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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 :
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Articles 64 Documents
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Search results for , issue "Vol 28, No 1: October 2022" : 64 Documents clear
Non-isolated high voltage gain DC to DC converter based on the diode a capacitor switches Ibraheem Jawad Billy; Jasim Farhood Hussein
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp67-75

Abstract

Many researchers have put great endeavor to develop DC converter’s designs, into studying how to increase voltage gain with low switching stress and low ripple current. This paper has proposed a circuit to boost the voltage with a high gain conversion ratio. It is a combined adverse parallel two boost conversion. Two inductors are connected on both sides of the input source to decrease the current-ripple of the input current and output sides utilizing the interleaving technique. The proposed converter integrated with an active-network circuit is based on multiplier cells and two output capacitors. The voltage gain and voltage stresses across power semiconductors have been determined using a steady-state analysis. In addition, the input current- ripple and output voltage-ripple are analysis have been reported. This converter's inductors operate in a continuous conduction mode (CCM). The designed converter is capable of achieving significant voltage gain while maintaining a low duty ratio. Furthermore, the active switches and output diodes are under low voltage stress. As a result, low voltage components can be used to decrease conduction loss and cost. Finally, this converter was simulated in MATLAB/Simulink software to verify the theoretical calculations.
Implementation of augmented reality on historical monuments in Gorontalo Province Moh Ramdhan Arif Kaluku; Nikmasari Pakaya; Galang Leoni Yagri Ms Punu
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp559-566

Abstract

Several historical monument buildings in the city of Gorontalo with important colonial historical features have been designated as cultural reserves. The unavailability of information media that can be accessed by visitors so that visitors do not know in detail about the historical place visited, by implementing augmented reality (AR) technology as access to information media using multimedia development life cycle (MDLC) methods, visitors can access information freely and in real time, by presenting information and also displaying three-dimensional (3D) monument buildings with android devices. Based on research conducted, the design of AR applications is used to create an information media, and also one of the methods of introducing gorontalo historical monuments that can be used for prospective visitors outside the area and within the area. Implementing AR on historical monuments in Gorontalo Provides a new alternative, in utilizing technology by providing an information medium for historical monuments in Gorontalo.
A systematic review of structural equation modeling in augmented reality applications Vinh The Nguyen; Chuyen Thi Hong Nguyen
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp328-338

Abstract

The purpose of this study is to present a comprehensive review of the use of structural equation modeling (SEM) in augmented reality (AR) studies in the context of the COVID-19 pandemic. IEEE Xplore Scopus, Wiley Online Library, Emerald Insight, and ScienceDirect are the main five data sources for data collection from Jan 2020 to May 2021. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach was used to conduct the analysis. At the final stage, 53 relevant publications were included for analysis. Variables such as the number of participants in the study, original or derived hypothesized model, latent variables, direct/indirect contact with users, country, limitation/suggestion, and keywords were extracted. The results showed that a variety of external factors were used to construct the SEM models rather than using the parsimonious ones. The reports showed a fair balance between the direct and indirect methods to contact participants. Despite the COVID-19 pandemic, few publications addressed the issue of data collection and evaluation methods, whereas video demonstrations of the augmented reality (AR) apps were utilized. The current work influences new AR researchers who are searching for a theory-based research model in their studies.
Comparative analysis of time series prediction model for forecasting COVID-19 trend Sri Ngudi Wahyuni; Eko Sediono; Irwan Sembiring; Nazmun Nahar Khanom
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp600-610

Abstract

The outbreak of the COVID-19 pandemic occurred some time ago, making the world a pandemic. Based on this condition is important to predict early to prevent the COVID-19 disease if someday pandemic occurs. The aim of the study is to compare the analysis result of cumulative cases of COVID-19 using multiple linear regression (MLR), ridge regression (RR), and long short term memory (LSTM) models for cases study Java and Bali islands. We chose both islands as a case study because they have very dense populations. These three models are the most widely used time series-based prediction models and have relatively high accuracy values.  The predictive variables used are the number of cumulative cases, the daily cases, and population density. The research data was taken from Kaggle and processed using google collabs. Data was taken from January 20, 2020, to August 8, 2020, and data training was carried out for 12 days. The results show the accuracy of LSTM is better than other models. it can be seen in the accuracy value (99.8 %) of the model test result. The testing model uses R2, mean square error (MSE), and root mean square error (RMSE).

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