Abstract-A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Six cluster was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter's scale magnitudes.
Midwives are one of the health workers who provide child and maternal health (CMH) services and family planning. At present, most of the recording of midwife services is still managed conventionally by manual book keeping. It is less effective and efficient which causes the workload to increase, the information retrieval process is quite long and the risk of missing important data is likely to occur frequently. On the other hand, maternal patients are required to visit the midwife directly if they want to know the information on the progress of the pregnancy and their child. Based on these facts, a CMH information system was built that was accessible to midwives and parents. The information system developed consists of two integrated applications, namely web-based applications for midwives and mobile applications for parents. The web application facilitates midwives to record transactions, make reports, and deliver information to patients. While the mobile application makes it easier for parents to monitor the development of maternal and child health and other information provided by midwives. The system was developed using the water-fall software development model. The test results using the black-box test method indicate that the CMH system has been able to meet the user's functional requirements.
Earthquake is a type of natural disaster. The Indonesian archipelago located in the world's three mega plates; they are Australian plate, Eurasian plate, and Pacific plate. Therefore, it is possible for applied of earthquake risk of mitigation. One of them is to provide information about earthquake occurrences. This information used for spatiotemporal analysis of earthquakes. This paper presented Spatial Analysis of Magnitude Distribution for Earthquake Prediction using adaptive neural fuzzy inference system (ANFIS) based on automatic clustering in Indonesia. This system has three main sections: (1) Data preprocessing, (2) Automatic Clustering, (3) Adaptive Neural Fuzzy Inference System. For experimental study, earthquake data obtained Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) and the United States Geological Survey’s (USGS), the year 2010-2017 in the location of Indonesia. Automatic clustering process produces The optimal number of cluster, that is 7 clusters. Each cluster will be analyzed based on earthquake distribution. Its calculate the b value of earthquake to get the seven seismicity indicators. Then, implementation for ANFIS uses 100 training epochs, Number of membership function (MFs) is 2, MFs type input is gaussian membership function (gaussmf). The ANFIS result showed that the system can predict the non-occurrence of aftershocks with the average performance of 70%.
E-business security becomes an important issue in the development of technology, to ensure the safety and comfort of transactions in the exchange of information is privacy. This study aims to improve security in e-business systems using a hybrid algorithm that combines two types of keys, namely symmetric and asymmetric keys. Encryption and decryption of messages or information carried by a symmetric key using the simple symmetric key algorithm and asymmetric keys using the Rivest Shamir Adleman (RSA) algorithm. The proposed hybrid algorithm requires a high running time in the decryption process compared to the application of a single algorithm. The level of security is stronger because it implements the process of message encryption techniques with two types of keys simultaneously.
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