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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
 638   639   640   641   642 
A smart wearable device based on internet of things for the safety of children in online transportation Elsyea Adia Tunggadewi; Eva Inaiyah Agustin; Riky Tri Yunardi
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.pp708-716

Abstract

The world needs to pay attention to children who often become victims of violence and cannot escape social problems. Various safety devices that are commonly known as smart wearable devices have been created, but they still have many shortcomings. Thus, in this research a safety device that can be held by children is designed and is equipped with a button that can be pressed, then it will automatically send the location and photo of the scene to the parent's cellphone via the telegram application. It uses the Raspberry Pi Zero W controller, the GNSS HMC5983 SAW LNA GPS Module to determine the location, and the 5MP Raspberry Pi Zero Camera Module to capture the incident. Based on the results, the average time needed to share locations is 0.91 seconds, and the average time needed to capture is 11.57 seconds, if the device and receiving cellphone use the same network. Additionally, the average time needed to share locations is 0.96 seconds, and the average time needed to capture is 12.09 seconds, if the device and receiving cellphone use a different network. Both conditions have 97.5% location accuracy rate and 100% photo accuracy rate.
Combating the hate speech in Thai textual memes Lawankorn Mookdarsanit; Pakpoom Mookdarsanit
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1493-1502

Abstract

Thai textual memes have been popular in social media, as a form of image information summarization. Unfortunately, many memes contain some hateful content that easily causes the controversy in Thailand. For global protection, the Hateful Memes Challenge is also provided by Facebook AI to enable researchers to compete their algorithms for combating the hate speech on memes as one of NeurIPS’20 competitions. As well as in Thailand, this paper introduces the Thai textual meme detection as a new research problem in Thai natural language processing (Thai-NLP) that is the settlement of transmission linkage between scene text localization, Thai optical recognition (Thai-OCR) and language understanding. From the results, both regular and irregular text position can be localized by one-stage detection pipeline. More scene text can be augmented by different resolution and rotation. The accuracy of Thai-OCR using convolutional neural network (CNN) can be improved by recurrent neural network (RNN). Since misspelling Thai words are frequently used in social, this paper categorizes them as synonyms to train on multi-task pre-trained language model. 
A multiple handover method by using the guide of mobile node Radhwan Mohamed Abdullah; Radhwan Basher; Ayad Hussain Abdulqader
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.pp1090-1102

Abstract

Today’s healthcare system can be characterised using the up-and-coming integral component of mobility management of wireless body area networks (WBANs). In general, remote sensor nodes of WBAN are positioned on the body of a subject. Meanwhile, recommendations for specific proxy mobile IPv6 (PMIP) approaches have emerged, but its comparatively unfeasible nature in terms of group mobility management with regards to WBAN. Therefore, it shows a likelihood for expansive registration and handover interruptions. Thus, this work offered an alternative aimed at curbing such restrictions via an enhanced group mobility management method. The approach underlined the integration of authentication, authorisation, and accounting (AAA) services into the local mobility anchor (LMA) as another option for independent practice. Moreover, the proxy binding update (PBU) and AAA inquiry messages were consolidated, whereas the AAA response and proxy binding acknowledge (PBA) message were amalgamated. The resulting outcomes depicted the proposed method’s superior performance in comparison with the current PMIP approaches in the context of registration delay time, handover interruption, and average signalling cost.
Soft computing techniques for early diabetes prediction Sabah Anwer Abdulkareem; Hussein Y. Radhi; Yousra Ahmed Fadil; Hussain Mahdi
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.pp1167-1176

Abstract

Diabetes mellitus is a chronic, life-threatening, and complicated condition. Around 1.5 million deaths due to diabetes have been documented, according to a World Health Organization (WHO) estimation in 2019. In the world of medicine, predicting diabetes risk is a difficult and time-consuming task. Many past studies have been conducted to investigate and clarify diabetes symptoms and variables. To solve these persisting issues, however, more critical clinical criteria must be considered. A comparative analysis based on three soft computing strategies for diabetes prediction has been carried out and achieved in this work. Among the computational intelligence methods used in this study are fuzzy analytical hierarchy processes (FAHP), support vector machine (SVM), and artificial neural networks (ANNs). The techniques reveal promising performance in predicting diabetes reliably and effectively in terms of several classification evaluation metrics, according to experimental analysis and assessment conducted on 520 participants using a publicly available dataset.
Sound to electric energy generating device Maricel G. Dayaday; Jordan-James S. Olivo
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.pp1761-1769

