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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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Inventory model having preservation technology with fix lifetime under two level trade credit policy Sharma, Deep Kamal; Singh, Ompal
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp610-619

Abstract

Supply-chain management involves moving storage supplies from origin to consumption, with manufacturers running production based on quadratic demand, distributors and retailers monitoring inventory. When a new product is released, demand often rises linearly and then declines dramatically when an alternative becomes available. Shortages are not allowed. Players' inventory will decrease at a rate of (1/(1+m-t)), where m is fixed lifetime, greater than the replenishment time. Deteriorating goods experience constant mass loss or usefulness, but preservation technology can help the damaged item to be consumed. Retailers with direct customer relationships can reduce stock spoilage through good warehouses. Manufacturers' storage systems have a higher deterioration rate. Two-tier trade credit financing is examined in this model. Distributors offer specific credit terms to stores, while manufacturers provide a grace period for invoicing. Distributors and retailers must pay interest on unsold inventories if invoices aren't settled on time. An integrated storage system reduces costs by minimizing costs through multiple shipments from manufacturers to distributors and retailers, and by adjusting replenishment times for each player. The resolution process is designed so that the supply chain operator gets the best possible decision. Therefore, results are authorized using mathematical examples for different scenarios. Management decisions are suggested.
ADEMNET architecture: An innovative solution for adaptive multi-class balancing problem in image classification Neetha Papanna Umalakshmi; Simran Sathyanarayana; Pushpa Chicktotlikere Nagappa; Thriveni Javarappa; Venugopal Kuppanna Rajuk
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1252-1260

Abstract

In the field of medical image processing, achieving high performance in the classification of four types of dementia poses a significant challenge. This research presents a novel approach that outperforms existing methodologies, bringing about a transformative impact in this specialized domain. The method integrates the adaptive synthetic–nominal (ADASYN) technique with a DEMNET framework, resulting in a substantial performance improvement of 95.45% compared to current benchmarks. Through meticulous experimentation on a dementia dataset encompassing four distinct types, we consistently demonstrate significant enhancements achieved by the refined strategy. This innovation not only raises the performance standard but also provides a robust and adaptable solution that can be easily integrated into existing systems. The implications of this advancement open up new avenues for both research and practical applications. This work exemplifies the power of innovative approaches to push the limits of performance and establishes a new benchmark for excellence within this specific domain.
Global software development agile planning model: challenges and current trends Hajar Lamsellak; Mohammed Ghaouth Belkasmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1774-1784

Abstract

Agile planning offers a number of benefits that make the customers active members of the team throughout the project. In global software development (GSD), geographic separations demand special attention to harness these benefits. Our paper conducted a systematic mapping study (SMS) to analyze GSD-specific agile planning challenges followed by a systematic literature review (SLR) for efficient solutions. These studies led to a model for agile planning in global software development supporting GSD practitioners during this process.
TQU-HG dataset and comparative study for hand gesture recognition of RGB-based images using deep learning Van-Dinh Do; Van-Hung Le; Huu-Son Do; Van-Nam Phan; Trung-Hieu Te
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1603-1617

Abstract

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.
For wireless LAN application, microstrip patch antenna design in S-band Md. Sohel Rana; Md. Soriful Islam Sourav; Md. Abdulla Al Mamun; Omer Faruk; Md. Mominur Rahaman; Md. Shehab Uddin Shahriar; Sukanto Halder; Md. Toukir Ahmed; Imran Chowdhury; Omar Faruq; Saikat Mondal; Md. Hasibul Islam; Shubhra Kanti Sinha Shuva
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp383-395

