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All Journal Journal International of Lingua and Technology Journal International Inspire Education Technology (JIIET) Scientechno: Journal of Science and Technology International Journal of Language and Ubiquitous Learning
Anis, Nina
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Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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The Role of Digital Learning Objects in Personalized Education Bungai, Joni; Permana, Indra; Anis, Nina; Zaman, Khalil
Journal International Inspire Education Technology Vol. 3 No. 3 (2024)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiiet.v3i3.727

Abstract

Digital Learning Objects (DLOs) have emerged as key tools in promoting personalized education, offering students flexible, adaptable resources that can be tailored to individual learning styles, preferences, and paces. With increasing demand for personalized learning experiences, DLOs provide an innovative approach to meet diverse educational needs by allowing educators to create and deploy interactive, multimedia-rich content. This research aims to explore the effectiveness of DLOs in enhancing personalized education in secondary school settings, examining their impact on student engagement, comprehension, and learning outcomes. A mixed-methods approach was employed, combining quantitative analysis of student performance data with qualitative feedback from students and educators. Data were collected from two groups: one group utilizing DLO-based lessons and a control group following traditional instructional methods. Surveys, interviews, and assessment scores were analyzed to determine the effect of DLOs on individualized learning experiences and overall academic success. The findings reveal that students in the DLO group demonstrated improved engagement and a 20% higher retention rate compared to the control group, with educators noting greater adaptability to varying skill levels and learning preferences. Students reported feeling more in control of their learning, highlighting DLOs’ potential in promoting autonomy and motivation. This study concludes that DLOs are effective tools for supporting personalized education, helping educators cater to individual learning needs. Future research is recommended to explore long-term impacts of DLO integration and its effectiveness across different educational contexts.
Pattern Recognition System for Automating Medical Diagnosis Based on Image Data Irianti, Evi; Anis, Nina; Aziz, Saifiullah
Scientechno: Journal of Science and Technology Vol. 4 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i1.2126

Abstract

The increasing volume and complexity of medical image data have presented significant challenges for healthcare professionals in delivering timely and accurate diagnoses. Traditional diagnostic processes are often time-consuming and prone to human error, underscoring the need for automated solutions. This study aims to develop a pattern recognition system to automate medical diagnosis using image data, thereby improving diagnostic accuracy and efficiency. A hybrid methodology was employed, combining image preprocessing, feature extraction using convolutional neural networks (CNNs), and classification through deep learning algorithms. The system was trained and validated using publicly available medical image datasets across various disease types. The results demonstrate high diagnostic accuracy, with the system achieving over 92% precision in identifying disease patterns from image inputs. Furthermore, the model exhibited robustness across different imaging modalities, such as X-rays, MRIs, and CT scans. These findings suggest that the proposed pattern recognition system can serve as a reliable support tool for medical practitioners. In conclusion, the integration of image-based pattern recognition in medical diagnostics holds significant promise in enhancing clinical decision-making processes and reducing diagnostic errors.
Prediction of Indonesian Learning Achievement Using Machine Learning Models Safar, Muh.; Anis, Nina
International Journal of Language and Ubiquitous Learning Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijlul.v3i1.1921

Abstract

Background. Student learning achievement is one of the important indicators in assessing the effectiveness of education. Various factors such as student attendance and socioeconomic status have been known to affect learning outcomes. However, the influence of access to technology in the context of education in Indonesia has not been studied in depth. In today's digital era, access to technology is an important aspect that can support or hinder the learning process of students. Purpose. This study aims to analyze the influence of student attendance, socioeconomic status, and access to technology on student learning achievement. In addition, this study also aims to test the accuracy of machine learning models in predicting student exam results based on these variables. Method. This study uses a quantitative approach with the application of machine learning models, including linear regression and decision trees. The data used includes students' test scores, attendance levels, socioeconomic status, and access to technology devices and networks. Results. The results of the analysis showed that student attendance, socioeconomic status, and access to technology had a significant influence on learning achievement. The machine learning model applied is able to predict students' exam results with a high level of accuracy, demonstrating the effectiveness of this approach in educational analysis. Conclusion. This study emphasizes the importance of external factors, especially access to technology, in predicting student learning achievement. A more inclusive education policy is needed by expanding access to technology and educational facilities, in order to support the equitable distribution of learning quality in all circles.
The Role of Podcasts in Enhancing Language Learning Syafei, Muh; Rahman, Rashid; Anis, Nina
Journal International of Lingua and Technology Vol. 3 No. 3 (2024)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiltech.v3i3.775

Abstract

In the digital age, podcasts have emerged as a versatile tool for language learning, offering accessible and engaging content for learners. Despite their growing popularity, there is limited empirical research on how podcasts specifically enhance language acquisition, particularly in diverse educational contexts. This study aims to address this gap by exploring the role of podcasts in improving language skills. This research investigates the effectiveness of podcasts in enhancing language learning outcomes, focusing on listening comprehension, vocabulary acquisition, and learner motivation. A mixed-methods approach was employed, combining quantitative pre- and post-tests with qualitative interviews. Participants included 120 intermediate-level language learners divided into experimental and control groups. The experimental group engaged with podcast-based activities over eight weeks, while the control group followed traditional methods. Data were analyzed using statistical software and thematic analysis. The findings revealed significant improvements in listening comprehension and vocabulary retention among the experimental group. Learners reported increased motivation and autonomy, attributing these gains to the flexibility and authenticity of podcast content. Podcasts are a valuable resource for language learning, fostering both linguistic and motivational benefits. Educators are encouraged to integrate podcasts into curricula to support diverse learning needs.  
Co-Authors Aziz, Saifiullah Indra Permana, Indra Irianti, Evi Joni Bungai, Joni Muh Syafei Muh. Safar, Muh. Rahman, Rashid Zaman, Khalil