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All Journal JOIN (Jurnal Online Informatika) JTIK (Jurnal Teknik Informatika Kaputama)
Putri, Fayza Nayla Riyana
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Computer Science & IT

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Enchancing Lung Disease Classification through K-Means Clustering, Chan-Vese Segmentation, and Canny Edge Detection on X-Ray Segmented Images Riyono, Joko; Pujiastuti, Christina Eni; Puspa, Sofia Debi; Supriyadi; Putri, Fayza Nayla Riyana
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1178

Abstract

The lungs are one of the vital organs in the human body. Not only play a role in the respiratory system, the lungs are also responsible for the human circulatory system. Supporting examinations can also facilitate medical workers in determining the diagnosis. Usually a lung examination is complemented by a chest X-ray examination procedure. This examination aims to see directly and assess the severity of lung conditions. With current technological advances, image analysis can be done easily. Through digital image processing methods, information can be obtained from images that can be used for analysis as a support for diagnoses in the world of health. Image segmentation is a method in which digital images are divided into several segments or subgroups based on the characteristics of the pixels in the image. In this study, clustering with the K-Means method will be carried out on the results of segmentation of x-ray images of lung diseases, namely Covid-19, Tuberculosis, and Pneumonia. The segmentation method that will be implemented is the Chan-Vese Method and the Canny Edge Detection Method. This research shows that the results of the accuracy of applying the K-Means Clustering method to Chan-Vese and Canny Edge-Based Image Segmentation are 80%.
STUDI KASUS: SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PENERIMA BANTUAN SOSIAL MENGGUNAKAN SIMPLE ADDITIVE WEIGHT Riyono, Joko; Pujiastuti, Christina Eni; Putri, Fayza Nayla Riyana
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 7 No. 1 (2023): Volume 7, Nomor 1, Januari 2023
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v7i1.36

Abstract

Wabah Covid-19 yang melanda di hampir penjuru belahan dunia saat ini, tidak hanya memberikan efek yang buruk pada sistem kesehatan masyarakat dunia tetapi juga telah memporak porandakan kestabilan perekonomian di berbagai negara, Indonesia sebagai satu diantara negara berkembang juga mengalami masalah perekonomian sebagai akibat Covid-19. Pemerintah Indonesia telah berupaya dengan berbagai cara melakukan perbaikan perekonomian diantaranya pemberian bantuan sosial kepada masyarakat terdampak Covid-19. Sebagai akibat keterbatasan dana yang dimiliki pemerintah, maka diperlukan suatu sistem pendukung keputusan untuk penyebaran bantuan sehingga bantuan yang diberikan akan tepat sasaran. Metode Simple Additive Weighting (SAW) merupakan satu metode yang dapat digunakan dalam proses sistem pendukung keputusan. Dalam tulisan ini, Metode Simple Additive Weighting akan digunakan untuk proses penentuan pengambilan keputusan pemberian bantuan sosial Covid-19 didasarkan pada lima kriteria yaitu penghasilan, bangunan tempat tinggal, moda transportasi yang digunakan, tingkat pendidikan dan penerangan yang digunakan. Hasil penelitian ini menunjukkan bahwa dengan metode SAW (Simple Additive Weighting), calon penerima bantuan sosial dapat ditentukan dengan hasil yang yang lebih efektif dan akurat
Co-Authors Joko Riyono Pujiastuti, Christina Eni Puspa, Sofia Debi Supriyadi