Article Per Year (5 Year)
p-Index From 2020 - 2025
1.704
P-Index
This Author published in this journals
All Journal Journal International Dakwah and Communication Journal Neosantara Hybrid Learning Journal of Noesantara Islamic Studies Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials
Rith, Vicheka
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Community-Based Social Education for Sustainable Development – An Indonesian Perspective on Collaborative Learning Models Vann, Rithy; Rith, Vicheka; Suyitno, Suyitno
Journal Neosantara Hybrid 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/jnhl.v3i1.2174

Abstract

Community-based social education plays a crucial role in fostering sustainable development, especially in countries with diverse social structures such as Indonesia. However, the implementation of collaborative learning models within this framework remains underexplored. This study aims to investigate the effectiveness of community-based social education in supporting sustainable development through the application of collaborative learning models from an Indonesian perspective. Employing a qualitative research method with a case study approach, data were collected through interviews, focus group discussions, and observations involving educators, community leaders, and learners in selected rural and urban communities. The findings reveal that collaborative learning models significantly enhance community engagement, improve critical thinking skills, and promote shared responsibility among participants. Furthermore, the integration of local wisdom and cultural values into learning processes strengthens the relevance and sustainability of educational programs. The study concludes that community-based social education, when supported by well-structured collaborative learning models, can serve as an effective strategy for achieving sustainable development goals. It emphasizes the need for policy support, capacity building, and continuous evaluation to ensure the scalability and impact of such educational initiatives.  
The Role of Body Language in Islamic Public Speaking to Influence Audiences in the Digital Era Waliulu, Habiba; Sok, Vann; Rith, Vicheka
Journal International Dakwah and Communication Vol. 5 No. 1 (2025)
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/jidc.v5i1.895

Abstract

The digital era has significantly transformed public speaking, especially in Islamic discourse, where the influence of body language plays a crucial role in engaging and persuading audiences. Body language, encompassing gestures, facial expressions, posture, and eye contact, can enhance the effectiveness of spoken words and create a stronger connection between the speaker and the audience. This research explores the role of body language in Islamic public speaking, focusing on its impact on influencing audiences within the context of digital platforms such as webinars, podcasts, and live-streamed sermons. Using a qualitative research design, this study analyzes interviews with experienced Islamic speakers, audience feedback, and case studies of prominent Islamic public speaking events conducted in digital spaces. The findings reveal that effective body language significantly improves audience engagement, comprehension, and emotional connection, even in virtual settings. Speakers who utilized expressive gestures and appropriate posture were more successful in delivering their messages and retaining the audience’s attention. The study concludes that body language remains a key tool for enhancing Islamic public speaking, even in the digital age, where it contributes to the effectiveness of communication and strengthens the delivery of religious messages. These findings offer insights into improving the quality of Islamic discourse on digital platforms.
Reconfiguring Islamic Authority in Indonesia: The Role of Ulama and Digital Media in Religious Practices Besari, Anam; Rith, Vicheka; Dara, Ravi
Journal of Noesantara Islamic Studies Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnis.v2i3.2396

Abstract

The digital revolution has profoundly reshaped the landscape of religious authority and practice in the Muslim world, challenging traditional clerical hierarchies. In Indonesia, the authority of the ulama, long central to the dissemination of Islamic knowledge, now coexists with a vibrant and often unregulated digital sphere. This study aimed to investigate how digital media is reconfiguring traditional Islamic authority and influencing the religious practices of Indonesian Muslims. The primary objective was to analyze the strategies employed by both traditional ulama and new digital religious figures in this evolving media ecosystem. A qualitative methodology was employed, combining digital ethnography of popular religious social media platforms with in-depth, semi-structured interviews with established ulama and emergent "cyber-preachers." The results reveal a significant fragmentation and democratization of religious authority. New media figures are leveraging platforms like YouTube and Instagram to bypass traditional institutions, offering direct religious guidance to a mass audience. In response, many traditional ulama are adapting by creating their own digital presence, yet often struggle to match the popular appeal of these new influencers. This study concludes that digital media is fostering a more contested, personalized, and networked religious landscape in Indonesia. This reconfiguration is not replacing the traditional ulama but is forcing them into a new, competitive role, fundamentally altering how religious authority is constructed, consumed, and maintained in the digital age.
Development of Machine Learning Algorithms for Anomaly Detection in Internet of Things (IoT) Networks Rith, Vicheka; Sok, Vann; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1560

