Analisis Data pada Jaringan Sensor Nirkabel Menggunakan Metode Support Vector Machine

The aims of this research are to implement Support Vector Machine for analyze abnormal data on sensor network and evaluate the implementation result. The data collection in the research were done through the searching of related libraries and software evaluate/testing. In this research, temperature, wind speed, and humidity tested using three kernels (linear, Gaussian, and polynomial). Evaluation result show that the implementation of Support Vector Machine can perform the best data validity analysis using Gaussian Kernel with the percentage of average accuracy, temperature 97.83%, humidity 94.5325%, and wind speed 96.93% for weather data 20-28 May and July 28-August 10, 2015. Meanwhile, for weather data June 5-6, 2017 obtained the percentage of average accuracy of temperature 92.855% and humidity 92.43%

Implementasi Teori Naive Bayes dalam Klasifikasi Ujaran Kebencian di Facebook

Hate Speech can be orally or in writing which is expressed intentionally by someone for the purpose of spreading and leading to hatred between groups of people. The phenomenon of Hate Speech has become a hot topic. This is motivated by netizens who often express Hate Speech either in the comments column or in their personal status on social media. The impact of this phenomenon is the emergence of hatred in society which can lead to conflict. The purpose of this study is to implement the Naïve Bayes Theory in the classification of Hate Speech on Facebook. In this study Naïve Bayes is used as a Classfifier. Naïve Bayes method is applied to find the probability of words in documents would be categorized as hate speech or not hate speach. This Classfifier is implemented using Python programming language. In the Classfifier design stage, 500 data are collected randomly on Facebook. Data is divided by 80% - 20% , 400 text data for training and 100 text data for testing. The accuracy for hate speech classification in this study is 83%. These results are obtained from Classfifier evaluations using test data where the Classfifier correctly labels 83 out of 100 test data