Abstract:The cognitive development of early childhood requires appropriate stimulation, one of which is through color recognition. Color block media serves as an educational tool that not only introduces various colors but also familiarizes…
amiliarizes children with geometric shapes, numerical concepts, and trains their thinking and memory skills. This study aims to implement color block media as an innovative learning method to effectively improve early childhood abilities in color recognition. Early childhood is a stage of exploration, where the learning process must be concrete, engaging, and enjoyable. Color block media combines visual and manipulative approaches that can foster curiosity and active involvement in the learning process. This learning-through-play activity encourages children to naturally identify, differentiate, and name colors. The study used a quantitative method with a pretest-posttest design to determine the effectiveness of the media. The research was conducted at KB Adduriyah 3 on October 29, 2024, and data analysis was performed using a t-test through SPSS 18 for Windows. The results showed a significance value (2-tailed) of 0.00. Since this value is smaller than the significance level (α = 0.05), the null hypothesis is rejected, and the alternative hypothesis is accepted. The results of the study demonstrate that color block media has a significant impact on improving children's color recognition skills. Additionally, the media also enhances children's active participation in learning activities. Therefore, color block media is highly recommended as a creative and effective learning strategy for educators and parents in supporting the cognitive development of early childhood.
Abstract:This study focuses on the development of an Augmented Reality (AR)–based learning application designed to assist students in understanding the mathematical concepts of volume and surface area of three-dimensional geometric…
tric shapes. The development process adopted the Multimedia Development Life Cycle (MDLC) model, which consists of six systematic stages: concept, design, material collecting, assembly, testing, and distribution. The research concentrated on the development and expert validation stages. Validation results from content and media experts indicate that the application meets pedagogical and technical feasibility standards. The content expert confirmed that the materials align with the national mathematics curriculum and are presented in a clear, contextual, and accurate manner, while the media expert highlighted the user-friendly interface, interactive features, and visual appeal of
the application. Theoretically, this AR-based medium bridges the gap between abstract mathematical concepts and concrete visualization by enabling students to interact directly with
virtual 3D objects. Practically, the application enhances learning motivation and engagement by providing dynamic, interactive experiences. Overall, this research contributes to the
advancement of educational technology by offering a systematic model for developing AR-based learning media that support active and meaningful learning in the digital
era.
Abstract:Abstract: Face recognition based on deep learning has become an important technology in many areas. However, these systems often face challenges in real-world conditions, such as when the face is partially covered by accessories…
essories such as masks or glasses. This study aims to evaluate the effect of data augmentation by adding facial accessories (masks, glasses, and a combination of both) and geometric augmentation on the accuracy of face recognition systems. There are three types of datasets used in this method: the original dataset (category 1), the dataset with facial accessories augmentation (category 2), and the dataset with geometric augmentation (category 3). Data augmentation was performed on the training dataset to increase diversity, followed by the face detection process using SCRFD and feature extraction with ArcFace. The model was then trained using Multi-Layer Perceptron (MLP). Based on the results, adding face accessories (category 2) made the model a lot more accurate, hitting 99% accuracy. In category 3, adding geometric features improved accuracy to 91%. Other evaluation metrics, such as precision, recall, and F1-score, also showed improvement after augmentation. This study concludes that facial accessories augmentation is more effective in improving the accuracy and robustness of face recognition models compared to geometric augmentation.
Keywords: augmentation; deep learning; face recognition; glasses.