Abstract

The proliferation of Internet of Things (IoT) devices has increased the vulnerability of networks to security threats, making anomaly detection essential for maintaining system integrity. Traditional security measures often fall short in identifying and mitigating complex attack patterns that can jeopardize IoT networks. This research aims to develop a machine learning algorithm specifically designed for anomaly detection in IoT environments. The goal is to enhance the ability to identify unusual behavior indicative of potential security breaches while minimizing false positives. A dataset comprising network traffic from various IoT devices was collected and preprocessed to extract relevant features. Several machine learning algorithms, including decision trees, support vector machines, and neural networks, were implemented and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were used to assess the effectiveness of each model. The results indicated that the proposed machine learning algorithm outperformed traditional methods, achieving an accuracy of 95% in detecting anomalies. The model demonstrated a significant reduction in false positives compared to existing techniques, thereby enhancing the reliability of anomaly detection in IoT networks. The research concludes that the developed machine learning algorithm is a robust solution for detecting anomalies in IoT environments. This advancement contributes to the field by providing an effective tool for improving security measures in the rapidly evolving landscape of IoT. Future work should focus on real-time implementation and further optimization of the algorithm to adapt to dynamic network conditions.
Sustainable Forest Management Practices in Tropical Asia: A Review Kiri, Ming; Rith, Vicheka; Sothy, Chak
Journal of Selvicoltura Asean Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i4.1662

Abstract

Tropical Asia is home to some of the world's most diverse and ecologically significant forests. However, these forests face immense pressures from deforestation, climate change, and unsustainable logging practices. Sustainable forest management (SFM) has emerged as a vital approach to balance ecological health, economic viability, and social equity in forest use. This review aims to evaluate current sustainable forest management practices in tropical Asia, identifying effective strategies and challenges faced in implementation. The objective is to provide insights into how SFM can enhance forest conservation while supporting local communities. A comprehensive literature review was conducted, analyzing peer-reviewed articles, policy documents, and case studies related to SFM in tropical Asia. Key themes were identified, including community participation, adaptive management, and certification schemes, with a focus on their effectiveness and applicability. The findings indicate that successful SFM practices often incorporate community involvement and traditional ecological knowledge. Certification systems, such as the Forest Stewardship Council (FSC), have proven effective in promoting sustainable practices among local and commercial stakeholders. However, challenges such as inadequate policy frameworks and lack of financial resources hinder broader implementation. This review concludes that sustainable forest management practices in tropical Asia are essential for biodiversity conservation and community resilience. Enhancing stakeholder collaboration and strengthening policy frameworks are crucial for overcoming existing challenges. Future efforts should focus on integrating local knowledge and adaptive management strategies to ensure the long-term sustainability of forest resources.
The Impact of Growth Mindset Interventions on Student Achievement Krit, Pong; Pong, Ming; Rith, Vicheka; Suyitno, Suyitno
Research Psychologie, Orientation et Conseil Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i3.2375

Abstract

A student’s underlying beliefs about intelligence—whether it is a fixed trait or can be developed (a “mindset”)—is a powerful predictor of academic resilience and achievement. Fostering a growth mindset, the belief that intelligence is malleable, has been identified as a critical target for educational interventions aimed at improving student success. This study aimed to quantitatively evaluate the impact of a targeted, school-based growth mindset intervention on the academic achievement and perseverance of middle school students in a challenging subject. A quasi-experimental, pre-test/post-test study was conducted with 250 8th-grade students. The intervention group (n=125) participated in six workshops focused on neuroplasticity and growth mindset principles. The control group (n=125) received standard study skills training. Academic achievement was measured by mathematics grades and standardized test scores. The intervention group demonstrated a statistically significant improvement in their mathematics grades (p < .01) and reported higher levels of academic perseverance compared to the control group. The control group showed no significant change in either measure over the same period. Targeted, low-cost growth mindset interventions are an effective strategy for improving student academic achievement. Fostering the belief that intellectual abilities can be developed through effort is a powerful pedagogical tool for enhancing student success and resilience.
Quantum Neural Network for Medical Image Pattern Recognition Vann, Dara; Rith, Vicheka; Sothy, Chak
Journal of Tecnologia Quantica Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i4.1679

