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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 36, No 3: December 2024" : 65 Documents clear
Data mining implementation: a survey Widayat, Wisnu; Assiroj, Priati; Sohirin, Sohirin; Prabadhi, Isidorus Anung; Kautsar, Pasha Adelia
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1960-1968

Abstract

In the current era, the relentless advancement of information technology necessitates efficient information acquisition, which relies on proper data processing. To address the challenges in data organization, data mining emerges as a pivotal solution. This study aims to delve into various methodologies for data grouping. Employing a survey approach, the research scrutinizes journals published from 2020 to 2024. The findings illuminate prevalent techniques, algorithms, and software tools utilized in similar research domains. Notably, the study reveals that the predominant approach entails clustering via K-Means leveraging RapidMiner. This insight underscores the significance of employing robust methodologies and tools to streamline data processing and analysis in the contemporary information landscape. By elucidating the prevalent techniques and tools, this study contributes to enhancing understanding and fostering advancements in data mining practices, thereby facilitating more efficient data utilization and decision-making processes.
Impacts of electric vehicle charging stations and DGs on RDS for improving voltage stability using honey badger algorithm Thiruveedula, Madhubabu; Asokan, K.; Subrahmanyam, JBV
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1379-1388

Abstract

The intelligent computational technique used in this research handles the multi-objective voltage stability optimization (MOVSO) problem in radial distribution systems (RDS). The objectives of the proposed research are to minimize network loss, lower the average voltage deviation index (AVDI), and improve the voltage stability index (VSI) of RDS by taking into account the recently created distributed generators (DGs) and electric vehicle charging stations (EVCSs). To address the MOVSO problem, a novel and innovative honey badger algorithm (HBA) optimization technique is put forth. The two stages of HBA, known as the "digging" and "honey" phases, are responsible for effectively identifying the ideal position and appropriate quantity of EVCSs and DGs. The standard IEEE 33 node test system with different case studies is considered to validate the performance of HBA. The simulation results of improved voltage profile, minimized power loss, AVDI and improved VSI are tabulated. The proposed HBA fine-tunes the ideal position and size of the EVCSs to significantly enhance RDS performance under higher loading circumstances. To demonstrate the efficacy and originality of the suggested HBA, the numerical results are contrasted with those of earlier soft computing techniques.
Advancing elderly care through big data analytics and machine learning for daily activity characterization Allali, Ayoub; Bouanani, Nouama; Abouchabaka, Ibtihal; Rafalia, Najat
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1969-1975

Abstract

Confronted with the ongoing demographic shift characterized by an aging population, society grapples with emerging challenges that extend beyond the provision of targeted health services for the elderly. The focus has broadened to encompass the promotion of well-being and vitality throughout the aging process. Addressing these multifaceted issues demands a comprehensive approach that integrates biomedical components with physical, psychological, and social interventions. In the context of my project, a unique strategy is employed, placing significant emphasis on leveraging big data analytics and machine learning. The primary objective is to systematically observe and characterize the physiological conditions of the elderly, facilitating healthcare professionals in monitoring behaviors and promoting active aging. This undertaking involves meticulous data collection and analysis, employing machine learning algorithms (support vector machine (SVM), gradient boosting) within a framework that harnesses extensive data analytics. Ultimately, this approach enables the identification and characterization of daily routines and physiological states of individuals, contributing to a holistic understanding of aging.
Design and development of coastal marine water quality monitoring based on IoT in achieving implementation of SDGs Kustija, Jaja; Fahrizal, Diki; Nasir, Muhamad; Setiawan, Deny; Surya, Irgi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1470-1484

Abstract

Indonesia, an archipelagic nation with about 70% ocean territory, relies on oceanographic data for efficient marine environment monitoring and natural resource sustainability. Current data collection is limited by tools measuring only single parameters and lengthy data collection times. This study proposes a marine coastal water quality monitoring tool based on the internet of things (IoT), capable of simultaneously measuring temperature, electrical conductivity, pH, and dissolved oxygen. Utilizing an Atmega328 and a battery lasting up to 119 hours, this system offers a cost-effective solution for real-time oceanographic data collection. Employing the ADDIE methodology, the results demonstrate high measurement accuracy compared to traditional methods, with accuracy of 90.5% for temperature, 93.50% for electrical conductivity, 93.67% for pH, and 96.82% for dissolved oxygen. The development of this tool aims to reduce costs and labor in capturing oceanographic data integrated with IoT, facilitate access and monitoring of water data, and make a significant contribution to achieving SDGs targets. The main focus on the goals of addressing climate change and life underwater, especially in the aspects of water resources management and protection of marine ecosystems in Indonesian.
Transfer learning based leaf disease detection model using convolution neural network Raut, Rahul; Bidve, Vijaykumar; Sarasu, Pakiriswamy; Kakade, Kiran Shrimant; Shaikh, Ashfaq; Kediya, Shailesh; Borde, Santosh; Pakle, Ganesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1857-1865

