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Daerah istimewa yogyakarta
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 64 Documents
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Search results for , issue "Vol 26, No 2: May 2022" : 64 Documents clear
Modeling phasmophobia (fear of ghosts) using electroencephalogram Safie Sairul Izwan; Puteri Zarina M. Khalid; Mohd Aimullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp743-753

Abstract

Extreme fears towards ghosts and entities are defined as phasmaphobia. Those diagnosed with phasmophobia symptoms should control their own fears to avoid phasmaphobia attack. In this work, we present the development of phasmophobia detection electroencephalogram database (PDED).PDEDconsistsofanaverageof45minutes electroencephalography (EEG) recordings from eight electrodes situated on the frontal lobe of the brain area. A real-time fear assessment was conducted simultaneously with the EEG recording by the participant. Five different stimuli were used to induce fear in our experiment. 599 EEG epochs related to fear were extracted based on the timestamp recorded by each individual. Asymmetry relation ratio (ARR) techniques were used on these EEG to detect the presence of fear. The quality of long duration of EEG recording from PDED in recognizing fear was thoroughly presented based on ARR. In this study, 91.5% of fear emotion managed to be detected from these epochs. Using PDED, it is also proven that the changes of ARR reflected positive correlation towards the changes of the level of fear. Analysis using emotion recognition rate (ERR) curves indicated that, two electrodes, namely F7 and F8, were sufficient to recognized 88% of fear from the recordings.
Real-time passenger social distance monitoring with video analytics using deep learning in railway station Iqbal Ahmad Dahlan; Muhammad Bryan Gutomo Putra; Suhono Harso Supangkat; Fadhil Hidayat; Fetty Fitriyanti Lubis; Faqih Hamami
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp773-784

Abstract

Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.
COVID-19 detection based on combined domain features Omar Munthir Al Okashi; Ismail Taha Ahmed; Leith Hamid Abed
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp965-973

Abstract

The computed tomography (CT) scan delivers more detailed information and higher judgment accuracy than a chest X-ray, which has a wide range of uses in diagnosing and decision-making to aid medical professionals. This paper proposed a method to detect COVID-19 from CT scan images using the combination of spatial domain and transform domain features. Using the lung segmentation step, the CT image is first processed and segmented, and then various domain features are extracted. From these domain features, the highest combined domain features (CDF) are obtained. Finally, the detection task is completed using random forest (RF) and Naive Bayesian (NB) classifiers. The proposed method is tested using a dataset of CT scan images, and the results are compared to several current techniques. The results showed that our method based on CDF outperforms previous methods, with an overall accuracy of nearly 98%. As can be shown, CDF is the best domain feature to apply for detecting COVID-19.
Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources Munther H. Abed; Mohd Nizam Mohmad Kahar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1036-1049

Abstract

This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm to solve the UPMR problem because of the robustness and the success of genetic algorithm in solving many optimization problems. An enhancement of GA (GGA) was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The GGA is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the SGA, but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time.
Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet Shimaa Janabi; Shaimaa Shukri Abd Alhaleem
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1173-1181

Abstract

Many types of medical pictures have to be fused, as single-modality medical images can give limited information because of the imagery and the complicated architecture of the human organ. This study proposes to offer a platform on which to make clinical diagnoses and to increase the accuracy of the target identification and the quality of the fused pictures by combining the benefits of nonsubsampled contourlet transform (NSCT) and fuzzy entropy. A picture is first broken down into low frequency or high frequency subbands through NSCT. In line with the various features of the low and high frequency components the respective fusion rules must be implemented. It calculates the level of membership of low frequency coefficients. The fusion of coefficients is also calculated and then utilized to retain picture features. By increasing regional energy, high-frequency components are merged. Inverse transformation produces the final fused picture. Experimental results have shown that, based on subjective visual effect and objective assessment standards, the suggested technique produces a satisfactory fusion effect. This process may also achieve high average gradient, standard deviation (SD), and edge preservation and maintain the fused picture features well. Effective reference can be provided by the outcome of the suggested algorithm for patients' assessment.
A model for blockchain-based privacy-preserving for big data users on the internet of thing Ihab L. Hussein Alsammak; Mohammed F. Alomari; Intedhar Shakir Nasir; Wasan H. Itwee
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp974-988

