Pemeriksaan Citra Mikroskop Menggunakan Graphical User Interface dengan Python pada Raspberry Pi
Abstract
Keywords
Full Text:
PDFReferences
K. Adi, K. S. Firdausi, R. Gemowo, B. Raharjo, I. Siena, dan B. A. Putranto, “Sistem Pencitraan Mikroskop Digital untuk Identifikasi Bakteri Tuberkulosis (TB),” Prosiding InSINas, p. 80, 2012.
Yohannes, S. Devella, dan K. Arianto, “Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency,” JUITA: Jurnal Informatika, vol. 8, hal. 37–44, 2020.
Y. Jusman, S. Riyadi, A. Faisal, S. N. Aqmariah, M. Kanafiah, Z. Mohamed, and R. Hassan, “Classification system for leukemia cell images based on Hu moment invariants and support vector machines” 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 137-141.
L. R. I. Melanika, H. Fitriyah, dan G. E. Setyawan, "Sistem Deteksi dan Perhitungan Otomatis Bakteri Salmonella dengan Pengolah Citra Menggunakan Metode Object Counting," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, p. 6401, 2018.
A. W. Setiawan, Y. A. Rahman, A. Faisal, M. Siburian, N. Resfita, M. W. Gifari, dan R. Setiawan, “Deteksi malaria berbasis segmentasi warna citra dan pembelajaran mesin,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 4, hal. 769–776, 2021.
Supatman, “Deteksi Pembesaran Kelenjar Getah Bening Pada Paru Dengan Pengolahan Citra Digital Untuk Mendiagnosa Penyakit Primer Kompleks Tuberkulosis (PKTB),” SNATI, pp. C-1, 2009.
Y. Jusman, E. Samudra, S. Riyadi, S. N. Aqmariah, M. Kanafiah, A. Faisal, R. Hassan, and Z. Mohamed, “Comparison of texture and shape features performance for leukemia cell images using support vector machine” 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2021.
S. R. Reshma and T. R. Beegum, “Microscope Image Processing for TB Diagnosis Using Shape Features and Ellipse Fitting,” IEEE Xplore, 2017.
G.O.F. Parikesit, M. Darmawan, and A. Faisal, “Quantitative low-cost webcam-based microscopy”, Optical Engineering, Vol 49, Issue 11, page 113-205, 2010.
N. K. C. Pratiwi, N. Ibrahim, Y. N. Fu’adah, dan S. Rizal, “Deteksi Parasit Plasmodium pada Citra Mikroskopis Hapusan Darah dengan Metode Deep Learning,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 9, no. 2, hal. 306, 2021.
A. W. Setiawan, A. Faisal, N. Resfita, and Y. A. Rahman, “Detection of Malaria Parasites using Thresholding in RGB , YCbCr and Lab Color Spaces.” International Seminar on Application for Technology of Information and Communication (iSemantic), 2021.
I. Susanti, S. Handayani, R. Ekowatiningsih, B. Prasetyorini, E. A. Yusnita, D. A. Ardianto, dan S. K. Widjaya, “Pengembangan Mikroskop Dengan Mikrokontroler dan Cahaya Monokromatik Untuk Mendeteksi Parasit Malaria,” Jurnal Teknologi Laboratorium, vol. 6, no. 2, hal. 75–82, 2017.
I. S. Faradisa, Taufikurrahman, E. Nurcahyo, “Aplikasi Arduino untuk Otomatisasi Apusan Darah Tepi dan Pengecatan Menggunakan Pewarna Giemsa.” Seminar Nasional Inovasi dan Aplikasi Teknologi di Industri (SENIATI), 2016.
V. Vadde, S. Shivkumar, A. Kulkarni, “An Innovative Wireless Digital Microscope for Enhanced Malaria Detection and Telepathology in Remote Villages.” 2015 IEEE Region 10 Humanitarian Technology Conference (R10-HTC).
Rohan Sangameswaran, “MAIScope: A low-cost portable microscope with built-in vision AI to automate microscopic diagnosis of diseases in remote rural settings”, ArXiv: Electrical Engineering and Systems Science, Image and Video Processing, 12 Aug 2022.
J. Yoon, W.S. Jang, J. Nam, D.C. Mihn, and C. S. Lim, “An Automated Microscopic Malaria Parasite Detection System Using Digital Image Analysis”, Diagnostics. 11(3), 527, March 2021.
N.C. Poojari, K. Pallavi, P.P. Rai, R. Abdullah, and K. Ankitha, “Detection of Malarial Parasites in Blood using Image Processing”, International Journal of Engineering Research & Technology (IJERT), Vol. 7. Issue 08, 2019.
U. Salamah, R. Sarno, A.Z. Arifin, A.S. Nugroho, I.E. Rozi, and P.B.S. Asih, “Segmentation of Malaria Parasite Candidate from Thickblood Smear Microscopic Images using Watershed and Adaptive Thresholding”, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 10, No. 2-4, Jul. 2018.
G. Hcini, I. Jdey, and H. Ltifi, “Improving Malaria Detection Using L1 Regularization Neural Network”, Journal of Universal Computer Science, vol. 28, no. 10, 1087-1107, 2022.
M. Muttaqin, M. C. Untoro, A. Febrianto, A. Faisal, A. W. Setiawan, B. P. Prabowo, and Y. A. Rahman, “CNN Classification of Malaria Parasites in Digital Microscope Images Using Python on Raspberry Pi”, Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 1, pp. 108–120, Feb. 2023.
DOI: https://doi.org/10.18196/mt.v5i2.18226
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Medika Teknika : Jurnal Teknik Elektromedik Indonesia
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Medika Teknika : Jurnal Teknik Elektromedik Indonesia is indexed by :
Our Office
Editorial of Medika Teknika UMY, Building D, Jl. Brawijaya, Tamantirto, Kasihan, Bantul, Yogyakarta.Telp : (0274) 387656, Ext : 455
Fax : (0274) 387646
Email : mt@umy.ac.id
website : http://journal.umy.ac.id/index.php/mt
Medika Teknika : Jurnal Teknik Elektromedik Indonesia is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.