Abstract

The plants are attacked from various micro-organisms, bacterial illnesses, and pests. The signs are normally identified via leaves, stem, or fruit inspection. Illnesses that generally appeared on vegetation are from leaves and causes big harm if not managed in the early ranges. To stop this huge harm and manipulate the unfold of disorder this work implements a software system. This research work customs deep neural network to gain knowledge of probable illnesses on leaves within the early phases so it can be stopped early. Deep neural network (DNN) used for image classification. This work mainly focuses a neural network model of leaves ailment detection. The commonly available plant leaves dataset is undertaken with a dataset having special training of disease detection. In this work VGG16, ResNet50, Inception V3 and Inception ResNetV2 architectural techniques are implemented to generate and compare the results. Results are compared on the factors like precision, accuracy, recall and F1-Score. The results lead to the conclusion, that the convolution neural network (CNN) is more impactful technique to perceive and predict plant diseases.
Big data and ensemble learning for effective student orientation in Morocco Badrani, Morad; Marouan, Adil; Kannouf, Nabil; Chetouani, Abdelaziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1904-1910

Abstract

Guiding high school students toward suitable educational paths is a complex challenge, particularly influenced by academic performance. In Morocco, first-year high school students in the scientific branch face a crucial decision when selecting between science mathematics (SM), physics (SF), and Science of Life and Earth (SVT) paths. This decision is critical as it can significantly impact their future academic and professional success. To address the issue of suboptimal student orientation, this study proposes an automated, personalized approach leveraging big data technology. By employing ensemble learning techniques, including random forest and neural network models, we developed a classification system to predict students’ optimal paths based on their academic performance. Our models achieved exceptional performance, with precision, accuracy, recall, and F-measure scores of approximately 98.59%, 98.60%, 98.60%, and 98.58%, respectively. This research demonstrates the potential of our approach to enhance educational support and decision-making, ultimately improving student outcomes and reducing dropout rates caused by wrong orientation.
Optimized deep neural network based vulnerability detection enabled secured testing for cloud SaaS Vallabhaneni, Rohith; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Vaddadi, Srinivas A.; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1950-1959

Abstract

Based on the information technology service model, an on-demand services towards user becomes cost effective, which is provided with cloud computing. The network attack is detected with research community that pays huge interest. The novel proposed framework is intended with the combination of mitigation and detection of attack. While enormous traffic is obtainable, extract the relevant fields decide with Software-as-a-service (SaaS) provider. According to the network vulnerability and mitigation procedure, perform deep learning-based attack detection model. The golf optimization algorithm (GOA) done the selection of features followed by deep neural network (DNN) detect the attacks from the selected features. The correntropy variational features validates the level of risk and performs vulnerability assessment. Perform the process of bait-oriented mitigation during the phase of attack mitigation. The proposed approach demonstrates 0.97kbps throughput with 0.2% packet loss ratio than traditional methods.
Battery charging system for electric vehicle Kotmire, Nayan J.; Kakade, Anandrao B.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1400-1408

Abstract

Selecting the appropriate charger for electric vehicles (EVs) is crucial for enhancing performance, with non-isolated DC-DC converters playing a significant role in charging EV batteries. The efficient conversion of input power into output as per the requirement is main perspective in the design of DC-DC converters. This paper delves into the landscape of non-isolated DC-DC converters utilized in EV charging, emphasizing their pivotal role. Additionally, it introduces a novel approach by incorporating machine learning-based pulse width modulation (PWM) control for the buck DC-DC converter. By integrating machine learning algorithms into the control scheme, the efficiency and performance of the charging system can be greatly enhanced, resulting in improved overall EV operation. This innovative application of machine learning not only optimizes charging efficiency but also enables adaptability to varying input/output conditions, ultimately leading to more efficient and effective charging processes for EVs.
Automated blood cancer detection models based on EfficientNet-B3 architecture and transfer learning Alshdaifat, Nawaf; Owida, Hamza Abu; Mustafa, Zaid; Aburomman, Ahmad; Abuowaida, Suhaila; Ibrahim, Abdullah; Alsharafat, Wafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1731-1738

Abstract

In blood smear images, there are difficulties in diagnosing blood cancer diseases like leukemia and lymphoma because of their various forms that appear in the human body. In this paper, a method for automatic detection of blood cancer is suggested that uses the EfficientNet-B3 architecture along with transfer learning techniques to improve accuracy and efficiency. We first fine-tuned the EfficientNet-B3 model, which was pre-trained on a large dataset consisting of annotated blood smear images, to capture pertinent features linked with blood malignant cells. To expedite the training process and adapt the model to our task, we use transfer learning. The proposed approach’s results from our experiments show that it outperforms traditional deep learning models and state-of-the-art methods in blood cancer detection. Additionally, with high precision and recall rates, this model also detects different types of blood cancers with robustness in its performance since its accuracy is over 99%. This means that when used together with the EfficientNet-B3 architecture, transfer learning can help the developed methods generalize among different types of blood cancers and conditions.
Energy baseline model enhanced based on artificial neural network in industrial buildings Ouaomar, Younes; Benkachcha, Said; Kaddiri, Mourad
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1493-1502

Abstract

In this article, a new energy-efficient reference model has been established for a plastic injection molding plant. However, the proposed model handled difficulties due to the lack of robust and complete data, such as production mix and cooling degree-days. In addition, the proposed model applies three distinct enhanced modeling methodologies, including regression modeling, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Furthermore, these performance parameters were established to assess the accuracy of each model in this work. Moreover, the numerical results show that among the methodologies used in this work, the ANN demonstrated effective performance despite uncertainties in the measured input variables. The ANN numerical results in this paper highlight the ability to accurately assess baseline consumption in the industrial sector, providing a practical tool for decision-makers to improve energy efficiency.

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