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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 adaptive combination algorithm based on deep learning and genetic algorithm for anomalous events detection Zainab K. Abbas; Ayad A. Al-Ani
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp902-908

Abstract

One of the most widely used human behavior detection methods is anomaly detection, which this article covers. Ensuring a person's safety is a crucial task in every community today due to the ever-increasing actions that can be dangerous, from planned crime to harm from an accident. Classic closed-circuit television is insufficient since a person must always be awake and available to monitor the cameras, which is costly. Also, someone's attention tends to decrease after a certain period of time. Due to these reasons, a surveillance system that is automated and able to detect unusual activities in real-time and give sufferers prompt aid is necessary. It should be noted that the identification process must be completed swiftly and correctly. In this paper, we employ a model based on mixes the machine learning (ML) model, namely genetic algorithms with deep learning (DL). In this study's experimentation, the UCF-Crime dataset was employed. The detection accuracy on the testing sample dataset was equal to 89.90%, while the area under the curve (AUC) was equal to 94.58%. The developed models have demonstrated reliability and the ability to achieve the greatest accuracy when compared to models that have already been designed.
Blind nonlinear unmixing using nonnegative matrix factorization based bi-objective autoencoder Sreejam Muraleedhara Bhakthan; Agilandeeswari Loganathan; Aashish Bansal
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1070-1079

Abstract

Hyperspectral image processing is one of the trending techniques used in many fields such as remote sensing, medical, agriculture, food processing, and military. The unique discrimination of hyperspectral images can be used for object identification, classification, and prediction. One of the main challenges of these tasks is the mixed pixel problem. Hyperspectral unmixing is the process of identifying the endmembers and their abundance in pixels. In linear unmixing, the mixture of the endmembers is assumed to be linear homogenous patches. Even though these models are simple and faster in performance, most of the real-world images are not linear. A modified nonlinear mixture-based sparsity regularized bi-objective autoencoder model based on nonnegative matrix factorization (NMF-BOA) is proposed in this article. The performance analysis shows that our model gives competitive results compared to the state-of-the-art models.
Network intrusion detection and classification using machine learning predictions fusion Harshitha Somashekar; Ramesh Boraiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1147-1153

Abstract

The primary objective of an intrusion detection system (IDS) is to monitor the network performance and to look into any indications of malformation over the network. While providing high-security network IDS played a vital role for the past couple of years. IDS will fail to identify all types of attacks, when it comes to anomaly detection, it is often connected with a high false alarm rate with accuracy and the detection rate is very average. Recently, IDS utilize machine learning methods, because of the way that machine learning algorithms demonstrated to have the capacity of learning and adjusting as well as permitting a proper reaction for real-time data. This work proposes a prediction-level fusion model for intrusion detection and classification using machine learning techniques. This work also proposes retraining of model for unknown attacks to increase the effectiveness of classification in IDS. The experiments are carried out on the network security layer knowledge discovery in database (NSL-KDD) dataset using the Konstanz information miner (KNIME) analytics platform. The experimental results showed a classification accuracy of 90.03% for a simple model to 96.31% for fusion and re-trained models. This result inspires the researchers to use machine learning techniques with a fusion model to build IDS.
Bluetooth low energy for internet of things: review, challenges, and open issues Mahmood A. Al-Shareeda; Murtaja Ali Saare; Selvakumar Manickam; Shankar Karuppayah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1182-1189

Abstract

As a result of its ultra-low power consumption, simple development, sufficient network coverage, and rapid data transfer speed, Bluetooth low energy (BLE) has emerged as the standard communication standard for internet of things (IoT) nodes. Therefore, in this review paper introduces the concept of Bluetooth low energy for the internet of things (BLE-IoT) in terms of Bluetooth classic, Bluetooth version, applications for BLE-IoT, and new features of BLE-IoT. We then provide a taxonomy of literature reviews based on the parameter adjustment approach (e.g., advertiser side schemes, scanner side schemes, hybrid schemes) and collision avoidance approach (e.g., advertiser side schemes and scanner side schemes). Finally, we discuss research challenges and future opportunities for BLE-IoT.
Optimal placement of the phasor measurement units using differential evolution algorithm Mahmoud Zadehbagheri; Alireza Abbasi; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1211-1222

Abstract

The increasing consumption of the electric energy aimed at develop the transmission networks and the demand for higher reliability from the network; in this regard, wide-area measurement systems using phasor measurement units (PMUs) have revolved the trend of power network management. In this paper, the optimal allocation of PMUs in order to reach the perfect observability of the network; based on a differential evolution algorithm, is proposed and it is shown that, the deployment of constraints related to the zero-injection busses (ZIB) aimed to decrease the number of PMUs and their corresponding cost. By comparing the proposed method to the other methods, its simplicity and good performance are approved.
Data mining technique for grouping products using clustering based on association Eka Praja Wiyata Mandala; Dewi Eka Putri
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp835-844

