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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 65 Documents
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Search results for , issue "Vol 38, No 3: June 2025" : 65 Documents clear
Energy efficient distributed intelligence on cognitive IoT gateway using MQTT protocols Krishna, Anitha; Balasubramanian, Muthu Kumar; Srinivas Desikachar, Venkatesh Prasad Kadaba
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.pp2059-2069

Abstract

Internet of Things (IoT) facilitates communication between machines and devices which plays a crucial role in the conservation of energy. In largescale multidomain environments securing the data exchange among various IoT devices and key sharing creates a significant challenge. However, the message queuing telemetry transport (MQTT) lacks functional security mechanisms as well as mutual authentication between brokers and clients. To address these issues, a novel Cognitive IoT in Teroperability Recognition USing deep learning (CITRUS) framework is developed for real-time decision-making and sharing information among multiple IoT systems. Initially, the healthcare and weather data are collected remotely by using interoperable sensors which are then fed to the deep learning (DL) module for efficient decision-making. The MQTT module makes an energy-efficient IoT data communication over a resource-constrained network and the QoS1 introduces an acknowledgment and retransmission mechanism to ensure message delivery. The efficacy of the CITRUS model has been analyzed in terms of accuracy (AC), recall (RC), F1-score (F1S), sensitivity, packet delivery ratio (PDR), transmission speed, communication overhead, packet loss ratio (PLR) and delay. The experimental result shows that the CITRUS method achieves 89.89% of delay whereas, the IHPEC, SemBox, and DynoIoT methods achieve 161.63%, 128.99%, and 111.70% respectively for efficient data transmission.
Holographic-based design, building, and testing of an RRP spherical robot for olive fruits harvesting Al-Habahbeh, Osama M.; Arabiat, Ayeh; Al-Kasasbeh, Riad Taha; Ayoub, Salam
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.pp1602-1612

Abstract

A revolute-revolute-prismatic (RRP) spherical robot has been designed, simulated, built, and tested. The robot is intended to perform olive fruit harvesting tasks. The design simulation is done using hologram tools. The design factors considered include reach, dexterity, accuracy, and productivity. Based on the results of the holographic simulation, a prototype was built and tested on real olive fruits. The end effector is equipped with a rake tool so that the robot can harvest multiple fruits in each stroke. The robot is controlled by Raspberry Pi while a stereovision camera enables 3-D vision. Once the camera detects the fruits, an inverse kinematics algorithm is initiated to find the location of the fruits. The fruit coordinates are commanded to the manipulator to perform the harvesting. The field tests showed that the manipulator is successful in performing the harvesting operations. To increase the harvesting efficiency, it is recommended to build a larger prototype.
CGDE-YOLOv5n: a real-time safety helmet-wearing detection algorithm Luo, Wanbo; Mohd Yassin, Ahmad Ihsan; Mohd Shariff, Khairul Khaizi; Raju, Rajeswari
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.pp1765-1781

Abstract

Due to numerous parameters and calculations, existing safety helmetwearing detection models are challenging to deploy on embedded devices. Therefore, this paper proposed a you only look once (YOLO) v5n-based lightweight detection algorithm called CGDE-YOLOv5n to address the shortcomings in the following areas: (i) the YOLOv5n algorithm was selected to minimize the model’s parameters and calculations, reducing the hardware cost. (ii) The convolutional block attention module (CBAM) was integrated into the backbone to enhance the network’s feature extraction capability. (iii) The neck was improved using the efficient re-parameterized generalized feature pyramid network (efficient RepGFPN) to enhance the multi-scale object detection capability. (iv) The C3 module was improved using the deformable ConvNets v2 (DCNv2) module to enhance the network’s adaptability to geometric changes of objects. (v) The complete intersection over union (CIoU) loss was replaced with focal-efficient IoU (focal-EIoU) loss to reduce the missed detection rate. Experimental results demonstrated that the customized gradient descent estimation (CGDE)- YOLOv5n achieved a mean average precision (mAP) 50 of 89.5% and recall of 84%, which is 1% and 0.8% higher than the YOLOv5n. In particular, the recall of workers not wearing safety helmets increased by 1.7%. Furthermore, the improved model achieved a detection speed of 68.5 frames per second (FPS), meeting the real-time requirements.
Ensuring transcript integrity with SHA-3 and digital signature standard: a practical approach Nur Alam, Wa Ode Siti; Sajiah, Adha Mashur; Bahtiar Aksara, La Ode Muhammad; Surimi, La; Ransi, Natalis; Nangi, Jumadil
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.pp1957-1969

