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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
Arjuna Subject : -
Articles 603 Documents
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Comparative study of K-Nearest Neighbor and Support Vector Machine methods in analyzing the consistency of college major based on high school majors Fiorenza Rizkyllah, Anabel; Meiriza, Allsela; Hardiyanti, Dinna Yunika
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER (In Press)
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Choosing a college major is a crucial decision that can influence a student's academic and career path. Ensuring that students' choices are consistent with their high school majors can help improve academic success and career readiness. This paper delivers a comparative analysis of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) methods in evaluating the consistency of college major selection based on high school majors. A dataset of 636 students was collected and processed for analysis. The findings indicates that the KNN algorithm achieved an average precision, recall, F1-Score, and accuracy of 78%. Meanwhile, the SVM algorithm achieved a higher average score of 85%. This indicates better performance in analyzing the consistency between students' high school majors and their chosen college majors. These findings show that SVM is more effective in supporting guidance in college major selection, highlighting its suitability as a reliable method for decision making.
Detection and Identification of Vehicle License Plates in Indonesia Transportation System Based on Deep Learning Using YOLOv11 and Easyocr Martadinata, Fendri; Firdaus, A; Amal, M.Ridho Tahsinul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER (In Press)
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2524

Abstract

The detection and identification of vehicle license plates in Indonesia still face significant challenges due to unstable environmental conditions during image capture, such as extreme lighting, varying angles of capture, physical damage to the plates, and diversity in design and font types. These conditions degrade the accuracy of existing recognition systems, especially if the model is not trained to handle such variability. In addition, the public's low understanding of license plate structure also hinders the optimal use of this information. This study aims to develop an accurate, adaptive license plate recognition system for real-world conditions that can interpret license plate information in real time. The model was created using the YOLOv11 algorithm for fast, high-precision plate detection, and EasyOCR for plate character identification. The dataset consisted of 709 images of two-wheeled (motorcycle) and four-wheeled (car) vehicle plates, collected from public datasets, the researchers' surroundings, and the campus area. Most of the data was collected through direct photography with cell phone cameras, reflecting real-world field conditions. The test results show that the YOLOv11 model has excellent detection performance, with mAP@50 of 94.2%, precision of 97.7%, and recall of 86.7%, while the EasyOCR method achieved a character recognition accuracy of 91.0%. The main innovation of this research is the application of a license plate recognition system to support intelligent transportation systems in campus environments, particularly for parking system implementation.
Performance Analysis of YOLOv8, YOLO11, and YOLOE in Detecting Patient Density under Complex Healthcare Conditions Ardiansyah, Ardiansyah; Syahputri, Rezyana Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER (In Press)
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2526

Abstract

Providing quality healthcare is a fundamental right of citizens as stipulated in the 1945 Constitution, making healthcare a national priority as outlined in the Ministry of Health's 2020-2024 Strategic Plan. However, high patient visitation rates can lead to overcrowding, impacting service efficiency and quality. Therefore, real-time patient monitoring technology is needed. Previous studies have shown promising results, but remain limited to ideal conditions for the machine. This study uses the YOLO algorithm to detect patient congestion in real healthcare facilities using CCTV footage from waiting rooms. This study uses three instance segmentation models —YOLOv8n-seg, YOLO11n-seg, and YOLOE-seg —that are tested on a custom dataset and compared with the official model. The results of training the custom dataset model are: YOLOv8n-seg Precision 96%, Recall 97%, mAP50 98%, mAP50-95 84%, and F1-score 97%. YOLO11n-seg precision 96%, Recall 97%, mAP50 98%, mAP50-95 84%, and F1-score 97%. and YOLOE-seg precision 96%, Recall 98%, mAP50 98%, mAP50-95 85%, and F1-score 97%. In addition, this study compared predictions with the official model, which found that all custom dataset models successfully detected objects with 100% density. In contrast, the official model correctly predicted density 70%-82% of the time. Therefore, this study concludes that models trained on custom datasets can improve the accuracy of patient density predictions, thereby enhancing the quality of real-time healthcare services.

Page 61 of 61 | Total Record : 603

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