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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Pustaka Keperawatan
Abdurrahman, Muhammad Farhan
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Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Nursing Public Health

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The Role of Deep Learning in Cancer Detection: A Systematic Review of Architectures, Datasets, and Clinical Applicability Abdurrahman, Muhammad Farhan; Rianto, Yan; Hamzah, Nasir; Firmansyah, Muhammad; Prawira, Nurul Adi; Nugraha, Thomas Fajar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4748

Abstract

Early cancer detection continues to be a significant challenge in clinical practice due to limitation of conventional diagnostic technique that often takes time and error prone. This systematic review evaluates the efficacy of deep learning (DL) architecture and datasets to improve cancer detection and diagnosis. We performed a structural analysis on 40 high-impact research paper published in Q1 journals between 2014 and 2025, considering DL model performance, datasets, and clinical relevance. Results indicate that fundamental architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) consistently report high diagnostic accuracy (>90%) on radiology- and histopathology-based imaging datasets. Conversely, DL performance on non-imaging clinical data, including electronic medical records (EMDs), is more varied. Evaluation metrics such as AUC and DICE shows the trade-off between classification precision and segmentation accuracy. Despite their potential, DL models have significant limitations in terms of generalization, interpretability, and integration within real-world clinical workflows. This review highlights the need for standardized evaluation, implementation of ethical models, and multi-modal data fusion to facilitate wider and more equitable clinical uptake of DL in cancer diagnostics.
Manajemen Perianestesi Subarachnoid Block Pada Pasien ORIF Fraktur Femur Dextra: Studi Kasus Abdurrahman, Muhammad Farhan; Agniansyah, Kakha; Delfiando, Dicky; Larasati, Putri; Susanto, Amin; Sebayang, Septian Mixrova
Jurnal Pustaka Keperawatan (Pusat Akses kajian Keperawatan) Vol 5 No 1 (2026): Jurnal Pustaka Keperawatan
Publisher : Pustaka Galeri Mandiri

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Abstract

Femoral fracture is a major orthopedic injury requiring operative management to prevent complications and restore limb function. Anesthetic technique plays a crucial role in the success of surgical intervention. This case report employed a descriptive observational method with a case study approach on a 36-year-old male patient with a complete fracture of the middle third of the right femur who underwent Open Reduction and Internal Fixation (ORIF). Data were collected from medical records, physical examination, laboratory tests, radiological findings, as well as intraoperative and post-anesthesia monitoring. Anesthetic management was performed using spinal anesthesia with 15 mg of intrathecal bupivacaine, supported by multimodal analgesia and fluid therapy to maintain hemodynamic stability. During surgery, the patient remained stable, although shivering occurred and was successfully managed with intravenous pethidine and external warming. Post-anesthesia, the patient demonstrated gradual motor recovery, stable vital signs, and no serious complications. In conclusion, spinal anesthesia proved effective in providing adequate analgesia, maintaining hemodynamic stability, and reducing intraoperative complications in patients undergoing ORIF for femoral fracture. This case highlights the importance of selecting an appropriate anesthetic technique, close monitoring, and prompt intervention to ensure patient safety and favorable clinical outcomes
Co-Authors Agniansyah, Kakha Delfiando, Dicky Muhammad Firmansyah, Muhammad Nasir Hamzah Nugraha, Thomas Fajar Prawira, Nurul Adi Putri Larasati, Putri Rianto, Yan Sebayang, Septian Mixrova Susanto, Amin