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All Journal Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics Jurnal Penelitian Fisika dan Aplikasinya (JPFA) JURNAL FISIKA JFA (Jurnal Fisika dan Aplikasinya) JOURNAL OF NATURAL SCIENCES AND MATHEMATICS RESEARCH JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI Journal of Natural Sciences and Mathematics Research Phenomenon : Jurnal Pendidikan MIPA Jurnal Biodjati JIPF (Jurnal Ilmu Pendidikan Fisika) Al-Kimia Edukasi Islami: Jurnal Pendidikan Islam JURNAL EKSAKTA PENDIDIKAN (JEP) Physics Education Research Journal Journal of Electrical Engineering and Computer (JEECOM) Analisa: Journal of Social Science and Religion
Hadi Kusuma, Hamdan
Universitas Islam Negeri Walisongo Semarang
Religion Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Journal : JURNAL FISIKA

 1 
Klasifikasi Kasus COVID-19 dan SARS Berbasis Ciri Tekstur Menggunakan Metode Multi-Layer Perceptron Azzahra, Jannatul Firdausa; Sumarti, Heni; Kusuma, Hamdan Hadi
Jurnal Fisika Vol 12, No 1 (2022)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v12i1.35685

Abstract

Corona Virus Disease 2019 (COVID-19) merupakan penyakit infeksi akut yang disebabkan oleh virus corona sebagai sindrom pernafasan akut parah. SARS (Severe Acute Respiratoty Syndrome) merupakan gangguan saluran pernapasan yang disertai gejala saluran perncernaan disebabkan oleh corona virus. Penelitian ini bertujuan untuk membedakan pasien COVID-19 dan SARS berdasarkan ciri tekstur dengan metode Multi-Layer Perceptron (MLP). Metode yang digunakan dalam penelitain ini terdiri dari tiga tahap, tahap pertama adalah pre-processing, tahap kedua adalah ekstraksi ciri tekstur menggunakan histogram dan GLCM (Gray Level Co-occurrence Matrix), dan tahap ketiga adalah klasifikasi data menggunakan metode Multi-Layer Perceptron (MLP). Hasil penelitian menunjukkan bahwa citra pasien SARS memiliki rerata kecerahan lebih tinggi, memiliki kontras lebih tajam, dan tingkat penyebaran data dalam piksel citra rontgen toraks lebih acak dibandingkan dengan citra pasien COVID-19. Metode yang digunakan dalam penelitian ini menghasilkan akurasi, sensitivitas, dan spesifisitas yang sama, yaitu sebesar 91,67%. Penelitian ini menunjukkan bahwa ciri tekstur mampu membedakan citra rontgen toraks pasien COVID-19 dan SARS secara akurat, sehingga dapat menjadi perangkat tambahan untuk memudahkan tenaga kesehatan.
Development of Non- Invasive Cholesterol Monitoring System Using TCRT5000 Sensor with Android Compatibilty Rahmawati, Tika; Tasyakuranti, Alvania Nabila; Sumarti, Heni; Kusuma, Hamdan Hadi
Jurnal Fisika Vol 13, No 2 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v13i2.45044

Abstract

High cholesterol levels cause several diseases, such as atherosclerosis (narrowing of the arteries), coronary heart disease, high blood pressure, obesity, thyroid disorders, diabetes mellitus, liver disease, and kidney disease. Generally, such checking is carried out invasively in clinical laboratories or hospitals. The checking can be done individually at a lower cost by using non invasive cholesterol measuring devices. This study aims to design and implement an android-based non-invasive cholesterol monitoring device using the TCRT5000 sensor. The tool developed was tested to measure cholesterol levels in 15 respondents aged 20-30 years. The research procedure consisted of several stages, starting with the design stage of the tool, which was carried out by assembling the components; the second stage was the tool coefficient of determination test, the third stage was the accuracy test, and the last stage was the data transfer speed test. The average accuracy of the tool is 83.18%, and the avarage of delay is 8.8 ms. This tool has considerable potential to be used in a telemedicine system that can be accessed remotely regularly to determine the estimated value of cholesterol levels in the blood.
Co-Authors Adeni, Adeni Adina Widi Astuti Adityaa, Farhan Al Afda, Mahrunnisa Anasom, Anasom Andi Muhammad Yusuf, Andi Muhammad Anshori Huroeroh Azizah, Nisa Awaliyah Nur Azzahra, Jannatul Firdausa Baehaqi Biaunik Niski Kumila Biaunik Niski Kumila Choeroni Choeroni Destyofrini, Fredina Faqih, Muhammad Izzatul Fariyani, Qisthi Fattah Syukur Fitri Fitri Hardianto, Firman Hidayat, Saifullah Hudawi, Ahmad Ibrahim, Zuhairi Intan, Wahyu Noor Isfaul Maulina Istikomah Jamhari Jamhari Karim, Abdul Kumila, Biaunik Niski Kumila, Biaunik Niski Kustomo Mufakhidin, Ayuf Mujiasih Mujiasih Mulyatun Mulyatun Niam, Muhammad Yusrun Nisa Awaliyah Nur Azizah Nugroho, Deni Ebit Nur Ahmad, Nur Pancorowati, Dhita Azzahra Pertiwi, Salsabiela Prastyo, Irman Said Rahmani, Tara Puri Ducha Sari, Fitri Nur Indah Sheilla Rully Anggita Shofani, Maya Sumarti, Heni Supriyono - Susilawati Susilawati Susilawati, Susilawati Tasyakuranti, Alvania Nabila Tika Rahmawati Tri Suryandari, Ervin Tria Nurmar’atin Watson, Lee James Zulkafli Othaman