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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 12 Documents
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Search results for , issue "Vol 6 No 2 (2024): April" : 12 Documents clear
Internet of Medical Things and the Evolution of Healthcare 4.0: Exploring Recent Trends Mukhopadhyay, Manishka; Banerjee, Subhrajyoti; Das Mukhopadhyay, Chitrangada
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.402

Abstract

Enhanced patient care and remote health monitoring have always been important issues. Internet of Medical Things (IoMT) is a subsection of Healthcare 4.0 that uses recent technologies like mobile computing, medical sensors, and cloud computing to track patients' medical information in real-time. These data are stored in a cloud computing framework that may be accessed and analyzed by healthcare experts. IoMT and Healthcare 4.0 have immense potential for revolutionizing patient care and diagnostics, despite facing numerous complex challenges. This paper thoroughly analyzes technical, structural, and regulatory obstacles encountered by the healthcare sector. Challenges in IoMT implementation include cost considerations, network stress, interoperability issues, ethical limitations, policy intricacies, security concerns, and vulnerabilities jeopardizing patient privacy. However, amidst these challenges, the study highlights the prospective long-term benefits, including diminished medical costs and enhanced patient care. In this study, we have portrayed a comprehensive exploration of the field of IoMT and different related technologies from more than 100 papers to represent the transformation and growth in this decade. We have illustrated some of the significant findings of applications and innovations in the domain of IoMT. This paper delves into IoMT's application in dementia detection and care, improved data management, fortified cybersecurity measures, and modernizing existing healthcare systems. The study also offers valuable insights into potential mitigation strategies, offered by ongoing research and innovation to address emerging trends and challenges, propelling the trajectory of Healthcare 4.0 towards an optimized and transformative future for patient well-being. Hence future research needs to integrate more prudent technologies addressing challenges including security, privacy, interoperability, and implementation costs.
Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction Putri Nabella; Rudy Herteno; Setyo Wahyu Saputro; Mohammad Reza Faisal; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.409

Abstract

Software Defect Prediction (SDP) is crucial for ensuring software quality. However, class imbalance (CI) poses a significant challenge in predictive modeling. This study delves into the effectiveness of the Synthetic Data Vault (SDV) in mitigating CI within Cross-Project Defect Prediction (CPDP). Methodologically, the study addresses CI across ReLink, MDP, and PROMISE datasets by leveraging SDV to augment minority classes. Classification utilizing Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF), also model performance is evaluated using AUC and t-Test. The results consistently show that SDV performs better than SMOTE and other techniques in various projects. This superiority is evident through statistically significant improvements. KNN dominance in average AUC results, with values 0.695, 0.704, and 0.750. On ReLink, KNN show 16.06% improvement over the imbalanced and 12.84% over SMOTE. Similarly, on MDP, KNN 20.71% improvement over the imbalanced and a 10.16% over SMOTE. Moreover, on PROMISE, KNN 13.55% improvement over the imbalanced and 7.01% over SMOTE. RF displays moderate performance, closely followed by LR and DT, while NB lags behind. The statistical significance of these findings is confirmed by t-Test, all below the 0.05 threshold. These findings underscore SDV's potential in enhancing CPDP outcomes and tackling CI challenges in SDV. With KNN as the best classification algorithm. Adoption of SDV could prove to be a promising tool for enhancing defect detection and CI mitigation

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