In modern times, the chatbot is implemented to store data collected through a question and answer system, which can be applied in the Python program. The data to be used in this program is the Cornell Movie Dialog Corpus which is a dataset containing a corpus which contains a large collection of metadata-rich fictional conversations extracted from film scripts. The application of chatbot in the Python program can use various models, the one specifically used in this program is the LSTM. The output results from the chatbot program with the application of the LSTM model are in the form of accuracy, as well as a data set that matches the information that the user enters in the chatbot dialog box input. The choice of models that can be applied is based on data that can affect program performance, with the aim of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in determining the selected model. Based on the application of the LSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size_layer 512, num_layers 2, embedded_size 256, learning_rate 0.001, batch_size 32, epoch 20) from File 3 is the LSTM Chatbot with the avg accuracy value of 0.994869 which uses the LSTM model is the best parameter pair.

The news is information about a recently changed situation or a recent event. Serving as popular media information the internet has the power spread the news not only real news but fake news as well. We propose an ensemble learning approach on Indonesian fake news in order to separate fake news from the real one and to tackle imbalanced data problem which we face on the given dataset. Our experiment result shows that random forest classifier as the ensemble classifier which obtained 0.98 f1-score is superior to multinomial naive bayes and support vector machine as non-ensemble classifiers which achieve 0.43 and 0.74 f1-score respectively across 660 evaluation documents. We also compare our result against other research that using the same data and our approach achieved better results.

Information explosion that can be generated by anyone may lead to the spread of fake news not only at the news channel, but also at social media, and so forth. Detection of fake news has become an urgent need on the society because of fake news spread of unrest in the society. Several related studies have been conducted in the news classification with the aim of providing a decision whether a news is included in fake news or original news. In the related research, a vector representation of documents is used. This vector representation is then given to the algorithm for further processing. This study aims to model vectors that can accommodate the characteristics of fake news before further processed by language algorithms using the Indonesian language. In this research, fake news and original news are represented according to the vector space model. Vector model combination of frequency term, inverse document frequency and frequency reversed with 10-fold cross validation using support vector machine algorithm classifier. Variations of phrase detection as well as name recognition entities (entity recognition names) are also used in vector representation. A vector representation that uses the term frequency shows promising performance. It can recognize news characteristics correctly 96.74% of 2516 documents across phrase detection and named entity recognition process.

In the modern world, chatbot programs are implementations that can be used to store data collected through a question and answer system and then can be applied in the Python program to optimize the results based on highly rated questions asked in a service center. The application of chatbots in the Python program can use various models. Specifically in this program, the BiLSTM model will be applied. The output produced from the chatbot program with the application of the BiLSTM model is in the form of accuracy and also data set that matches the information the program user enters in the chatbot’s input dialog box. The selection of models that can be applied to the program is based on data which can affect program performance, with the objective of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in deciding the selected model. Based on the various considerations that are the requirements for choosing a model of a program, in the end the BiLSTM model is selected will be applied to the program. In addition to model selection, the next step is to determine the method used in the program, in this program the greedy method is a form of implementation of the BiLSTM model with the aim that when running the program, data processing time can be faster, and increase the value of the model selected in program. In addition, supporting attributes such as the seq2seq model are a determining factor in a program that can function to verify whether data processing matches the criteria that can be used as new in data processing. In addition, a program evaluation method is needed that can be used to verify whether the program output matches the data expected by the user. Based on the application of the BiLSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size-layer 512, num-layers 2, embedded-size 256, learning-rate 0.001, batch-size 32, epoch 20) from File 3 is the BiLSTM Chatbot with the avg accuracy value of 0.995217 which uses the BiLSTM model is the best parameter pair.