Article Per Year (5 Year)
p-Index From 2021 - 2026
2.808
P-Index
This Author published in this journals
All Journal Sosiohumaniora MediaTor: Jurnal Komunikasi Journal of Dentistry Indonesia Record and Library Journal Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population) Dental Journal (Majalah Kedokteran Gigi) Jurnal Ilmiah Peuradeun Jurnal Studi Komunikasi Knowledge Engineering and Data Science Biosfer: Jurnal Pendidikan Biologi International Journal of Research in Counseling and Education GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Jurnal Komunikasi Nusantara Jurnal Komunikasi Nusantara Biokultur Amerta Berkala Ilmiah Kedokteran dan Kesehatan Masyarakat Folia Medica Indonesiana COUNS-EDU: The International Journal of Counseling and Education
Myrtati Dyah Artaria, Myrtati Dyah
Unknown Affiliation
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Neuroscience Social Sciences Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Corona virus (COVID- 19) and education for all achievement: artificial intelligence and special education needs- achievements and challenges Bah, Yahya Muhammed; Artaria, Myrtati Dyah
COUNS-EDU: The International Journal of Counseling and Education Vol. 5 No. 2 (2020)
Publisher : Indonesian Institute for Counseling, Education, and Therapy & Indonesian Counselor Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23916/0020200528630

Abstract

The shortage of well-trained teachers especially in special education is a serious problem worldwide. To attain education for all as enshrined in the Sustainable Development Goals (SDGs), there is urgent need for robot ways of solving this problem with grave consequences for the future of children with disabilities and special education needs. Thus, education delivery methods like other services need to be innovative. The purpose of this studi is to examine the achievements and challenges in the application of AI for teaching children with special education needs. This research used the literature review method. The result of this study shows that AI has the power to enhance learning for children with special needs while curbing some of the problems such children are encountering in accessing quality and relevant education. In conclusion the findings revealed some significant achievements and the possibilities of more if the appropriate technologies are applied consistently with the right environment both in schools and homes.
Automatic 3D Cranial Landmark Positioning based onSurface Curvature Feature using Machine Learning Suputra, Putu Hendra; Sensusiati, Anggraini Dwi; Artaria, Myrtati Dyah; Verkerke, Gijsbertus Jacob; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious volumetric issue. This work uses the Surface Curvature Feature as a three-dimensional descriptor. It extracts the local surface curvature shape into a projection sequential value (depth). A machine learning method is developed to determine the position of landmarks based on local surface shape characteristics. Classification is carried out from the top-n prediction probabilities for each landmark class, from a set of predictions, then filtered to get pinpoint accuracy. The landmark prediction points are hypothetically clustered in a particular area, so a cluster-based filter is appropriate to isolate them. The learning model successfully detected the landmarks, with the average distance between the prediction points and the ground truth being 0.0326 normalized units. The cluster-based filter is implemented to increase accuracy compared to the ground truth. Thus, SCF is suitable as a 3D descriptor of cranial landmarks.
Co-Authors Akhirul Aminulloh Alaydrus, Sayf Muhammad Ana, Rizka Fitri Ari Gunawan Avianto, Rahmat Bah, Yahya Muhammed Budianto, Azzah Kania Bull, Ronald Lukens Chen, Yao-Fong Dinara Maya Julijanti Dinglasa, Ivan Nikkimor Lao Eko Mulyanto Yuniarno Emei Dwinanarhati Setiamandani Fathul Qorib Fitrianti, Mia Yulia Fitrianti hakim, lukman Handini, Retno Handini, Retno Harry Octavianus Sofian, Harry Octavianus I Ketut Eddy Purnama I Made Geria, I Made Ike Herdiana Katili, Rifany Humairah Purnama Lalu Murdi Miftakhurahma, Rizki Moorthy, Tharmar Nataraja Nugraheni, Edlin Yanuar Nur Mujaddidah, Nur Prasetyo, Bagyo Prasetyo, Bagyo Putu Hendra Suputra Sensusiati, Anggraini Dwi Simanjuntak, Truman Simanjuntak, Truman Suen, Mein-Woei Suryandaru, Yayan Sakti Suryani Dyah Astuti Susilowati Andajani, Susilowati Sutinah Sutinah Syarifah, Mustika Chasanatussy Ula, Ma’rifatul Unggul Prasetyo Wibowo, Unggul Prasetyo Verkerke, Gijsbertus Jacob Yuyun Wahyu Izzati Surya