Abstract

Academic transcripts are essential documents in higher education, reflecting students’ academic performance and capabilities. However, the current management of transcript data at Halu Oleo University (UHO) lacks safeguards against unauthorized alterations, compromising their authenticity. This study proposes a method using the secure hash algorithm 3 (SHA-3) and the digital signature standard (DSS) scheme to ensure the integrity of transcript data. A Python-based web module for managing transcripts and a signing program using SHA-3 and DSS were developed and implemented. This method digitally signs transcript files, ensuring that subsequent changes invalidate the current digital signature. Efficiency tests demonstrated an average signing time of 0.242 seconds, indicating a practical and efficient solution. The study’s findings emphasize how SHA-3 and DSS effectively authenticate academic transcript files, preventing unauthorized modifications and safeguarding the integrity of critical educational records. This method presents a robust and efficient solution for educational institutions to strengthen the security and reliability of their academic record management systems.
Remove glasses diffusion model an innovative conditioned of eye glasses removal with image diffusion model Yuliza, Yuliza; Muwardi, Rachmat; Yehezkiel, Galatia Erica; Yunita, Mirna; Lenni, Lenni
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.pp1503-1516

Abstract

The presence of eyeglasses in facial images poses challenges for image processing, particularly in facial recognition. This paper introduces the remove glasses diffusion model (RGDM), a conditioned denoising diffusion probabilistic model (DDPM) designed for precise glasses removal. RGDM employs conditional modeling to focus on the glasses region while seamlessly restoring facial features. An eyes position accuracy mechanism, leveraging facial landmarks, ensures accurate eye restoration post-removal. Comprehensive evaluations on the CelebA dataset demonstrate RGDM’s superior performance, achieving the lowest Fréchet inception distance (FID) of 27.09 and learned perceptual image patch similarity (LPIPS) of 0.299, outperforming state-of-the-art methods such as 3D synthetic, cycleconsistent generative adversarial network (CycleGAN), and eyeglasses removal generative adversarial network (ERGAN). These results highlight the model’s effectiveness in producing natural and high-fidelity facial reconstructions. This work advances glasses removal technology and underscores the significance of conditional models in image processing. The proposed approach has practical implications for facial recognition and image enhancement, paving the way for more accurate and robust real-world applications.
Region based lossless compression for digital images using entropy coding Vamsikrishna, Mangalapalli; Sudhakar, Oggi; Bugge, Bhagya Prasad; Kumar, Asileti Suneel; Thankachan, Blessy; Subrahmanyam, K.B.V.S.R.; Deepthi, Natha; Mande, Praveen
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.pp1870-1879

Abstract

Image compression is a method for reducing video and image storage space. Moreover, enhancing the performance of the transmission and storage processes is important. The region based coding technique is important for compressing and sending medical images. In the medical field, lossless compression can help telemedicine applications achieve high efficiency. It affects image quality and takes a long time to encode. As a result, this study proposes region-based lossless compression for digital images using entropy coding. The best performance is achieved by segmenting these areas. In this case, an integer wavelet transform (IWT) is utilized after the ROI of the image was manually generated. The IWT compression method is helpful for reversibly reconstructing the original image to the required quality. For enhancing the quality of compression, entropy coding is utilized. By passing images of varying sizes and formats, various quantitative metrics can be determined. The simulation results demonstrate that the region based lossless compression technique utilizing range blocks and iterations resulted in reduced encoding time and improved quality.
Intelligent transportation network-based congestion forecasting with federated learning and a convolutional neural network Pandurangan, Kamaleswari; Nagappan, Krishnaraj; Galeebathullah, B.; Karpagam, N. Shunmuga; Kumaran, N.; Navaneethan, S.
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.pp2041-2049

