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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 22 Documents
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Search results for , issue "Vol 12, No 3: September 2024" : 22 Documents clear
Malware Classification Using Machine Learning and Dimension Reduction Techniques on PE File Data Pradipta, Arif Harsa; Wulandhari, Lili Ayu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5657

Abstract

The digital transformation has enhanced efficiency, transparency, and accessibility but has also led to a notable increase in cyber incidents, including malware attacks. According to the 2022 annual report from the Honeynet Project by the National Cyber and Encryption Agency, Indonesia experienced over 370 million cyber attacks, with 800,000 of these being malware attacks. The increasing complexity of Portable Executable files further complicates accurate classification in machine learning models. This research aims to develop an effective malware detection approach using machine learning classifiers—Random Forest, XGBoost, and AdaBoost—on raw feature dataset and integrated feature dataset. Dimension reduction techniques such as Principal Component Analysis and Linear Discriminant Analysis were utilized to enhance classification efficiency. The results demonstrated that Random Forest and XGBoost consistently outperformed AdaBoost, particularly in classifying ransomware, achieving recall values ranging from 0.72 to 0.85 and F1-scores from 0.74 to 0.81 For the trojan class, both Random Forest and XGBoost achieved recall values ranging from 0.96 to 0.97, with corresponding F1-scores between 0.95 and 0.97. Both classifiers maintained high precision, recall, and F1-scores across all malware classes, even with reduced feature sets.
Feature Optimization for Machine Learning Based Bearing Fault Classification Mohiuddin, Mohammad; Islam, Md Saiful; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5671

Abstract

The most critical and essential parts of rotating machinery are bearings. The main problem of the bearing fault classification is to select the fault features effectively because all extracted features are not useful, and the high-dimensional features give poor performances and slow down the training process. Due to the effective feature selection problem, the bearing fault diagnosis method does not achieve a satisfactory result. The main goal of this paper is to extract the effective fault features with an optimization technique to classify the bearing faults using machine learning algorithms. Since wavelet entropy can determine complexity and degree of order of a vibration signal, this research uses it in features optimization.  The proposed wavelet entropy-based optimization technique reduces the dimensionality of input, elapsed time and raises the learning process. Four Machine learning algorithms (naïve Bayes, support vector machine, artificial neural network and KNN) are applied to classify the bearing faults using the optimized features.    To evaluate the proposed method, Case Western Reserve University’s (CWRU’s) bearing dataset is used which consists of three types of bearing faults. The accuracy and robustness of the bearing fault classification are tested by adding noise to the vibration raw signals at various levels of Signal-to-Noise Ratio (SNR). Experimental results show that the proposed method is very highly reliable in detecting bearing faults compared to the conventional methods.
Solving Dynamic Combined Economic Environemental Dispatch Problem with Renewable Energies and Constraints Using Gorilla Troops Optimizer Abrouche, Amel; Bouzeboudja, Hamid; Dahmani, Kaouthar Lalia; Naama, Bakhta
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5486

Abstract

The primary goal is to optimize the hourly allocation of power generation outputs by minimizing operational costs, pollutant emissions, and transmission losses, and ensuring compliance with a range of equality and inequality constraints. To tackle this challenge, a novel metaheuristic algorithm inspired by gorilla’s behavior is proposed. Gorilla Troops Optimizer (GTO) was applied to 5- and 10-generator unit systems, integrating variable wind and solar energies over a day with varying load demands. To demonstrate the effectiveness of the GTO algorithm in handling the hybrid dynamic combined economic and environmental dispatch problem, including equality constraints, transmission losses, valve-point effects, prohibited operating zones, ramp rates, and power limits, its performance was compared with other optimization techniques. The findings indicate that GTO provides the optimal scheduling of power generators, leading to significant reductions in daily operational costs and emissions with high percentages. Moreover, the integration of renewable energy significantly reduces pollutant gas emissions, fuel costs, and transmission losses, while meeting all imposed constraints. This research positively contributes to enhancing the reliability of power supply systems, while simultaneously reducing environmental pollution, transmission losses, and fuel costs.
Multi-Objective Reinforcement Learning Based Algorithm for Dynamic Workflow Scheduling in Cloud Computing Sudhakar, Rayapati V.; Dastagiraiah, C.; Pattem, Sampurnima; Bhukya, Sreedhar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5728

