LIMEEMAS: Jurnal Ilmiah Pendidikan

Gambo Traditional Cloth as a Tourism and Business Product

M Bambang Purwanto, Fitria Marisya, Ariya Agustin, Sri Porwani — Sat, 10 Oct 2026 00:00:00 +0000

This study discusses the traditional jumputan gambo cloth which is a typical cloth of Musi Banyuasin Regency. Gambo cloth is a jumputan cloth made from gambir sap waste as a natural dye in the manufacturing process. The purpose of the study was to develop local wisdom on the use and productivity of gambir plants in Toman Village, Babat Toman District, Musi Banyuasin Regency. The result of the research is that the gambir plant is a plant that has many benefits such as being used as mixture of drugs, cosmetics and fabrics dyes. The process of making jumputan gambo cloth starts from the process making cloth patterns, the process of sewing the cloth, the process of binding the cloth, the process of soaking and drying the cloth. Jumputan gambo fabric continues to develop following the trend of the times according to the tastes of today’s society. The developing is carried out to preserve the jumputan gambo fabric by providing training to the community in the process of making jumputan gambo fabric. The conclusion that jumputan gambo fabric is an icon of the handicarft industry based on community empowerment especially for Gambo fabric craftsmen and also gambo farmers. Promotion of jumputan gambo fabric must continue tobe carried out especially for the local community because quite a lot people do not know and know what jumputan gambo fabric is because jumputan gambo fabric is a local product typical of Musi Banyuasin Regency that must be developed and preserved.

Breaking the Data Barrier

Fitri Yani, Theo Vhaldino, Arpa Pauziah — Thu, 01 Oct 2026 00:00:00 +0000

The rapid digitalization of healthcare has increased the need for graduates who can understand, interpret, and responsibly use health-related data. However, students entering health and technology-related programs may encounter barriers when learning health informatics and big data, particularly when technical concepts must be connected with clinical and professional contexts. This qualitative study explored students' experiences of learning health informatics and big data literacy through an interdisciplinary learning approach at Universitas ‘Aisyiyah Palembang. The study involved 30 undergraduate students, consisting of 20 Information Technology students and 10 Nursing students, and was conducted from 12 March to 1 April 2026. Data were collected through classroom observations, students' reflective responses, semi-structured interviews, and learning artifacts, and were analyzed using thematic analysis. The findings revealed five interconnected themes: encountering the data barrier, moving from technical complexity toward meaningful understanding, learning through disciplinary complementarity, connecting healthcare data with professional practice, and developing confidence and responsible data awareness. The findings indicate that students' difficulties were not limited to technical skills but also involved unfamiliar terminology, contextual interpretation, disciplinary differences, and uncertainty about the responsible use of health information. Interdisciplinary learning provided opportunities for Information Technology and Nursing students to exchange complementary perspectives, connecting technological knowledge with clinical meaning. The study concludes that health data literacy should be understood as a multidimensional competency involving data understanding, interpretation, contextualization, communication, and ethical use. Interdisciplinary health informatics education can therefore serve as a meaningful pedagogical pathway for preparing future professionals to participate in increasingly data-driven healthcare environments.

AI-Assisted Problem-Based Learning to Enhance Computational Thinking and Digital Problem-Solving Skills among Vocational Higher Education Students

Abiyasa Eka Saputra, Rahma Fitriyani, Muhammad Hairul, Sri Nuryeni — Tue, 06 Oct 2026 00:00:00 +0000

The rapid development of artificial intelligence (AI) has transformed the ways students access information, solve problems, and construct knowledge in technology-mediated learning environments. However, the availability of generative AI tools does not automatically guarantee the development of students' computational thinking and digital problem-solving abilities. This study investigated the effectiveness of AI-assisted problem-based learning in improving computational thinking and digital problem-solving skills among vocational higher education students. A quasi-experimental pre-test and post-test control group design was employed. The study involved 60 undergraduate students who were divided into an experimental group receiving AI-assisted problem-based learning and a control group receiving conventional problem-based learning without direct generative AI assistance. The intervention was conducted over eight instructional sessions. Data were collected using a computational thinking performance test, a digital problem-solving assessment, and a student perception questionnaire. The data were analyzed using descriptive statistics, independent-samples t-tests, paired-samples t-tests, and ANCOVA. The simulated empirical results indicated that the experimental group demonstrated a substantially greater improvement in computational thinking than the control group. The experimental group increased from a pre-test mean of 55.13 to a post-test mean of 84.27, whereas the control group increased from 54.60 to 72.13. A similar pattern was observed in digital problem-solving performance. The findings suggest that AI-assisted problem-based learning can provide students with immediate feedback, alternative solution pathways, debugging support, and opportunities to critically evaluate AI-generated solutions. Nevertheless, the findings also indicate that AI should function as a cognitive support mechanism rather than a substitute for students' own reasoning. The study concludes that structured AI integration can strengthen computational thinking when students are required to explain, evaluate, modify, and validate AI-supported solutions.