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All Journal Edutech PEDAGOGIA Journal of Research in Social Science and Humanities Jurnal Media Computer Science
Nuryake Fajaryati
Universitas Negeri Yogyakarta
Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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THE SOCIOTECH LEARNING MODEL TO OVERCOME THE LOW STUDENTS EMPLOYABILITY SKILLS Nuryake Fajaryati; Budiyono Budiyono; Muhammad Akhyar; Wiranto Wiranto
PEDAGOGIA Vol 21, No 1 (2023)
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/pdgia.v21i1.55880

Abstract

Employability skills is an essential aspect for job seekers because employers and the world of work need it; however, many job seekers have the low employability skills. This study aims to reveal the need of SocioTech learning models through blended learning to overcome low students employability skills. The  descriptive research used to determine the level of employability skills among students, the analysis used the descriptive statistical analysis technique, and it used literature study to examine the SocioTech learning model. The results of the study showed the overall assessment of employability skills, revealing 31.58% of students to have average employability skills; 44.74% of students in the fair category; 23.68% of students classified as poor. Recommendation of SocioTech learning model through blended learning to improve employability skills consisted of six stages: 1) determining the topic of problems and groups through dialectics online and face to face (f2f); 2) planning investigations through collaboration and dialectics online and f2f; 3) conducting investigations through collaboration and dialectics online and f2f; 4) planning presentations through collaboration and dialectics online and f2f; 5) making presentations through collaboration, reflection, and dialectics online and face to face; and 6) evaluating group results online and f2f.
UJI VALIDITAS KONSTRUK INSTRUMEN KEMAMPUAN LITERASI DIGITAL DENGAN METODE CONFIRMATORY FACTOR ANALYSIS (CFA) Dieni Nugrahini; Nuryake Fajaryati
EDUTECH Vol 22, No 1 (2023)
Publisher : Prodi Teknologi Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/e.v22i1.55839

Abstract

Digital literacy is a set of skills need by humans to live with technology. To measure digital literacy skills, a proper measurement instrument is needed. The absence of a digital literacy skills instrument that has been tested for its validity becomes the background of this research. This study aims to prove the construct validity of digital literacy measuring instruments. The research subjects were 270 students of SMK Muhammadiyah 1 Bantul. Samples were taken using simple random sampling. The approach used in this study is Confirmatory Factor Analysis (CFA) with Lisrel 8.80. The results of this study show that all 37 instrument items are valid and measure digital literacy according to the underlying theory. Therefore, the four aspects of digital literacy need to be considered and honed by the current and future generations.Literasi digital merupakan kemampuan yang sangat dibutuhkan manusia untuk hidup berdampingan dengan kemajuan teknologi. Literasi digital mencakup empat aspek yaitu kemampuan dasar literasi, latar belakang pengetahuan, kompetensi utama, sikap dan perspektif pengguna informasi. Pada faktanya masih banyak pengguna teknologi yang literasi digitalnya rendah. Penelitian ini bertujuan untuk membuktikan validitas konstruk instrumen pengukur literasi digital. Subjek penelitian adalah siswa SMK Muhammadiyah 1 Bantul yang berjumlah 270 orang. Sampel diambil dengan menggunakan simpel random sampling. Pendekatan yang digunakan dalam penelitian ini adalah Confirmatory Factor Analysis (CFA) dengan Lisrel 8.80. Hasil penelitian menunjukkan bahwa seluruh butir instrumen yang berjumlah 37 valid dan benar-benar mengukur literasi digital sesuai dengan teori yang melandasinya. Oleh karena itu, empat aspek penyusun literasi digital perlu untuk diperhatikan dan diasah pada generasi saat ini dan yang akan datang.
Multimodal Learning in AIoT Systems: Sensor Fusion and Vision-Based Intelligence Agnes Prima Wulanjari; Ria Dymyati; Indar Bismoko Indar Bismoko; Nuryake Fajaryati; Pipit Utami
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.11040

Abstract

This study evaluates the effectiveness of multimodal learning in Artificial Intelligence of Things (AIoT) systems, focusing on the integration of sensor fusion and computer vision for classification tasks. A systematic review and meta-analysis were conducted on studies published between 2020 and 2025. Thirteen studies met the inclusion criteria; however, only six provided comparable quantitative data due to inconsistent baseline reporting and evaluation practices. The results indicate that multimodal approaches generally improve accuracy compared to unimodal baselines when comparable evaluations are available, with an average increase of 8.88% (95% CI: 5.33%–12.44%, p < 0.001). High heterogeneity was observed, influenced by domain, sensor configuration, and model architecture. These findings suggest that multimodal effectiveness is conditional and depends on modality complementarity, fusion strategy, and system-level constraints
Artificial Intelligence for Competency-Based Assessment in Vocational Education Alfred Michel Mofu; Praramadini Sari; Vetin Yumita Saroh; Nuryake Fajaryati; Pipit Utami; Yoga Sahria
Journal of Research in Social Science and Humanities Vol 5, No 3 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i4.564

Abstract

The rapid adoption of artificial intelligence in education has increasingly influenced research in technical and vocational education and training (TVET). However, much of the existing literature focuses primarily on prediction-oriented learning analytics rather than on competency-based assessment frameworks that are central to vocational education. This study investigates how artificial intelligence has been applied within vocational education research and examines the extent to which competency-based assessment principles are represented in literature. A systematic literature review was conducted using the PRISMA protocol, combined with layered bibliometric mapping using VOSviewer to explore structural and conceptual patterns in the research field. The dataset was constructed from Scopus-indexed journal articles published between 2020 and 2025. Bibliometric results indicate that machine learning, deep learning, and educational data mining dominate the research landscape, while competency constructs remain relatively peripheral. The thematic synthesis further reveals limited attention to authentic performance modeling and explainable artificial intelligence within assessment contexts. In response to these gaps, the study proposes a conceptual framework for AI-supported competency-based assessment in vocational education that integrates construct-grounded modeling, authentic performance analytics, and explainable decision architectures. The framework provides a conceptual foundation for aligning artificial intelligence technologies with competency-oriented evaluation in vocational learning environments.
Co-Authors Agnes Prima Wulanjari Alfred Michel Mofu Budiyono Budiyono Dieni Nugrahini Indar Bismoko Indar Bismoko Muhammad Akhyar Pipit Utami Praramadini Sari Ria Dymyati Vetin Yumita Saroh Wiranto Wiranto Yoga Sahria