Abstract

It is essential to consider the infrastructures of workflows as a critical research area where even slight optimizations can significantly impact infrastructure efficiency and the services provided to users. Traditional workflow scheduling approaches using heuristics may not be efficient due to the dynamic workloads and diverse resources of cloud infrastructure. Additionally, the resources at any given time have different states that must be considered during workflow scheduling. The emergence of artificial intelligence has made it possible to address the dynamics and diverse resources of cloud computing during workflow management. In particular, reinforcement learning enables understanding the environment at runtime with an actor and critic approach to make well-informed decisions. Our paper introduces an algorithm called Multi-Objective Reinforcement Learning based Workflow Scheduling (MORL-WS). Our empirical study with various workflows has demonstrated that the proposed multi-objective reinforcement learning-based approach outperforms many existing scheduling methods, especially regarding makespan and energy efficiency. The proposed method with the Montage workflow demonstrated superior performance compared to scheduling 1000 tasks, achieving a least makespan of 709.26 and least energy consumption of 72.11 watts. This indicates that the proposed method is suitable for real-time workflow scheduling applications.
A Development of Supporting System for Historical Heritage Based Tourism Boonmee, Salinun; Somsuphaprungyos, Suwit; Natho, Parinya; Boonying, Sangtong
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5089

Abstract

Tourism is a major economic contributor in Thailand. With the richness of historical heritage recognized as world heritages, Phra Nakhon Si Ayutthaya province is a famous destination for tourists who enjoy historical and cultural tourism. This work presents a development of a supporting system for tourism in Phra Nakhon Si Ayutthaya province in regarding of historical and cultural aspects of heritages. This work designs an ontology to represent a relation network of properties from tourist attractions based on historical and cultural relationship among them. Instances which are the heritages hence are related and can be visualized in a form of a graph. The suggestion module is designed to provide related tourist destination following the relations from the generated knowledge graph based on the initial query of a user. The experiment results signify that the system revealed hidden historical relations of destinations to users and made them learn the values of history lied within heritages. Furthermore, 87.5% of participants decided to make a tour plan following the suggested destinations since they found the linking in historical values to be more meaningful and interesting.
A Compact Inset Coupled-Fed Triangular Patch Antenna For Wideband 5G Applications C, Mohan; J, Silamboli; S, Divya; R, Shantha Sheela
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5677

Abstract

For 5G applications, a compact inset coupled-fed high bandwidth triangle antenna is demonstrated. A large bandwidth can be achieved by combining the inset and coupling feeding with a triangle-shaped patch. With a VSWR of less than 2, the suggested antenna's working frequency of 3.6 GHz spans the frequency range needed for 5G applications, which is between 2.8 and 5.6 GHz. The primary characteristics of the suggested antenna are its smaller dimensions (20.5 × 17.5 mm2) and about 35% increased bandwidth. Significant factors that match the simulated results exactly are S11, radiation pattern, radiation efficiency, and peak gain in the proceeding of the proposed antenna. With the addition of two parallel rectangular strips with a triangular-shaped patch, the antenna is capable to achieve 40% reductions in size, 81.74% radiation efficiency, and 2.61 dB peak gain for the suggested antenna. With a center frequency of 3.6 GHz and a reflection coefficient of 28.6 dB, the fractional bandwidth is 66.67% (2.8 GHz to 5.6 GHz).  With a smaller surface wave and an excellent omnidirectional radiation pattern, the antenna's inset coupling feeding arrangement makes it appropriate for Sub-GHz 5G applications. 
Performance Evaluation of Advanced PLL Techniques For Accurate FFPS Component Extraction Saritha, M; Sidram, M H
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5686

Abstract

It is very necessary to adopt fundamental frequency positive sequence (FFPS) element extraction methods in order to maximise the efficiency of integrating and handling the use of renewable energy sources (RES). The decision to act in this manner is made with the purpose of contributing to the accomplishment of the aforementioned aim. The capability of the synchronous references frame phase-locked loop to reject variations over a broad variety of grid conditions is enhanced as a result of this. This is particularly true for voltage sags and surges that are accompanied by harmonics, irrespective of whether the harmonics are the result of balanced or unbalanced electrical current fluctuations. The accuracy of the extraction of FFPS components is significantly influenced by the frequency deviation in SRF-PLL systems. The frequency deviation is another critical component. This is a result of the frequency deviation not remaining constant. An investigation is being conducted to ascertain the effectiveness of a various advanced PLL techniques, such as the cascaded delayed signal cancellation (CDSC), the dual second-order generalized integrator (DSOGI) and the multiple delayed signal cancellation (MDSC). The objective of conducting this assessment is to facilitate the evaluation of the efficacy of these strategies, which is the reason for its implementation. The CDSC and MDSC PLL have been demonstrated to be preferable to other PLLs due to their ability to distinguish between even and odd harmonics. This is due to the fact that each of these harmonics possesses its own distinctive characteristics. This may be attributable to its capacity to independently identify either harmonic. The MATLAB simulation results is provided to demonstrate the exceptional performance of these highly advanced PLLs.
Novel Polar Coded MIMO Power Domain NOMA Scheme for 5G New Radio (NR) Pavithra, B; Chakraborty, Parnasree
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5670

