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All Journal JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Ismail, Juni
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Computer Science & IT Electrical & Electronics Engineering

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Implementasi Algoritma Fifo Terhadap Sistem Antrian Pasien di Rumah Sakit Berbasis Web Online Ismail, Juni; Gea, Muhammad Nasri; Satria, Habib; Tammamah Lubis, Hartati; Prasetya, Hardi; Hanani Hutabarat, Jamina; Sihombing, Rotua; Wanayumini, Wanayumini
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol. 7 No. 2 (2024): Journal of Electrical and System Control Engineering
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v7i2.10665

Abstract

The development of queuing systems in hospitals continues to be developed to optimally support patient service, especially regional general hospitals (RSUD). This is because the manual system is still inefficient, resulting in patients queuing for a long time and conflicts often occur. Based on these problems, a Web system was designed with the support of the Fifo algorithm so that the queuing system becomes simpler and more optimal. An easier and more flexible queuing system will support better and more excellent hospital services. Implementation of the Fifo Algorithm, designed to determine and calculate the patient queue system and orderly service for patient registration at the hospital. The implementation of this automatic queuing application will have an impact on health services, especially at Dr. Djasamen Saragih, Pematang Siantar city. The results of using a web-based application in this hospital have an impact in making it easier for operators or admins to queue up calls for patient serial numbers. The operating system on the website is monitored. If the web status has a data result of 1, the patient has been called by admin, but if the patient has not been called, the monitoring site is in Web with value 0 or null.
HYBRID TRANSFER LEARNING AND ADVANCED DATA AUGMENTATION FOR MULTICLASS BRAIN TUMOR CLASSIFICATION USING EFFICIENTNET Pardede, A M H; Winanjaya, Riki; Ismail, Juni
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7524

Abstract

Accurate Accurate brain tumor diagnosis from MRI images remains challenging due to dataset limitations, class imbalance, and high morphological variability across tumor types. Existing deep learning approaches often yield suboptimal results when trained on small or imbalanced datasets. This study proposes a hybrid learning strategy that integrates transfer learning with advanced data augmentation to classify four brain tumor categories: glioma, meningioma, pituitary adenoma, and normal tissue. Using a large-scale dataset of 7,023 MRI images, the proposed framework incorporates Mixup, CutMix, and a comprehensive augmentation pipeline with an optimized EfficientNet-B0 architecture. The model achieves a test accuracy of 99.05% with F1-scores of 0.99, representing a 4.05 percentage point improvement over a baseline InceptionV3 model (95.00%) and outperforming ResNet-based approaches (93.80%) reported in previous studies. This quantitative improvement demonstrates the effectiveness of combining modern CNN architectures with advanced augmentation strategies. The streamlined architecture and high accuracy make the method suitable for deployment in resource-constrained healthcare environments. These results indicate that hybrid augmentation and transfer learning can deliver clinically meaningful performance for early brain tumor identification, offering a scalable and practical solution for computer-aided medical diagnosis
Co-Authors Gea, Muhammad Nasri Habib Satria Hanani Hutabarat, Jamina Pardede, Akim Manaor Hara Prasetya, Hardi Sihombing, Rotua Tammamah Lubis, Hartati Wanayumini Winanjaya, Riki