Automatic Text Summarization for Indonesian Language Using TextTeaser

Text summarization is one of the solution for information overload. Reducing text without losing the meaning not only can save time to read, but also maintain the reader’s understanding. One of many algorithms to summarize text is TextTeaser. Originally, this algorithm is intended to be used for text in English. However, due to TextTeaser algorithm does not consider the meaning of the text, we implement this algorithm for text in Indonesian language. This algorithm calculates four elements, such as title feature, sentence length, sentence position and keyword frequency. We utilize TextRank, an unsupervised and language independent text summarization algorithm, to evaluate the summarized text yielded by TextTeaser. The result shows that the TextTeaser algorithm needs more improvement to obtain better accuracy

Water Pipeline Monitoring and Leak Detection Using Flow Liquid Meter Sensor

Water distribution is generally installed through underground pipes. Monitoring the underground water pipelines is more difficult than monitoring the water pipelines located on the ground in open space. This situation will cause a permanent loss if there is a disturbance in the pipeline such as leakage. Leaks in pipes can be caused by several factors, such as the pipe's age, improper installation, and natural disasters. Therefore, a solution is required to detect and to determine the location of the damage when there is a leak. The detection of the leak location will use fluid mechanics and kinematics physics based on harness water flow rate data obtained using flow liquid meter sensor and Arduino UNO as a microcontroller. The results show that the proposed method is able to work stably to determine the location of the leak which has a maximum distance of 2 metres, and it's able to determine the leak location as close as possible with flow rate about 10 litters per minute

Fast Learning for Big Data Using Dynamic Function

This paper discusses an approach for fast learning in big data. The proposed approach combines momentum factor and training rate, where the momentum is a dynamic function of the training rate in order to avoid overshoot weight to speed up training time of the back propagation neural network engine. The two factors are adjusted dinamically to assure the fast convergence of the training process. Experiments on 2-bit XOR parity problem were conducted using Matlab and a sigmoid function. Experiments results show that the proposed approach signifcantly performs better compare to the standard back propagation neural network in terms of training time. Both, the maximum training time and the minimum training time are significantly faster than the standard algorithm at error threshold of 10-5

Eaodv: A*-based Enhancement Ad-hoc on Demand Vector Protocol to Prevent Black Hole Attacks

Black hole attack is an attack where a node that responds to RREQ from the source node by replying a fake freshness information and false hop count. The black hole nodes do not respond to distributed co-operation in routing protocol to absorb all the packets, as a result, the network performance will drop. Most previous works are focused on anomaly detection through dynamic trusted of the neighbouring nodes. We find out that the internal comparisons take a long time. This loss can be shortened by changing the routing mechanism. We propose an enhancement of AODV protocol, named EAODV, that is able to prevent black hole attacks. The EAODV can find a shortest path of routing discovery using A* heuristic search algorithm. Values of hop count and estimate time to reach the destination node are used as input in the heuristic equation and one-way hash function is used to make a secure value and then to casting it to all neighbouring nodes. Experiments were conducted in NS2 to simulate EAODV in different running time with and without black hole nodes. The EAODV performance results are indicated better in terms Packet loss and Average End-to-End delay

Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels

Organization Readiness and ERP Implementation in Albaha University

This work studies the correlation between the organizational readiness in Albaha University and the respective Critical Success Factors with regards to the Enterprise Resource Planning (ERP) implementation. The study also considers some suggestions to improve the ABU’s ERP systems and roadmap towards the self –development strategy and to reduce vendor-dependency. A survey regarding ERP to the end-users, experts and developers in Albaha University was conducted. The analysis of the results in this work confirmed with the results of an existing work. The four significance success factors: Project Management, Business Process Re-engineering, System Integration, and Training and Education are recommended to be adopted to assure the smooth adoption of ERP at Albaha University

MPKMS: a Matrix-based Pairwise Key Management Scheme for Wireless Sensor Networks

Due to the sensitivity of the Wireless Sensor Networks (WSN) applications and resource constraints, authentication and key management emerge as a challenging issue for WSN. In general, various approaches have been developed for the key management in WSN. This paper has come up with a new robust key pre-distribution scheme using random polynomial functions and matrix. This new proposed scheme significantly increases the storage efficiency and provides resilience to network against node capture by using random prime numbers, polynomial functions and matrix properties. The effectiveness of the scheme is demonstrated through a security analysis and comparison with the existing schemes

Enhancement of FMIPv6 by Utilising Concurent Binding Update Process

The world is progressing toward the Mobile Internet Protocol Television (MIPTV) era where people are able to watch television while roaming. The MIPTV technology requires high bandwidth and low latency handover. This paper enhances the binding updates process in the Fast Handover Mobile IPv6 (FMIPv6) to improve its handover process performance in term of secureness and robustness, by implementing concurant binding update process thru the use of the International Mobile Subscriber Identifier (IMSI). Simulation results show that the proposed idea reduces the handover latency to about 63% compared to standard FMIPv6

Vulnerability Analysis of a Mutual Authentication Protocol Conforming to EPC Class-1 Generation-2 Standard

In this paper we scrutinize the security properties of an RFID authentication protocol conforming to the EPC Class-1 Generation-2 standard. The protocol is suitable for Gen-2 passive tags and requires simple computations. The authors claim that the scheme provides privacy protection and authentication and offers resistant against commonly assumed attacks. We propose a de-synchronization and an impersonation attack in which the disclosing of the secret information (i.e. secret key and static identifier) shared between the tag and the reader is unnecessary to success in these attacks