KLASIFIKASI POSTING TWITTER KEMACETAN LALU LINTAS KOTA BANDUNG MENGGUNAKAN NAIVE BAYESIAN CLASSIFICATION; TWITTER POSTING CLASSIFICATION OF BANDUNG TRAFFIC JAM USING NAIVE BAYESIAN CLASSIFICATION

Sandi Fajar Rodiyansyah, Edi Winarko

2012 | Disertasi | PROGRAM STUDI S2 ILMU KOMPUTER

Every day the Twitter server receives a very large number of data tweet. Thus, the system should be developed to perform data mining of the heap data 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. Prior to classification, tweet has been through a preprocessing and term frequency weighting and tf-idf. Then the weight is to perform classification with naive bayes classifier. After the data is classified, tweet visualization is then performed using the Google Maps map and chart. 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, traffic jam, classification, preprocessing, naive bayesian classification, support vector machine, accuracy, visualization, Google Map

Kata Kunci : Twitter, tweet, kemacetan, lalu lintas, klasifikasi, preprocessing; naive bayesian classifier; support vector machine; akurasi; visualisasi; Google Map