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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Borneo Educational Journal (Borju)
Yuniarti
Universitas Kuningan
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics

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Translation Analysis of Romanticism In 50 First Dates Movie Robi Aji Sugihartono; Muh. Aprianto Budie Nugroho; Yuniarti
Borneo Educational Journal (Borju) Vol. 5 No. 2 (2023): August
Publisher : Teacher Training and Education Faculty, Widya Gama Mahakam Samarinda University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/bej.v5i2.1390

Abstract

50 First Dates is a movie that broadly known around the globe in 2004 as one of the best romantic comedy movie in the 2000s. This research aims to analyze type of romanticism and the translation techniques used in the 50 First Dates movie subtitle in Amazon Prime Video. This research uses qualitative method with case study as a research design. This research uses document analysis with transcript and subtitle of the movie. The data are analyzed using theory from Lomas (2018) in finding the type of romanticism and theory by Molina and Albir (2002) to find out the types of translation techniques. Based on findings, there are 6 types of love found out of 14, namely: Philia (31.03%), Storge (25.28%), Paixnidi (21.83%), Ananke (13.79%), Epithymia (5.74%) and Pragma (2.29%). Regarding the translation techniques, 9 out of 18 types of translation techniques are found, namely; Established Equivalent (47.61%), Linguistics Compression (15.87%), Borrowing (8.73%), Reduction (8.73%), Compensation (7.93%), Linguistics Amplification (4.76%), Modulation (3.17%), Amplifications (2.38%) and Particularization (0.79%). Based on the data, the researcher found that the translation used more than one translation techniques. Therefore, the researcher grouped them into single (59.77%), duplet (31.03%), triplet (8.04%), and quadruplet group (1.14%). From the findings, it can be concluded that the most found type of love is Philia. On the other hand, the most dominant type of translation techniques is Established Equivalent with various combinations.
EXTENDING THE HOT-FIT MODEL WITH INFORMATION LITERACY TO EXPLAIN AI ADOPTION IN HIGH SCHOOLS Fahmi Yusuf; Yuniarti; Heru Budianto; Rio Priantama
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7270

Abstract

Artificial Intelligence (AI) is increasingly transforming educational practices by enabling more adaptive and personalized learning experiences in secondary schools. Nevertheless, previous applications of the Human–Organization–Technology Fit (HOT-Fit) model have given limited attention to the roles of information literacy and trust in influencing AI adoption. To address this gap, the present study expands the HOT-Fit framework by incorporating three additional constructs: information literacy, perceived validity, and perceived trust, in order to better explain AI readiness and adoption in educational settings. A quantitative approach was employed involving 316 senior high school students from Kuningan Regency, Indonesia. Data were gathered using a structured questionnaire based on a five-point Likert scale and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS v3.0 to evaluate 29 proposed hypotheses. The findings revealed that 21 hypotheses were statistically supported. Information literacy demonstrated a strong positive effect on perceived trust (β = 0.708; p < 0.001), as well as on system use, organizational structure, environmental support, and user satisfaction. In addition, system quality significantly contributed to user satisfaction, whereas service quality affected both system use and satisfaction. Among all relationships, net benefit exerted the strongest effect on action to use (β = 0.547; p < 0.001). The R² results for several endogenous constructs were above 0.50, indicating acceptable explanatory capability of the proposed model. Practically, the findings offer implications for educators, policymakers, and system developers in designing AI-supported learning environments by emphasizing the enhancement of digital literacy, service support, and system effectiveness for sustainable AI integration in schools.
Co-Authors Fahmi Yusuf Heru Budianto Muh. Aprianto Budie Nugroho Rio Priantama Robi Aji Sugihartono