Disaster is a series of events that threaten and disrupt human life caused by natural factors, non-natural factors and human factors themselves. Therefore, disasters cause casualties, environmental damage, property losses, and psychological impacts. In this study will be discussed about the prediction of the number of victims affected by the disaster, either died, lost, injured, suffered or displaced. Data sources were obtained by the National Disaster Management Agency and the Indonesian Central Statistics Agency. The method used to predict is the Incremental Sequential Order method. This method is one part of the Artificial Neural Network method. With this method, network architecture patterns will be established to predict the number of victims affected by the disaster for years to come. The network architecture models used are 4-5-1, 4-10-1, 4-5-10-1, 4-10-20-1 and 4-15-30-1. Of the five models, the best models will be obtained, namely 4-15-30-1 with an accuracy rate of 80%. With this architectural model, predictions will be made on the number of victims affected by the disaster for years to come.

Facilities are a support for the implementation of a process in a business in this case the Dharmawangsa University campus, by increasing the level of student satisfaction with the facilities available, student comfort in learning will be achieved. This study used questionnaire data on 70 respondents who were students of Dharmawangsa University. Previously, the questionnaire consisted of 42 questions. After being tested for validity and reliability, 20 questions were obtained. Then from the results of the questionnaire data, a classification of student satisfaction levels will be carried out using one of the algorithms in data mining, namely Naive Bayes. The results obtained by using the rapid miner application with 50 training data and 19 data testing data, the results obtained are a classification accuracy of 73.68% with a recall value of 83.33% and a precision of 83.33%, then there are 9 attributes that have a value dissatisfaction is higher than the satisfaction score given by respondents, this can be a concern of the leadership to improve these facilities so as to increase the level of satisfaction with the facilities provided by Dharmawangsa University.Keywords: Student Satisfaction, Data Mining, Naive Bayes

Information about the weather is crucial in assisting human activities and labor because the weather is a factor that cannot be separated and is closely related to all human activities. The purpose this study to compare performance the Autoregressive Integrated Moving Average (AIMA) and Long-Short Term Memory (LSTM) algorithm models with case studies of weather forecasting. This study uses comparison of two methods, forecasting using AIMA and LSTM methods. LSTM method provides the best forecasting performance for attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.45 and the Mean Absolute Error value below 1.14. For attributes of average humidity and solar radiation with a Root mean squared error value of 2.62 to 3.82 and a Mean Absolute Error value of 2.21 to 3.2. Precipitation forecasting has the highest error value with a root mean squared error value of 9.99 and a mean absolute error of 6.5. The AIMA method provides the best forecasting performance on the attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.47 and the Mean Absolute Error value below 1.16. For the sun exposure attribute with a Root mean squared error value of 2.91 to 3.05. Whereas the average humidity attribute has the highest error with the Root mean squared error value reaching 4.97 and the Mean Absolute Error reaching 3.99. LSTM method is better in terms of forecasting results and in terms of computation time. From every forecast made, the LSTM method produces a smaller error value.