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All Journal DIKSI Jurnal Nuansa Akademik: Jurnal Pembangunan Masyarakat
Atymtaevna, Begimbetova Guldana
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Clustering ECE and NFE Accredited Statuses with Unsupervised Possibilistic Fuzzy C-Means Prihantoro, Agung; Kartianom, Kartianom; Atymtaevna, Begimbetova Guldana
Nuansa Akademik: Jurnal Pembangunan Masyarakat Vol. 10 No. 2 (2025): In Progress
Publisher : Lembaga Dakwah dan Pembangunan Masyarakat Universitas Cokroaminoto Yogyakarta (LDPM UCY)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47200/jnajpm.v10i2.3025

Abstract

The research aims to have clusters of accredited statuses of early childhood education (ECE) and non-formal education (NFE) institutions in Yogyakarta Special Province in Indonesia, which are created by unsupervised possibilistic fuzzy c-means (UPFC) and to organize the institutions into the clusters created. The Board of National Accreditation for ECE and NFE determined four accredited statuses of A, B, C, and TT. The research employs a method of machine learning, especially UPFC. The dataset is a data of accreditation 2022 from the Board of National Accreditation for ECE and NFE of Yogyakarta Special Province. The data consists of 760 institutions composed of 749 (98.55%) ECE institutions and 11 (1.45%) NFE institutions. The analysis of UPFC created two clusters of accredited statuses of the institutions, thar are Accredited A that consists 437 (57.5%) institutions and Accredited B consisting of 323 (42.5%) institutions. The names of the clusters have political impact.
Is there any item or test bias in the Business English Test at Universitas Terbuka? Santoso, Agus; Retnawati, Heri; Pardede, Timbul; Rahayu, Dyah Paminta; Rosyada, Munaya Nikma; Tuanaya, Rugaya; Sotlikova, Rimajon; Atymtaevna, Begimbetova Guldana
Diksi Vol. 33 No. 2: DIKSI (SEPTEMBER 2025)
Publisher : Fakultas Bahasa, Seni, dan Budaya, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/diksi.v33i2.84570

Abstract

In an exemplary test implementation, the items used should be fair and free from bias. This biased content threatens the validity that will affect the interpretation of the test results. This study aims to analyze the biased content of items and tests on the test device for the Commercial English Course, with the code ADBI4201, a course in the Business Administration Study Program at the Faculty of Law, Social, and Political Sciences, Open University (UT). It uses the quantitative approach. Data were collected through documentation in the form of grids and responses from final semester test participants in January 2024. Data analysis was carried out by (1) estimating item parameters using classical test theory and item response theory for tests, (2) estimating item parameters based on regional groups and based on gender, which are used to identify DIF content, (3) testing the significance of DIF with a maximum likelihood comparison. The study's findings showed 26 items contained DIF by gender and 25 items contained DIF by region. The results of the test bias detection showed that the test slightly favored the female group and the students from the Java region.
Co-Authors Agung Prihantoro Agus Santoso Heri Retnawati Kartianom, Kartianom Pardede, Timbul Rahayu, Dyah Paminta Rosyada, Munaya Nikma Sotlikova, Rimajon Tuanaya, Rugaya