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All Journal Jurnal Pendidikan IPA Indonesia International Journal of Artificial Intelligence
Kusuma, M.
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Determination of Gender Differential Item Functioning in Tegal Students' Scientific Literacy Skills with Integrated Science (SLiSIS) Test Using Rasch Model Susongko, P.; Arfiani, Y.; Kusuma, M.
Jurnal Pendidikan IPA Indonesia Vol 10, No 2 (2021): June 2021
Publisher : Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v10i2.26775

Abstract

The emergence of Differential Item Functioning (DIF) indicates an external bias in an item. This study aims to identify items at scientific literacy skills with integrated science (SLiSIS) test that experience DIF based on gender. Moreover, it is analyzed the emergence of DIF, especially related to the test construct measured, and concluded on how far the validity of the SLiSIS test from the construct validity of consequential type. The study was conducted with a quantitative approach by using a survey or non-experimental methods. The samples of this study were the responses of the SLiSIS test taken from 310 eleventh-grade high school students in the science program from SMA 2 and SMA 3 Tegal. The DIF analysis technique used Wald Test with the Rasch model. From the findings, eight items contained DIF in a 95 % level of trust. In 99 % level of trust, three items contained DIF, items 1, 6, and 38 or 7%. The DIF is caused by differences in test-takers ability following the measured construct, so it is not a test bias. Thus, the emergence of DIF on SLiSIS test items does not threaten the construct validity of the consequential type.
Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture Kamath, Deepthi; Fathima, Misba Firdose; K. P., Monica; Kusuma, M.
International Journal of Artificial Intelligence Vol 8 No 1 (2021)
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-0801.217

Abstract

Alzheimer’s disease is a condition that leads to, progressive neurological brain disorder and destroys cells of the brain thereby causing an individual to lose their ability to continue daily activities and also hampers their mentality. Diagnostic symptoms are experienced by patients usually at later stages after irreversible neural damage occurs. Detection of AD is challenging because sometimes the signs that distinguish AD MRI data, can be found in MRI data of normal healthy brains of older people. Even though this disease is not completely curable, earlier detection can aid in promising treatment and prevent permanent damage to brain tissues. Age and genetics are the greatest risk factors for this disease. This paper presents the latest reports on AD detection based on different types of Neural Network Architectures.
Co-Authors Arfiani, Y. Fathima, Misba Firdose K. P., Monica Kamath, Deepthi P. Susongko, P.