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All Journal Journal on Education Jurnal Ilmiah Manajemen Kesatuan RESEARCH AND DEVELOPMENT JOURNAL OF EDUCATION (RDJE) West Science Interdisciplinary Studies Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah Ipso Jure Aksioma Education Journal Journal of Computer Science Advancements Journal Markcount Finance Journal Emerging Technologies in Education
Apriyanto Apriyanto
Politeknik Tunas Pemuda
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : journal of computer science advancements

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Implementation of Augmented Reality Technology in History Learning: Experimental Study Apriyanto Apriyanto; Kailie Maharjan; Zhang Wei
Journal of Computer Science Advancements Vol. 2 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i4.1321

Abstract

The integration of technology into education continues to evolve, with augmented reality (AR) emerging as a promising tool in enhancing learning experiences. In the context of history education, traditional methods often struggle to engage students and provide immersive experiences. This study investigates the implementation of augmented reality technology in history learning to assess its impact on student engagement and understanding. The research aims to determine whether AR can effectively improve students' comprehension of historical events and concepts compared to conventional teaching methods. A mixed-methods experimental design was employed, involving a sample of high school students who were divided into an experimental group using AR technology and a control group following traditional teaching methods. Data were collected through pre- and post-intervention assessments, surveys, and interviews. The analysis focused on comparing learning outcomes and engagement levels between the two groups. Results indicated that the experimental group demonstrated significantly higher scores on post-intervention assessments and reported greater engagement and interest in the subject matter compared to the control group. These findings suggest that AR technology can enhance students' understanding of historical content by providing interactive and immersive learning experiences. The study concludes that incorporating AR technology into history education can significantly improve student learning outcomes and engagement. The use of AR provides a novel approach to teaching history, offering immersive experiences that can complement traditional methods. Future research should explore the long-term effects of AR on learning and its applicability across various educational contexts.
Implementation of Deep Learning in a Voice Recognition System for Virtual Assistants Apriyanto Apriyanto; Rohmat Sahirin; Snyder Bradford
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1533

Abstract

Voice recognition technology has become a vital component in virtual assistants, enabling more natural and efficient user interactions. However, traditional voice recognition systems face challenges in accurately interpreting diverse accents, dialects, and background noise, which can limit their usability. This study investigates the implementation of deep learning techniques to improve the accuracy and adaptability of voice recognition systems within virtual assistant applications. The research aims to enhance voice recognition performance by leveraging deep learning models that can process complex speech patterns and adapt to varied linguistic nuances. A convolutional neural network (CNN) architecture combined with recurrent neural networks (RNN) was used to train the voice recognition model on a large, diverse dataset of audio samples. The dataset included multiple languages, accents, and noisy environments to test the robustness of the model. Results indicate a 25% improvement in word error rate (WER) and a significant increase in recognition accuracy across diverse voice inputs compared to traditional voice recognition systems. The model demonstrated high adaptability, accurately interpreting speech in varying acoustic conditions, thus improving user experience with virtual assistants. These findings suggest that deep learning can significantly enhance voice recognition systems, offering more reliable performance in real-world applications. Implementing deep learning models in voice recognition systems can bridge the gap between human and machine communication, making virtual assistants more accessible and user-friendly.
Co-Authors Ahmed Hossam Andro Ruben Runtu Bobby Ferly Camelia Bakker Dwi Purnomo, Adi Ediaman Sitepu Iswadi Iswadi Kailie Maharjan Loso Judijanto Mardiati Mardiati Muhammad Wahid Murniawan Narrong Hassanee Nur Hakim Purwinahyu Purwinahyu Rahman Tanjung Reddy Anggara Rizky Wisudawan Katjong Rohmat Sahirin Saefudin Zuhdi Siti Maria Siti Mudawanah Snyder Bradford sutanto, Edi Syamsul Anwar Tekni Megaster Titik Haryanto Wiwik Handayani Yara Abed Zhang Wei