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All Journal Creative Information Technology Journal JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JNANALOKA Jurnal Informatika Teknologi dan Sains (Jinteks)
Buyut Khoirul Umri
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Transportation Other

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Penerapan transfer learning pada convolutional neural networks dalam deteksi covid-19. Buyut Khoirul Umri; Visq Delica
JNANALOKA Vol. 02 No. 02 September Tahun 2021
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2021.v2-no2-53-61

Abstract

The Covid-19 pandemic has become a serious problem in the world, including Indonesia, until now, the virus that emerged at the end of 2019 is still a serious problem. The number of cases of infected people continues to increase and reaches more than two hundred million cases worldwide. To carry out this rapid test, it did not run smoothly but experienced many obstacles experienced by the Medical team, one of which was the limitation of the Covid-19 test kit, so scientists took other diagnostic steps. In the field of informatics, scientists use several diagnoses, one of which is X-ray images of the lungs. CXR images are currently often used for the detection process using the CNN algorithm. This research uses transfer learning method which will be tested in large and small scale datasets. The best result of all the models tested is MobileNet with an accuracy of 98.11% which was tested on a large-scale dataset and the lowest was obtained by ResNet50 which was tested on a small-scale dataset with an accuracy of 41.94%. The large-scale dataset also shows improved accuracy across all tested transfer learning models.
Tinjauan Literatur Sistematik tentang Deteksi Covid-19 menggunakan Convolutional Neural Networks Buyut Khoirul Umri; Ema Utami; Mei P Kurniawan
Creative Information Technology Journal Vol 8, No 1 (2021): Januari - Juni
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/citec.2021v8i1.261

Abstract

Covid-19 menyerang sel-sel epitel yang melapisi saluran pernapasan sehingga dalam kasus ini dapat memanfaatkan gambar x-ray dada untuk menganalisis kesehatan paru-paru pada pasien. Menggunakan x-ray dalam bidang medis merupakan metode yang lebih cepat, lebih mudah dan tidak berbahaya yang dapat dimanfaatkan pada banyak hal. Salah satu metode yang paling sering digunakan dalam klasifikasi gambar adalah convolutional neural networks (CNN). CNN merupahan jenis neural network yang sering digunakan dalam data gambar dan sering digunakan dalam mendeteksi dan mengenali object pada sebuah gambar. Model arsitektur pada metode CNN juga dapat dikembangkan dengan transfer learning yang merupakan proses menggunakan kembali model pre-trained yang dilatih pada dataset besar, biasanya pada tugas klasifikasi gambar berskala besar. Tinjauan literature review ini digunakan untuk menganalisis penggunaan transfer learning pada CNN sebagai metode yang dapat digunakan untuk mendeteksi covid-19 pada gambar x-ray dada. Hasil sistematis review menunjukkan bahwa algoritma CNN dapat digunakan dengan akruasi yang baik dalam mendeteksi covid-19 pada gambar x-ray dada dan dengan pengembangan model transfer learning mampu mendapatkan performa yang maksimal dengan dataset yang besar maupun kecil.Kata Kunci—CNN, transfer learning, deteksi, covid-19Covid-19 attacks the epithelial cells lining the respiratory tract so that in this case it can utilize chest x-ray images to analyze the health of the lungs in patients. Using x-rays in the medical field is a faster, easier and harmless method that can be utilized in many ways. One of the most frequently used methods in image classification is convolutional neural networks (CNN). CNN is a type of neural network that is often used in image data and is often used in detecting and recognizing objects in an image. The architectural model in the CNN method can also be developed with transfer learning which is the process of reusing pre-trained models that are trained on large datasets, usually on the task of classifying large-scale images. This literature review review is used to analyze the use of transfer learning on CNN as a method that can be used to detect covid-19 on chest x-ray images. The systematic review results show that the CNN algorithm can be used with good accuracy in detecting covid-19 on chest x-ray images and by developing transfer learning models able to get maximum performance with large and small datasets.Keywords—CNN, transfer learning, detection, covid-19
EVALUASI AUGMENTED REALITY BANGUN RUANG SEBAGAI MEDIA PEMBELAJARAN SISWA KELAS IV SEKOLAH DASAR Buyut Khoirul Umri; Ika Asti Astuti; Achmad Choirul Sholihan
Journal of Information System Management (JOISM) Vol. 5 No. 1 (2023): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2023v5i1.1093

Abstract

Model pembelajaran menggunakan metode ceramah masih digunakan dalam pelajaran matematika pada materi bangun ruang di SD Muhammadiyah Noyokerten kelas IV. Dalam mempelajari bangun ruang, siswa dituntut memiliki imajinasi yang tinggi serta konstentrasi yang mendalam dalam mendeskripsikan unsur-unsur bangun ruang (sisi, sudut, jaring, tepi). Salah satu metode yang dapat digunakan untuk membantu visualisasi bangun ruang yakni menggunakan teknologi Augmented Reality. Penelitian dimulai dengan melakukan analisis masalah pada SD Muhammadiyah Noyokerten kelas IV, melakukan perancangan sistem, coding program hingga testing. Testing yang dilakukan meliputi black box atau menguji fungsionalitas aplikasi, validasi kepada 2 ahli materi dan uji coba kepada 31 siswa. Hasil uji black box menandakan bahwa fungsional aplikasi telah berjalan dengan baik. Hasil validasi kepada ahli materi menandakan bahwa aplikasi reliable untuk digunakan. Kebergunaan aplikasi dinilai dengan membandingkan hasil belajar pemahaman materi siswa dari model pembelajaran ceramah (pretest) dan menggunakan aplikasi augmented reality (post test) yang dikembangkan. Hasil uji siswa menunjukan bahwa nilai rata-rata pretes adalah 49.68 dan rata-rata nilai post test adalah 74.84. Hal ini berarti ada peningkatan hasil belajar materi bangun ruang sesudah menerapkan media pembelajaran dengan teknologi augmented reality.
PENERAPAN TEKNOLOGI AUGMENTED REALITY DALAM VISUALISASI ARSITEKTUR BERBASIS ANDROID Buyut Khoirul Umri; Ayub Pangestu Ari Wibowo; Giles Palendya Thessa Widyananda; Arizka Indah Dwi Nugraheni; Muhammad Iqbal Hafizh
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 4 (2023): EDISI 18
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i4.3458

Abstract

The development of technology in the current digital era is rapidly advancing in all fields, and it is something that we cannot avoid. One of the areas closely linked to the continuous technological progress is multimedia. In its evolution, multimedia has led to numerous new breakthroughs in the digital age, such as the emergence of Augmented Reality (AR) technology. One industry that has been performing well but can be enhanced through the integration of AR technology is architectural house design. The purpose of this paper is to provide a detailed explanation of the implementation of AR in architectural visualization using affordable devices accessible to all, specifically Android. The researcher adopted the Waterfall method in the application development process. The AR application underwent several stages, including the analysis of functional and non-functional requirements, UML design, asset creation, application development, testing, and maintenance. From all the button testing performed, all buttons functioned successfully.
Co-Authors Achmad Choirul Sholihan Arizka Indah Dwi Nugraheni Ayub Pangestu Ari Wibowo Ema Utami Giles Palendya Thessa Widyananda Ika Asti Astuti Mei P Kurniawan Muhammad Iqbal Hafizh Visq Delica