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Journal of New Trends in Sciences
Published by CV. Aksara Global Akademia
ISSN : 29641799     EISSN : 29641624     DOI : https://doi.org/10.59031/jnts.v3i1
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Earth & Planetary Sciences
Journal of New Trends in Sciences merupakan jurnal ilmiah yang bertujuan untuk mempublikasikan hasil-hasil penelitian terbaru, inovatif, dan multidisipliner dalam berbagai bidang ilmu pengetahuan dan teknologi. Jurnal ini memfasilitasi diskusi akademik mengenai tren mutakhir, pendekatan interdisipliner, serta pengembangan teori dan aplikasi ilmiah dalam konteks global. Cakupan topik dalam jurnal ini mencakup (namun tidak terbatas pada): Ilmu alam (fisika, kimia, biologi) Ilmu komputer dan teknologi informasi Matematika dan statistika terapan Teknologi lingkungan dan keberlanjutan Rekayasa dan inovasi teknologi Kesehatan dan sains kehidupan Sains data dan kecerdasan buatan Pendidikan sains dan STEM Interdisiplin ilmu dalam konteks sains dan teknologi Jurnal ini menerima artikel asli hasil penelitian, review, serta case study yang relevan dengan perkembangan dan tantangan baru dalam ilmu pengetahuan.
Arjuna Subject : Umum - Umum
Articles 59 Documents
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Pengembangan Media Pembelajaran Virtual Reality untuk Meningkatkan Pemahaman Konsep Fisika pada Siswa SMA Dini Rohmayani; Castaka Agus Sugianto
Journal of New Trends in Sciences Vol. 2 No. 1 (2024): Februari: Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i1.783

Abstract

The learning of physics, particularly mechanics, poses significant challenges for high school students. Concepts such as Newton’s laws, energy, and three-dimensional vectors are often difficult to grasp using traditional teaching methods. Virtual Reality (VR) has emerged as a promising solution by providing an immersive and interactive learning environment. This study aims to evaluate the effectiveness of VR-based learning media in enhancing students’ understanding of physics concepts, with a specific focus on mechanics. An experimental design was employed, consisting of two groups: an experimental group using VR for learning and a control group receiving traditional instruction. Pre-test and post-test assessments were used to measure the improvement in students' conceptual understanding of physics. The findings indicate that students in the experimental group demonstrated a significant improvement in their understanding of complex physics concepts, such as projectile motion, force, and Newton’s laws, compared to the control group. Students in the experimental group also exhibited higher levels of engagement and motivation, with VR's immersive nature encouraging active participation in learning. The study concludes that VR is an effective tool for enhancing students’ comprehension of abstract and complex physics concepts, improving their visualization and problem-solving skills. Furthermore, VR-based learning provides students with opportunities to conduct virtual experiments and simulations that may not be possible in traditional classroom settings. The implications of this study suggest that VR should be integrated into the physics curriculum to improve learning outcomes, especially in schools with access to the necessary technology. Educators and curriculum developers are encouraged to explore VR’s potential in fostering a more engaging and effective physics education.
Pemanfaatan Teknologi Internet of Things untuk Monitoring Kualitas Air Sungai di Wilayah Perkotaan Danang Danang; Nuris Dwi Setiawan; Eko Siswanto
Journal of New Trends in Sciences Vol. 2 No. 1 (2024): Februari: Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i1.784

Abstract

The rapid urbanization and industrialization of cities have significantly contributed to the rising pollution levels, especially in urban rivers, where water quality is often compromised. Monitoring water quality in real-time is essential for mitigating the adverse effects of water contamination. This research aims to design and implement an Internet of Things (IoT)-based system for real-time monitoring of water quality in urban rivers, focusing on the continuous collection and analysis of environmental data. The system utilizes a range of sensors to measure critical water quality parameters, including pH, temperature, dissolved oxygen (DO), turbidity, and various contaminants, all of which transmit data wirelessly to a central server for further processing. The study evaluates the accuracy, reliability, and efficiency of the IoT system in detecting water pollution and its ability to deliver real-time insights. Findings demonstrate that the IoT system offers a higher level of precision and faster detection compared to conventional monitoring methods, making it an effective tool for real-time pollution detection and decision-making. Additionally, the integration of the IoT system with a user-friendly visualization platform enhances the accessibility of the data for stakeholders, enabling them to monitor the water quality effectively. The study suggests that IoT-based water quality monitoring systems present a sustainable long-term solution for urban water management, offering cost and time savings. Moreover, the research highlights the importance of cross-sector collaboration to support the development and deployment of IoT technologies and recommends further advancements in sensor technologies to monitor additional water quality parameters.  
Analisis Big Data dalam Deteksi Dini Wabah Penyakit Menular untuk Mendukung Sistem Kesehatan Publik Ayu Hendrati Rahayu; Castaka Agus Sugianto; Dini Rohmayani
Journal of New Trends in Sciences Vol. 2 No. 1 (2024): Februari: Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i1.785

