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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Implementation of the C4.5 algorithm for micro, small, and medium enterprises classification Sri Lestari; Yulmaini Yulmaini; Aswin Aswin; Sylvia Sylvia; Yan Aditiya Pratama; Sulyono Sulyono
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6707-6715

Abstract

The coronavirus disease-19 (COVID-19) pandemic has spread to various countries including Indonesia. Thus, implementing large-scale social restrictions (Bahasa: Pembatasan Sosial Berskala Besar (PSBB)) has resulted in the paralysis of the economy in Indonesia. including micro, small, and medium enterprises (MSMEs) have decreased turnover and even went out of business. The Department of Cooperatives and Small and Medium Enterprises (SMEs) in Pesawaran Regency, Lampung, oversees 3,808 MSMEs, whose development should be monitored as a basis for determining policies. However, there are problems in classifying MSMEs according to their categories because they have to check the existing data one by one, so it takes a long time. Therefore, this study proposed the C4.5 algorithm to solve this problem. In addition, this research compared with the naïve Bayes algorithm to find out which algorithm had a good performance and is suitable for this case. The results showed that 91% of MSMEs were included in the micro category, 8% was in a small category, and 1% was in the medium category. Based on the results, it explained that the C4.5 algorithm was bigger than naïve Bayes with a difference in the value of 3.79%. It had an accuracy value of 99.2%. Meanwhile, naive Bayes was 95.41%.
Enhanced sentiment analysis based on improved word embeddings and XGboost Amina Samih; Abderrahim Ghadi; Abdelhadi Fennan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1827-1836

Abstract

Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.
Application of artificial intelligence in early fault detection of transmission line-a case study in India Prashant P. Mawle; Gunwant A. Dhomane; Prakash G. Burade
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5707-5716

Abstract

Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue.
The cross-association relation based on intervals ratio in fuzzy time series Etna Vianita; Muhammad Sam'an; A. Nafis Haikal; Ina Salamatul Mufaricha; Redemtus Heru Tjahjana; Titi Udjiani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2040-2051

Abstract

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modified steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross-association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals.
Cloud server design for heavy workload gaming computing with Google cloud platform Airlangga Baihaqi Wicaksono; Rendy Munadi; Sussi Sussi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2197-2205

Abstract

Cloud servers are generally used for data storage and remote office activities, but it can be applied for gaming purposes, where cloud servers can be paired with virtual machines and gaming platform that can be accessed by users via an internet connection. This makes the device used by user no longer needs to process resources because the workload is carried out by virtual machines on cloud server. The author designs a cloud gaming system using Google cloud platform as a cloud server and parsec as an optimizer that is attached to a virtual machine for game computing purposes. Author takes measurements of the cloud gaming system using 2 test games varying from low to middle specifications. Resource testing on central processing unit (CPU) and random-access memory (RAM) usage on the user side is below 40% when running game 1 and below 44% when running game 2, while on the system it reaches a capacity above 40% for CPU and RAM and 99% maximum on graphics processing unit (GPU). Quality of service testing of the system is carried out at bandwidths of 5, 10, and 30 Mbps with a minimum bandwidth of 10 Mbps. In general, there are a little difference that occurred between test game and different bandwidths.
A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle Md. Abdullah Al Noman; Zhai LI; Firas Husham Almukhtar; Md. Faishal Rahaman; Batyrkhan Omarov; Samrat Ray; Shahajan Miah; Chengping Wang
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp347-357

Abstract

Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.
A new approach of scalable traffic capture model with Pi cluster Kristoko Dwi Hartomo; April Firman Daru; Hindriyanto Dwi Purnomo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2186-2196

Abstract

The development of the internet of things (IoT), which functions as servers, device monitors, and controllers of several peripherals inside the smart home, eased workload in many sectors. Most devices are accessible through the internet because they communicate with wired or wireless interfaces. However, this feature makes them prone to the risk of being exposed to the public. The exposed devices are an easy target for the third party to launch a flooding attack through the network. This attack overloads the system due to the low processing capability, thereby interrupting any running process and harming the device. Therefore, this study proposed a scalable network capturing model that utilized multiple Raspberry Pi boards in parallel to monitor the network traffics simultaneously. An isolated experiment was used for evaluation by running simultaneous flooding attacks on each device. The result showed that the model consumed 30.44% more memory with 14.66% lower central processing unit (CPU) usage and 3.63% faster execution time. This means that this model is better in terms of performance and effectiveness than the single capture model.
Compressive speech enhancement using semi-soft thresholding and improved threshold estimation Smriti Sahu; Neela Rayavarapu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2788-2800

Abstract

Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basis-function based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silence-region of the signal. This study suggests that thresholding should be applied to both detail coefficients and approximation coefficients to remove noise effectively. The performances of the hard, soft, garrote and semi-soft thresholding approaches are compared based on objective quality and speech intelligibility measures. The normalized covariance measure is introduced as an effective intelligibility measure as it has a strong correlation with the intelligibility of the speech signal. A visual inspection of the output signal is used to verify the results. Experiments were conducted on the noisy speech corpus (NOIZEUS) speech database. The experimental results indicate that the proposed method of semi-soft thresholding using improved threshold estimation provides better enhancement compared to the other thresholding approaches.
An intelligent system to detect slow denial of service attacks in software-defined networks Prathima Mabel John; Rama Mohan Babu Kasturi Nagappasetty
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3099-3110

Abstract

Slow denial of service attack (DoS) is a tricky issue in software-defined network (SDN) as it uses less bandwidth to attack a server. In this paper, a slow-rate DoS attack called Slowloris is detected and mitigated on Apache2 and Nginx servers using a methodology called an intelligent system for slow DoS detection using machine learning (ISSDM) in SDN. Data generation module of ISSDM generates dataset with response time, the number of connections, timeout, and pattern match as features. Data are generated in a real environment using Apache2, Nginx server, Zodiac FX OpenFlow switch and Ryu controller. Monte Carlo simulation is used to estimate threshold values for attack classification. Further, ISSDM performs header inspection using regular expressions to mark flows as legitimate or attacked during data generation. The proposed feature selection module of ISSDM, called blended statistical and information gain (BSIG), selects those features that contribute best to classification. These features are used for classification by various machine learning and deep learning models. Results are compared with feature selection methods like Chi-square, T-test, and information gain.
Detecting COVID-19 in chest X-ray images Worapan Kusakunniran; Punyanuch Borwarnginn; Thanongchai Siriapisith; Sarattha Karnjanapreechakorn; Krittanat Sutassananon; Trongtum Tongdee; Pairash Saiviroonporn
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3290-3298

Abstract

One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Self-attention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are non-COVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where non-COVID-19 cases could cover more than just a normal condition.

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