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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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Deep learning approaches, platforms, datasets for behaviorbased recognition: a survey Jeddah, Yunusa Mohammed; Abdallah Hashim, Aisha Hassan; Khalifa, Othman Omran
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1880-1895

Abstract

Video surveillance is an extensively used tool due to the high rate of atypical behavior and many cameras that enable video capture and storage. Unfortunately, most of these cameras are operator dependent for stored content analysis. This limitation necessitates the provision of an automatic behavior identification system. This behavior identification can be achieved using unsupervised (generative) computer vision methods. Deep learning makes it possible to model human behavior regardless of where they could be. We attempt to classify current research work to report the ongoing trends in human behavior recognition using deep learning algorithms. This paper reviews various aspects, like the ones associated with machine learning and deep learning models, human activity recognition (HAR), deep learning frameworks/tools, abnormal behavior datasets, and a variety of other current trends in the field of automatic learning. All these are to give the researcher a sense of direction in this area.
Design and implementation of an automatic irrigation system for plants in Lima-Perú Astuhuaman-Medina, Jean Piere; Granados-Zárate, Adrian Humberto; Marcatinco-Gonzales, Jhonel Wilfredo; Segura-Viteri, Martin Fernando; Morante-Medina, Aldhair; Castro-Vargas, Cristian
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1580-1590

Abstract

In many regions of the world, water used in agriculture becomes a scarce and costly resource over time. It is necessary to make efficient use of this vital resource. For this reason, we opted for an innovative project that can be of great use for agriculture, incorporating information and communication technologies such as the internet of things (IoT), databases, and smartphone applications. The research proposes an IoT system to control and monitor crops in a specific area based on the ESP32 microcontroller, using the DHT11 sensor to collect temperature and relative humidity data. The sensors send the information to the central node for the wireless communication part. The central node activates the actuators to control and store the information in a database for corresponding monitoring. The mobile application displays the results from the database and causes them to be turned on and off manually. The system was implemented for home plant cultivation but can be used for other types of cultivation due to its flexibility.
EMG-based hand gesture classification using Myo Armband with feedforward neural network Mohd Said, Sofea Anastasia; Thamrin, Norashikin M.; Amin Megat Ali, Megat Syahirul; Hussin, Mohamad Fahmi; Mohamad, Roslina
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp159-166

Abstract

This paper presents the development of an electromyography (EMG)-based hand gesture identification system for remote-controlled applications. Even though the Myo Armband is no longer commercially supported, the research discusses its use in EMG data collecting. Open-source libraries were utilized to capture EMG data from this device to solve this problem. Using the developed data acquisition platform, data was collected from 30 participants who performed three (3) gestures - a fist, an open hand, and a pinch. The energy spectral density (ESD) and power ratio (pRatio) were extracted to describe gesture-specific patterns. A feedforward neural network (FFNN) was implemented for classification, initially configured with 10 hidden neurons and later optimized to 40 neurons to improve the performance. The box plot analysis showed channels CH1, CH4, CH5, and CH7 as the most significant for enhancing classification accuracy. The optimized FFNN achieved 80% and 70% for the training and testing accuracies, respectively. However, the results suggest that implementing a systematic protocol during data acquisition to reduce signal overlap between movements could improve classification accuracy. In conclusion, the study successfully developed an open-source EMG data acquisition platform for MYO Armband and demonstrated acceptable hand gesture recognition using an optimized FFNN.
An innovative image encryption scheme integrating chaotic maps, DNA encoding and cellular automata Kukaram, Gaverchand; Ramasamy, Venkatesan; Abdul, Yasmin
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp710-719

