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All Journal Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Cognitionis Civitatis et Politicae Journal of Biomedical and Techno Nanomaterials
Vicheka Rith
National University Cambodia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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Development of Machine Learning Algorithms for Anomaly Detection in Internet of Things (IoT) Networks Vicheka Rith; Vann Sok; Arnes Yuli Vandika
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 Ming Kiri; Vicheka Rith; Chak Sothy
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 Pong Krit; Ming Pong; Vicheka Rith; 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.  
The Role of Empathy in Reducing Prejudice: A Cross-Cultural Study in Diverse Elementary School Settings Budiawan Budiawan; Vicheka Rith; Chenda Dara
Research Psychologie, Orientation et Conseil Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i5.2711

Abstract

Prejudice is a pervasive issue in diverse societies, and addressing it at an early age can help reduce its long-term impact. This study explores the role of empathy in reducing prejudice among elementary school students in diverse cultural settings. The research aims to examine whether empathy training can effectively decrease prejudicial attitudes in students from different cultural backgrounds and whether such interventions are equally impactful across various cultural contexts. A mixed-methods design was employed, combining pre- and post-intervention surveys with in-depth interviews in a sample of 300 students from three culturally diverse elementary schools. The quantitative data, analyzed using paired t-tests and ANOVA, revealed a significant reduction in prejudicial attitudes among students who participated in empathy-building activities. The qualitative findings, derived from interviews with teachers and students, supported these results, suggesting that empathy training fostered greater understanding and respect for diversity. The study concludes that empathy plays a crucial role in reducing prejudice in young children and that culturally tailored empathy interventions are effective in diverse elementary school environments. This research emphasizes the importance of early interventions in promoting inclusivity and tolerance, offering practical insights for educators and policymakers in multicultural education.
Quantum Neural Network for Medical Image Pattern Recognition Dara Vann; Vicheka Rith; Chak Sothy
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 Vicheka Rith; Dara Vann; Luis Santos
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.
Error Correction Codes for Fault-Tolerant Quantum Computation in Superconducting Qubit Architectures Loso Judijanto; Vicheka Rith; Vanna Sok
Journal of Tecnologia Quantica Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.2790

Abstract

Fault-tolerant quantum computation remains a central challenge in superconducting qubit architectures, where decoherence, crosstalk, and gate infidelities significantly degrade computational reliability. Although quantum error correction (QEC) codes are widely assumed to provide scalable protection, their practical performance depends critically on hardware-specific noise characteristics that are often underexamined. This study aims to evaluate the effectiveness of leading QEC codes specifically the surface code, Bacon-Shor code, and low-density parity-check (LDPC) quantum codes when implemented on contemporary superconducting qubit platforms. A simulation-based methodological approach is employed, integrating stochastic noise modeling, syndrome extraction analysis, and threshold estimation using density-matrix simulations calibrated with experimentally reported parameters. The results indicate that while the surface code maintains the highest threshold under realistic two-qubit gate fidelities, LDPC-based schemes exhibit superior logical qubit compression but suffer from decoding overhead that limits near-term applicability. The study also identifies parameter regimes where Bacon-Shor codes offer competitive performance due to their reduced measurement complexity. The findings suggest that no single QEC code uniformly outperforms others; instead, code selection must be matched to hardware-specific noise anisotropy and architectural constraints. The research concludes that optimizing QEC for superconducting qubits requires hybrid design strategies that integrate code efficiency with architecture-aware gate scheduling.
Resource-Efficient Fault-Tolerant Quantum Computing Architectures Based on Surface Codes with Dynamic Error Suppression Vicheka Rith; Ming Kiri; Adam Idris
Journal of Tecnologia Quantica Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i6.3199

Abstract

Quantum computing has the potential to revolutionize industries by solving complex problems that are intractable for classical computers. However, achieving fault tolerance in large-scale quantum systems remains a significant challenge due to the high resource overhead required for error correction. Surface codes, a leading quantum error correction technique, provide robust fault tolerance but demand a large number of physical qubits. This research explores a resource-efficient approach by integrating dynamic error suppression with surface codes to reduce qubit overhead while maintaining fault tolerance in quantum computing architectures. The objective of this study is to investigate how dynamic error suppression can enhance the performance of surface code-based quantum computing architectures by minimizing resource usage and improving system reliability. The research employs computational simulations to model quantum systems under varying error rates, qubit numbers, and dynamic error correction strategies. The results demonstrate that combining dynamic error suppression with surface codes significantly reduces the physical qubit overhead while maintaining or improving fault tolerance. The proposed architecture achieves higher efficiency and robustness in large-scale systems, especially at higher error rates. In conclusion, this study offers a practical solution for scaling quantum computing systems by optimizing resource usage without compromising fault tolerance. These findings have important implications for the development of efficient, fault-tolerant quantum computers suitable for real-world applications.  
Analysis of factors that influence student creativity in solving mathematical problems Vicheka Rith; Vann Sok; Ravi Dara
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.
OPTIMIZATION OF THE HUMAN RESOURCE TRANSFER AND PROMOTION SYSTEM FOR PROPER JOB PLACEMENT TO SUPPORT ORGANIZATIONAL PERFORMANCE Moch Aji Widayat; Hardiman Hardiman; Vicheka Rith
Cognitionis Civitatis et Politicae Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/politicae.v3i2.3700

Abstract

Human resource (HR) management plays a pivotal role in achieving organizational goals by ensuring the right people are placed in the right roles. The optimization of HR transfer and promotion systems is critical to fostering employee satisfaction and organizational performance. However, many organizations face challenges in aligning HR processes with strategic objectives, leading to inefficiencies in job placement and performance. This study investigates the optimization of HR transfer and promotion systems to enhance job placement accuracy and support organizational performance. The research aims to analyze existing HR practices, identify key factors influencing job placement, and propose an optimized framework that aligns employee capabilities with organizational goals. A mixed-method approach was employed, combining qualitative case studies, expert interviews, and quantitative surveys. The findings reveal that organizations with structured and transparent HR systems experience higher employee satisfaction and better alignment between individual skills and job roles. In contrast, organizations with informal HR processes face misalignments, lower engagement, and suboptimal performance. The study concludes that formalizing HR transfer and promotion systems is essential for improving job fit, boosting employee satisfaction, and enhancing organizational performance. The research provides actionable recommendations for organizations to optimize HR practices and contribute to long-term success.
Co-Authors Adam Idris Arnes Yuli Vandika Budiawan, Budiawan Chak Sothy Chenda Dara Dara Vann Hardiman Hardiman Loso Judijanto Luis Santos Ming Kiri Ming Pong Moch Aji Widayat Pong Krit Ravi Dara Suyitno Suyitno Vann Sok Vanna Sok