Abstract

This article presents a 3.5 GHz rectangular microstrip patch antenna (RMPA) designed, studied, and analyzed for wireless LAN applications. Using Fr-4 as substrate material, whose dielectric permittivity is 4.3, patch thickness is 1.65 mm, and loss tangent is 0.025. A feeding line with an impedance of 50 Ω is utilized to supply the antenna with power. Computer simulation technology (CST) software has been used to design the antenna and origin pro software has been used to display the resulting figures from the simulation. The antenna simulation showed that the return loss is -56.82 dB; the directivity gain is 6.02 dBi, the bandwidth is 0.148 GHz, and the voltage standing wave ratio (VSWR) is 1.0028. The paper aims to increase the return loss, develop a standard VSWR, increase the directivity gain of the antenna, and improve the antenna bandwidth. The results of the proposed antenna were much better than previously published papers, which were suitable for wireless applications. This proposed antenna can be used for future wireless LAN applications.
Hybrid encryption based on a generative adversarial network Amir, Iqbal; Suhaimi, Hamizan; Mohamad, Roslina; Abdullah, Ezmin; Pu, Chuan-Hsian
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp971-978

Abstract

In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob’s and Eve’s accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob’s neural network effectively secured the messages from Eve’s neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection.
Integrated energy-efficient and location-aware routing in wireless sensor networks Karur Mohammed Saifuddin; Geetha D. Devangavi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1708-1717

Abstract

Sensor nodes in wireless sensor networks are commonly distributed randomly across a given landscape, and their placement may be randomized for specific applications, even extending to national deployments. The energy consumption associated with data transmission and reception by the cluster’s leader is notably higher compared to other nodes. To address this issue, it is recommended that wireless sensor networks adopt a more energy-efficient routing technique. This proposed technique assumes a spatial separation between different node types. Elevating the threshold enhances the likelihood that nodes with ample remaining power will endure as cluster leaders. Ultimately, a hybrid data transfer strategy is formulated, wherein data is directly exchanged between the base station and cluster heads among the super nodes containing advanced nodes. Most nodes employ a combination of single-hop and multi-hop approaches for data transport, aiming to minimize the power required for transmission between the cluster’s control node and the base station. According to simulation results, this proposed method surpasses the stable election protocol (SEP), demonstrating superiority over the improved threshold-sensitive stable election protocol in terms of the operational duration of a wireless sensor network.
Automatic segmentation of human ear in the wild Rahul Lahkar; Khurshid Alam Borbora
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp333-341

Abstract

Ear biometrics has been a challenging and distinctive research area in recent times. The human ear possesses unique promising attributes that are being used by the researchers to carry out significant improvements in the field of human recognition using ear as a biometric. In order to achieve efficiency on any ear biometric system, the detection and segmentation of the human ear need to be performed precisely. Feeding accurately segmented images to the recognition system will result in higher recognition accuracy. In this paper, we present our work of segmentation of human ears from the images captured in unconstrained environment by employing the U-Net architecture on our own dataset and presented the results of ear segmentation. The U-Net model is also tested on the annotated web ears (AWE) segmentation dataset. We obtained 92.38% accuracy and 79.33% intersection over union (IoU) on the test data on our own dataset and 76.2% IoU on AWE segmentation dataset.
Effectiveness of VGG19 in deep learning for brain tumor detection Arlis, Syafri; Putra, Muhammad Reza; Yanto, Musli
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1210-1218

Abstract

Image processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients.
Enhanced ARIA-based counter mode deterministic random bit generator random number generator implemented in verilog Eugene Rhee; Jihoon Lee
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1416-1423

Abstract

This paper presents a study aimed at effectively implementing a deterministic random bit generator (DRBG) IP in verilog language, based on the standard encryption algorithm. By controlling the existing round generation and key generation blocks, the internal modules of the counter mode deterministic random bit generator (CTR-DRBG) were successfully implemented and operated, ensuring the secure and efficient generation of random bit sequences. The research focused on parallel operation of modules and optimized module placement to achieve improved clock frequencies. By concurrently operating two modules in the derivation and internal update modules of CTR-DRBG, the processing speed was enhanced compared to the conventional algorithm. Additionally, integrating the reseeding and initialization modules of CTR-DRBG into a single module successfully reduced size. Furthermore, this IP supports the special function register (SFR) interface. The safety of the CTR-DRBG was validated through known answer test (KAT) verification utilizing test vectors from certification. Future research should explore additional studies on CTR-DRBG operating on real FPGA or ASIC, not only using normal algorithm but also employing other block cipher algorithms.

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