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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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An integrated machine learning model for indoor network optimization to maximize coverage Ahmed Wasif Reza; Abdullah Al Rifat; Tanvir Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp394-402

Abstract

Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), support vector machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross-validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.
The rogue access point identification: a model and classification review Diki Arisandi; Nazrul Muhaimin Ahmad; Subarmaniam Kannan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1527-1537

Abstract

Most people around the world make use of public Wi-Fi hotspots, as their daily routine companion in communication. The access points (APs) of public Wi-Fi are easily deployed by anyone and everywhere, to provide hassle-free Internet connectivity. The availability of Wi-Fi increases the danger of adversaries, taking advantages of sniffing the sensitive data. One of the most serious security issues encountered by Wi-Fi users, is the presence of rogue access points (RAP). Several studies have been published regarding how to identify the RAP. Using systematic literature review, this research aims to explore the various methods on how to distinguish the AP, as a rogue or legitimate, based on the hardware and software approach model. In conclusion, all the classifications were summarized, and produced an alternative solution using beacon frame manipulation technique. Therefore, further research is needed to identify the RAP.
Novel deep learning model for vehicle and pothole detection Gayathri K.; Thangavelu S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1576-1582

Abstract

The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural network (Faster R-CNN) with single shot detector (SSD) and you only look once (YOLO) algorithms. The proposed method shows good progress than the existing methods such as SSD and YOLO. The measure of performance evaluation is Accuracy. The proposed method shows an improvement of 5% once compared with the previous methods such as SSD and YOLO.
Efficient hardware implementation for lightweight mCrypton algorithm using FPGA Yasir Amer Abbas; Ahmed Salah Hameed; Safa Hazim Alwan; Maryam Adnan Fadel
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1674-1680

Abstract

The lightweight cryptography is used for low available resources devices such as radio frequency identification (RFID) tags, internet of things (IoTs) and wireless sensor networks. In such case, the lightweight cryptographic algorithms should consider power consumption, design area, speed, and throughput. This paper presents a new architecture of mCrypton lightweight cryptographic algorithm which considers the above-mentioned conditions. Resource-shared structure is used to reduce the area of the new architecture. The proposed architecture is implemented using ISE Xilinx V14,5 and Spartan 3 FPGA platform. The simulation results introduced that the proposed design area is 375 of slices, up to 302 MHz operating frequency, a throughput of 646 Mbps, efficiency of 1.7 Mbps/slice and 0.089 Watt power consumption. Thus, the proposed architecture outperforms similar architectures in terms of area, speed, efficiency and throughput.
Design of a portable radio-frequency-identification reader capable to reading a user memory bank for smart-building energy management Ajib Setyo Arifin; M. B. Fathinah Hanun; Eka Maulana; I Wayan Mustika; Fitri Yuli Zulkifli
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1538-1549

Abstract

Communication is an important factor in smart-building energy management (SBEM). Many communications technologies have been applied to SBEM, including radio-frequency identification (RFID). RFID has been used not only for identification but also for carrying information, which is stored in a user memory bank attached to the tag. To access the user memory bank, an RFID reader should comply with ISO 18000-6C standards. The greatest challenge of RFID-reader technology is its short communication range, which limits the sensing area. To overcome this problem, this paper proposes a portable RFID reader built to an ISO 18000-6C standard to extend the sensing area due to its moveability. The reader is designed using low-cost devices widely available on the market for ease of duplication and assembly by researchers, educators, and startups. The proposed RFID reader can read passive tags with distances up to 12 and 5.5 m for line-of-sight (LOS) and non-line-of-sight (NLOS) communication, respectively. The minimum received-signal-strength indicators (RSSIs) for LOS and NLOS are found to be −63.75 and −59.66 dBm, respectively. These results are comparable with those of non-portable RFID readers on the market.
Image-based lime size grading using the comparison ratio of the pixel radius and the actual size of lime fruit Pawat Chimlek; Sutasinee Jitanan
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp279-286

Abstract

Lime is a commercially important fruit in Thailand whose sale price depends on the fruit’s size; hence, farmers must grade limes by size before distribution. However, as lime grading machines are very expensive and each province has different size grading limits, grading is often performed manually, which is time-consuming and error-prone. Agricultural production systems for automatic selection and grading use image processing techniques for extracting key features. Therefore, this study proposes techniques to extract features of limes and to develop analytical methods for grading them. This method can reduce time and cost, and increase accuracy and flexibility for selecting different lime sizes according to each province’s size criteria. To verify our method, we classified limes according to criteria from four Thailand provinces as sample data in an experiment. The focal image feature was the radius or diameter of the lime and the grading conditions were defined by the maximum comparison ratio of the fruit’s radius in pixels to the measured radius of the actual lime in centimeters. The average grading accuracy was 99.59%, which outperformed that of mechanical grading. The processing time was 1.70 seconds per individual fruit.
Classification of hand gestures from forearm electromyogram signatures from support vector machine Diaa Albitar; R. Jailani; Megat Syahirul Amin Megat Ali; Anwar P. P. Abdul Majeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp260-268

