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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 25 Documents
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Search results for , issue "Vol 12, No 4: December 2024" : 25 Documents clear
DermAI: An Innovative AI-Driven Chatbot for Enhanced Dermatological Diagnosis and Patient Interaction Rajeshkumar, Pradeep; Kharche, Shubhangi; Poojari, Prithvi; Utekar, Sachet; Saini, Sahil; Bidwai, Samriddhi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5806

Abstract

Skin disorders constitute a noteworthy public health concern globally, with earnest impacts on both physical and mental well-being. However, effective dermatological care faces challenges in resource-limited regions due to poor infrastructure, limited access to medical facilities & expertise, and inadequate advanced diagnostic tools. The existing research work majorly focuses on cancer and uncommon skin diseases with models trying to achieve a higher training accuracy with no regards to misclassification rate. The products currently available in the market provide a limited initial diagnosis and suggest consulting a doctor to get an accurate diagnosis or offer a list of other possible skin disorders. To address these challenges, we propose DermAI, an innovative AI-based Chatbot made entirely of open-source technologies, which integrates the ResNet50 model and LLM via Chainlit, with Retrieval Augmented Generation(RAG), utilising AstraDB vector database and OpenAI embedding model for personalised responses. enabling accurate classification of common skin diseases. The proposed DermAI ensures minimal misclassification and comprehensive coverage of diseases, leveraging Retrieval-Augmented Generation and comparative model analysis. The metrics indicate that the model has a high true positive rate, with a misclassification rate of 2.17%, mean sensitivity, specificity & AUC of 92.6%, 99.8% & 99.9% respectively. This is demonstrated in the situations of melanoma, chickenpox, shingles, impetigo, and nail fungus, where it obtained 100% validation accuracy, a feat not attained by previous studies. Additionally, the model is highly capable of correctly identifying negative cases. The hallucination metric suggests the model may have a minimal tendency to hallucinate as the average hallucination score of 7% which falls far within the manually set threshold value of 50%. By setting the threshold value to 50%, the model generates grounded answers that are pertaining to the knowledge base and also allows it to be flexible with its responses. Overall, DermAI outperforms all solutions proposed in research literature.
Design of Solar Home Charging for Individual Electric Vehicles: Case Study for Indonesian Household Nurwidiana, Nurwidiana; Nugroho, Dedi; Fatmawati, Wiwiek
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5078

Abstract

This study aims to examine the use of solar energy through Rooftop Photovoltaic (RPV) technology for solar-powered home charging of individual electric vehicles (EVs) in Indonesia. Simulations using HOMER Pro software are carried out to analyze both the energy and financial performance of the designed RPV system. According to the calculations, a 4 kW RPV system is required to meet the daily energy demand for EVs in the household sector. The off-grid RPV system design consists of 12 unit 325 wp PV panels, 36 units of 100AH battery as power storage, and a 4000-watt inverter to convert DC from RPV system into AC for battery charging. Simulation results from HOMER Pro software confirm that the designed RPV system can adequately supply the electricity needed for home charging, generating a total of 6449 Wh per year. With an annual energy consumption of approximately 4,190 kWh/year, the proposed system not only meets the daily energy needs of EVs but also provides excess power to be used by additional electrical equipment. Additionally, the proposed system can reduce 77.89 tons of CO2 emissions over the 25-year project lifespan.
Machine Learning-Driven Pre-Broadcast Video Codec Validation: Ensuring Seamless Television Transmission El Fayq, Khalid; Tkatek, Said; Idouglid, Lahcen
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5845

Abstract

This study addresses the critical challenge of ensuring uninterrupted television broadcasting by proactively detecting video codec errors, focusing on TV Laayoune, a prominent Moroccan channel. We developed a machine learningbased methodology that identifies incompatible codecs before they disrupt live broadcasts. The approach involves data collection from multiple sources, including TV Laayoune's archives, metadata extraction via FFmpeg, and a hybrid model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. Integrated into the broadcasting pipeline, this model achieved a 95% accuracy rate, significantly enhancing broadcast reliability and operational efficiency. Additionally, we propose a user-friendly interface for real-time error detection, comprehensive workflow integration, and automated alerts. This innovative solution addresses common broadcast challenges, reducing operational risks and improving the viewer experience.
Improved Lung Sound Classification Model Using Combined Residual Attention Network and Vision Transformer for Limited Dataset Jurej, Muhammad; Roslidar, Roslidar; Yunida, Yunida
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5530

Abstract

According to WHO data, the prevalence of respiratory disorders is increasing, exacerbated by a shortage of skilled medical professionals. Consequently, there is an urgent need for an automated lung sound classification system. Current methods rely on deep learning, but limited lung sound data resulted in low model accuracy. The widely used ICBHI 2017 dataset has an imbalanced class distribution, with a normal class at 52.8%, wheezing at 27.0%, crackles at 12.8%, and combined wheeze and crackles at 7.3%. The imbalance of the dataset may affect the model's efficiency and performance in classifying lung sounds. Given these data limitations, we propose a hybrid model, combining residual attention network (RAN) and vision transformer (ViT), to construct an effective respiratory sound classification model with a small dataset. We employ feature fusion techniques between convolutional neural network (CNN) feature maps and image patches to enrich lung sound features. Additionally, our preprocessing involves bandpass filtering, resampling sounds to 16 kHz, and normalizing volume to 15 dB. Our model achieves impressive ICBHI scores with 97.28% specificity, 92.83% sensitivity, and an average score of 95.05%, marking a 10% improvement over state-of-the-art models in previous research.
Cyber Security Threat Prediction using Time-Series Data With LSTM Algorithms Hakim, Lukman; Wulandhari, Lili Ayu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5648

Abstract

Cyber security remains a paramount concern in the digital era, with organizations and individuals increasingly vulnerable to sophisticated cyber-attacks. This study aims to develop and evaluate Long Short-Term Memory (LSTM) regression models to predict three types of cyber attacks: flood, spyware, and vulnerability. The LSTM algorithm is used to construct regression models for spyware, flood, and vulnerabilities within a firewall log dataset. The experiments demonstrate that preprocessing techniques such as normalization and standardization can positively impact model performance by reducing prediction errors and enhancing accuracy. The results of the experiments show that the model developed in this research exhibits potential in predicting cyber attacks. For the flood attack model, the best performance was achieved with an RMSE of 59.8810 and an R-Squared of 0.9214 after data standardization. The spyware attack model's best results were an RMSE of 133.9567 and an R-Squared of 0.7685 after standardization. In contrast, the vulnerability attack model showed limited improvement, with the best RMSE of 503.5521 and an R-Squared of 0.2358 after standardization. Moreover, real-time implementation and testing of these models in live network environments could validate their practical applicability and effectiveness.

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