Abstract

In the current digital era, securing image transmission is crucial to ensure data integrity, prevent tampering, and preserve confidentiality as images traverse unsecured channels. This paper presents an innovative encryption scheme that synergistically combines a two-dimensional (2-D) logistic map, deoxyribonucleic acid (DNA) encoding, and 1-D cellular automata (CA) rules to significantly bolster encryption robustness. The proposed model initiates with the generation of a key image via the 2-D logistic map, yielding intricate chaotic sequences that fortify the encryption mechanism. DNA cryptography is employed to amplify randomness through diffusion properties, providing robust defense against various cryptographic attacks. The integration of 1-D CA rules further intensifies encryption complexity by iteratively processing DNA-encoded sequences. Experimental results substantiate that the proposed encryption scheme demonstrates exceptional endurance against a vast spectrum of attacks, affirming its superior security.
Blue light therapy device for wound healing Kamal, Minahil; Kamal, Aleena; Abid, Azka; Ahmed, Sarah; Hussain, Syed Muddusir; Ur Rahman, Jawwad Sami; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1527-1539

Abstract

Cuts, diabetic ulcers, and pressure sores are examples of chronic skin wounds that pose a serious healthcare danger because of their delayed healing rates. This problem emphasizes the necessity of creating noninvasive, economical, and successful wound treatment plans. Conventional treatments, such as skin grafting, negative pressure wound therapy, and hyperbaric oxygen therapy, have demonstrated effectiveness; nevertheless, they are frequently costly, intrusive, and have possible side effects. On the other hand, blue light treatment has become a viable substitute due to its antimicrobial characteristics and capacity to encourage cellular restoration. However, there is a crucial gap in the development of a portable, noninvasive, and cost-effective photobiomodulation device for wound treatment and monitoring, despite its demonstrated potential in wound healing. This work aims to address this gap by creating a novel blue light therapy tool specifically suited for wound healing. The gadget allows for controlled blue light exposure and real-time temperature monitoring to minimize overheating. It has a portable arm housing with integrated blue light strips, a temperature sensor, and an integrated fan. An STM 32 microcontroller powers the system’s pulse width modulation (PWM) technology, which modifies light intensity and therapy duration in response to conditions unique to each wound. This novel strategy seeks to improve the effectiveness of wound healing, lower the likelihood of adverse effects, and offer patients and healthcare providers a workable alternative that is noninvasive, inexpensive, and easy to use.
Weierstrass scale space representation and composite dilated U-net based convolution for early glaucoma diagnosis Zahir Hussain, Abdul Basith; Mohamed Sulaiman, Sulthan Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1661-1672

Abstract

Glaucoma is one of the common causes of blindness in the current world. Glaucoma is a blinding optic neuropathy characterized by the degeneration of retinal ganglion cells (RGCs). Accurate diagnosis and monitoring of glaucoma are challenging task through eye examinations and additional tests. To achieve accurate diagnosis of glaucoma with higher sensitivity and specificity, novel method called Weierstrass scale space representation and composite dilated U-net based convolution (WSSR-CDC) is introduced. At first, the Weierstrass transform scale space representation is employed to enhance image structures at various scales with higher accuracy of region of interest (ROI) detection using Euler’s identity. Next, CDC model is utilized with several layers. In input layer, preprocessed input images are taken as input. Fragment derivative are formulated for every preprocessed input. Log cosh dice loss function and optic cup to disc ratio are computed for segmented glaucoma detected results. With this, the accurate diagnosis of glaucoma is made with minimal error. The WSSR-CDC method was evaluated using the glaucoma fundus imaging dataset with several factors. The results show that the WSSR-CDC method outperforms conventional techniques, improving accuracy by 24% and sensitivity by 18%. It demonstrates promising results in fast, accurate, diagnosis of glaucoma.
Analysis of real-time multi-surveillance detection model using YOLO v5 Pramanik, Tapas; Burade, Prakash Gajananrao; Sharma, Sanjeev
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1634-1641