Abstract

Robotic prosthetics is increasingly adopted as an enabling technology for amputees. These are vital not only for activities of daily living but to display expression and affection. A vital element to this system is an intelligent model that can identify signatures from the remaining limb that can be mapped to specific effector movements. Therefore, the study proposes the use of forearm electromyogram to classify between different types of hand gestures; fingers spread, wave out, wave in, fist, double tap, and relaxed state. Data are acquired from 32 subjects using Myo armband. Initially, a total of 248 time-and frequency-domain features are extracted from the eightchannel device. Neighborhood component analysis has reduced them to a total of fourteen features. A hand gesture classification model based on electromyogram signal has been successfully developed using support vector machine with overall accuracy of 97.4% for training, and 88.0% for testing.
The impact of influencers on the companies reputation in developing countries: Case of Morocco Mohamed Chiny; Marouane Chihab; El Mahdi Juiher; Khaoula Jabari; Omar Bencharef; Younes Chihab
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp410-419

Abstract

With the emergence of social networks and their adoption by a large number of users, the importance of influencers continues to grow and companies are in a frantic race to recruit those most likely to promote their reputation and brand image. However, in the existing literature, there is little work that conducts quantitative studies on this subject in developing countries. For this reason, we conducted a study that attempts to understand the importance of influencers in reshaping public opinion of a company or brand. We chose as a subject of study a large Moroccan company operating in the telecommunications sector that hired a popular influencer among young Moroccans. We then adopted an approach based on scraping and analyzing the occurrences of the influencer's posts on Instagram and the content of the company's website and then publishing a questionnaire to 180 respondents in the age range of most of the followers of the influencer in question. The results suggest that a positive relationship exists between the influencer and brand reputation, meaning that if the person is following the influencer who has published content on the brand, that person is expected to be systematically aware of the brand, and vice versa.
Machine learning for decoding linear block codes: case of multi-class logistic regression model Chemseddine Idrissi Imrane; Nouh Said; Bellfkih El Mehdi; El Kasmi Alaoui Seddiq; Marzak Abdelaziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp538-547

Abstract

Facing the challenge of enormous data sets variety, several machine learning-based algorithms for prediction (e.g, Support vector machine, multi layer perceptron and logistic regression) have been highly proposed and used over the last years in many fields. Error correcting codes (ECCs) are extensively used in practice to protect data against damaged data storage systems and against random errors due to noise effects. In this paper, we will use machine learning methods, especially multi-class logistic regression combined with the famous syndrome decoding algorithm. The main idea behind our decoding method which we call logistic regression decoder (LRDec) is to use the efficient multi-class logistic regression models to find errors from syndromes in linear codes such as bose, ray-chaudhuri and hocquenghem (BCH), and the quadratic residue (QR). Obtained results of the proposed decoder have a significant benefit in terms of bit error rate (BER) for random binary codes. The comparison of our decoder with many competitors proves its power. The proposed decoder has reached a success percentage of 100% for correctable errors in the studied codes.
Enhancement of WiMAX networks using OPNET modeler platform Noor Nateq Alfaisaly; Suhad Qasim Naeem; Azhar Hussein Neama
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1510-1519

Abstract

Worldwide interoperability microwave access (WiMAX) is an 802.16 wireless standard that delivers high speed, provides a data rate of 100 Mbps and a coverage area of 50 km. Voice over internet protocol (VoIP) is flexible and offers low-cost telephony for clients over IP. However, there are still many challenges that must be addressed to provide a stable and good quality voice connection over the internet. The performance of various parameters such as multipath channel model and bandwidth over the Star trajectoryWiMAX network were evaluated under a scenario consisting of four cells. Each cell contains one mobile and one base station. Network performance metrics such as throughput and MOS were used to evaluate the best performance of VoIP codecs. Performance was analyzed via OPNET program14.5. The result use of multipath channel model (disable) was better than using the model (ITU pedestrian A). The value of the throughput at 15 dB was approximately 1600 packet/sec, and at -1 dB was its value 1300 packet/se. According to data, the Multipath channel model of the disable type the value of the MOS was better than the ITU Pedestrian A type.

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