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.
The Puskesmas is a health service center at the first level in the community. Medication is an important requirement in health agencies, including health centers. The Puskesmas must provide medicine for patients for a period of a month. As well, the puskesmas also has to plan drug requests for the period of the following month. The problem that often arises is about drug supplies. If the demand for drugs is too much, it will cause the drug to be removed for a long period of time, so that it will result in drug expiration. Likewise, if the demand for drugs is too little then it becomes less good, which results in less optimal service to the community. During this time, planning for drug demand for use in the next period, still using instinctive techniques by the head of the health center of the puskesmas. So, this will lead to excess or even reduced drug supply. In this study a system that is able to predict future drug needs was built. The results of this prediction can be used as a reference for drug requests to the health department. The method used to predict is the Least Square method, while the system to control the upper and lower limits uses the Minimum Maximum Stock Level (MMSL) method. The test system for prediction errors in this study uses Mean Absolute Percentage Error (MAPE). This systems was implemented using the PHP language and visualized on a web-based basis. The system test results showed an average prediction error rate of 12.70%. The existence of this system is expected to be able to assist the planning process of drug needs in the future at the puskesmas.
Pengelolaan hasil tangkap ikan nelayan dan retribusi di Kecamatan Muncar selama ini menjadi tanggung jawab KUD Mina Blambangan. Permasalahan yang dihadapi oleh KUD Mina Blambangan adalah pengelolaan data nelayan, hasil tangkap dan retribusi yang masih konvensional, sehingga banyak mengalami kendala seperti kehilangan, kerancuan, dan manipulasi data. Kegiatan pengabdian ini dilaksanakan program digitalisasi dari masalah dihadapi oleh mitra melalui pemanfaatan aplikasi NelayanKita agar masalah administrasi yang dihadapi dapat minimimalisir. Metode yang digunakan dalam kegiatan pengabdian ini menggunakan rapid application development yang diadaptasi sesuai dengan kebutuhan program. Hasil yang didapatkan menunjukkan semua program dapat berjalan dengan lancar, mitra memberikan respon yang baik dan sangat puas terhadap pelaksanaan program. Hal ini ditunjukkan berdasarkan hasil pengisian survey kepuasan mitra menunjukkan prosentase tingkat kepuasan mencapai 96,31%.
Madrasah Aliyah Negeri (MAN) Banyuwangi using a worksheet which can lead to error occurrences and slow decision making. A system for decision support that can improve the ranking process and quality were developed in this paper. The proposed system implemented the codeigniter framework, MySQL database, and PHP programming language. The system provided three user roles which are teacher, student, and administrator role. These four parameters are used as ranking system input, including academic values, non-academic values, violation scores, and student attendance. The ranking process was conducted by applying the analytic hierarchy process (AHP) method. The developed decision support system was tested using two ways: the black box testing method and providing questionnaires. Black box testing result shows that the system has functionally worked, while user’s questionnaire gives 92,29% well accepted by users. The results show that the decision support system can help manage values and determine the parallel ranking list.
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