Abstrak: Pengenalan wajah berbasis deep learning telah menjadi salah satu teknologi penting dalam berbagai aplikasi. Namun, sistem ini sering kali menghadapi tantangan dalam kondisi dunia nyata, seperti saat wajah tertutup sebagian oleh aksesori seperti masker atau kacamata. Penelitian ini bertujuan untuk mengevaluasi pengaruh augmentasi data dengan menambahkan aksesori wajah (masker, kacamata, dan kombinasi keduanya) serta augmentasi geometris terhadap akurasi sistem pengenalan wajah. Metode yang digunakan melibatkan tiga kategori dataset: dataset asli tanpa augmentasi (kategori 1), dataset dengan augmentasi aksesoris wajah (kategori 2), dan dataset dengan augmentasi geometris (kategori 3). Augmentasi data dilakukan pada dataset pelatihan untuk meningkatkan keberagaman, diikuti dengan proses deteksi wajah menggunakan SCRFD dan ekstraksi fitur dengan ArcFace. Model kemudian dilatih menggunakan Multi-Layer Perceptron (MLP). Hasil penelitian menunjukkan bahwa augmentasi aksesoris wajah (kategori 2) memberikan peningkatan signifikan pada akurasi model, mencapai 99%, sedangkan kategori 3 dengan augmentasi geometris mencapai akurasi 91%. Metrik evaluasi lainnya, seperti precision, recall, dan F1-score, juga menunjukkan peningkatan setelah augmentasi. Penelitian ini menyimpulkan bahwa augmentasi aksesoris wajah lebih efektif dalam meningkatkan akurasi dan ketahanan model pengenalan wajah dibandingkan dengan augmentasi geometris.
Kata kunci: augmentasi; deep learning; kacamata; pengenalan wajah.
Abstract:Face recognition has become a common thing used in the field of surveillance and security in computer technology and image devices. This study aims to identify the usefulness of a person's face on 3 test images. This study…
dy examines the methods of cropping techniques, image enhancement through intensity measurement, and histogram analysis to improve the contrast and distribution of image intensity. In addition, the Viola-Jones algorithm is used to detect key facial features such as eyes, nose, and mouth. The results of the analysis are then applied in the feature evaluation stage, where usually between facial features are applied to measure the ratio of facial proportions. Furthermore, the comparison of proportional ratios of several images was analyzed using bar graphs and line graphs to evaluate the trend and stability of facial proportions. The results showed the best ratio stability with a smaller variation of the on-off ratio of image 2 which is 0.4762 pixels to 0.4983 pixels. Image 2 is the most ideal for face measurement systems based on geometric ratios because it provides more consistent and visible results.
Abstract:This study aims to analyze the types of errors made by 11th grade students at SMAS Amir Hamzah Medan in solving geometric transformation problems involving translation, reflection, rotation, and dilation. The study employs…
ys a qualitative descriptive approach with a sample of 15 students selected based on high, medium, and low ability categories. Data were collected through essay tests and analyzed using Newman's Error Analysis, which consists of five stages of errors: reading, understanding, transformation, process skills, and writing the final answer. The analysis results indicate that the highest incidence of errors occurred in the understanding stage and process skills stage, each accounting for 36%. Transformation errors accounted for 21.33%, while reading and final answer writing errors were in the very low category, each at 9.33%. These findings indicate that students still face difficulties in understanding basic concepts and technical steps in geometric transformations. Based on these results, it is recommended that teachers implement systematic learning strategies such as Learning Therapy, emphasizing the understanding of prerequisite concepts, writing down known and asked information, and developing the habit of structuring and evaluating solution steps systematically to minimize similar errors in the future.
Abstract:This study aims to identify the dominant error patterns of students in geometry learning based on Newman's Error Analysis (NEA) and to examine their implications for readiness to understand Non-Euclidean Geometry. The method…
thod employed is a Systematic Literature Review (SLR) of 10 empirical articles published between 2019 and 2026. Data were extracted based on the five stages of NEA — reading, comprehension, transformation, process skill, and encoding — and subsequently analyzed through narrative synthesis, percentage comparison, and descriptive effect size. The results indicate that comprehension errors and encoding errors are the most dominant categories, with the highest percentages of 56.92% and 50%, respectively, followed by transformation errors, which consistently fall within a moderate effect range (20–40%), while reading errors and process skill errors are classified as low. The primary causes of errors include weak understanding of geometric concepts, inability in spatial visualization, limited mathematical language, and lack of procedural precision. The findings contribute as a diagnostic foundation for designing more effective geometry learning, while simultaneously serving as a conceptual bridge toward higher-level geometry