Abstract

The use of Polar coded Multiple Input Multiple Output Power Domain Non-Orthogonal Multiple Access (MIMO PD-NOMA) technology has the potential to greatly improve the capacity and spectral efficiency of 5G NR systems. From the on-going research, there is a combination of polar coded NOMA and Polar coded MIMO techniques are approached separately with other channel coding techniques. This paper introduces a novel approach to combine polar coded with MIMO power domain NOMA to enhance the system performance. MIMO Power Domain NOMA that utilizes polar codes for channel coding and power allocation. By combining the benefits of NOMA and MIMO, which permits multiple users to share frequency-time resources simultaneously and the MIMO employs multiple antennas to increase diversity gain and spatial multiplexing gain. The proposed scheme provides effective utilization of radio resources where the polar codes are an optimal choice for 5G NR systems due to their strong error correction capability and low complexity decoding. Successive Cancellation List -Singular Value Decomposition adaptive scaling algorithm (SCL-SVD) is proposed in the polar decoding process. The suggested method attains 6.5 dB coding gain and improved throughput of 80.34% using MATLAB simulation. The proposed model compared with the other existing model such as Power Domain NOMA (PD-NOMA), multiple input single output NOMA (miso-NOMA) and multiple input multiple output NOMA (mimo-NOMA) in terms of Bit Error Rate (BER) and Signal to Noise Ratio (SNR). This scheme has the potential for practical implementation and can play a crucial role in meeting the increasing demands of future wireless communication systems.
BERT-BiLSTM model for hierarchical Arabic text classification Hamzaoui, Benamar; Bouchiha, Djelloul; Bouziane, Abdelghani; Doumi, Noureddine
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5659

Abstract

Text classification is a fundamental task in natural language processing (NLP) aimed at categorizing text documents into predefined categories or labels. Leveraging artificial intelligence (AI) tools, particularly deep learning and machine learning, has significantly enhanced text classification capabilities. However, for the Arabic language, which lacks comprehensive resources in this domain, the challenge is even more pronounced. Hierarchical text classification, which organizes categories into a tree-like structure, presents added complexity due to inter-category similarities and connections across different levels. In addressing this challenge, we propose a deep learning model based on BERT (Bidirectional Encoder Representations from Transformers) and BiLSTM (Bidirectional Long Short-Term Memory). Experimental evaluations demonstrate the effectiveness of our approach compared to existing methods, yielding promising results. Our study contributes to advancing text classification methodologies, particularly in the context of Arabic language processing.
ML-ACID: a Modified Machine Learning Algorithm Coupled With a Novel Ant Colony Approach for Intrusion Detection in IOT Belkhiri, Hamza; Messai, Abderraouf; Belhadad, Yehya; Andre-Luc, Beylot; Salheddine, Sadouni
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5533

Abstract

Software Defined Networks is becoming increasingly important in IoT because it allows devices to communicate more easily it provides the flexibility and centralized management, however in recent years these networks have witnessed a widespread spread of cyber-attacks that has a significant and negative impact on the availability of services. In this paper, we propose a novel approach for intrusion detection in Software Defined Networks for IoT. our work inspired by the self-defense mechanism of ant colonies. The approach uses a self-adaptable colony fingerprint and based on multiple parameters, it makes the detection of intrusions easy and filters out every other legitimate communication within the network. A machine learning model is used to provide basic predictions about the communication that later drives the evolution of the colony in terms of self-defence. The whole approach is implemented in a simple switch using Ryu-controller and analyses OpenFlow datagrams. The meta-heuristic implication of using ant colony optimization improved approach provides the system with reliability and high performance of detecting and blocking threats. in the end interesting results based on several scenarios shows the usability of our approach.

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