Yusuf Priyo Anggodo

Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm

Inflation is a benchmark of a countrys economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimisation with hybrid automatic clustering and particle swarm optimisation (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results.

A Novel Forecasting Based on Automatic-Optimized Fuzzy Time Series

In this paper, we propose a new method for forecasting based on automatic-optimized fuzzy time series to do forecasting on Indonesia Inflation Rate (IIR). First, we propose the forecasting model of two-factor high-order fuzzy-trend logical relationships groups (THFLGs) for predicting the IIR. Second, we propose the interval optimization using automatic clustering and particle swarm optimization (ACPSO) to optimize the interval of main factor IIR and secondary factor SF, where SF = {Customer Price Index (CPI), the Bank of Indonesia (BI) Rate, Rupiah Indonesia /US Dollar (Rp/USD) Exchange rate, Money Supply}. The main contribution of this paper, we propose a new model of fuzzy forecasting based on interval optimization with THFLGs, ACPSO, and similarity between measured subscripts from FS to do forecasting IIR. Forecasting results of the proposed method get high accuracy and better than previous methods.

K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem

Received Mar 3, 2018 Revised Apr 11, 2018 Accepted Apr 21, 2018 Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population.N. N. S. Abdul Rahman, N.M. Saad, A. R. Abdullah, M. R. M. Hassan, M. S. S. M. Basir, N. S. M. Noor 1,2,4,6Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 2,3Center for Robotics and Industrial Automation, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3,5Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaLight rail transit (LRT), or fast tram is urban public transport using rolling stock similar to a tramway, but operating at a higher capacity, and often on an exclusive right-of-way. Indonesia as one of developing countries has been developed the LRT in two cities of Indonesia, Palembang and Jakarta. There are opinions toward the development of LRT, negative and positive opinions. To reveal the level of LRT development acceptance, this research uses machine learning approach to analyze the data which is gathered through social media. By conducting this paper, the data is modeled and classified in order to analyze the social sentiment towards the LRT development.Mohamad, S., Nasir, F.M., Sunar, M.S., Isa, K., Hanifa, R.M., Shah, S.M., Ribuan, M.N., Ahmad, A. 1,4,6,7,8Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 1,2,3UTM-IRDA Digital Media Centre, Media and Game Innovation Centre of Excellence, Universiti Teknologi Malaysia, Johor, Malaysia 1,2,3Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia 5Centre for Diploma Studies, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 6Research Centre for Applied Electromagnetics, Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaReceived Jan 31, 2018 Revised Apr 21, 2018 Accepted Apr 30, 2018 Bluetooth is an emerging mobile ad-hoc network that accredits wireless communication to connect various short range devices. A single hop network called piconet is the basic communication topology of bluetooth which allows only eight active devices for communication among them seven are active slaves controlled by one master. Multiple piconets are interconnected through a common node, known as Relay, to form a massive network called as Scatternet. It is obvious that the performance of Scatternet scheduling is highly dependent and directly proportionate with the performance of the Relay node. In contrary, by reducing the number of Relays, it may lead to poor performance, since every Relay has to perform and support several piconet connections. The primary focus of this study is to observe the performance metrics that affects the inter-piconet scheduling since the Relay node’s role is like switch between multiple piconets. In this paper, we address and analyze the performance issues to be taken into consideration for efficient data flow in Scatternet based on Relay node.

Optimization of Dempster-Shafer’s Believe Value Using Genetic Algorithm for Identification of Plant Diseases Jatropha Curcas

Received Jan 19, 2018 Revised Apr 28, 2018 Accepted Jun 14, 2018 Nowadays, Aspect-Oriented Programming (AOP) paradigm is getting more popularity in the field of software development. But testing an AspectOriented Software System (AOSS) is not well matured. Therefore, many researchers have been focusing on testing an AOSS. Moreover, the literature indicates that very few papers have devoted to literature survey but still there is need to study in depth of various testing techniques used for AOSS. Therefore, in this paper, a comprehensive study of existing various testing techniques for AOSS have been conducted and present a comparative analysis result of various testing techniques based on various parameters.Received Apr 8, 2018 Revised Jun 9, 2018 Accepted Jun 23, 2018 This paper proposes an effective technique to solve Distribution System Expansion Planning (DSEP) problem by using the artificial neural network. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer quadratic programming problem. It consists of two layers neural network which combine Hopfield network and Boltzmann machine (BM) in upper and lower layer respectively named as Modified BM. The originality of the proposed technique is it will delete the unit of the second layer, which is not selected in the first layer in its execution. Then, the second layer is restructured using the selected units. Due to this feature, the proposed technique will improve time consuming and accuracy of solution. Referring to the case study demonstrated in this paper, the significance outputs obtained are the improvement in computational time and accuracy of solution provided. As the solution provided various of options, the proposed technique will help decision makers in solving DSEP problem. As a result, the performance of strategic investment planning in DSEP certainly enhanced.Internet Protocol version 6 (IPv6) is a next-generation internet protocol that is devised to replace its predecessor, the IPv4. With the benefit of ample address space, flexible header extensions and its many specific features, IPv6 is the future of the Internet and Internetworking. A significant advantage of IPv6 is its capabilities in the domain of security and mobility, where it scores in comparison with its predecessor. One of the many features specific to IPv6, such as the mandatory IPsec messaging or address auto-configuration, is the Neighbor Discovery Protocol (NDP). Even though the concept of security is more pronounced in the IPv6 protocols, there still exist loopholes. These loopholes when exploited target the foundation of the Internetworking. The extensive applications of NDP make it even more necessary to identify and address these gaps to ensure network security. Hence, this paper investigates such loopholes in the applications of NDP in creating a network and analyzes the process of the denial-of-service attacks that endanger the safety of an established network. Also, the paper proposes a new method to mitigate Denial-of-Service (DoS) in network mobility of IPv6 networks. This proposed approach is a hybrid of existing solutions and is capable of overcoming the significant disadvantages of these methods. Also, the paper discusses the comparative analysis among the various existing solutions and illustrates the effect of the proposed method in MIPv6 security.Received Jun 1, 2018 Revised Jul 10, 2018 Accepted Jul 25, 2018 Big Data is a new technology and architecture. It can work on a very large volume of a variety of data with high-velocity, discovery, and/or analysis. Big Data is about the fast-growing sources of data such as web logics, Sensor networks, Social media, Internet text and documents, Internet pages, Search Index data, scientific research. Big data also formally introduces a complex range of analysis. Big data can evaluate mixed data (structured and unstructured) from multiple sources. As there are some security issues in big data which are no longer solved using the hashing techniques on large amount of data, this paper shows an idea of new approach of designing a Knox‟ified Hadoop cluster.

AUTOMATIC CLUSTERING AND OPTIMIZED FUZZY LOGICAL RELATIONSHIPS FOR MINIMUM LIVING NEEDS FORECASTING

Forecasting of minimum living needs is useful for companies in financial planning next year. In this study, the firescasting is done using automatic clustering and optimized fuzzy logical relationships. Automatic clustering is used to form a sub-interval time series data. Particle swarm optimization is used to set and optimze interval values in fuzzy logical relationships. The data used as many as 11 years of historical data from 2005-2015. The optimal value of the test results obtained by the p = 4, the number of iterations = 100, the number of particles = 45, a combination of Vmin and Vmax = [-0.6, 0.6], as well as combinations Wmax and Wmin = [0, 4, 0 , 8]. These parameters values produce good forecasting results. Keywords: minimum living needs, automatic clustering, particle swarm optimization, fuzzy logical relationships