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Dive into the research topics where Anak Agung Putri Ratna is active.

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Featured researches published by Anak Agung Putri Ratna.


ieee region 10 conference | 2011

The design of embedded web server for remote laboratories microcontroller system experiment

F. Yudi Limpraptono; Harry Sudibyo; Anak Agung Putri Ratna; Ajib Setyo Arifin

Remote laboratories are innovations in educational technology which allows students to access and interact with laboratory equipment remotely via the Internet network. There are several advantages of using a remote laboratory is the laboratorys performance is better and more efficient because of laboratory equipment can be used by students throughout the day for 24 hours. This paper presents a new design remote laboratory-based embedded web server for practical microcontroller systems. This system has several advantages compared with traditional remote laboratory based on computer server, which are small size, low power consumption, low cost and easy installation and maintenance. Design an embedded web server using 8 bit microcontroller AT89S52. The system consists of a computer server, some experiment module microcontroller system and some embedded web server for controlling modules. The computer server is used as a web server remote lab gateway that serves as a homepage and user management. Embedded web server serves as the user interface to control the lab module microcontroller via the internet. Experiment module is evaluation board AT89SXX microcontroller and is equipped with some I/O circuit. Users can perform remote process on experiment module that is: upload binary files to the target board, observing the changes via a web cam and can interact with the target board through a virtual I/O. Embedded web server is working with pure HTML instructions so that the system is more secure because it only uses port 80 only. Design remote labs are multiuser, so that at the same time can be used by more than one user.


international conference on remote engineering and virtual instrumentation | 2012

Remote laboratories multiuser based on embedded web server

F. Yudi Limpraptono; Anak Agung Putri Ratna; Harry Sudibyo

This paper presents a new design of remote laboratory multiuser based embedded web server that is used for the lab microcontroller system. Architecture of Remote Lab System consists of a computer server, two experiment module microcontroller system and two embedded web server for controlling lab modules. The computer server is used as a web server remote lab that serves as a homepage and user management. Embedded web server serves as the user interface to control the lab module microcontroller via the internet. The novelty of the system proposed is the remote laboratory architecture is multi user, and embedded web server designed more intelligently that function as lab modul controller, such as ability to send log event data to database server and ability to send message to chat applications. This research activity has resulted a remote laboratory based on embedded web server, the system is multiuser so that at the same time can be used by more than one user. Test result, the system can work quite well.


international conference of the ieee engineering in medicine and biology society | 2015

EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network

Prima Dewi Purnamasari; Anak Agung Putri Ratna; Benyamin Kusumoputro

In EEG-based emotion recognition, feature extraction is as important as the classification algorithm. A good choice of features results in higher recognition rate. However, there is no standard method for feature extraction in EEG-based emotion recognition, especially for real time monitoring, where speed of computation is crucial. In this work, we assess the use of relative wavelet energy as features and Back Propagation Neural Network (BPNN) as classifier for emotion recognition. This method was implemented in simulated real time emotion recognition by using a publicly accessible database. The results showed that relative wavelet energy and BPNN achieved an average recognition rate of 92.03%. The highest average recognition rate was achieved when the time window was 30s.


Algorithms | 2017

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

Prima Dewi Purnamasari; Anak Agung Putri Ratna; Benyamin Kusumoputro

The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.


international conference network communication and computing | 2016

Artificial Neural Networks Based Emotion Classification System through Relative Wavelet Energy of EEG Signal

Prima Dewi Purnamasari; Anak Agung Putri Ratna; Benyamin Kusumoputro

Emotion classification from EEG brain signal has been a widely research topic recently, because of the complexity of processing the multi channels of the brain signal and also the problem on mapping the human emotion itself. This paper discusses the technique to determine the emotion classification from EEG brain signal using a relative wavelet energy (RWE) as a feature vector and an artificial neural networks (ANN) as a classifier. In this research, two types of ANN classifier was utilized and analyzed, namely, Back-propagation Neural Networks (BPNN) and Probabilistic Neural Networks (PNN). Also reducing the number of the EEG channel to be processed is investigated, in order to decrease the computational cost of the classification system. Results showed that the recognition rate of the reduced utilized channels up to 4 channel are incomparable with that of the full utilization of 32 channels. However, 8 and 14 channels still produced sufficient recognition rate. It is also confirmed from experiments that the BPNN shown as a more reliable classifier compare with the PNN method.