Abstract

The heavy traffic in growing cities hurts the environment, commuters, and economy. Predicting such difficulties early helps increase road network capacity and efficiency and reduce congestion. Many academicians and transportation engineers ignore traffic congestion prediction despite its importance. Insufficient computationally efficient traffic forecast systems and high-quality city-wide traffic data contribute to this. Provide useful information to reduce traffic and construct shorter, more energy-efficient routes. Data storage increases traditional traffic forecasting training, storage costs, and delay. Smarter algorithms can handle today’s city expectations because sensors can now communicate with their environment. A vibrant economy requires decent roads. Improving transportation requires uninterrupted highway traffic. To overcome these issues, smart city roadway traffic flow must be monitored in real time using enhanced internet of things (IoT) capabilities. Training data may contain sensitive information, raising privacy problems. This work addresses these issues by training the prediction model near data sources using federated learning (FL). The suggested strategy was tested using Mumbai, Chennai, and Bangalore traffic data. We compared the proposed method to centralized strategies to assess its efficacy. Our experiments confirm the model’s traffic jam prediction accuracy. Our approach outperforms auto-encoder and convolutional neural network (CNN) in computer efficiency and prediction.
Crop prediction using an enhanced stacked ensemble machine learning model Sudhan Reddy, D. Madhu; Rani, N. Usha
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.pp1840-1850

Abstract

In India, agriculture is a major sector that fulfils the population's food requirements and significantly contributes to the gross domestic product (GDP). The careful selection of crops is fundamental to maximizing agricultural yield, thereby elevating the economic vitality of the farming community. Precision agriculture (PA) leverages weather and soil data to inform crop selection strategies. Conventional machine learning (ML) models such as decision trees (DT), support vector classifier, K-nearest neighbors (KNN), and extreme gradient boost (XGBoost) have been deployed to predict the best crop. However, these model's efficiency is suboptimal in the current circumstances. The enhanced stacked ensemble ML model is a sophisticated meta-model that addresses these limitations. It harnesses the predictive power of individual ML models, stratified in a layered architecture to improve the prediction accuracy. This advanced model has demonstrated a commendable accuracy rate of 93.1% prediction by taking input of 12 soil parameters such as Nitrogen, Phosphorus, Potassium, and weather parameters such as temperature and rainfall, substantially outperforming the accuracies achieved by the individual contributing models. The efficacy of the proposed meta-model in crop selection based on agronomic parameters signifies a substantial advancement, fortifying the economic resilience of India's agriculture.
Ba3GdNa(PO4)3F:Eu2+ phosphor with blue-red emission colors on white-LED properties Dung, Nguyen Van; Quoc Anh, Nguyen Doan
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.pp1564-1571

Abstract

The blue/red-emission phosphor Ba3GdNa(PO4)3F:Eu2+ (BGN(PO)F-Eu) is used in this work for diodes emit white illumination (wLED). The phosphor is prepared using the solid-phase reaction. The suitable concentrations of Eu2+ ion dopant is about 0.7% and 0.9%. The BGN(PO)F-Eu phosphor can provide wLED light with the spectral wavelength in the region of blue (480 nm) and orange-red colors (595-620 nm). With the resulted emissions the phosphor can be appropriate for plant growing because they compatible with absorption spectra of plants’ carotenoids and chlorophylls for stimulating the photosynthesis. The phosphor influences on the wLED lighting properties depending on the doping dosages. It is possible to enhance the luminous intensity of the wLED with higher BGN(PO)F-Eu phosphor amount. Meanwhile, the color properties does not get significant improvements. Thus, the BGN(PO)F-Eu phosphor could be used with other luminescent materials to stimulate the hue rendering performance.
Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks Kannan, Deeba; Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan; Venkatarajan, Shanmugasundaram; Mahajan, Rashima; Gunasekaran, Brindha; Murugamani, Pandi Maharajan; Dhandapani, Karthikeyan
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.pp2090-2100

Abstract

In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.

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