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Journal : Journal of ICT Research and Applications

Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients Tjong Wan Sen; Bambang Riyanto Trilaksono; Arry Akhmad Arman; Rila Mandala
Journal of ICT Research and Applications Vol. 3 No. 2 (2009)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2009.3.2.4

Abstract

To improve the performance of phoneme based Automatic Speech Recognition (ASR) in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT) coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited) and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA). These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4) from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.
Automatic Tailored Multi-Paper Summarization based on Rhetorical Document Profile and Summary Specification Masayu Leylia Khodra; Dwi Hendratmo Widyantoro; E. Aminudin Aziz; Bambang Riyanto Trilaksono
Journal of ICT Research and Applications Vol. 6 No. 3 (2012)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2012.6.3.4

Abstract

In  order  to  assist  researchers  in  addressing  time  constraint  and  low relevance  in  using  scientific  articles,  an  automatic  tailored  multi-paper summarization  (TMPS)  is  proposed.  In  this  paper,  we  extend  Teufel's  tailored summary  to  deal  with  multi-papers  and  more  flexible  representation  of  user information needs. Our TMPS extracts Rhetorical Document Profile (RDP) from each paper and  presents a summary based on user information needs.  Building Plan  Language  (BPLAN)  is  introduced  as  a  formalization  of  Teufel's  building plan  and  used  to  represent summary  specification,  which  is  more  flexible representation user information needs. Surface repair is embedded within the BPLAN  for  improving  the  readability  of  extractive summary.  Our  experiment shows that the average performance of RDP extraction module is 94.46%, which promises  high  quality  of  extracts  for  summary  composition.  Generality evaluation  shows  that  our  BPLAN  is  flexible  enough  in  composing  various forms  of summary.  Subjective  evaluation  provides evidence that  surface repair operators can improve the resulting summary readability.
A Multiclass-based Classification Strategy for Rethorical Sentence Categorization from Scientific Papers Dwi H. Widyantoro; Masayu L. Khodra; Bambang Riyanto Trilaksono; E. Aminudin Aziz
Journal of ICT Research and Applications Vol. 7 No. 3 (2013)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2013.7.3.5

Abstract

Rapid identification of content structures in a scientific paper is of great importance particularly for those who actively engage in frontier research. This paper presents a multi-classifier approach to identify such structures in terms of classification of rhetorical sentences in scientific papers. The idea behind this approach is based on an observation that no single classifier is the best performer for classifying all rhetorical categories of sentences. Therefore, our approach learns which classifiers are good at what categories, assign the classifiers for those categories and apply only the right classifier for classifying a given category. This paper employsk-fold cross validation over training data to obtain the category-classifier mapping and then re-learn the classification model of the corresponding classifier using full training data on that particular category. This approach has been evaluated for identifying sixteen different rhetorical categories on sentences collected from ACL-ARC paper collection. The experimental results show that the multi-classifier approach can significantly improve the classification performance over multi-label classifiers.
DIDS Using Cooperative Agents Based on Ant Colony Clustering Muhammad Nur Kholish Abdurrazaq; Bambang Riyanto Trilaksono; Budi Rahardjo
Journal of ICT Research and Applications Vol. 8 No. 3 (2015)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2015.8.3.3

Abstract

Intrusion detection systems (IDS) play an important role in information security. Two major problems in the development of IDSs are the computational aspect and the architectural aspect. The computational or algorithmic problems include lacking ability of novel-attack detection and computation overload caused by large data traffic. The architectural problems are related to the communication between components of detection, including difficulties to overcome distributed and coordinated attacks because of the need of large amounts of distributed information and synchronization between detection components. This paper proposes a multi-agent architecture for a distributed intrusion detection system (DIDS) based on ant-colony clustering (ACC), for recognizing new and coordinated attacks, handling large data traffic, synchronization, co-operation between components without the presence of centralized computation, and good detection performance in real-time with immediate alarm notification. Feature selection based on principal component analysis (PCA) is used for dimensional reduction of NSL-KDD. Initial features are transformed to new features in smaller dimensions, where probing attacks (Ra-Probe) have a characteristic sign in their average value that is different from that of normal activity. Selection is based on the characteristics of these factors, resulting in a two-dimensional subset of the 75% data reduction.
Tweet-based Target Market Classification Using Ensemble Method Muhammad Adi Khairul Anshary; Bambang Riyanto Trilaksono
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.2016.10.2.3

Abstract

Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting). To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree) algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras) and three categories of sentiments (positive, negative and neutral) were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%). On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter.
Design and Implementation of Moving Object Visual Tracking System using μ-Synthesis Controller Saripudin Saripudin; Modestus Oliver Asali; Bambang Riyanto Trilaksono; Toto Indriyanto
Journal of ICT Research and Applications Vol. 13 No. 3 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.3.1

Abstract

Considering the increasing use of security and surveillance systems, moving object tracking systems are an interesting research topic in the field of computer vision. In general, a moving object tracking system consists of two integrated parts, namely the video tracking part that predicts the position of the target in the image plane, and the visual servo part that controls the movement of the camera following the movement of objects in the image plane. For tracking purposes, the camera is used as a visual sensor and applied to a 2-DOF (yaw-pitch) manipulator platform with an eye-in-hand camera configuration. Although its operation is relatively simple, the yaw-pitch camera platform still needs a good control method to improve its performance. In this study, we propose a moving object tracking system on a prototype yaw-pitch platform. A m-synthesis controller was used to control the movement of the visual servo part and keep the target in the center of the image plane. The experimental results showed relatively good results from the proposed system to work in real-time conditions with high tracking accuracy in both indoor and outdoor environments.