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INDONESIA
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 13 Documents
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Search results for , issue "Vol. 7 No. 4 (2025): November" : 13 Documents clear
CoAtNet for Chest X-Ray Report Generation with Bi-LSTM and Multi-Head Attention Akbar, Rafy Aulia; Putra, Ricky Eka; Yustanti, Wiyli
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.271

Abstract

In clinical environments, Chest X-Ray (CXR) represents the most prevalent diagnostic instrument, particularly facilitating diagnostic procedures through medical report. However, manual report preparation is time-consuming, highly dependent on the expertise of radiologists, and carries the risk of errors due to high workloads and limited expert staff. Therefore, an automated system based on artificial intelligence is needed to ease the workload of radiologists while increasing consistency. This study aims to develop an automated medical report generation system with balanced data distribution, reliable encoder, and bidirectional contextual understanding. The main contributions of this study include the implementation of an undersampling strategy based on majority captions followed by oversampling on minority labels while maintaining a proportion of labels with higher frequencies, the use of Bi-LSTM with Multi Head Attention (MHA) to strengthen text context understanding, and the use of CoAtNet as a visual encoder that combines the strengths of CNN and Transformer. The methodology incorporates image preprocessing via gamma correction for contrast improvement, data selection, balancing through combined undersampling and oversampling, and CoAtNet implementation as encoder paired with Bi-LSTM and MHA as decoder. Experimental execution employed the IU X-ray dataset, with assessment conducted using BLEU and ROUGE-L metrics. Outcomes revealed that the CoAtNet configuration with Bi-LSTM and MHA, coupled with the undersampling-oversampling strategy, delivered superior performance evidenced by a cumulative score of 1.642, with BLEU-1 to BLEU-4 and ROUGE-L achieving 0.480, 0.329, 0.245, 0.183, and 0.405, respectively. These findings prove that the combination of data balancing strategies with CoAtNet and Bi-LSTM is able to produce more accurate automated medical reports and reduce bias towards the majority label.
Classification Of Cyber Attack And Anomaly In Web Server Using Transformer and Transfer Learning Prasetyo, Edi Dwi; Rahmat, Basuki; Sari, Anggraini Puspita
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.119

Abstract

Cybersecurity is a crucial aspect in maintaining the integrity and availability of information systems, especially on web servers which are vulnerable to various types of attacks and anomalies. This research aims to investigate the application of transfer learning in the classification of cyber attacks and anomalies on web servers. Transfer learning, a powerful deep learning approach, enables pre-trained models to adapt to new tasks with limited data, offering an efficient solution for detecting malicious activities and unusual patterns in web server logs. The goal is to improve detection accuracy while reducing the time and resources required to train models from scratch. This study uses a bi-layer classification approach with pre-trained Transformer models, RoBERTa and BERT, through transfer learning to detect cyber attacks and anomalies in web server log data. The process includes preprocessing the log data, extracting relevant features, and fine-tuning BERT to classify known attacks in the first layer, followed by RoBERTa in the second layer to detect unusual or unknown behaviors. Model performance is evaluated using accuracy, precision, recall, and F1-score, and results are compared with traditional deep learning methods like RoBERTa and BERT to highlight the advantages of this bi-layer transfer learning approach. The result of this proposed bi-layer classification method is improved performance in detecting cyber attacks and anomalies compared to using RoBERTa and BERT individually. By combining both models, the system is anticipated to achieve higher accuracy, better precision in identifying true threats, improved recall for detecting a wider range of attacks, and a more balanced F1-score. This layered approach leverages the strengths of both RoBERTa and BERT, enabling more robust and reliable threat detection, with reduced false positives and false negatives compared to single-model implementations. 
Gait Variability and Phase Segmentation in Obese and Normal Individuals Using Multi-Location IMUs and Hidden Markov Models Supervised Marginal Setiyadi, Suto; Muktar, Husneni; Cahyadi, Willy Anugrah; Widiyasari, Diyah; Ramadhani, Mohamad; Tang, Nigel Bryan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.269

Abstract

Obesity is known to disrupt motor control and biomechanics; however, detailed gait alterations in individuals with obesity remain underexplored, particularly in dynamic and real-world walking conditions. This study aims to quantitatively characterize gait differences between individuals with obesity and those of normal weight by analyzing postural and temporal gait parameters. The investigation focuses on pitch, roll, and cadence dynamics using body-worn inertial sensors, with phase transition modeling via Hidden Markov Models. This work proposes a novel framework that integrates multi-location Inertial Measurement Unit (IMU) sensors and a Hidden Markov Model–Supervised Marginal (HMM-SM) approach to detect and classify gait phases with high accuracy, offering practical value for clinical gait assessment and personalized rehabilitation. IMU sensors were placed on the waist, thigh, calf, and heel to record gait data from participants in both obese and normal-weight groups. Gait segmentation and phase modeling were conducted using 4-, 5-, and 8-state HMMs. Quantitative analysis revealed significantly greater postural variability in the obese group during slow walking, with standard deviations in roll and pitch reaching 20.68° and 9.23°, respectively—much higher than the normal-weight group (0.60° and 0.26°). Hidden state transitions from 5-state pitch HMMs showed a very strong effect size for the obese group (Cramér’s V = 0.72) compared to a moderate effect for the normal-weight group (V = 0.33). Similar patterns were observed for roll and cadence. In terms of segmentation accuracy, the 4- and 5-state HMMs outperformed the 8-state model, achieving accuracy levels above 99%, while the 8-state model reached only ~93%. The findings demonstrate that obesity significantly alters gait dynamics, particularly in postural stability and gait phase transitions. The proposed IMU-based HMM-SM framework effectively captures these changes, offering a reliable tool for gait analysis in clinical and biomechanical applications.

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