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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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An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm Palayanoor Seethapathy Ramapraba; Moorthy Radhika; Sokkanarayanan Sumathi; Jayavarapu Karthik; Nachiappan Senthamilarasi
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.pp1698-1707

Abstract

Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.
Water quality monitoring with an early warning system for enhancing the shrimp aquaculture production Guruh Aryotejo; Prajanto Wahyu Adi; Eko Adi Sarwoko
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1042-1051

Abstract

Aquaculture provided 43% of the aquatic animal food consumed by humans in 2007, and it is anticipated to expand even more to meet the increasing future demand. Marine Science Techno Park (MSTP) is one of the Techno Parks in Indonesia and is located in Teluk Awur, Jepara. MSTP has an intensive system of Vannamei shrimp farming activities. The challenge that has been faced annually is the organic matter of the remaining shrimp feed that accumulates in the waters can cause a decrease in pond water quality. Ammonia-N anions derived from the decomposition of feed residues can cause toxins in shrimp culture ponds which can further interfere with shrimp survival. The objective of this study is to design a self-controlled water quality monitoring system that is equipped with an early warning system based on internet of things (IoT) and to implement the design for analyzing the water quality including dissolved oxygen (DO), pH, and temperature in the Vannamei farming pond of MSTP UNDIP through integrating IoT which is equipped with an early warning system. If the water quality reaches a threshold value, the monitoring system will send a sound signal to the buzzer followed by sending an alert notification in real-time conditions automatically to a smartphone.
Text Classification on Customer Feedback: A Systematic Literatures Review Zuleaizal Sidek; Sharifah Sakinah Syed Ahmad; Yogan Jaya Kumar; Noor Hasimah Ibrahim Teo
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1258-1267

Abstract

User feedback in text classification serves multiple purposes, including refining models, enhancing datasets, adapting to user preferences, identifying emerging topics, and evaluating system performance, all of which contribute to the creation of more effective and user-centric classification systems. Many text classification techniques, including data mining, machine learning, and deep learning approaches, have been employed in previous literature, each making significant contributions to the field. This paper aims to contribute by guiding researchers seeking commonly used classification techniques and evaluation metrics in text processing. Additionally, it identifies the classification technique that generates higher accuracy and works as a basis for researchers to synthesize studies within their respective fields. PRISMA methodology is adapted to systematically review 28 current literatures on text classification on user feedback. The results obtained are guided by four research questions; paper distribution year, dataset source and size; evaluation metric and model accuracy. The review has shown that support vector machines (SVM) are frequently employed and consistently achieve high levels of accuracy as high as 97.17% with various datasets used. The future direction of this work could explore models that integrate sentiment analysis and natural language understanding to more accurately capture nuanced user opinions and preferences.
Improving night driving behavior recognition with ResNet50 Muhammad Firdaus Ishak; Fadhlan Hafizhelmi Kamaru Zaman; Ng Kok Mun; Syahrul Afzal Che Abdullah; Ahmad Khushairy Makhtar
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.pp1974-1988

Abstract

The issue of driving behavior at night poses significant challenges due to reduced visibility and increased risk of accidents. Recent works have leveraged deep learning techniques to enhance night-time driving safety. However, the limited availability of high-quality training data and the lack of robustness in existing models present significant problems. In this work, we propose a novel approach to improve driving behavior recognition at night using ResNet50 with contrast limited adapted histogram equalization (CLAHE). We collected a new dataset and developed a more effective and robust model that can accurately recognize driving behaviors under low-illumination conditions, thereby reducing the likelihood of collisions and improving overall road safety. The experimental results demonstrate significant improvements in the deep learning model’s performance compared to conventional methods. Notably, the ResNet50 model delivers the best performance with accuracy rates of 90.73% using NIGHT-VIS-CLAHE data, demonstrating a 16% improvement in accuracy. For benchmark purposes, the InceptionV3, GoogleNet, and MobileNetV2 models also show enhanced accuracy through CLAHE implementation. Furthermore, NIGHT-VIS-CLAHE implementation in ResNet50 achieved 90.29% accuracy, surpassing the best NIGHT-IR InceptionV3 at 89.27%, highlighting the advantage of ResNet50 with CLAHE in low-light conditions even against infra-red sensor.
Substations power quality improvement by flexible alternating current transmission system devices Faissl G Chremk Chremk; Hanene Medhaffar
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.pp671-686

Abstract

The increase in load demand has a negative impact on power system quality, reliability, and stability. Transporting reactive power from the generating side to the distribution side reduces the transmission line’s capacity to deliver active power to loads. In conventional power systems, during faults or sudden loads, the load flow changes and causes extra voltage drop. Reactive power compensation at the load side can improve voltage quality at distribution busbars, but the conventional compensation method doesn’t offer the required flexibility. With the development of the power electronics industry, flexible alternating current transmission system (FACTS) devices have been introduced. There are many categories for FACTS controllers, including series, shunt, and hybrid, which can exhibit dynamic operating characteristics. In this paper, two types of FACTS devices (STATCOM and fixed capacitor-thyristor-controlled reactors (FCTCR)) have been used to improve voltage profile in a part of Iraq’s high voltage power system during unusual operating conditions, such as sudden loads and the absence generation. Load flow calculations were conducted to determine the critical bus, and then the performance of the FACTS devices was investigated during heavy loading when the devices were connected separately to the critical bus. The voltage drop in the bus was 1.58%, which reduced to 0.78% with STATCOM and 0.96% with FCTCR.
Efficient and robust disaster recovery system using cloud-based algorithms with data integrity Gurulakshmanan, Gurumoorthi; Amarnath, Raveendra Nandhavanam
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.pp388-396

