Dedy Hartama

Searching Process with Raita Algorithm and its Application

Searching is a common process performed by many computer users, Raita algorithm is one algorithm that can be used to match and find information in accordance with the patterns entered. Raita algorithm applied to the file search application using java programming language and the results obtained from the testing process of the file search quickly and with accurate results and support many data types.

Keylogger Application to Monitoring Users Activity with Exact String Matching Algorithm

The development of technology is very fast, especially in the field of Internet technology that at any time experiencing significant changes, The development also supported by the ability of human resources, Keylogger is a tool that most developed because this application is very rarely recognized a malicious program by antivirus, keylogger will record all activities related to keystrokes, the recording process is accomplished by using string matching method. The application of string matching method in the process of recording the keyboard is to help the admin in knowing what the user accessed on the computer.

Combination Base64 and Hashing Variable Length for Securing Data

Keeping the data intact without changing becomes an important factor in a communication, the data itself has many forms such as text data, audio data, image data and for secure every form data an algorithm Base64 are needed, Base64 algorithm is used as data format to transmit data due to the result of base64 itself, but Base64 algorithm is not safe enough because it is easy to decoding and get the original form data, therefore need additional security and in this research combined with Hashing Variable Length (HAVAL) algorithm, HAVAL algorithm has a way work secures and compresses plaintext, so the encoding result from Base64 is re-secured and compressed using HAVAL algorithm with length of hashing 32 bit or 4 bytes so when data transmission process will not take many bytes of data compared with Base64 algorithm

A framework of training ANFIS using Chicken Swarm Optimization for solving classification problems

The result of training parameters described Adaptive Neuro-Fuzzy Inference System (ANFIS) performance. The speed and reliability of training effect depend on the training mechanism. There have been many methods used to train the parameters of ANFIS as using GD, metaheuristic techniques, and LSE. But there are still many methods developed to achieve efficiently. One of the proposed algorithm to improve the performance of ANFIS is Chicken swarm optimization (CSO) algorithm. The experimental results of training ANFIS network for classification problems show that ANFIS-CSO algorithm achieved better accuracy.

Smart City: Utilization of IT resources to encounter natural disaster

This study proposes a framework for the utilization of IT resources in the face of natural disasters with the concept of Smart City in urban areas, which often face the earthquake, particularly in the city of North Sumatra and Aceh. Smart City is a city that integrates social development, capital, civic participation, and transportation with the use of information technology to support the preservation of natural resources and improved quality of life. Changes in the climate and environment have an impact on the occurrence of natural disasters, which tend to increase in recent decades, thus providing socio-economic impacts for the community. This study suggests a new approach that combines the Geographic Information System (GIS) and Mobile IT-based Android in the form of Geospatial information to encounter disaster. Resources and IT Infrastructure in implementing the Smart Mobility with Mobile service can make urban areas as a Smart City. This study describes the urban growth using the Smart City concept and considers how a GIS and Mobile Systems can increase Disaster Management, which consists of Preparedness, mitigation, response, and recovery for recovery from natural disasters.

Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

Modification Of Learning Rate With Lvq Model Improvement In Learning Backpropagation

One type of artificial neural network is a backpropagation, This algorithm trained with the network architecture used during the training as well as providing the correct output to insert a similar but not the same with the architecture in use at training.The selection of appropriate parameters also affects the outcome, value of learning rate is one of the parameters which influence the process of training, Learning rate affects the speed of learning process on the network architecture.If the learning rate is set too large, then the algorithm will become unstable and otherwise the algorithm will converge in a very long period of time.So this study was made to determine the value of learning rate on the backpropagation algorithm. LVQ models of learning rate is one of the models used in the determination of the value of the learning rate of the algorithm LVQ.By modifying this LVQ model to be applied to the backpropagation algorithm. From the experimental results known to modify the learning rate LVQ models were applied to the backpropagation algorithm learning process becomes faster (epoch less).

Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves In The Predicting Process

Backpropagation is a good artificial neural network algorithm used to predict, one of which is to predict the rate of Consumer Price Index (CPI) based on the foodstuff sector. While conjugate gradient fletcher reeves is a suitable optimization method when juxtaposed with backpropagation method, because this method can shorten iteration without reducing the quality of training and testing result. Consumer Price Index (CPI) data that will be predicted to come from the Central Statistics Agency (BPS) Pematangsiantar. The results of this study will be expected to contribute to the government in making policies to improve economic growth. In this study, the data obtained will be processed by conducting training and testing with artificial neural network backpropagation by using parameter learning rate 0,01 and target error minimum that is 0.001-0,09. The training network is built with binary and bipolar sigmoid activation functions. After the results with backpropagation are obtained, it will then be optimized using the conjugate gradient fletcher reeves method by conducting the same training and testing based on 5 predefined network architectures. The result, the method used can increase the speed and accuracy result.

The Planning of Smart City to Mitigate the Impacts of Natural Disaster in North Sumatera

This article introduces the smart urban planning in the mitigation of natural disasters in urban areas in Indonesia especially North Sumatera. A smart city is a city-based social development, capital, citizen participation, transportation and information technology, natural resources and quality of life. Frequency and socio-economic impacts of natural disasters frequent in recent decades due to climate change and the environment. The approach used in this paper is a combination of Geographic Information System (GIS) and mobile IT in the form of geospatial information. Mobile services sector in which the city government is involved in the formation of smart cities. This article reviews the growth of smart cities and considers how a systems can improve mitigation and adaptation approaches to these risks and to recovery from the natural disasters.

A soft set approach for fast clustering attribute selection

Attribute-based data clustering has been proven as one of the efficient methods in data clustering. Set theory approaches for data clustering exist to handle attribute-based data clustering. The MDDS, a soft set based technique has proven its applicability in data clustering. However, in reviewing MDDS, where its calculations are based on comparing all constructed multi-soft sets, it still suffers from high computational time. This research presents a modification of the MDDS by generating an alternative technique to reduce its computational complexity. To provide alternative solutions from MDDS algorithm, we derive a new algorithm that can lesser response time. It is using theory of soft set by selecting and excluding the set having no effect domination on other sets. The experiments are implemented in MATLAB software thought to UCI benchmark datasets. The computation experiment illustrate that the time response can be speed up to 67.56 % by proposed algorithm compared with MDDS.