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All Journal Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Sunan Kalijaga Journal of Physics
Iswanto Suwarno
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Humanities Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics Public Health Social Sciences

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Journal : Sunan Kalijaga Journal of Physics

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Sensors and Transducers for Stroke Detection Systematic Literature Review Iswanto Suwarno; Rezarta Lalo; Elena Costru-Tasnic; Jan van der Merwe; Husitha Vanguru
Sunan Kalijaga Journal of Physics Vol. 5 No. 1 (2023): Sunan Kalijaga Journal of Physics
Publisher : Prodi Fisika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/physics.v5i1.4242

Abstract

Along with changes in lifestyle, stroke is not only a disease that attacks the elderly population, but also often attacks people of productive age. Actions are proposed to the public to control risk factors, such as changing lifestyle behavior or taking medication, and variations in stroke risk are tracked by evaluating stroke risk annually. The symptom of stroke that is currently widely known by the public is paralysis, even though many stroke symptoms often appear without realizing it. So it is necessary to detect stroke using sensors and transducers to detect it. The aim of this research is to examine sensors and transducers for stroke detection. This research uses a systematic literature review using Preferred Reporting Items for Systematic Reviews (PRISMA). The results of article screening and selection found 84 potential articles that met the inclusion criteria. The research results show that the development of sensors and transducers for stroke detection is currently starting to develop through artificial intelligence based on the internet of thought which uses sensors and transducers within it. Judging from the production of tools that use sensors and transducers for stroke detection. Optimization of sensors and transducers for stroke detection must be carried out with appropriate use, detailed regulatory supervision, and continuous innovation of sensors and transducers for stroke detection which is useful in reducing the number of strokes in the world
A Artificial Intellegent algorithms for Tumor Disease Detection: systematic Literature Review Muhammad Hafid Alkarim; Iswanto Suwarno; Muhammad Ahmad Baballe; Mukhtar Ibrahim Bello
Sunan Kalijaga Journal of Physics Vol. 5 No. 2 (2023): Sunan Kalijaga Journal of Physics
Publisher : Prodi Fisika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/physics.v5i2.4275

Abstract

A Tumor is a swelling in the body caused by cells that multiply abnormally. Tumors or neoplasms consisting of benign tumors and malignant tumors. Benign tumors can grow larger but do not spread to other body tissues. Malignant tumors are cancers that attack the entire body and are uncontrollable. Comparison between the cell nucleus with the cytoplasm of malignant tumors, while benign tumors are the same as normal cells. Cancer cells can develop rapidly. These cells attack and damage body tissues through the bloodstream and lymph vessels so that they can grow in new places. One way to detect tumor disease is by utilizing Artificial intelligence algorithms for tumor Disease Detection. The purpose of this paper is for the development of Artificial Intellegent algorithms for the detection of tumor Diseases and optimization of Artificial Intellegent algorithms for the detection of tumor Diseases. This research uses systematic literature review by using preferred Reporting Items for Systematic Review (PRISMA). The results of screening and selection of articles obtained 64 potential articles that have met the inclusion criteria. The results showed that with earlier detection, a person can check tumor disease earlier using the help of Artificial intelligence algorithms. The results of research on the development of Artificial intelligence algorithms for detection of tumor Diseases have found Artificial intelligence algorithms that can be used to reduce the risk of tumor disease. Optimization of Artificial Intelegency algorithms for tumor classification, performing new data processing methods such as artificial intelligence can be selected to provide the accuracy of classification and diagnosis, exploration of detection limits is a very important aspect in tumor diagnosis based on SERS, finding improved and suitable nanoparticle substrates so as to significantly improve the original Raman signal.
Co-Authors Elena Costru-Tasnic Husitha Vanguru Idiani Darmawati Jan van der Merwe Lilis Kurniasari Muhammad Ahmad Baballe Muhammad Hafid Alkarim Mukhtar Ibrahim Bello Novi Caroko Novi Diah Wulandari Ratna Indriawati Rezarta Lalo