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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Two-versions of descent conjugate gradient methods for large-scale unconstrained optimization Hawraz N. Jabbar; Basim A. Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1643-1649

Abstract

The conjugate gradient methods are noted to be exceedingly valuable for solving large-scale unconstrained optimization problems since it needn't the storage of matrices. Mostly the parameter conjugate is the focus for conjugate gradient methods. The current paper proposes new methods of parameter of conjugate gradient type to solve problems of large-scale unconstrained optimization. A Hessian approximation in a diagonal matrix form on the basis of second and third-order Taylor series expansion was employed in this study. The sufficient descent property for the proposed algorithm are proved. The new method was converged globally. This new algorithm is found to be competitive to the algorithm of fletcher-reeves (FR) in a number of numerical experiments.
Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis Hima Keerthi Sagiraju; Shashi Mogalla
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1759-1771

Abstract

Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).
A comparative study of deep learning based language representation learning models Mohammed Boukabous; Mostafa Azizi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1032-1040

Abstract

Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. In this paper, we highlight the most important language representation learning models in NLP and provide an insight of their evolution. We also summarize, compare and contrast these different models on sentiment analysis, and thus discuss their main strengths and limitations. Our obtained results show that BERT is the best language representation learning model.
Delineation of electrocardiogram morphologies by using discrete wavelet transforms Annisa Darmawahyuni; Siti Nurmaini; Hanif Habibie Supriansyah; Muhammad Irham Rizki Fauzi; Muhammad Naufal Rachmatullah; Firdaus Firdaus; Bambang Tutuko
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp159-167

Abstract

The accuracy of electrocardiogram (ECG) delineation can affect the precise diagnose for cardiac disorders interpretation. Some nonideal ECG presentation can make a false decision in precision medicine. Besides, the physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration is also affected. This paper proposes a discrete wavelet transform (DWT), non-stationary signal analysis for noise removal, and onset-offset of PQRST feature extraction. A well-known database from Physionet: QT database (QTDB) is used to validate the DWT function for detecting the onset and offset of P-wave, QRS-complex, and T-wave localization. From the results, P-peak detection gets the highest result that achieves 2.19 and 13.62 milliseconds of mean error and standard deviation, respectively. In contrast, Toff has obtained the highest error value due to differences in the T-wave morphology. It can be affected by inverted or biphasic T-waves and others.
A secure cloud service deployment framework for DevOps Rao Ravinder; V. Sucharita
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp874-885

Abstract

The advancements in cloud computing and leveraging the benefits from cloud computing to the service providers have increased the deployment of traditional applications to the cloud. The applications once deployed on the cloud, due to various reasons, need migration from development infrastructure to operational infrastructure, one operational instance to other operational instances due to load balancing and the cycle continues due to the use of DevOps as development strategies for cloud computing applications. Advocates of hybrid and public clouds observe cloud computing makes it possible for organizations to avert or minimize upfront IT infrastructure expenses.  Proponents also assert that cloud computing systems permit businesses to receive their software up and running faster, using improved manageability and less maintenance, so it empowers IT teams to rapidly adapt tools to meet the varying and unpredictable requirements. DevOps is a lot of practices that mechanizes the procedures between programming improvement and IT groups, all together that they can fabricate, test, and discharge programming quicker and even more dependably. The idea of DevOps is established on building a culture of a joint effort between groups that generally worked in relative siloes. The guaranteed advantages incorporate expanded trust, quicker programming discharges, capacity to explain basic issues rapidly and better oversee impromptu work. Thus, this work identifies the need for providing multiple security protocols during the complete life cycle of cloud application development and deployment. This work proposes a novel framework for automatic selection and deployment of the security protocols during cloud service deployments. The framework identifies the need for security aspects and selects the appropriate security algorithms for virtual machines. The proposed framework demonstrates nearly 80% improvement over the security policy deployment time. 
Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier Millee Panigrahi; Dayal Kumar Behera; Krishna Chandra Patra
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp884-891

