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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 9,138 Documents
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Development of an analysis capacity model for high electron mobility transistor AlGaN/GaN Farti, Azzeddine; Touhami, Abdelkader
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1261-1269

Abstract

In this paper, we demonstrate the analytical model developed to characterize the gate-to-drain capacitance Cgd and the gate-to-source capacitance Cgs, and the impact of the gate length on those capacitances, for the high electronic mobility transistor based on GaN. This model is developed from our previous work on the current voltage characteristic (I, V), and small signal parameters for AlGaN/GaN HEMT. The research study examined the impact of parasitic resistances (drain, source), low field mobility, the aluminum amount in the AlGaN barrier, and high-speed saturation. The developed model has matched the experimental data well, confirming the validity, accuracy, and robustness of the model we have developed.
Evaluation of the performance of mobile telephone networks: literature review Valandi, Pascal; Temoa, Djorwe; Jean Luc, Nsouandele; Pascal, Tsama Eloundou; Froumsia, Dokrom
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp128-139

Abstract

Improving the quality of service (QoS) of telephone networks inevitably involves studying previous work on the evaluation of its performance indicators. Several researchers have addressed the subject of evaluating the performance of service of mobile telephone networks. Some proceeded through user surveys and others opted for more objective methods using either professional scanners or developed: hyper text markup language (HTML) or Android applications. The results show that whether by subjective or objective methods, this work has made it possible to advance research and allow other researchers to progress further in the process of evaluating mobile networks. In this study which constitutes a review of the literature, we investigated the different approaches, methods, and most recent results mentioned by researchers to evaluate the QoS by relying much more on objective evaluation. Despite the advances and their limits, in our proposal we intend to rely on data sciences through their tools to evaluate the QoS with more precision.
Mobile application for diagnosing alzheimer's based on clinical dementia rating Supriyanti, Retno; Putra Yubiksana, Muhammad; Mahardika Wijonarko, Bintang Abelian; Ramadhani, Yogi; Syaiful Aliim, Muhammad; Irham Akbar, Mohammad; Budi Widodo, Haris; Widanarto, Wahyu; Alqaaf, Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1607-1617

Abstract

Alzheimer's is a neurodegenerative disease characterized by memory loss, impaired thinking abilities, and changes in behavior. It is the most common form of dementia, significantly affecting a person's ability to carry out daily activities. Statistics indicate that the number of individuals suffering from Alzheimer's worldwide continues to rise as the population ages. Diagnosing Alzheimer's is a complex process that typically requires a skilled medical team. One diagnostic tool that can be utilized is an MRI machine. Previous research focused on extracting features from MRI images taken from three different cross-sections: axial, coronal, and sagittal. Based on these three types of cross-sectional images, we developed a system to classify the severity of Alzheimer's. This paper focuses on creating an Alzheimer's classification system accessible through a mobile application. The results indicate that our system has a performance accuracy of 90% in classifying the severity of the disease.
Lossy ECG signal compression based on RR intervals detection with wavelet transform and optimized run-length encoding Boukhennoufa, Nabil; Garah, Messaoud
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp109-118

Abstract

It is expensive to transmit or store significant amounts of electrocardiogram (ECG) records, particularly when using telecommunications channels that charge according to the volume of transferred data. The advancement of telemedicine renders compressing ECG signals even more necessary. Compression aims to reduce the size of data while maintaining the features of ECG signals. This paper presents a novel strategy for compressing ECG signals based on 3D format conversion. After identifying the RR intervals, we divide the signal into cardiac cycles and proceed with the cut and align process. A 3D discrete wavelet transform (DWT) is employed to minimize the correlation existing between two adjacent voxels. Moreover, an optimized run-length encoding (RLE), a novel lossless compression technique, has been proposed to increase the compression ratio (CR). The proposed strategy is applied to different types of ECG records of the Arryyhmia database. This algorithm demonstrates improved performance in terms of CR and percentage root-mean-square difference (PRD) compared to several recently published works.
Efficient PAPR reduction technique in OFDM system using amplitude clipping and selective filtering M., Supriya; R., Sukumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1308-1316

Abstract

One of the most important transmission methods for the next generation of wireless communication systems is orthogonal frequency division multiplexing (OFDM). Transmitting an OFDM signal in a noisy environment with a low bit error rate (BER) is the primary goal. High peakto-average power ratio (PAPR) at the transmitter, which lowers the transmission peak power, is one of OFDM's biggest drawbacks. In this paper, we propose efficient PAPR reduction technique in OFDM system using amplitude clipping and selective filtering. The efficient multiefficiency PAPR reduction strategies with pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) modulation are employed with selective filtering and evaluated in terms of percentage reduction level to lowest PAPR of 3.841 db. It is observed that QAM modulation produces better results compared to PAM modulation with less BER of 0.003 for signal-to-noise ratio (SNR) of 20 db.
Artillery fire control based on artificial intelligence algorithm of unmanned aerial vehicle Bayramov, Azad Agalar; Suleymanov, Samir Suleyman; Abdullayev, Fatali Nariman
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp83-89

