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DETEKSI OBJEK ASET RUMAH SAKIT MENGGUNAKAN COMPUTER VISION DENGAN METODE GENERATIVE ADVERSARIAL NETWORKS Suakanto, Sinung; Hidayat, Muhammad Fahmi; Hamami, Faqih; Raffei, Anis Farihan Mat; Nuryatno, Edi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1277

Abstract

Hospital asset monitoring systems encounter significant challenges in managing partially occluded medical equipment, which affects inventory management and operational efficiency. Conventional object detection methods have shown limitations in accurately detecting occluded medical equipment, potentially leading to asset management inefficiencies. This study presents an integrated framework that combines Generative Adversarial Networks (GAN) inpainting with YOLOv8 to improve the detection accuracy of partially occluded medical equipment. The proposed system was evaluated using three distinct training configurations of 500, 750, and 1000 epochs on a comprehensive medical equipment dataset. The experimental results indicate that the 1000-epoch GAN model demonstrated superior reconstruction performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 39.68 dB, Structural Similarity Index Measure (SSIM) of 0.9910, and Mean Squared Error (MSE) of 7.0030. Furthermore, the integrated YOLOv8-GAN framework maintained robust detection performance with an F1-score of 0.933, comparable to the 0.938 achieved with unoccluded original images. The detection confidence scores exhibited improvement at higher epochs, ranging from 0.824 to 0.861, suggesting enhanced performance with extended training duration. The findings demonstrate that the integration of GAN inpainting with YOLOv8 effectively enhances occluded object detection in hospital environments, offering a viable solution for improved asset monitoring systems.
User Experience Development in Elderly Heart Patient Monitoring System Alaric Rasendriya Aniko; Tien Fabrianti Kusumasari; Sinung Suakanto; Muhammad Ivan Fadilah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.18783

Abstract

Heart disease is a major global cause of death, particularly among the elderly. Elderly often face challenges in accessing healthcare due to physical and cognitive limitations, making remote health monitoring systems a crucial solution. However, the effectiveness of these systems depends heavily on a good user experience (UX), which is often a challenge for the elderly. This research aims to develop a user-centered design (UCD) method and design a remote patient monitoring prototype that is specifically tailored to the needs of the elderly. The research employed a design science research methodology (DSRM) and included an in-depth literature review, interviews with five elderly patients and two medical professionals, a needs analysis, and the development of the FlowBeat prototype. The developed UCD method consisted of seven phases, and its validity was assessed by six UI/UX experts using the content validity ratio (CVR) and the content validity index (CVI), including item-CVI (I-CVI) and scale-CVI (S-CVI). The results showed that the research users and test the design phases were rated as the most essential (CVR = 1) and relevant (I-CVI = 1). Conversely, the creating a personal manifesto phase scored the lowest (CVR = -0.33). The overall S-CVI score was 0.93, indicating strong content validity for most of the framework. In conclusion, the creating a personal manifesto and evaluate against requirements phases performed poorly, necessitating their removal. Furthermore, further research requires testing the prototype on elderly to ensure successful implementation in the real world.
Co-Authors A., Simon Filippus Abdulaziz, Rifqi Abdulaziz Adillah, Muhammad Fauzan Nur Adyartama, Arya Putra Agustien, Ferry Ahmad Musnansyah Alaric Rasendriya Aniko Albert, Vincentius Alfi Zahra Hafizhah Amanah, Raisyah Nurul Andreas Andreas Angela, Dina Anis Farihan Mat Raffei Anisa, Gia Annastasia, Syifa Aprilita Firsty Hazdia Arifudin, Nanang Bagastio, Shobrun Jamil Bayuwindra, Anggera Christy, Aldi Cristian Richardo Anin Daniel Hadi Wijaya Dila, Revyolla Ananta Dina Angela Echo, Ruth Edi Triono Nuryatno Ekky Novriza Alam Evan Reswara Fa'rifah, Riska Yanu Fahrizky, Bimo Agung Faishal Mufied Al Anshary Faqih Hamami Fauzi, Rokhman Febriyani, Widia Ferda Ernawan Firdaus, Taufiq Maulana Gamaliel, Yoyok Yusman Hadiningrum, Tiara Rahmania Handoko, Mahardika Maulana Al Mahdi Hardiyanti, Margareta Hazdia, Aprilita Firsty Herry Imanta Sitepu Herry Sitepu Herry Sitepu Hutagalung, Maclaurin Hutahaean, Bernad Robinson Ismail, Mohd Arfian Isnaeni, Rizqullah Maziyah Krisna Dwi Permana Mahardika Maulana Al Mahdi Handoko Margareta Hardiyanti Mat Raffei, Anis Farihan Mifta Ardianti Muhammad Fahmi Hidayat, Muhammad Fahmi Muhammad Ivan Fadilah Muharman Lubis Mulyati, Rika Munansyah, Ahmad Nia Ambarsari Nugroho, Tunggul Nugroho, Tunggul Arief Nur Ichsan Utama Nuraliza, Hilda Nuryanto, Edi Nuryatno, Edi Parameswari, Sang Dara Priyadi, Djoko Rachmadita Andreswari Raffei, Anis Farihan Mat Rafi Adinegoro Raharjo, Adi Rahmat Fauzi Raina, Apriani Nur Ramadhan, Yumna Zahran Randy Ferdiawan Rivero Novelino Roberd Saragih S. Suhardi Safara Cathasa Riverinda Rijadi Satria , Ryan Muhammad Sebastian, Kelvin See, Tan Lian Seno Adi Putra Shaffiei, Zatul Alwani Siregar, Amril Mutoi Suhono H. Supangkat Sulingallo, Irwansa Ryan Syfa Nur Lathifah Thaha, Taufik Kemal Tien Fabrianti Kusumasari Tjong Wan Sen Ulinuha, Zulfa Ventje Jeremias Lewi Engel Widyadhari, Dinda Putri Widyatasya Agustika Nurtrisha Wijaksana, Syifa Nuurunnisa Wijaya, Yohanes Rico Yoyok Gamaliel Zulkarnaen, Rizky Zaki