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Yuliah Qotimah
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Journal of ICT Research and Applications
Published by Institut Teknologi Bandung
ISSN : 23375787     EISSN : 23385499     DOI : https://doi.org/10.5614/itbj.ict.res.appl.
Core Subject : Science,
Computer Science & IT
Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2007)" : 5 Documents clear
A Rural Next Generation Network (R-NGN) and Its Testbed Armein Z. R. Langi
Journal of ICT Research and Applications Vol. 1 No. 1 (2007)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2007.1.1.1

Abstract

Rural Next Generation Networks (R-NGN) technology allows Internet protocol (IP) based systems to be used in rural areas. This paper reports a testbed of R-NGN that uses low cost Ethernet radio links, combined with media gateways and a softswitch. The network consists of point-to-point IP Ethernet 2.4 GHz wireless link, IP switches and gateways in each community, standard copper wires and telephone sets for users. It uses low power consumption, and suitable for low density users. This combination allows low cost systems as well as multiservices (voice, data, and multimedia) for rural communications. An infrastructure has been deployed in two communities in Cipicung Girang, a village 10 km outside Bandung city, Indonesia. Two towers link the communities with a network of Institut Teknologi Bandung (ITB) campus. In addition, local wirelines connect community houses to the network. Currently there are four houses connected to each community node (for a total of eight house), upon which we can perform various tests and measurements.
DEWA: A Multiaspect Approach for Multiple Face Detection in Complex Scene Digital Image Setiawan Hadi; Adang S. Achmad; Iping Supriana Suwardi; Farid Wazdi
Journal of ICT Research and Applications Vol. 1 No. 1 (2007)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2007.1.1.2

Abstract

A new approach for detecting faces in a digital image with unconstrained background has been developed. The approach is composed of three phases: segmentation phase, filtering phase and localization phase. In the segmentation phase, we utilized both training and non-training methods, which are implemented in user selectable color space. In the filtering phase, Minkowski addition-based objects removal has been used for image cleaning. In the last phase, an image processing method and a data mining method are employed for grouping and localizing objects, combined with geometric-based image analysis. Several experiments have been conducted using our special face database that consists of simple objects and complex objects. The experiment results demonstrated that the detection accuracy is around 90% and the detection speed is less than 1 second in average.
Improvement of CB & BC Algorithms (CB* Algorithm) for Learning Structure of Bayesian Networks as Classifier in Data Mining Benhard Sitohang; G. A. Putri Saptawati
Journal of ICT Research and Applications Vol. 1 No. 1 (2007)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2007.1.1.3

Abstract

There are two categories of well-known approach (as basic principle of classification process) for learning structure of Bayesian Network (BN) in data mining (DM): scoring-based and constraint-based algorithms. Inspired by those approaches, we present a new CB* algorithm that is developed by considering four related algorithms: K2, PC, CB, and BC. The improvement obtained by our algorithm is derived from the strength of its primitives in the process of learning structure of BN. Specifically, CB* algorithm is appropriate for incomplete databases (having missing value), and without any prior information about node ordering.
Discovery of Frequent Itemsets: Frequent Item Tree-Based Approach A. V. Senthil Kumar; R. S. D. Wahidabanu
Journal of ICT Research and Applications Vol. 1 No. 1 (2007)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2007.1.1.4

Abstract

Mining frequent patterns in large transactional databases is a highly researched area in the field of data mining. Existing frequent pattern discovering algorithms suffer from many problems regarding the high memory dependency when mining large amount of data, computational and I/O cost. Additionally, the recursive mining process to mine these structures is also too voracious in memory resources. In this paper, we describe a more efficient algorithm for mining complete frequent itemsets from transactional databases. The suggested algorithm is partially based on FP-tree hypothesis and extracts the frequent itemsets directly from the tree. Its memory requirement, which is independent from the number of processed transactions, is another benefit of the new method. We present performance comparisons for our algorithm against the Apriori algorithm and FP-growth.
Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification M. Sarosa; A. S. Ahmad; B. Riyanto; A. S. Noer
Journal of ICT Research and Applications Vol. 1 No. 1 (2007)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2007.1.1.5

Abstract

Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.

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