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All Journal International Journal of Electrical and Computer Engineering Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic
Siti Mariyam Shamsuddin
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Intelligent Caching Wireless Data Access in the Wireless Spectrum Syazwa Mad Jais; Sarina Sulaiman; Siti Mariyam Shamsuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 5: May 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The evolution wireless technologies are growing rapidly in upcoming years. It is expected that many users will shift to more advanced devices that will contribute to gain higher demand in wireless spectrum. However, the capacity for the allocation frequency in the wireless spectrum is typically limited in wireless data transmission. Therefore, when the loads of wireless users in the wireless communications are increasing, the need of cache mechanisms such as Web caches or Web servers are crucial. The scalability demands on internet infrastructure keep increasing as the internet continues to grow in popularity and size. Therefore, the existence and development in Web caching technologies will contribute to bandwidth savings, network latency reduction, improve content availability and subsequently server load balancing. This paper will studies and investigates the cache performance in wireless spectrum with the purpose of dealing with the data growth since the spectrum crisis becomes a serious matter lately. The performance improvement will be observed using caching scheme which allows for time shifting and load shifting in accessing the wireless data with the better cache deployment in the network system. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2506
Food Traceability in Supply Chain Based on EPCIS Standard and RFID Technology Evizal Abdul Kadir; Siti Mariyam Shamsuddin; Eko Supriyanto; Wahyudi Sutopo; Sri Listia Rosa
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

The growth of retailer lately very significant especially in food product, selling of food product is not only in conventional market but moreover now is in such mini market, mall, shopping complex, etc. Food traceability is required in supply of food product to make sure items are safe to consume and not in expired date. This paper present on food traceability in supply chain based on Electronic Product Code Information Services (EPCIS), the use of EPCIS standard is refer to Global Standard-1 (GS1) for logistic and supply chain based on Radio Frequency Identification (RFID) Technology. RFID is used for items tagging on food product instead of barcode that currently widely used the advantages of RFID tag compare than barcode make the system is more applicable to used in food traceability. In this case one of food product take into example in supplying which is banana that the process started from farmer until reach to retail house are monitored and recorded by the system. End process of this system is to give services to the consumer of customer based on EPCIS database collected in all the way of process. DOI: http://dx.doi.org/10.11591/telkomnika.v13i1.6919
Near Optimal Convergence of Back-Propagation Method using Harmony Search Algorithm Abdirashid Salad Nur; Nor Haizan Mohd Radzi; Siti Mariyam Shamsuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

Training Artificial Neural Networks (ANNs) is of great significanceand a difficult task in the field of supervised learning as its performance depends on underlying training algorithm as well as the achievement of the training process. In this paper, three training algorithms namely Back-Propagation Algorithm, Harmony Search Algorithm (HSA) and hybrid BP and HSA called BPHSA are employed for the supervised training of Multi-Layer Perceptron feed  forward type of Neural Networks (NNs)  by giving special attention to hybrid BPHSA. A suitable structure for data representation of NNs is implemented to BPHSA-MLP, HSA-MLP and BP-MLP. The proposed method is empirically tested and verified using five benchmark classification problemswhich are Iris, Glass, Cancer, Wine and Thyroid dataset on training NNs. The MSE, training time, and classification accuracy of hybrid BPHSA are compared with the standard BP and meta-heuristic HSA. The experiments showed that proposed method has better results in terms of convergence error and classification accuracy compared to BP-MLP and HSA-MLPmaking the BPHSA-MLPa promising algorithm for neural network training. DOI: http://dx.doi.org/10.11591/telkomnika.v14i1.7233
Radial Basis Function Network Learning with Modified Backpropagation Algorithm Usman Muhammad Tukur; Siti Mariyam Shamsuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

Radial Basis Function Network (RBFN) is a class of Artificial Neural Network (ANN) that was used in many classification problems in science and engineering. Backpropagation (BP) algorithm is a learning algorithm that was widely used in ANN. However, BP has major disadvantages of slow error rate convergence and always easily stuck at the local minima. Hence, Modified BP algorithm was proposed in this study to improve the learning speed of RBFN using discretized data. C programming language was used to develop the program for the proposed method. Performance measurement of the method was conducted and the experimental results indicate that our proposed method performs better in error rate convergence and correct classification compared to the result with continuous dataset. T-test statistical analysis was used to check the significance of the results and most were found to be satisfactorily significant. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.7032 
Big Data Platforms and Techniques Salisu Musa Borodo; Siti Mariyam Shamsuddin; Shafaatunnur Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 1, No 1: January 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v1.i1.pp191-200

Abstract

Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in big data environments. Techniques used for big data are machine learning, Data Mining, Neural Network and Deep Learning. There are big data architecture offerings from Microsoft, IBM and National Institute of Standards and Technology. Big data potentials can transform economies and reduce running cost of institutions. Big data has challenges such as storage, computation, security and privacy
Co-Authors Abdirashid Salad Nur Bariah binti Yusob Detri Karya Eko Supriyanto Evizal Abdul Kadir Maslina Darus Nor Haizan Mohd Radzi Norsharina Abu Bakar Salisu Musa Borodo Sarina Sulaiman Shafaatunnur Hasan Sri Listia Rosa Syamsuryadi Syamsuryadi Syazwa Mad Jais Usman Muhammad Tukur Wahyudi Sutopo