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All Journal Journal of Applied Computer Science and Technology (JACOST) Nusantara Hasana Journal
Danis Rifa Nurqotimah
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Nursing

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HUMAN-CENTERED DESIGN UNTUK PERANCANGAN MEDIA REHABILITASI PASCA STROKE BERBASIS AUDIO VISUAL Danis Rifa Nurqotimah; Ahsanun Naseh Khudori; Risqy Siwi Pradini
Nusantara Hasana Journal Vol. 4 No. 3 (2024): Nusantara Hasana Journal, August 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i3.1193

Abstract

Stroke is a condition that makes a person's whole body or part of his body paralyzed. The leading cause of death and disability in the world is Stroke. Each year, there are more than 12.2 million new Stroke cases, and more than 101 million people with this condition. With proper care and prompt therapy, Stroke patients can recover. The study aims to design visual media for post-Stroke rehabilitation. The method applied in this research is a combination of ISO 9241-210:2019 Human-Centered Design, persona, and expert validation. The stages of designing audio visual media consist of pre-production, production and editing. Finally, this study produces audio-visual media for post-Stroke rehabilitation that has been validated by experts.
Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke Danis Rifa Nurqotimah; Naseh Khudori, Ahsanun; Siwi Pradini, Risqy
Journal of Applied Computer Science and Technology Vol 5 No 2 (2024): Desember 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i2.817

Abstract

Stroke or known as Cerebrovascular Accident (CVA) is a functional disorder caused by impaired blood flow function from within the human brain. Stroke carries a high risk of brain damage, paralysis, speech disorders, visual impairment, even death. Classification is one of a few methods in predicting stroke symptoms with the aim of obtaining accurate prediction of disease. The researchers implemented a method to classify stroke with the Support Vector Machine (SVM) algorithm. The SVM is a learning method used in medical diagnosis for classification, the researchers processed data sets using the Orange tool. The study used data sets from the data.world.com site with a total of 40,910 data. Using the Orange tool, the study managed to classify stroke disease well using the RBF kernel with cross validation techniques resulting in an accuracy of 94.8%. The results of this study can be concluded that the stroke classification model developed has excellent performance. Overall, these results indicate that the Stroke classification model developed is highly reliable and effective, with excellent ability to detect stroke cases and provide accurate predictions. Making better and quicker medical judgments can be aided by using this approach to diagnose strokes.
Co-Authors Ahsanun Naseh Khudori Risqy Siwi Pradini Siwi Pradini, Risqy