PENINGKATAN KEMAMPUAN PENGGUNAAN TANDA BACA MELALUI MEDIA PAPAN DETEKTIF TANDA BACA PADA MURID KELAS III SD
DOI:
https://doi.org/10.23969/jp.v11i03.61250Keywords:
punctuation ability, learning media, punctuation detective boardAbstract
This study addresses the suboptimal punctuation skills of third-grade students at SDN 05 Madiun Lor, primarily caused by conventional learning methods. The research aims to improve students' punctuation usage through the Punctuation Detective Board Media. This Classroom Action Research was conducted in two cycles, encompassing planning, acting, observing, and reflecting. The subjects were 29 third-grade students. Data were collected via tests, observations, and documentation, then analyzed using descriptive quantitative and qualitative techniques. The results indicate a significant improvement in students' punctuation abilities. The average score increased from 70.34 in the pretest to 88.62 in Cycle I, and further to 94.14 in Cycle II. Moreover, the learning completeness percentage rose from 27.59% initially to 93.10% in Cycle I, and reached 96.55% in Cycle II. Beyond academic outcomes, the media successfully enhanced student activity, participation, and enthusiasm during the learning process. It is concluded that the Punctuation Detective Board Media is highly effective in improving punctuation skills among third-grade elementary students.
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References
Bhatt, H., Das, S., Han, Y. (Jade), Moghaddam, M., & Nanda, G. (2026). A machine learning based framework for identifying consumer product injuries from social media data. Injury, 57(2). https://doi.org/10.1016/j.injury.2025.112927
Birthriya, S. K., Ahlawat, P., & Jain, A. K. (2026). Machine learning-based smishing detection using fuzzy logic and TF-IDF feature engineering. Franklin Open, 14. https://doi.org/10.1016/j.fraope.2026.100506
Chakarverti, M., Goswami, A., & Kumar Kaliyar, R. (2026). FusionNet: A bias-aware multimodal deep learning framework for detecting misinformation in noisy social media environments. Knowledge-Based Systems, 335. https://doi.org/10.1016/j.knosys.2025.115170
Chutia, T., Dutta, B., & Baruah, N. (2025). Harnessing Deep Learning for Assamese Sarcastic Comment Detection from Social Media Text. Procedia Computer Science, 258, 3115–3125. https://doi.org/10.1016/j.procs.2025.04.569
Fkih, F., Rhouma, D., & Alharbi, T. (2025). Mental disorder preventing by worry levels detection in social media using deep learning based on psycho-linguistic features: application on the COVID-19 lockdown period. Computers in Biology and Medicine, 191. https://doi.org/10.1016/j.compbiomed.2025.110162
Islam, M. J., Rafi, M. F. Al, Podder, P., Siddika, A., Kabir, M., Khan, E. M., Islam, N., & Mukta, S. (2026). Irregular sleep pattern identification and analysis from social media dataset using hybrid deep learning based attention mechanism. Data and Information Management, 10(1). https://doi.org/10.1016/j.dim.2025.100104
Kolesnikova, O., Shahiki Tash, M., Ahani, Z., Agrawal, A., Monroy, R., & Sidorov, G. (2025). Advanced machine learning techniques for social support detection on social media. Heliyon, 11(10). https://doi.org/10.1016/j.heliyon.2025.e43437
Mali, M. K., Pawar, R. R., Shinde, S. A., Kale, S. D., Mulik, S. V., Jagtap, A. A., Tambewagh, P. A., & Rajput, P. U. (2025). Automatic detection of cyberbullying behaviour on social media using Stacked Bi-Gru attention with BERT model. Expert Systems with Applications, 262. https://doi.org/10.1016/j.eswa.2024.125641
Niu, K., Si, X., Qi, X., & Kang, H. (2025). Event-Aware Sarcasm Detection in Chinese Social Media Using Multi-Head Attention and Contrastive Learning. Computers, Materials and Continua, 85(1), 2051–2070. https://doi.org/10.32604/cmc.2025.065377
Rao, K., & Dixit, G. (2026). Suspiciousness of social media accounts: Examining the role of conversational consistency using machine learning approach. Data Science and Management, 9(2). https://doi.org/10.1016/j.dsm.2025.09.003
Shannak, Y., & Duwairi, R. M. (2026). Transfer learning for Arabic spelling correction: a comprehensive study with dynamic error synthesis. International Journal of Intelligent Computing and Cybernetics, 19(2), 320–344. https://doi.org/10.1108/IJICC-08-2025-0558
Thoppil, I. J., Saha, J., & Gopu, A. (2025). Leveraging Social Media Analytics for Student Project Allocation Using Deep Learning. Procedia Computer Science, 258, 386–397. https://doi.org/10.1016/j.procs.2025.04.275
Tran, N., Ta, P., Nguyen, H., Nguyen, H. D., & Le, A. C. (2025). Hybrid contextual and sentiment-based machine learning model for identifying depression risk in social media. Expert Systems with Applications, 291. https://doi.org/10.1016/j.eswa.2025.128505
V, M., & Mohan, A. K. (2026). Advanced success-driven lightweight deep learning network with data augmentation for detecting offensive text on unstructured social media data. Knowledge-Based Systems, 343. https://doi.org/10.1016/j.knosys.2026.115991
Vyas, D., Shah, M., Kothari, A., Golakia, J., & Parikh, V. (2025). Enhancing Digital Forensics: Machine Learning Techniques for Social Media Investigation. Procedia Computer Science, 258, 2290–2301. https://doi.org/10.1016/j.procs.2025.04.483
Wagay, F. A., & Jahiruddin. (2026). Capsule-augmented deep learning architectures for mental health detection from social media text. Intelligence-Based Medicine, 13. https://doi.org/10.1016/j.ibmed.2025.100319
Zulfiker, M. S., Kabir, N., Biswas, A. A., Adan, M. M. I. Y., & Uddin, M. S. (2025). Identifying suicidal ideations from social media posts using deep learning and explainable AI-driven approach. Applied Soft Computing, 184. https://doi.org/10.1016/j.asoc.2025.113813
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