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rizal ula ananta fauzi
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rizalmanajemen@gmail.com
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+6282139474255
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jl raya solo no 11 suratmajan Maospati, Magetan JAWA tIMUR
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Kab. magetan,
Jawa timur
INDONESIA
International Journal of Education, Vocational and Social Science
ISSN : -     EISSN : 29634954     DOI : https://doi.org/10.99075/ijevss.v1i01.1192
International Journal of Education Vocational and Social Science(IJEVSS ) is  a peer-reviewed journal which welcomes submissions involving a critical discussion of policy and practice, as well as contributions to conceptual and theoretical developments in the field. It includes articles based on empirical research and analysis (quantitative, qualitative and mixed method) and welcomes papers from a wide range of disciplinary and inter-disciplinary perspectives. The journal embraces the broad range of settings and ways in which vocational and professional learning takes place and, hence, is not restricted by institutional boundaries or structures in relation to systems of education and training. This journal covers the topics of the educational profession, curriculum, educational philosophy, educational technology, learning strategies, educational management, educational psychology, student development, general issues in primary, secondary and higher education, educational foundations, teacher professional development, out-of-school education, and excellent education, the study of curriculum, pedagogy, and assessment, as well as economic, cultural and political aspects related to the role of vocational and professional education and training in society
Articles 522 Documents
Development of Learning Management System For Nava Dhammasekha Teachers In Indonesia Tri Suyatno; Sulaiman Sulaiman; Santi Paramita; Putri Cakkhavati; Aurel Vimalla Pannadayika; Ratna Paramitha
International Journal of Education, Vocational and Social Science Vol. 5 No. 03 (2026): International Journal of Education, Vocational and Social Science( IJVESS)
Publisher : Cita konsultindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijevss.v5i03.5230

Abstract

This study aims to develop and evaluate a Learning Management System (LMS) at STAB Negeri Raden Wijaya as part of higher education digital transformation. The research employed the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The results indicate that the developed LMS achieved a media validity score of 86.67%, categorized as highly valid. The system demonstrates strong design quality, well-structured learning content, and evaluation features that support learning transparency. The needs analysis score of 65.2% suggests the necessity for further development, particularly in terms of system personalization and flexibility. The LMS also implements a scaffolding strategy in content organization, which enhances students’ cognitive engagement. The integration of security systems through SSO and LDAP strengthens academic accountability. Overall, the LMS is considered feasible for implementation without major revisions. Nevertheless, its long-term sustainability depends on continuous support, including digital literacy improvement, ongoing training, and the optimization of learning analytics to support data-driven decision making
Performance Analysis of IndoBERT Fine-Tuning for Indonesian Text-Based Hoax Detection Tiara Siti; Ari Muzakir
International Journal of Education, Vocational and Social Science Vol. 5 No. 03 (2026): International Journal of Education, Vocational and Social Science( IJVESS)
Publisher : Cita konsultindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijevss.v5i03.5764

Abstract

Abstract The spread of misinformation (hoaxes) on social media, particularly on the X platform, has become a significant challenge that can influence public understanding and perceptions of various issues, including those related to education. This study aims to analyze and improve the performance of an Indonesian-language hoax detection model through a fine-tuning approach using the IndoBERT model. The dataset used in this research was collected through web scraping from the X platform and classified into two categories: hoax and non-hoax. Prior to model training, the data underwent several preprocessing stages, including data cleansing, normalization, tokenization, stopword removal, and stemming. This study utilized the pre-trained IndoBERTweet model with two data-splitting scenarios, namely 70:30 and 80:20, along with various combinations of learning rates and epochs to determine the optimal configuration. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the best performance was achieved under the 70:30 data-splitting scenario with a learning rate of 2e-5 and 20 epochs. On the testing dataset, the model achieved an accuracy of 80%, precision of 83%, recall of 78%, and an F1-score of 80%. The findings demonstrate that the fine-tuning approach applied to IndoBERT is effective in enhancing the performance of Indonesian-language hoax classification and is capable of maintaining strong performance when evaluated on previously unseen data. Keywords: IndoBERT, IndoBERTweet, fine-tuning, hoax detection, social media, natural language processing (NLP), text classification.

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