Proceedings of the 3rd International Conference on Communication and Information Processing | 2017

Cloud computing network design for high performance computing implementation on openstack platform

Anak Agung Putri Ratna; Tomi Wirianata; F. Astha Ekadiyanto; Ihsan Ibrahim; Diyanatul Husna; Prima Dewi Purnamasari

This paper presents networking design strategies in cloud infrastructure built using Openstack platform and explore scalability issues related to its infrastructure. Experiments discussed in this paper are conducted to study the performance of Openstack network based on the implementation of Neutron in order to provide recommendations for optimal network design suitable for high performance computing. Network performance parameters such as throughput, packet loss and latency were evaluated based on TCP and UDP data transmission using IPerf benchmarking tool. The results show that in general, Openstack did not exhibit network bandwidth bottlenecks. However, the location where virtual machine was instantiated and how the network was segmented will affect network performance. This paper also discussed the traffic flow used to analyze the network performances of different virtual machines network topology in the cloud and provide an optimal cloud networking design and recommendations that yields to a better high-performance computing platform.


Proceedings of the 3rd International Conference on Communication and Information Processing | 2017

Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network

Prima Dewi Purnamasari; Muhammad Taqiyuddin; Anak Agung Putri Ratna

One biggest challenge in sentiment analysis is that it should include Natural Language Processing (NLP), to make the machine understand the human language. With the current development of Artificial Neural Network (ANN), with its implementation, computer can learn to understand human language by such learning mechanism There are many types of ANN and for this research Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were used and compared on their performance. The text data for the sentiment analysis was taken from Stanford publication and transformation from text to vectors were conducted using word2vec. The result shows that RNN is better than CNN. Even the difference of accuracy is not significant with 88.35% ± 0.07 for RNN and 87.11% ± 0.50 for CNN, the training time for RNN only need 8.256 seconds while CNN need 544.366 seconds.


Proceedings of the 3rd International Conference on Communication and Information Processing | 2017

Automatic essay grading system with latent semantic analysis and learning vector quantization

Anak Agung Putri Ratna; Budi Selamet Raharjo; Prima Dewi Purnamasari; Randy Sanjaya

Automatic essay grading system called SIMPLE-O (Sistem Penilai Esai Otomatis) that developed by Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia was built using PHP. This system was developed for helping lecturer assessing the examination, especially with essay form. Currently, the SIMPLE-O is still developed using C programming language for implementing more methods in development that can only be done in that language to improve the performance of the system. To increase the accuracy, Learning Vector Quantization (LVQ) algorithm is implemented in the development due to its ability for supervised classification. The number of data samples in LVQ training phase are affecting the essay scoring performance, less data used will lead to decrease in the result accuracy of the validation phase. Moreover, singular value that generated by Frobenius norm and vector angle pre-processing will also affect the scoring accuracy. But, the number of words-per-column when creating the LSA matrix did not have any significant effect. At the end, SIMPLE-O with LVQ has an average accuracy of 53.57%, 41.66% higher than the system that did not use LVQ. This accuracy performance was still low due to the missing of the words similarity feature. In the previous version of SIMPLE-O, this feature can improve the performance of the essay grading system significantly.


Proceedings of the 2017 International Conference on Computer Science and Artificial Intelligence | 2017

Parallel Processing Design of Latent Semantic Analysis Based Essay Grading System with OpenMP

Anak Agung Putri Ratna; Ihsan Ibrahim; Prima Dewi Purnamasari

In this paper, a parallel processing design for an short-answer essay grading system based on Latent Semantic Analysis (LSA) is presented. This system was developed to ease the lecturers for short-essay assessment process. With the number of documents to be assessed in an exam period, efficiency of processing and time are needed. Parallel processing is a solution for advancing the system to be implemented for wide scope of use. The design was implemented using OpenMP Application Programming Interface (API) to achieve this research purpose. Most of the systems processes utilize the outer loop or repetitive process with data level parallelism to process the same process for different data at a time. Early experiment on SVD process has promising result for parallelization implementation compared to the serial process. With parallelization, SVD could process 50 documents in 1.773 microseconds (ms), 3 times faster than the serial process in 5.676 ms. There were 2 other experiments to obtain the optimum number of matrix size and number of threads for parallelization; the experiment showed that two threads division on SVD process is the best option for this system and more threads are appropriate for more complex implementation and larger LSA based system.


Algorithms | 2017

Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization

Anak Agung Putri Ratna; Prima Dewi Purnamasari; Boma Anantasatya Adhi; F. Astha Ekadiyanto; Muhammad Salman; Mardiyah Mardiyah; Darien Jonathan Winata

Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.

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