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August 17, 2024
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May 30, 2025
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Abstract

Diabetic wounds are serious complications often experienced by people with diabetes. Regular wound monitoring is crucial to prevent further complications. This study aims to design and develop an artificial intelligence-based healthcare information system called LukaDia Wound Care for monitoring diabetic wounds in diabetes care facilities. The research method used a waterfall software development approach. Development stages include needs analysis, system design, implementation, and testing. The results showed that LukaDia Wound Care was successfully developed with features that support the diabetic wound monitoring process, such as uploading wound images, measuring wound parameters, and generating wound progress reports. System testing at Klinik Pratama Muhammadiyah Kajen showed that the system is easy to use and beneficial for healthcare professionals in monitoring patients' diabetic wounds. The conclusion of this study indicates that LukaDia Wound Care has the potential to improve the quality of healthcare for people with diabetes

Keywords

Diabetic Wound Healthcare information system artificial intelligence (AI) wound monitoring frontend application

Article Details

References

  1. Alfaqih et al. (2021) ‘Manajemen Penatalaksanaan Diabetes Mellitus - Google Books’, Guepedia [Preprint].
  2. Anisuzzaman, D.M. et al. (2022) ‘Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review’, Advances in Wound Care. Available at: https://doi.org/10.1089/wound.2021.0091.
  3. Ansori (2015) ‘Tinjauan Pustaka Diabetes Melitus Type 2’, Paper Knowledge . Toward a Media History of Documents, 3(April).
  4. Bhatt, H., Saklani, S. and Upadhayay, K. (2016) ‘Definisi Diabetes Melitus Tipe 2’, Indonesian Journal of Pharmacy, 27(2).
  5. Casal-Guisande, M. et al. (2020) ‘Proposal of a Methodology based on Expert Systems for the Treatment of Diabetic Foot Condition’, in ACM International Conference Proceeding Series. Available at: https://doi.org/10.1145/3434780.3436625.
  6. Chandu, A. (2013) ‘Diabetes, wound healing and complications’, Australian Dental Journal. Available at: https://doi.org/10.1111/adj.12117_1.
  7. Dabas, M. et al. (2023) ‘Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review’, Advances in Wound Care. Available at: https://doi.org/10.1089/wound.2021.0144.
  8. Emhandyksa, M., Soesanti, I. and Susilowati, R. (2023) ‘Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram’, Jurnal Teknologi Informasi dan Ilmu Komputer, 10(6). Available at: https://doi.org/10.25126/jtiik.1067382.
  9. Fadillah, M.A. and Ikasari, I.H. (2023) ‘Literature Review: Implementasi Perancangan Sistem Informasi Absensi Karyawan Berbasis Web Menggunakan Metode Waterfall’, JRIIN: Jurnal Riset Informatika dan Inovasi, 1(3).
  10. Farid Riyan Wijaya, A., Orisa, M. and Primaswara Prasetya, R. (2023) ‘SISTEM PAKAR DETEKSI PENYAKIT DIABETES DAN TINGKAT KEPARAHAN LUKA PADA PENDERITA DIABETES MENGGUNAKAN METODE DEMPSTER-SHAFER’, JATI (Jurnal Mahasiswa Teknik Informatika), 6(2). Available at: https://doi.org/10.36040/jati.v6i2.5373.
  11. Febrianti, T. (2020) ‘… SISTEM INFORMASI KEPERAWATAN KESEHATAN KOMUNITAS BERBASIS APLIKASI PADA PONSEL UNTUK MANAJEMEN DIRI PASIEN DIABETES MELITUS …’, Jurnal Mitra Kesehatan [Preprint].
  12. Foltynski, P. and Ladyzynski, P. (2023) ‘Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks’, Biocybernetics and Biomedical Engineering, 43(1). Available at: https://doi.org/10.1016/j.bbe.2022.11.004.