Abstract

Implementation of this advanced nighttime monitoring system provides one of the basic requirements toward the creation of an intelligent urban environment. The nighttime effective monitoring is highly enabled due to seamless integration of multi-directional cameras working as advanced sensors enhancing security measures in smart cities. This paper addresses the mentioned issues directly by proposing the you only look once version 5 (YOLOv5) model dedicated to object detection. It is experimentally confirmed, based on the dataset results, that the mean average precision of YOLOv5 multi-scale (YOLOv5MS) reaches an impressive 88.7%. The results unmistakably confirm domination of the model and its good ability to work over a network of more than 50 security cameras under the high restrictions of our operation. The use of state-of-art nighttime surveillance systems is an important constituent element in the construction of smart urban environment. The smooth interaction between multiple-angle cameras, which work as perceptive sensors, substantially upgrades the functionality of nighttime surveillance and strengthens security practices for smart cities. The current work presented the YOLOv5 model specifically designed for the task of target detection, targeting these issues head-on. The empirical data obtained from the dataset point to an outstanding mean average precision (mAP) of 88.7% for YOLOv5MS. Such results clearly prove the superiority of the model and demonstrate its excellent performance in a network of more than 50 security cameras under our harsh operational conditions.
Enhancing business analytics predictions with hybrid metaheuristic models: a multi-attribute optimization approach Syah, Rahmad B. Y.; Elveny, Marischa; Nasution, Mahyuddin K. M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1830-1839

Abstract

This approach aims to optimize business analytical predictions through multiattribute optimization using a hybrid metaheuristic model based on the modified particle swarm optimization (MPSO) and gravitational search optimization (GSO) algorithms. This research uses a variety of data, such as revenue, expenses, and customer behavior, to improve predictive modeling and achieve superior results. MPSO, an interparticle collaborative mechanism, efficiently explores the search space, whereas GSO models’ gravitational interactions between particles to solve optimization problems. The integration of these two algorithms can improve the performance of business analytical predictions by increasing model precision and accuracy, as well as speeding up the optimization process. Model validation test results, precision 95.60%, recall 96.35%, accuracy 96.69%, and F1 score 96.11%. This research contributes to the development of more sophisticated and effective business analysis techniques to face the challenges of an increasingly complex business world.
Random forest method for predicting discharge current waveform and mode of dielectric barrier discharges Abdelhamid, Laiadi; Abdellah, Chentouf; Mostafa, Ezziyyani
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp101-109

Abstract

This study addresses the classification of Homogeneous and Filamentary discharge modes in dielectric barrier discharge (DBD) systems and predicts the Homogeneous current waveform using machine learning (ML). The motivation stems from the need for accurate modelling in non-thermal plasma systems. The problem tackled is distinguishing between these two modes and predicting the current waveform for Homogeneous discharge. A random forest classification algorithm is applied, using experimental features such as applied voltage, frequency, gas gap, dielectric material, and gas type. An exponential model is proposed for the discharge current, with Gaussian regression transforming the model’s parameters. The classification results are evaluated through a confusion matrix, showcasing 80% accuracy in distinguishing discharge modes. The regression analysis reveals strong Pearson correlation coefficients between predicted and experimental waveforms. In conclusion, the results demonstrate the efficacy of ML techniques in enhancing DBD system modelling, though improvements can be made by expanding the dataset and refining feature selection for better classification and prediction performance.
Low-resolution image quality enhancement using enhanced super-resolution convolutional network and super-resolution residual network Riftiarrasyid, Mohammad Faisal; Halim, Rico; Novika, Andien Dwi; Zahra, Amalia
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp634-643

Abstract

This research explores the integration of enhanced super-resolution convolutional network (ESPCN) and super-resolution residual network (SRResNet) to enhance image quality captured by low-resolution (LR) cameras and in internet of things (IoT) devices. Focusing on face mask prediction models, the study achieves a substantial improvement, attaining a peak signal-to-noise ratio (PSNR) of 28.5142 dB and an execution time of 0.34704638 seconds. The integration of super-resolution techniques significantly boosts the visual geometry group-16 (VGG16) model’s performance, elevating classification accuracy from 71.30% to 96.30%. These findings highlight the potential of super-resolution in optimizing image quality for low-performance devices and encourage further exploration across diverse applications in image processing and pattern recognition within IoT and beyond.

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