Imam Tahyudin

The Combination of Evolutionary Algorithm Method for Numerical Association Rule Mining Optimization

The numerical problem of association rule mining is an updated issue. Numerous authors propose some methods to solved it. A number of them are using the optimization approach by Particle Swarm Optimization (PSO). The problem is that the PSO trapped in local optima when searched the best particle in every iteration. Many researchers solved this problem by combining with Cauchy distribution because it is tremendous for searching in a large neighborhood. Hence, that combination will be implemented to accomplish the numerical association rule mining problem for some objective functions such as confidence, comprehensibility, interestingness. Based on the result the multi-objective of PSO for Numerical Association Rule Mining Problem with Cauchy Distribution (PARCD) showed the better result than the method of Multi-objective Particle Swarm Optimization for Association Rule Mining (MOPAR).

SARIMA Model of Bioelectic Potential Dataset

Bioelectric potential of plant produces a low electrical signal because of the plant activities like photosynthesis and respiration. Furthermore, the electrical signal will change because of the environmental factors such as temperature, humidity and human behavior. Some authors successfully used the bioelectric potential of plant for detecting the various human activities, like walking, jumping, open the door, and etc. They used decision tree (DT) J48, multi layer perceptron (MLP) and convolution neural network (CNN) as the analysis method. However, the previous accuracy was no satisfied and estimating the human position globally not specifically an exact location. This research has aim to construct a time series model for bioelectric potential dataset which is SARIMA model and to build infrastructure design of human position estimation in an exact location. For constructing the SARIMA model we use one observation location and obtained the best model is SARIMA (1,0,0) with accuracy of 80%. In addition, this research successfully designed the infrastructure of human position estimation using three locations.

The Rules Determination of Numerical Association Rule Mining Optimization by Using Combination of PSO and Cauchy Distribution

One of the optimization methods to solve the numerical association rule mining problem is particle swarm optimization (PSO). This method is popularly used in various fields such as in the job scheduling problem, evaluating stock market, inferring gen regulatory networks and numerical association rule mining optimization. The weakness of the PSO is often premature for searching the optimal solution because it traps in local optima when the best particle is being searched in every iteration. Combining the PSO with Cauchy distribution for numerical association rule mining problem (PARCD) is a solution because it is robust for finding the optimal solution in a large neighborhood. The important point in this proposed method is particle representation which to know the association between one attribute to another. Therefore, this study has the aim to determinate rules of numerical association rule mining and also to calculate the multi-objective function using combination of PSO and Cauchy distribution. The results show that all of them explain every attribute to formulate the rule well. In addition, the multi-objective function value of PARCD method generally produces results which better than the previous method, MOPAR.

Comparison Between K-Means and Fuzzy C-Means Clustering in Network Traffic Activities

A network traffic utilization in order to support teaching and learning activities are an essential part. Therefore, the network traffic management usage is requirements. In this study, analysis and clustering network traffic usage by using K-Means and Fuzzy C-Means (FCM) methods have been implemented. Then, both of method were used Euclidean Distance (ED) in order to get better results clusters. The results showed that the FCM method has been able to perform clustering in network traffic.

Implementation of a Mobile Augmented Reality Application with Location Based Service for Exploring Tourism Objects

The development of tourism in Indonesia especially Purbalingga District showed a positive trend which was the number two of favorite destination in Central Java province after Borobudur Temple. One attempt to participate for promoting the tourism objects is by creating a promotion idea based on Augmented Reality technology. The previous research already used a tourist brochure with Augmented Reality technology which giving the impression of a real interactive as well as providing a great experience to get complete information. However, the problem is difficult to find the location. Therefore, this study has aim to develop the promotion media by using a mobile augmented reality application with location-based service (LBS) by using extreme programming (XP). The implemented system includes various types of content such as text, pictures, and video. By using this application can display information as well as the detection of POI (Point of Interest) via Android smartphone to determine and indicate the location of tourism objects. The future work is integrating database both of mobile and web application for updating information. Also, there is the additional content for tourist object support such as hotel, restaurant, transportation and the other public facilities.