Abstract

Incorporating cloud-based algorithms for disaster recovery (DR), it explores data replication, failover, virtual machine (VM) migration, and consistency algorithms. These algorithms play a pivotal role in safeguarding data and system continuity during unforeseen disruptions. Data replication ensures redundancy, failover algorithms swiftly transition to backup resources, VM migration facilitates resource optimization, and consistency algorithms maintain data integrity. Leveraging cloud technology enhances the effectiveness of these algorithms, providing robust DR solutions critical for business continuity in today's digital landscape. The recent growth in popularity of internet services on a massive scale has also raised the demand for stable underpinnings. Despite the fact that DR for big data is frequently overlooked in security research, the majority of existing approaches use a narrow, endpoint-centric approach. The significance of DR strategies has grown as cloud storage has become the norm for more data. But traditional cloud-centric DR techniques may be expensive, thus less expensive alternatives are being sought. There is persistent concern in the information technology (IT) community about whether or not cloud service providers (CPs) can guarantee data and service continuity in the event of a disaster.
Cybersecurity integration in distance learning: an analysis of student awareness and attitudes Adnan Hnaif; Areej Mofeed Derbas; Sally Almanasra
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.pp1057-1066

Abstract

With the rapid growth of distance learning, especially since the COVID-19 pandemic, cybersecurity has become increasingly essential to protect students, instructors, and institutions from cyber threats. This paper examines the role of cybersecurity in enhancing students’ security awareness during distance learning. A literature review covers critical cyber threats in distance learning and strategies to mitigate risks through cybersecurity tools, policies, training, and promoting a culture of cybersecurity. Primary research was conducted by surveying 531 university students engaged in distance learning to assess their cybersecurity awareness, attitudes, and behaviors. Results indicate relatively low awareness and adoption of secure practices. Recommendations include implementing multi-layered cybersecurity defenses, student security awareness training, and nurturing a “human firewall” through a cyber-aware campus culture. Cyber risks can be reduced through proactive partnerships between students, faculty, information technology (IT) staff, and administrators to secure distance learning environments.
Analysis of linear congruent methods and multiplicative random number generator in computer-based test Amrullah Amrullah; Al-Khowarizmi Al-Khowarizmi; Firahmi Rizky
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.pp1521-1532

Abstract

This research focuses on the implementation of computer - based test exams in high schools which face the problem of not having differences in exam questions which results in weak security and validity of exam results. Therefore, a randomization method is needed to overcome this problem. The method used in random ization is linear congruent methods and multiplicative random number generator. There are 101 random questions, but only 40 questions are displayed for each student with a reference value of Xn and C, 2 will be added for each package of exam questions and to avoid question code=0, the calculation results will be added 1. The linear congruent methods (LCM) results achieve 100% accuracy, while the method The multiplicative random number generator (MRNG) only achieved 62.5% accuracy in randomizing the exam que stions. This accuracy comparison highlights the difference in the ability of the two methods to generate random permutations of test item packages. LCM randomization accuracy ensures that each student will receive a different set of test questions in a con sistent manner. However, the low accuracy of randomization using MRNG indicates a weakness in generating permutations of exam question packages. The results of this study show that the LCM method is better than the MRNG in conducting exams.
Intelligent preliminary diagnosis system for diseases with similar clinical presentation Laberiano Andrade-Arenas; Cesar Yactayo-Arias
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1131-1143

Abstract

Accurately identifying diseases with similar symptoms, especially in resource-limited medical settings, is a key challenge to diagnostic accuracy. This paper presents a preliminary diagnostic system to address the challenge of diagnosing diseases with similar symptoms. The system has been implemented using the PyQt5 library and employs a unique symptom identification algorithm developed in Python. Furthermore, to carry out the diagnosis, it uses comma-separated values (CSV) and excel files as databases where the diseases and their respective symptoms are stored. The results show that the system has a precision of 95%, a sensitivity of 90%, and a specificity of 93% after evaluating 35 clinical cases covering seven diseases with similar initial symptoms, namely: dengue, zika, chikungunya, COVID-19, influenza, monkey-pox, and the common cold. Furthermore, the positive evaluation of the technical performance of the system by experts supports its practical feasibility and its potential as a valuable tool in medical practice. In conclusion, the system diagnoses diseases by analyzing the symptoms of the information file, highlighting its usefulness in improving the diagnostic accuracy of similar cases and optimizing medical care for the benefit of patients.
The development of color histogram method to identify air quality index based on sky images Sofika Enggari; Sumijan Sumijan; Muhammad Tajuddin
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.pp186-196

Abstract

Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.

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