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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 35, No 3: September 2024" : 65 Documents clear
Combination certainty factor method and fuzzy expert system module to determine the dose of leukemia drugs Krisbiantoro, Dwi; Wanti, Linda Perdana; Adi Prasetya, Nur Wachid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1915-1923

Abstract

Leukemia is a type of blood cancer. Treatment for leukemia patients can last for years because the dose of medication given is adjusted to the patient's immune system. The aim of this research is the use of information technology through a combination of certainty factors and the development of a fuzzy expert system (FES) module to determine the therapeutic schedule for administering leukemia drugs. The urgency of this research is to help medical personnel in measuring the dose of leukemia medication to be given to patients so as to increase the cure rate for leukemia patients. The method used is certainty factor and fuzzy logic. The combination of the certainty factor method and the FES module which is carried out using input variables in the form of the severity of the leukemia suffered by the patient is to produce an appropriate therapeutic schedule for administering leukemia drugs. The result of this research is a combination of the factor certainty method and the FES module which has been tested and the accuracy level is 95.17%, the same as recommendations from experts.
Microarray classification using genetic algorithm and latin hypercube sampling Awangditama, Bangun Rizki; Suciati, Nanik
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1976-1985

Abstract

Cancer, the second leading cause of global death, requires advanced diagnostic technology. Microarray gene expression technology plays an important role in comprehensively analyzing the genetic aspects of cancer. However, challenges such as high-dimensional attributes, limited samples, and varying gene presence rates hinder the accurate classification of microarray data. This study proposes a model that uses latin hypercube sampling (LHS) in genetic algorithms (GA) for Feature Selection in microarray data classification. LHS makes the chromosome samples in the initial population of GAs representative and diverse. The study used three microarray datasets with different numbers of features and classes. The results reveal that first, the use of GA alone tends to limit the exploration of the resulting feature space, while the use of LHS can expand the feature selection possibilities in the context of feature selection. Secondly, this study shows that microarray classification using GA with LHS (GALHS) consistently outperforms other feature selection methods such as based correlation features (BCF), principal component analysis (PCA), relief, and lasso. Thus, this research contributes to feature selection by applying LHS and GA to optimize the performance of microarray data classification models.
Evaluating the impact of downsampling on 3D MRI images segmentation results based on similarity metrics Fajar, Aziz; Sarno, Riyanarto; Fatichah, Chastine
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1590-1600

Abstract

Medical imaging plays a crucial role in diagnosing patient conditions, with magnetic resonance imaging (MRI) standing as a significant modality for numerous years. However, leveraging convolutional neural network (CNN) architectures like U-Net and its variations for anatomical segmentation demands considerable memory, particularly when working with full 3D image sets. Therefore, downsampling 3D MRIs proves advantageous in reducing memory consumption. Nevertheless, downsampling leads to a reduction in voxel count, potentially impacting the performance of commonly used segmentation metrics. The jaccard similarity index (JSI), dice similarity coefficient (DSC), and structural similarity index (SSIM) are extensively employed in image segmentation contexts. Hence, this study employs all three metrics to assess downsampled images and evaluate the robustness of the metrics when used to evaluate the downsampled 3D MRI images. The results show that JSI and DSC are more robust than SSIM when handling the downsampled data.
Bio-inspired wireless sensor networks - a protocol for an enhanced hybrid energy optimization routing Joshi, Rati D.; Banu, Sameena
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1808-1816

Abstract

Recently, there has been a focus on the significance of swarm intelligence-inspired routing algorithms for achieving optimum solutions in biologically inspired wireless sensor networks (WSNs). These protocols depict a network of wireless mobile nodes forming an infrastructure that is agile, dynamic, and independent of a central administrative facility. Among the challenges faced by bio-inspired WSNs, mobility awareness and excessive energy consumption (EC) stand out as significant hurdles, particularly in dynamic models with intermittent connections. This project seeks to tackle these obstacles by deploying the hybrid energy efficiency (HEED) approach to distributed clustering for network system cluster formation, along with fusion routing protocol of particle swarm optimization (PSO) and PIO to select cluster-heads and optimize solutions in bio-inspired WSNs. The success of the suggested approach is assessed using a variety of criteria, such as energy usage, rate of packet delivery, EC, and routing overhead and network lifetime. The methods like ad hoc on-demand distance vector's (AODV) and ant colony optimization (ACO) methods are employed in the testing and validation. In comparison to the reactive AODV routing protocol and ACO, the suggested routing protocol (HPSOPIO) reduces energy usage and increases network lifespan.
Optimizing feature extraction for tampering image detection using deep learning approaches Muniappan, Ramaraj; Sabareeswaran, Dhendapani; Jothish, Chembath; Raja, Joe Arun; Selvaraj, Srividhya; Nainan, Thangarasu; Ilango, Bhaarathi; Sumbramanian, Dhinakaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1853-1864

Abstract

Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.
Enhanced query performance for stored streaming data through structured streaming within spark SQL Jose, Benymol; N., Rajesh; Joseph, Lumy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1744-1750