Abstract

Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for the medical practitioners. Various machine learning techniques are applied to identify the seizure's occurrence and its classification in multiple domains. A classification model based on extreme gradient boosting (SCLXGB) is proposed here for the classification of the EEG signals. The SCLXGB model implements binary seizure classification on the benchmark dataset. Compared with K-nearest neighbor, linear regression, and Decision treebased models, the proposed model achieves the best area under receiver operating curve (AUC) of 0.9462 and an accuracy of 96% which signifies accurate prediction of seizure and non seizure period. The proposed model SCLXGB was validated by taking different performance metrics to indicate the occurrence and non-occurrence of seizures in patients more appropriately.
Monte Carlo analysis for solar PV impact assessment in MV distribution networks Dilini Almeida; Jagadeesh Pasupuleti; Shangari K. Raveendran; M. Reyasudin Basir Khan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp23-31

Abstract

The rapid penetration of solar photovoltaic (PV) systems in distribution networks has imposed various implications on network operations. Therefore, it is imperative to consider the stochastic nature of PV generation and load demand to address the operational challenges in future PV-rich distribution networks. This paper proposes a Monte Carlo based probabilistic framework for assessing the impact of PV penetration on medium voltage (MV) distribution networks. The uncertainties associated with PV installation capacity and its location, as well as the time-varying nature of PV generation and load demand were considered for the implementation of the probabilistic framework. A case study was performed for a typical MV distribution network in Malaysia, demonstrating the effectiveness of Monte Carlo analysis in evaluating the potential PV impacts in the future. A total of 1000 Monte Carlo simulations were conducted to accurately identify the influence of PV penetration on voltage profiles and network losses. Besides, several key metrics were used to quantify the technical performance of the distribution network. The results revealed that the worst repercussion of high solar PV penetration on typical Malaysian MV distribution networks is the violation of the upper voltage statutory limit, which is likely to occur beyond 70% penetration level.
The general design of the automation for multiple fields using reinforcement learning algorithm Vijaya Kumar Reddy Radha; Anantha N. Lakshmipathi; Ravi Kumar Tirandasu; Paruchuri Ravi Prakash
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp481-487

Abstract

Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.
The effect of the TF-IDF algorithm in times series in forecasting word on social media Arif Ridho Lubis; Mahyuddin K. M. Nasution; Opim Salim Sitompul; Elviawaty Muisa Zamzami
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp976-984

Abstract

Forecasting is one of the main topics in data mining or machine learning in which forecasting, a group of data used, has a label class or target. Thus, many algorithms for solving forecasting problems are categorized as supervised learning with the aim of conducting training. In this case, the things that were supervised were the label or target data playing a role as a 'supervisor' who supervise the training process in achieving a certain level of accuracy or precision. Time series is a method that is generally used to forecast based on time and can forecast words in social media. In this study had conducted the word forecasting on twitter with 1734 tweets which were interpreted as weighted documents using the TF-IDF algorithm with a frequency that often comes out in tweets so the TF-IDF value is getting smaller and vice versa. After getting the word weight value of the tweets, a time series forecast was performed with the test data of 1734 tweets that the results referred to 1203 categories of Slack words and 531 verb tweets as training data resulting in good accuracy. The division of word forecasting was classified into two groups i.e. inactive users and active users. The results obtained were processed with a MAPE calculation process of 50% for inactive users and 0.1980198% for active users.
Service landscape for private universities in indonesia based on service oriented architecture and cloud technology Faiza Renaldi; Irma Santikarama; Esmeralda C. Djamal; Agya Java Maulidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp497-506

Abstract

Information technology (IT) has been widely adopted and is believed to improve academic processes’ efficiency and run private universities’ academic functions (PTSs) in Indonesia. Nonetheless, adopting diverse technologies for them will also create many challenges. PTSs are struggling to survive in terms of technological implementation, in the sense that the investment and implementation rate in the PTSs just cannot catch up with the technological advancement rate. Even when more PTSs are trying to transform into digital entities, the next problem will be system integration and flexibility. This study aims to overcome this problem by implementing a framework that can be both integrated and flexible while also serving the efficiency of investments. Many studies already suggested that service oriented architecture (SOA) and cloud technology are the solutions. Nevertheless, none has been able to define what standard services can be applied within those platforms. To determine this, we use the BIAN service landscape, which was translated from the banking industry, offering a comprehensive view of the business domain and business capabilities alongside its service functions. While BIAN offers common services throughout the same platform, we modify the framework using the OASIS model from SOA, which allows the framework to be flexible in complying with many platforms of databases, programming languages, and network infrastructures. We completed our study by defining one business area: academic processes, three business domains, 19 business capabilities, and 84 service functions. We are strongly confident that our findings and study results will act as a reference in creating a cloud-based platform for Indonesia’s higher education academic systems.

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