Abstract

Recently, with the emergence and growth of the IoT as a promising vehicle for sustainable development, the concept of ‘smart cities’ has advanced significantly. However, many challenges inhibit the development of using IoT applications in smart cities, such as issues of privacy, scalability, trust, security, and centralisation. On a daily basis in smart cities, the IoT generates a large amount of data (big data) which could potentially be used for questionable or suspect purposes by attackers. The weight of the security issues surrounding big data must be acknowledged as the associated technology is continuously developing. To solve this issue, a strategy that secures important and potentially sensitive user information on a distributed blockchain and transmits non-sensitive information to the primary system by controlling the size of the blockchain is proposed. This solution cannot be achieved in traditional blockchain because it requires too many resources. The model is composed of three proposed algorithms: the first aims to allocate data to each user; the second performs the process of searching for data, and the third confirms the communication process. Experiments have proved that this proposed protocol for blockchain has excellent byzantine fault tolerance. The final experimental results of the proposed model established that the algorithms effectively meet the performance requirements.
Functionality of the learning platform and its effect on the satisfaction of students in the online teaching environment Omar Chamorro-Atalaya; Guillermo Morales-Romero; César León-Velarde; Lourdes Quevedo-Sánchez; Yurfa Medina-Bedón; Abel Tasayco-Jala; Maritte Fierro-Bravo
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1073-1081

Abstract

The objective of this article is to identify the results of the functionality of the learning platform and its effect on the satisfaction of mechanical and electrical engineering students, the results will serve as a basis for the continuous improvement of teaching-learning online from the higher institution. The research findings indicate that the indicators that present a better perception regarding the functionality of the learning platform are related to the design and ease of navigation. However, 21.4% of the students are not entirely satisfied with its functionality due to the technical problems presented when downloading the study material. According to the results, we can point out that the functionality of the learning platform generates an effect of 70.87% on student satisfaction, the relationship was validated through the Chi-square test, in which it is determined that the indicators that generate a Greater satisfaction in students refers to the design, availability (connectivity) and the ease of communication and interaction with the teacher and classmates.
Optimizing irrigation for boosting gynura procumbens growth in Malaysia urban area Nooryani Abdul Salim; Marsyita Hanafi; Siti Mariam Shafie; Syamsiah Mashohor; Norhashila Hashim
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp924-931

Abstract

Growing herbs traditionally could not meet the increasing demand for high medicinal value herbs in the pharmaceutical industry and domestic market. One of the solutions is by growing herbs using vertical structures in urban areas. Even so, the amount of water needed to optimise the growth of Asian local perennial herbs in vertical structures is yet to be explored. Hence, this paper investigated the performance of a fuzzy based irrigation method in optimising irrigation for boosting the local perennial herb growth. The understudy local perennial herb is Gynura Procumbens. The decision-making for irrigation relies on the data given by soil moisture, temperature, and humidity sensors. The performance of the proposed system is compared with a timer-based system, in terms of plant growth rates, given by average leaves diameter, height, and plant crown of local perennial herbs. The results have shown that the proposed intelligent irrigation system has reduced water consumption by 16.93% and the average plant growth rate has increased by at least 1.5% and to a maximum rate of 76.64%.
Orchid conservation development at Mudal River by using remote sensing Baritoadi Buldan Rayaganda Rito; Muhammad Muhajir; Rahmadi Yotenka; Novendri Isra Asriny; R.A. Nurul F.A. Asnawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1009-1017

Abstract

Daerah Istimewa Yogyakarta (DIY) is a district in Yogyakarta that contains various tourist attractions (ODTW) such as the Mudal River, attracting many local and international visitors each year. However, this region must be developed by identifying the best conservation position. Technology is required to explore the geographical aspects in establishing ecotourism in order to complete this process. This study sought to determine the most appropriate new development for the Mudal River, taking into account environmental variables, site area, orchid growing demands, ease of access, and commercialization. This is accomplished by employing a remote sensing technique based on the overlay technique. As a consequence of the investigation, three sites have been identified as the most plausible candidates. The analysis identified three areas that are most likely to be used as conservation sites: an orchid cultivation house, an orchid garden area that may be used as a photo location, and an orchid education garden.
Review on maintenance of photovoltaic systems based on deep learning and internet of things Younes Hammoudi; Idriss Idrissi; Mohammed Boukabous; Youssef Zerguit; Hicham Bouali
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1060-1072

Abstract

Many solar plants have been installed globally, and they must be continuously protected and supervised to ensure their safety and reliability. Photovoltaic plants are susceptible to many defects and failures, and fault detection technology is used to protect and isolate them. Despite numerous international standards, invisible photovoltaic defects continue to cause major issues. As a result, smart technologies like AI (Artificial Intelligence) and IoT are being developed for remote sensing, problem detection, and diagnosis of photovoltaic systems. Solar plants generate not only green electricity but also a lot of data, such as power output. With AI, a clear picture of electricity yields should be possible. The output of entire solar parks could be monitored and analyzed. The AI could also detect malfunctions within a solar park, according to the research. This would speed up and simplify maintenance work. Deep learning (DL) and IoT applications for photovoltaic plants are discussed. The most advanced techniques, such as DL, are discussed in terms of precision and accuracy. Incorporating DL and IoT approaches for fault detection and diagnosis into simple hardware, such as low-cost chips, maybe cost-effective and technically feasible for photovoltaic facilities located in remote locations.

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