  13. Goss, A.N. and Sambrook, P.J. (2013) ‘Diabetes, wound healing and complications: Authors’ reply’, Australian Dental Journal. Available at: https://doi.org/10.1111/adj.12117_2.
  14. Hakim, A., Ismunandar, H. and Wahyuni, A. (2022) ‘Manajemen Diabetes Melitus : An Update’, Journal Medula, 12(1).
  15. Herlina, M. (2019) ‘GAMBARAN PENGETAHUAN PASIEN DIABETES MELITUS TIPE 2 TENTANG SENAM KAKI PADA DIABETES DI LINGKUNGAN XXIV PEKAN LABUHAN KECAMATAN MEDAN LABUHAN’, Jurnal Ilmiah Keperawatan Imelda, 5(2). Available at: https://doi.org/10.52943/jikeperawatan.v5i2.318.
  16. Leizaola, D. et al. (2023) ‘Deep learning algorithms to classify Fitzpatrick skin types for smartphone-based NIRS imaging device’, in. Available at: https://doi.org/10.1117/12.2665179.
  17. Maghfuri, A. (2020) Buku Pintar Perawatan Luka Diabetes Mellitu, Salma Medika.
  18. Mahendra, G.D.D. et al. (2021) ‘Sistem Informasi Peminjaman dan Pengembalian Rekam Medis di Fasilitas Pelayanan Kesehatan (Literature Review)’, JPKM: Jurnal Profesi Kesehatan Masyarakat, 2(2). Available at: https://doi.org/10.47575/jpkm.v2i2.236.
  19. Monteiro-Soares, M. and Dinis-Ribeiro, M. (2016) ‘A new diabetic foot risk assessment tool: DIAFORA’, Diabetes/Metabolism Research and Reviews, 32(4). Available at: https://doi.org/10.1002/dmrr.2785.
  20. MUH. ARDIN TIRO (2020) ‘A. Tinjauan Teori Diabetes Melitus 1. Definisi Diabetes Melitus’, ASUHAN KEPERAWATAN Ny. M DENGAN GANGGUAN PEMENUHAN KEBUTUHAN NUTRISI AKIBAT PATOLOGI SISTEM ENDOKRIN DIABETES MELITUS [Preprint].
  21. Niken, R. et al. (2021) ‘Implementasi Algoritma K-Means Clustering Untuk Pengelompokan Penyebaran Penyakit Diabetes Melitus Di Kecamatan Bagor’, Jurnal Mahasiswa Matematika ALGEBRA, 02(01).
  22. Nuraini, F. (2017) ‘RANCANG BANGUN SISTEM INFORMASI DIAGNOSA KEPERAWATAN AKTUAL PADA PENDERITA DIABETES MELITUS’, Respati, 11(31). Available at: https://doi.org/10.35842/jtir.v11i31.122.
  23. Okonkwo, U.A. and Dipietro, L.A. (2017) ‘Diabetes and wound angiogenesis’, International Journal of Molecular Sciences. Available at: https://doi.org/10.3390/ijms18071419.
  24. Poon, T.W.K. and Friesen, M.R. (2015) ‘Algorithms for size and color detection of smartphone images of chronic wounds for healthcare applications’, IEEE Access, 3. Available at: https://doi.org/10.1109/ACCESS.2015.2487859.
  25. Rostami, B. et al. (2021) ‘Multiclass wound image classification using an ensemble deep CNN-based classifier’, Computers in Biology and Medicine, 134. Available at: https://doi.org/10.1016/j.compbiomed.2021.104536.
  26. Sabrina, E. and Buditjahjanto, I.G.P.A. (2017) ‘Klasifikasi Penyakit Diabetic Retinopathy menggunakan Metode Learning Vector Quantization (LVQ)’, Jurnal Teknik Elektro, 6(2).
  27. Safaruddin, S. and Permatasari, H. (2022) ‘Teknologi Kesehatan Digital Dalam Penanganan Masalah Diabetes Melitus Literature Review’, Malahayati Nursing Journal, 4(4). Available at: https://doi.org/10.33024/mnj.v4i4.6201.
  28. Santoso, B.J., Qona’ah, A. and Erye Frety, E. (2022) ‘Digitalisasi Tatalaksana Pasien Diabetes Melitus (DM) Melalui Aplikasi DM Assistant sebagai Upaya Peningkatan Kepatuhan Pilar Diabetes Melitus’, Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat, 2(2). Available at: https://doi.org/10.54082/jippm.23.
  29. Soegondo, S. (2013) Penatalaksanaan Diabetes Mellitus Terpadu, Fakultas Kedokteran Universitas Indonesia, Jakrta.
  30. Supardianto (2020) Fuzzy expert system untuk membantu diagnosis awal, Universitas Islam Indonesia.
  31. Wardani, A.K., Santoso, N. and Asmara, R.A. (2014) ‘SISTEM PAKAR DIAGNOSA PENYAKIT DIABETES MELITUS’, Jurnal Informatika Polinema, 1(1). Available at: https://doi.org/10.33795/jip.v1i1.93.