Abstract

The article presents the developed artillery fire remote control complex using unmanned aerial vehicles (UAVs) based on an artificial intelligence (AI) algorithm. The developed complex for artillery fire control includes sensor modules for assessing the environment, collecting and processing information, planning and decision-making, and developing a command for the commander of an artillery battalion, division, or brigade. The main advantage of the developed artillery fire control system using UAVs based on an AI algorithm is the most rapid decision-making without human intervention, based on a quick assessment of the environment, the type of enemy weapons, and their category of importance, and an assessment of the distance to the enemy’s military arms. An algorithm is proposed to minimize the power of artillery fire to suppress the enemy.
Design and construction of an Arduino-based baby incubator simulator using IoT Rusdiyana, Liza; Jamot Damanik, Joel Juanda; Sampurno, Bambang; Suhariyanto, Suhariyanto; Mursid, Mahirul; Widianti, Ika Silviana
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp99-108

Abstract

This study aims to create a baby incubator simulator equipped with an internet of things (IoT)-based temperature control system using Arduino UNO. We use a DHT22 sensor to measure temperature and humidity, as well as fuzzy logic to ensure more accurate and responsive temperature control. The Thinger.io platform enables real-time monitoring and control of the incubator, providing flexibility and ease of supervision. With fuzzy logic, the temperature control system can handle changes and uncertainties in the incubator environment, providing a smoother response compared to traditional on-off methods. Testing shows that this system has a very low error rate, with an error value of only 0.97%, meaning that the measured temperature is almost identical to the actual conditions inside the incubator. Additionally, the authors used mice as a model for premature infants in the testing. The results showed that the mice's body temperature increased gradually and stably in line with the incubator conditions, reaching the desired temperature within 90 minutes. This demonstrates that our temperature control system is capable of maintaining optimal environmental conditions for premature infants.
Hybrid TCP SYN attack detection model in SDN Muzafar, Saira; Zaman Jhanjhi, Noor
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1345-1356

Abstract

Software defined network (SDN) is a developing concept that emerged recently to overcome the constraints of traditional networks. The distinguishing characteristic of SDN is the uncoupling of the control plane from the data plane. This facilitates effective network administration and enables efficient programmability of the network. Nevertheless, the updated architecture is susceptible to cyberattacks including distributed denial of service (DDoS) attacks, that can impair network regular functions and hinder the SDN controller from assisting authorized users. This paper introduces hybrid deep learning model, to detect DDoS assaults triggered by TCP SYN attacks in SDN environments. Our proposed model integrates a temporal convolutional network (TCN) with a stacking classifier that leverages logistic regression, which is an innovative hybrid approach. We assessed the performance of our model by utilizing the benchmark CICDDoS2019 dataset. When compared to other benchmarking techniques, our model significantly improves attack detection. The experimental results indicate that the proposed hybrid model attains 99.9% accuracy for attack detection compared to the available approaches.
Meta-model integration with attention mechanisms for advanced decision-level fusion in machine learning Shobha, Shobha; Narasimhaiah, Nalini
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1325-1336

Abstract

This work proposes an advanced meta-model approach that incorporates forecasts from multiple machine learning models to improve classification accuracy in complex tasks. The approach employs decision-level data fusion, where predictions from random forest (RF), XGBoost, neural networks (NN), and support vector machine (SVM) are combined within a meta-model framework. The meta-model incorporates an attention mechanism and a gated model selection process to dynamically emphasize the most relevant model outputs based on input features. The results demonstrate superior accuracy in predicting explicit content compared to traditional fusion methods. This research highlights the potential of attention-enhanced meta-models in improving interpretability and accuracy across various domains. The integration of meta-models with attention mechanisms has the potential to significantly enhance decision-level fusion in machine learning applications. This study investigates the development of an advanced fusion framework leveraging attention mechanisms to improve decision-making accuracy in multi-source data environments. The proposed method is evaluated across multiple datasets, demonstrating its efficacy in increasing predictive performance and robustness.
Weighted fine-tuned BERT-based sparse RNN for fake news detection Kathigi, Asha; Nair, Gautam Vinod; Raghu, Kruthika Kadurahalli; Prakash, Kavya Pujar; Duttargi, Meghana Deepak
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp331-343

Abstract

Fake news refers to misinformation or false reports shared in the form of images, articles, or videos that are disguised as real news to try to manipulate people’s opinions. However, detection systems fail to capture diverse features of fake news due to variability in linguistic styles, contexts, and sources, which lead to inaccurate identification. For this purpose, a weighted fine-tuned-bidirectional encoder representation for transformer based sparse recurrent neural network (WFT-BERT-SRNN) is proposed for fake news detection using deep learning (DL). Initially, data is acquired from Buzzfeed PolitiFact, Fakeddit, and Weibo datasets to evaluate WFT BERT-SRNN. Pre-processing is established using stopword removal, tokenization, and stemming to eliminate unwanted phrases or words. Then, WFT-BERT is employed to extract features. Finally, SRNN is employed to detect and classify fake news as real or fake. Existing techniques like deep neural networks for Fake news detection (DeepFake), BERT with joint learning, and multi-EDU structure for Fake news detection (EDU4FD), Image caption-based technique, and fine-grained multimodal fusion network (FMFN) are compared with WFT-BERT-SRNN. The WFT-BERT-SRNN achieves a better accuracy of 0.9847, 0.9724, 0.9624, and 0.9725 for Buzzfeed, Politifact, Fakeddit, and Weibo datasets compared to existing techniques like DeepFake, BERT-joint framework, EDU4FD, Image caption-based technique, and FMFN.

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