncertain Objects Based on the Theory of Interval Probability

2016 ◽  
Vol 25 (03) ◽  
pp. 1650012 ◽  
Author(s):  
Hongmei Chen ◽  
Weiyi Liu ◽  
Lizhen Wang
Keyword(s):  
Data Mining ◽  
Uncertain Data ◽  
Bayesian Classification ◽  
Bayesian Classifier ◽  
Interval Probability ◽  
Naïve Bayesian Classifier ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Intuitive Concept

The potential applications and challenges of uncertain data mining have recently attracted interests from researchers. Most uncertain data mining algorithms consider aleatory (random) uncertainty of data, i.e. these algorithms require that exact probability distributions or confidence values are attached to uncertain data. However, knowledge about uncertainty may be incomplete in the case of epistemic (incomplete) uncertainty of data, i.e. probabilities of uncertain data may be imprecise, coarse, or missing in some applications. The paper focuses on uncertain data which miss probabilities, specially, value-uncertain discrete objects which miss probabilities (for short uncertain objects). On the other hand, classification is one of the most important tasks in data mining. But, to the best of our knowledge, there is no method to learn Naïve Bayesian classifier from uncertain objects. So the paper studies Naïve Bayesian classification of uncertain objects. Firstly, the paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities. Secondly, based on the theory of interval probability, the paper defines conditional interval probabilities including the intuitive concept and the canonical concept, and the conditional independence of the intuitive concept. Further, the paper gives a formula to effectively compute the intuitive concept. Thirdly, the paper presents a Naïve Bayesian classifier with interval probability parameters which can handle both uncertain objects and certain objects. Finally, experiments with uncertain objects based on UCI data show satisfactory performances.

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.  

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