Abstract

Traditional database systems like relational databases can store data which are structured with predefined schema, but in the case of bigdata, the data comes in different formats or are collected from diverse sources. The distributed databases like not only spark querying language (NoSQL) repositories are often used in relation to bigdata analytics, but a continual updating is required in business because of the streaming data that comes from stock trading, online activities of website visitors, and from the mobile applications in real time. It will not have to delay, for some report to show up, to assess and analyse the current situation, to move forward with the next business choice. Apache Spark’s structured streaming offer capabilities for handling streaming data in a batch processing mode with faster responses compared to MongoDB which is a document-based NoSQL database. This study completes similar queries to evaluate Spark SQL and NoSQL database performance, focusing on the upsides of Spark SQL over NoSQL databases in streaming data exploration. The queries are completed with streaming data stored in a batch mode.
Hybrid deep learning with pelican optimization algorithm for M2M communication on UAV image classification Sharmili, Kasturi Chandrahaasan; Kumar, Chevella Anil; Subbaiyan, Arunmurugan; Beena Bethel, Gundemadugula Nelson; Puliyanjalil, Ezudheen; Sapkale, Pallavi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1526-1534

Abstract

Machine-to-machine (M2M) communication for unmanned aerial vehicle (UAVs) and image classification is essential to current remote sensing and data processing. UAVs and ground stations or other linked devices exchange information seamlessly using M2M communication. M2M connectivity helps UAVs with cameras and sensors communicate aerial pictures in real time or post-mission for image categorization and analysis. During flight, UAVs acquire massive volumes of picture data. Image classification, commonly using deep learning (DL) methods like convolutional neural network (CNN), automatically categorizes and annotates photos based on predetermined classes or attributes. This work uses UAV photos to produce hybrid deep learning with pelican optimization algorithm for M2M communication (HDLPOA-M2MC). HDLPOA-M2MC automates UAV picture class identification. GhostNet model is used to derive features in HDLPOA-M2MC. The HDLPOA-M2MC approach leverages pelican optimization algorithm (POA) for hyperparameter adjustment in this investigation. Finally, autoencoder-deep belief network (AE-DBN) model can classify. The HDLPOA-M2MC method’s enhanced outcomes were shown by several studies. The complete results showed that HDLPOA M2MC performed better across measures.
MobileNet based secured compliance through open web application security projects in cloud system Vallabhaneni, Rohith; Vaddadi, Srinivas A; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1661-1669

Abstract

The daunting issues that are promptly faced worldwide are the sophisticated cyber-attacks in all kinds of organizations and applications. The development of cloud computing pushed organizations to shift their business towards the virtual machines of the cloud. Nonetheless, the lack of security throughout the programmatic and declarative levels explicitly prone to cyber-attacks in the cloud platform. The exploitation of web pages and the cloud is due to the uncrated open web application security projects (OWASP) fragilities and fragilities in the cloud containers and network resources. With the utilization of advanced hacking vectors, the attackers attack data integrity, confidentiality, and availability. Hence, it’s ineluctable to frame the application security-based technique for the reduction of attacks. In concern to this, we propose a novel Deep learning-based secured advanced web application firewall to overcome the lack of missing programmatic and declarative level securities in the application. For this, we adopted the MobileNet-based technique to ensure the assurance of security. Simulations are effectuated and analyzed the robustness with the statistical parameters such as accuracy, precision, sensitivity, and specificity and made the comparative study with the existing works. Our proposed technique surpasses all the other techniques and provides better security in the cloud.
Monitoring water quality parameters impacted by Indonesia’s weather using internet of things Riftiarrasyid, Mohammad Faisal; Soewito, Benfano
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1426-1436

Abstract

Increasing need for food resources, State of Indonesia to strive to maximize the output of food production. Not only in agriculture but also aquaculture results are also trying to be improved. This is also supported by the increase of Indonesia’s national fish consumption rate from 50.69 Kg per capita in 2018 to 55.37 Kg per capita in 2021. Recent aquaculture research only explored topics about monitoring the cultivation environment. But there have been no studies exploring how bad the impact of weather on the process of farming. Hence, this study aims to measure the influence of weather on freshwater aquaculture pond water quality and analyze its impact on fish growth namely Oreochromis Sp., using pH sensors and dissolved oxygen (DO). Then a weather simulation was carried out based on Indonesia’s tropical climate, which majorly consists of sunny and rainy weather. The experimental results indicate the instability of the pH value during the rainy period. DO values tend to decrease at the end of periods of sunny weather. Moreover, fish growth analysis showed that there was a decrease in food conversion ratio (FCR) by 0.956, specific growth rate (SGR) by 2.13% and survival rate (SR) by 5.715% during rainy weather.
Sustainable energy harvesting system for low-power underwater sensing devices Salagare, Sahana; Sudha, Pattipati Naga; Palani, Karthik
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1379-1387

Abstract

In marine scientific research, ocean monitoring is crucial where the battery powered sensor devices are placed under the water to collect different information like temperature, pressure, and turbidity in underwater sensor networks (UWSNs). Thus, keeping these devices active for longer periods is challenging. In the last decades, the piezoelectric transducer (PZT) material has been used widely for constructing more environmentally friendly energy harvesting systems. The PZT harvester offers a promising solution by eliminating the need for batteries for running devices in the future with less maintenance. The PZT harvester allows the system to generate higher voltage to run low-power devices. This paper designed and developed a new renewable energy harvester system using PZT transducers for running different types of underwater sensor devices like temperature, turbidity, and obstacle sensors. The proposed PZT-based energy harvester employs a two stage amplification model for generating higher voltage and current to run multiple devices. The sensing information collected from these sensors is transmitted to the cloud which is later utilized for analysis and decision making. Experiment results show the proposed PZT-based energy harvester can generate a voltage of 13 volts (V) and a current of 43.3 milliampere (mA) equivalent to 562 milliwatt (mW) which is very good to run multiple low-power underwater sensor devices.

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