Abstract

This paper presents the potential of an electromagnetic transducer device in a form of audio speaker that is used to capture sound waves to be converted into electricity. It is an interesting concept but less explored by researchers. The objective of the study is to measure the potential of electromagnetic transducer as a way to generate electricity. It deals with the creation of electricity through movement and magnetism. Sound waves can induce movement on the surface which in turn moves the transducer thus creating electricity. The source of sound was coming from an 8-inch subwoofer speaker with a frequency of 80 Hz that was held constant throughout the experiment. Furthermore, using simple linear regression analysis, the study showed that for every linear increase of sound intensity level and distance of the source, there is an exponential increase and an exponential decrease in the voltage root mean square (RMS) respectively. The functionality assessment of the device was statistically analyzed using completely randomized design. It was found that the energy level significantly increased as the sound intensity level increases given a fixed distance of 15 mm from the source. The device could generate enough energy to power small electronics such as light emitting diodes (LED), transistor and resistor.
A proposal of ethical competence model for cyber security organization Nor Hapiza Mohd Ariffin; Ruhaila Maskat
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.pp1711-1717

Abstract

A proactive cyber security plan to safeguard confidential information and privacy still lacks initiatives to avoid frequent harmful attacks. Cybersecurity professionals must possess ethical competence and prove worthy of overseeing valuable information for efficient decision‐making since ethical competence is fundamental for daily practice. There is a need to define what it means to be ethically competent in the era of IR4.0. The previous competence models still lack consideration of both artificial intelligence (AI) and emotional intelligence (EI) skills. AI brings new opportunities to cyber security organizations that focus on AI skills related to cognitive Intelligence or intelligent quotient (IQ). EI, which refers to emotional quotient (EQ), is a good predictor of ethical competence as it can perceive and express emotions precisely to facilitate thought to understand and manage emotions. However, practically, most cyber security organizations focused on AI skills and disregarded EI skills' roles. This research proposes a cyber artemotional model that blends AI skills and EI skills for cyber security employees. This research would benefit cyber security organizations with cyber artemotional model as employees ethical competence assessment, and it is in line with the demand of IR4.0.
Semantic feature extraction method for hyperspectral crop classification M. C. Girish Baabu; Padma M. C.
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.pp387-395

Abstract

Hyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus induces time and space complexity, resulting in high computational overhead and Hughes phenomenon in processing these images. Dimensional reduction technique such as band selection and feature extraction plays an important part in enhancing performance of hyperspectral image classification. However, existing method are not efficient when put forth in noisy and mixed pixel environment with dynamic illumination and climatic condition. Here the proposed Sematic Feature Representation based HSI (SFR-HSI) crop classification method first employ Image Fusion (IF) method for finding meaningful features from raw HSI spectrally. Second, to extract inherent features that keeps spatially meaningful representation of different crops by eliminating shading elements. Then, the meaningful feature set are used for training using Support vector machine (SVM). Experiment outcome shows proposed HSI crop classification model achieves much better accuracies and Kappa coefficient performance. 
Denoising of EEG signal based on word imagination using ICA for artifact and noise removal on unspoken speech Efy Yosrita; Rosida Nur Aziza; Rahma Farah Ningrum; Givary Muhammad
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.pp83-88

Abstract

The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.
Impact of pointing error on SISO/MISO drones swarm-based free space optical system in weak turbulence regime Abdullah Jameel Mahdi; Wamidh Jalil Mazher; Osman Nuri Ucan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp918-926

Abstract

Applying the drone-based free space optical (FSO) technology is recent in communication systems. The FSO technology hashigh-security features dueto narrow beamwidth, insusceptible to interferences, free license and landline connection is not appropriate. However, these advantages face many obstacles that affect the system's performance, such as random weather conditions and misalignment. The pointing error Hpis one of the critical factors of the channel gain H. The related parameters of the Hp factor: the pointing error angles θr and the path length Z, were manipulated to extract the applicable values at various receiver diameter values. The proposed system has two topologies: single input single output (SISO) and multiple input single output (MISO), flying in weak atmospheric turbulence. The simulation was done using MATLAB software 2020. The average bit error rate (ABER) for the system versus signal-to-noise ratio (SNR) were verified and analyzed. The results showed that at θr=10−3rad, Z increased in the range 10~100m for each one-centimeter increase of DR. At θr=10−2rad, the applicable Z was nearly 10% of the link distance Z when θr=10−3rad was applied. Consequently, an increase in θr must correspond decrease in Z and vice versa to maintain the system at high performance.
Real-time switching thirteen-level modified CHB-Multilevel inverter using artificial neural network technique based on selective harmonic elimination Moataz M.A. Alakkad; Zulhani Rasin; Mohammed Rasheed; Wahidah Abd Halim; Rosli Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1642-1652

Abstract

Recently, global research is giving more attention to the renewable energy sources due to its sustainability and environmentally friendly nature. The necessity of DC to AC conversion to integrate these sources to the well established AC power system create a significant development of multi-level inverter with its advantages of operating at higher rating system with lower component rating as well as better harmonics performance at it output voltage and current. In this research paper, a modified topology of CHB-MLI to provide 13-level output AC waveform is proposed based on the SHE-PWM strategy using the ANN optimization technique. The system modeling is done with Matlab Simulink software and verification are carried out by both simulation and experiment. Results show that the ANN technique able to reduce the THD significantly as the level of waveform is increased as low as 5.16% THD for the 13-level output voltage. Results from the experiment shows a good agreement with the simulation, thus verifying the effectiveness of the proposed ANN technique as an optimization method. 

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