Abstract

There is high competition between these minimarkets so many products sold in each minimarket are not sold until they expire. The aim of this study is to help retail managers cluster products in minimarkets. The data obtained will be processed using the hybrid data mining approach by combining two methods in data mining. In the first section, association uses the FP-Growth algorithm, and in the second section, clustering uses the K-means algorithm. From the experimental results, it can be seen that the proposed approach can minimize the number of products to be grouped. After the association process is carried out, from 29 products in 12 transactions, 6 products can be obtained that has a frequency above the minimum support and minimum confidence. After the clustering process, 6 products are grouped into 2 clusters, so that 1 product is included in the most interested product cluster and 5 products are included in the interested product cluster. We minimize data processing so that retail managers can process data directly from sales transaction data on the cashier's computer and can quickly get the results of product grouping.
Unorthodox technique in sensing with the metamaterial-based resonator sensor at millimetre frequencies Suhail Asghar Qureshi; Zuhairiah Zainal Abidin; Huda A. Majid; Adel Y. I. Ashyap; Bashar A. F. Esmail
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp785-793

Abstract

A metamaterial-based resonator is presented in this paper for liquid-sensing applications. The designed sensor operates at millimetre-wave (mm-w) frequencies, and it can characterise the samples that may possess identical characteristics. This paper relies on the extracted permittivity of the structure in the characterisation of the samples, mainly liquids. The sensor requires a very small amount of samples for sensing and it is used in distinguishing oil, ethanol, methanol, glycerol and water. A shift in the resonance frequency of about 200 MHz per unit increase in the epsilon value of samples was achieved. The oil sample showed the lowest value in the extracted permittivity value, while water showed the nearest to zero extracted permittivity. This relationship of variation in extracted permittivity parameter with the change in the sample’s epsilon value is found to be linear and reliable regardless of the change in thickness of the sample.
Human activity monitoring system with commodity WiFi infrastructure using channel state information Hicham Boudlal; Mohammed Serrhini; Ahmed Tahiri
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp763-776

Abstract

Human activity detection is a research field that has been growing rapidly for the last few decades. It opens a wide field of applications in the fields of healthcare, smart homes, robotics, human-media interaction, surveillance and security. WiFi-based solutions have received a lot of attention lately. These are based on the idea that nearby wireless signals are affected by human bodies. Reflections are produced by the presence of static objects like walls and furniture, while additional propagation paths are produced by the presence of dynamic objects such humans. This paper proposes using WiFi for a low-cost, device-free human activity monitoring system, as it is readily available in the office and home these days. It is also ubiquitous in nature as it collects information about the environment while providing Internet. The idea behind this approach is to acquire and model changes in multipath WiFi radio waves due to human movement. Experimental validation in practical situations with varying occupants, different environmental conditions, and interference from WiFi devices is used to demonstrate robustness and scalability.
Assessment of control and monitoring system design security using the attack security tree analysis method Mustafa Qahtan Alsudani; Israa Fayez Yousif; Ahmed Nooruldeen Alsafi; Hassan Falah Fakhruldeen
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp820-827

Abstract

Because of the efficiency of the system and the fact that it successfully completed the tasks that were given to it under specific conditions, we are compelled to look for a way to measure these requirements according to the conditions and guidelines that were established by the people who make use of the system. Conduct an investigation into the many techniques that are available for use in analysis in light of the following conditions: i) sufficient time to detect the mistake, ii) time to maintenance, iii) the total number of constituents involved in the analytical process, and iv) an explanation of the level of complexity provided to the user. In this article, we will provide a concise overview of a number of different approaches, along with our recommendations for the most effective ones based on the issues raised earlier.
Associating deep learning and the news headlines sentiment for Bursa stock price prediction Zuleaizal Sidek; Sharifah Sakinah Syed Ahmad; Noor Hasimah Ibrahim Teo
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1041-1049

Abstract

Accurate stock price prediction is appealing to academics, economists, and financial analysts for its potential to increase profits. Although remarkable progress has been made in stock prediction accuracy, studies to explore the relationship between public sentiments and the prediction of stock price movement based on online news portals in Malaysia context are limited. Therefore, this study aims to determine whether news sentiments influence the movement of the Bursa stock price. The stock prediction model was implemented using long short-term memory (LSTM), with stock data from Bursa Malaysia between January 2017 and April 2022, and the root mean squared error (RMSE) value was calculated. In addition, LSTM prediction model was compared to the decision tree algorithm, and LSTM performed significantly better than the decision tree, particularly when using the New York stock exchange (NYSE) dataset. Furthermore, sentiment analysis was carried out using a Malaysian online news portal's business and financial news. The findings showed that i) news has a significant impact on Malaysian stock market price movement; ii) the RMSE of the LSTM model was improved by adding a parameter (news polarity values); and iii) the RMSE value generated is less than one for every company stock and is influenced by stock price and price change magnitude.

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