Abstract

The background of this research focuses on the recognition of medical image patterns for disease detection using artificial intelligence technology. Although Convolutional Neural Networks (CNNs) have been widely used, the models are limited in terms of accuracy and efficiency in processing complex medical images. Quantum Neural Networks (QNNs) are considered as a potential solution to address this problem, by leveraging quantum computing to improve speed and accuracy. The purpose of this study is to explore the application of QNN in the recognition of medical image patterns, as well as to compare its performance with more conventional CNN models. The study used a dataset of medical images from cancer and heart disease, which were divided into training and testing data. QNN and CNN were tested on the same dataset to compare accuracy, speed, and efficiency. The results showed that QNN produced 92% accuracy in breast cancer detection, higher than CNN which only reached 88%. QNN is also more efficient in terms of processing speed, with lower use of computing resources. The conclusion of this study shows that QNN has great potential to be used in the recognition of medical image patterns, with significant advantages in terms of accuracy and efficiency. This research paves the way for the further development of QNN technology in medical applications and disease diagnosis.
Quantum Computing to Forecast Extreme Weather Rith, Vicheka; Vann, Dara; Santos, Luis
Journal of Tecnologia Quantica Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i3.1970

Abstract

The background of this research focuses on the challenges in forecasting extreme weather that is increasingly frequent due to climate change. Conventional weather models still face limitations in terms of accuracy and computational time, especially in predicting extreme weather phenomena. The purpose of this study is to explore the potential of quantum computing in predicting extreme weather by improving prediction accuracy and accelerating computational processes. The research method used involves the development and testing of weather prediction models based on quantum algorithms on extreme weather phenomena such as tropical storms, heavy rains, and heat waves. The results show that the quantum model is able to improve prediction accuracy by up to 92% for tropical storms and accelerate the computational time from 48 hours to 5 hours. The conclusion of the study is that quantum computing offers a more efficient and accurate solution in forecasting extreme weather, with great potential for practical applications in early warning and mitigation of weather disasters.
Analysis of factors that influence student creativity in solving mathematical problems Rith, Vicheka; Sok, Vann; Dara, Ravi
Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v1i4.1707

Abstract

Creativity in solving mathematical problems is a critical skill for students, enabling them to think innovatively and apply knowledge in diverse contexts. However, the development of mathematical creativity is influenced by various factors, including cognitive, environmental, and instructional aspects. Understanding these factors is essential to designing effective strategies to foster creativity in mathematics education. Despite its importance, there is limited research exploring the interplay of these factors in influencing student creativity. This study aims to analyze the factors that influence student creativity in solving mathematical problems and determine which factors have the most significant impact. A mixed-method approach was employed, involving 150 high school students from three schools. Data were collected using a creativity assessment test, a questionnaire on cognitive and environmental factors, and semi-structured interviews. Quantitative data were analyzed using regression analysis, while qualitative data were subjected to thematic analysis. The findings revealed that cognitive factors, such as critical thinking and prior knowledge, were the strongest predictors of mathematical creativity. Environmental factors, including classroom climate and teacher support, also played a significant role. Instructional methods, particularly problem-based learning, were found to enhance creativity by encouraging exploration and independent thinking. The study highlights the multifaceted nature of mathematical creativity and the need for comprehensive strategies that address cognitive, environmental, and instructional factors to foster creativity in mathematics education.
Al-Augmented Spectroscopy for Early Detection of Cervical Cancer Biomarkers Zani, Benny Novico; Rith, Vicheka; Dara, Ravi
Research of Scientia Naturalis Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i4.2387

Abstract

Cervical cancer remains a leading cause of mortality among women worldwide, primarily due to challenges in early and accurate detection. Conventional screening methods like Pap smears are subject to human error and have moderate sensitivity. This study aimed to develop and validate a novel, non-invasive diagnostic platform combining Raman spectroscopy with artificial intelligence (AI) for the rapid and highly accurate detection of early-stage cervical cancer biomarkers. The objective was to create a system that could overcome the limitations of current screening techniques. We collected cervical cell samples from clinically diagnosed healthy, pre-cancerous (CIN I-III), and cancerous patients. Raman spectroscopy was used to acquire high-resolution biochemical fingerprints from these samples. A custom-developed convolutional neural network (CNN) was then trained on the spectral data to learn and identify subtle biomarker-associated patterns indicative of neoplastic transformation. The AI-augmented system achieved a diagnostic accuracy of 96.5%, with a sensitivity of 98% and a specificity of 95% in differentiating high-grade lesions and cancerous samples from healthy ones. The model successfully identified key spectral shifts related to nucleic acid and protein conformational changes, correlating them with disease progression.
Co-Authors Arnes Yuli Vandika Besari, Anam Dara, Ravi Kiri, Ming Krit, Pong Pong, Ming Santos, Luis Sok, Vann Sothy, Chak Suyitno Suyitno Vann, Dara Vann, Rithy Waliulu, Habiba Zani, Benny Novico