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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Abdimas Madani dan Lestari (JAMALI)
Ahnaf, Kern Cesarean
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Pengembangan GEULIS: Prototipe Gelang Emergensi Lansia sebagai Upaya Peningkatan Keamanan Lansia Hidup Sendiri: Indonesia Rahmawati, Riana; Ahnaf, Kern Cesarean; Pudail, Muhammad
Jurnal Abdimas Madani dan Lestari (JAMALI) Volume 07, Issue 02, September 2025
Publisher : UII

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jamali.vol7.iss2.art23

Abstract

The growing elderly population in Indonesia highlights the need for support systems that can ensure their safety and well-being, particularly for those living alone. Living independently increases the vulnerability of older adults to risks such as falls, sudden illness, and emergency conditions that require immediate assistance. However, the use of simple, elderly-friendly emergency technologies at the community level remains limited. This community service program in Purbayan village in Yogyakarta City initiated the development of the Emergency Bracelet for the Elderly Living Alone (GEULIS), an innovative solution based on Internet of Things (IoT) technology. The prototype bracelet was designed with a simple emergency push button system capable of sending alerts via WhatsApp to family members or community health workers, when the elderly require help. The program comprised need assessments, designing and developing the prototype, and pilot testing with elderly individuals living alone. The results demonstrated that the device functioned effectively in transmitting emergency signals. While the design was relatively wearable and user-friendly, further refinement is needed to optimize ease of use. This program highlights the potential for continued development and broader application of GEULIS within the community.
Wound Depth Measurement System in Forensic Cases using Image Processing and Machine Learning Wahyuni, Elvira Sukma; Ahnaf, Kern Cesarean; Firdaus, Firdaus; Abdul-Kadir, Nurul Ashikin; Zakaria, Nor Aini; Wiraagni, Idha Arfianti; Kadarmo, Diwangkoro Aji
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1636

Abstract

Accurate evaluation of wound depth is crucial in forensic investigations, as it significantly affects case assessments and outcomes. This study introduces a method for classifying wound depth using a Support Vector Machine (SVM) model and compares its performance with Decision Tree and Logistic Regression models. The classification was based on color features extracted from HSV and LAB color spaces. The da-taset consisted of 76 images categorized into three stages: stage 2 (36 images), stage 3 (12 images), and stage 4 (28 images). Model performance was evaluated using confusion matrices, precision, recall, and F1-score. The SVM model achieved an overall accuracy of 85%, demonstrating higher precision and re-call across all stages compared to the Decision Tree and Logistic Regression models, which achieved 50% and 70%, respectively. The results indicate that the SVM model performed particularly well in distinguish-ing stage 2 wounds, although differentiating between stages 3 and 4 remained challenging. Overall, the proposed system shows potential to enhance the accuracy and efficiency of forensic wound evaluation by providing a rapid and objective classification tool. However, as the system was tested on a limited dataset under controlled conditions, further research should expand the dataset, incorporate additional features, and explore other machine learning algorithms to improve robustness and applicability in real forensic contexts.
Co-Authors Abdul-Kadir, Nurul Ashikin Elvira Sukma Wahyuni Firdaus Firdaus Kadarmo, Diwangkoro Aji Pudail, Muhammad Rahmawati, Riana Wiraagni, Idha Arfianti Zakaria, Nor Aini