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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Journal of ICT Research and Applications
Achmad Imam Kistijantoro
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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Conceptual Design of Multi-agent System for Suramadu Bridge Structural Health Monitoring System Seno Adi Putra; Bambang Riyanto; Agung Harsoyo; Achmad Imam Kistijantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.1382

Abstract

Wireless Sensor Network (WSN) is small embedded devices deployed in large scale network with capability to sense, compute, and communicate. It combines modern sensor, microelectronic, computation, communication, and distributed processing technology. WSN has been taking an important contribution in structural health monitoring system, especially in Suramadu Bridge, one of the longest span bridges in Indonesia connecting Surabaya (East Java) and Madura Island. Due to subjected by environmental circumstance, it is necessary to implement intelligent and autonomous WSN to monitor the bridge condition, detect the bridge damage, and send warning message to bridge users when unsafe condition occurs. The multi-agent system is a promising approach to be implemented on intelligent and autonomous WSN, especially in the bridge structural health monitoring system. In this approach agents are empowered to have several intelligent learning capabilities for structural monitoring, damage detection, and prediction. This paper describes multi-agent system conceptual design that will be implemented as model of long span bridge structural health monitoring system considering system architecture and agent organization.
The influence of data size on a high-performance computing memetic algorithm in fingerprint dataset Priati Assiroj; Harco Leslie Hendric Spits Warnars; Edi Abdurachman; Achmad Imam Kistijantoro; Antoine Doucet
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2760

Abstract

The fingerprint is one kind of biometric. This biometric unique data have to be processed well and secure. The problem gets more complicated as data grows. This work is conducted to process image fingerprint data with a memetic algorithm, a simple and reliable algorithm. In order to achieve the best result, we run this algorithm in a parallel environment by utilizing a multi-thread feature of the processor. We propose a high-performance computing memetic algorithm (HPCMA) to process a 7200 image fingerprint dataset which is divided into fifteen specimens based on its characteristics based on the image specification to get the detail of each image. A combination of each specimen generates a new data variation. This algorithm runs in two different operating systems, Windows 7 and Windows 10 then we measure the influence of data size on processing time, speed up, and efficiency of HPCMA with simple linear regression. The result shows data size is very influencing to processing time more than 90%, to speed up more than 30%, and to efficiency more than 19%.
High Performance CDR Processing with MapReduce Mulya Agung; Achmad Imam Kistijantoro
Journal of ICT Research and Applications Vol. 10 No. 2 (2016)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2016.10.2.1

Abstract

A call detail record (CDR) is a data record produced by telecommunication equipment consisting of call detail transaction logs. It contains valuable information for many purposes in several domains, such as billing, fraud detection and analytical purposes. However, in the real world these needs face a big data challenge. Billions of CDRs are generated every day and the processing systems are expected to deliver results in a timely manner. The capacity of our current production system is not enough to meet these needs. Therefore a better performing system based on MapReduce and running on Hadoop cluster was designed and implemented. This paper presents an analysis of the previous system and the design and implementation of the new system, called MS2. In this paper also empirical evidence is provided to demonstrate the efficiency and linearity of MS2. Tests have shown that MS2 reduces overhead by 44% and speeds up performance nearly twice compared to the previous system. From benchmarking with several related technologies in large-scale data processing, MS2 was also shown to perform better in the case of CDR batch processing.  When it runs on a cluster consisting of eight CPU cores and two conventional disks, MS2 is able to process 67,000 CDRs/second.
Co-Authors Agung Harsoyo Antoine Doucet Bambang Riyanto Edi Abdurachman Mulya Agung Priati Assiroj Seno Adi Putra Warnars, Harco Leslie Hendric Spits