Abstract

The rapid spread of infectious diseases remains a major global health threat, and early detection is vital to minimize their impact. This research investigates the role of predictive modeling using big data in the early detection of infectious disease outbreaks. The primary objective of this study is to assess the effectiveness of big data systems in forecasting potential outbreaks and the implications of these forecasts for public health systems. The study employs machine learning-based predictive models to process large health datasets, including electronic health records, sensor data, and social media information. The results demonstrate that the predictive model achieved an accuracy rate of 87%, significantly surpassing traditional methods in terms of early detection. By integrating various data sources such as medical records, sensor networks, and real-time digital traces, the system is capable of providing more accurate, timely predictions, which can greatly improve the ability of public health authorities to respond effectively to emerging health threats. Furthermore, the application of big data in public health not only improves the speed of response but also enhances the allocation of resources, allowing for more targeted and efficient interventions. Despite these successes, challenges remain, particularly in relation to data quality, privacy, and regulatory issues, which could hinder the broader implementation of such systems. Thus, collaboration between government agencies, healthcare institutions, and technology developers is essential to overcome these obstacles and ensure the sustainable integration of big data into public health infrastructures. This research highlights the significant potential of big data to transform public health responses, offering valuable insights for future epidemic management strategies.
Analisis Gelombang Elektromagnetik terhadap Efisiensi Sel Surya Generasi Baru Dika Ayu Wulandar; Hijrawati Ayu Wardani
Journal of New Trends in Sciences Vol. 2 No. 2 (2024): Mei : Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i2.786

Abstract

Solar energy is one of the most promising renewable energy sources to support the transition toward clean energy, yet conventional solar cell technology still faces efficiency limitations. Perovskite materials offer a potential solution due to their superior optoelectronic properties, although challenges related to stability and toxicity remain. This study aims to analyze the interaction of electromagnetic waves with perovskite materials and to evaluate the effect of nano-scale anti-reflective coatings on improving energy conversion efficiency. The research employed a combination of electromagnetic simulations using specialized software and laboratory experiments with miniature perovskite solar cell prototypes. Simulation results demonstrated higher electromagnetic field intensity within the active layer after the addition of anti-reflective coatings, with photon absorption increased by 15–18%. Experimental validation revealed that the energy conversion efficiency improved from 16.8–17.5% without anti-reflective layers to 20.5–21.3% with TiO₂- and SiO₂-based coatings. Optical characterization using a spectrophotometer confirmed enhanced light absorption up to 90% at specific wavelengths, while SEM analysis showed that smoother and more uniform SiO₂ coatings contributed to superior performance compared to TiO₂. Minor discrepancies between simulation and experimental results were attributed to fabrication variations, yet both approaches exhibited consistent improvement trends. These findings highlight that integrating perovskite materials with nano-scale anti-reflective coatings is a key strategy for enhancing the efficiency of next-generation solar cells while supporting future clean energy sustainability.
Inovasi Sensor Wearable untuk Monitoring Kesehatan Mental melalui Variabilitas Denyut Jantung Cici Widowati; Kasih Purwantini
Journal of New Trends in Sciences Vol. 2 No. 2 (2024): Mei : Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i2.787

Abstract

Mental health has become a major global issue, particularly after the COVID-19 pandemic, which significantly increased the prevalence of psychological disorders. Early detection of stress and other mental health problems remains a major challenge, as traditional methods are generally subjective and unable to provide real-time results. This study aims to design and test a wearable sensor based on Heart Rate Variability (HRV) as a physiological indicator for detecting stress levels. The research employed an experimental approach through the development of a wearable sensor prototype equipped with a stress detection algorithm based on HRV analysis, including both time-domain and frequency-domain parameters. The prototype was tested on 100 respondents with varying stress levels under controlled conditions. Instruments used in this study included the HRV sensor prototype, psychological questionnaires, and standard validation devices. Data were analyzed by comparing the sensor detection results with respondents’ psychological data and calculating prediction accuracy. The findings showed that the wearable sensor was able to predict stress conditions with an accuracy rate of 80%. The distribution of sensor detection results was generally consistent with psychological data, especially in the low-stress category, although slight deviations were observed in moderate and high-stress categories. These results demonstrate that an HRV-based wearable sensor can serve as a practical and non-invasive tool to monitor mental conditions in real time. The implications of this research highlight the potential of wearable technology as an innovative solution for mental health monitoring, both for individual use and as support for healthcare systems. Therefore, this study contributes to the development of adaptive and responsive health technologies in addressing global mental health challenges.
Pemanfaatan Gelombang Akustik untuk Deteksi Dini Retakan Struktur Jembatan Lismin Dirwanto; Shally Joncicilia
Journal of New Trends in Sciences Vol. 3 No. 2 (2025): Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v3i2.791

Abstract

Bridge infrastructure is a vital component of transportation systems that is vulnerable to structural damage caused by dynamic loads, environmental factors, and aging. Early crack detection is crucial to prevent structural failures that may lead to catastrophic consequences. This study aims to develop a non-destructive detection method based on acoustic sensors to identify cracks in bridge structures with higher sensitivity and accuracy compared to conventional visual inspections. The research was conducted through laboratory experiments and field tests using acoustic sensors, data acquisition devices, and signal analysis software. The procedure included sensor installation on a bridge model, simulation of artificial cracks with varying sizes and positions, recording of acoustic wave signals, and data analysis using frequency spectrum, amplitude, and waveform pattern approaches. The results show significant differences between normal and cracked conditions in the frequency spectrum, where cracks produced amplitude anomalies at specific frequencies. Amplitude analysis revealed a positive correlation between crack size and acoustic signal intensity, while waveform pattern analysis demonstrated the influence of crack position on distortion levels. Cracks located at the center generated the highest distortion, followed by joints and edges. These findings confirm that acoustic sensors, particularly fiber-optic-based ones, offer advantages such as high sensitivity, reliability under complex environmental conditions, and the ability to detect subsurface cracks. The implications of this research highlight the potential development of an acoustic sensor-based structural health monitoring system integrated with real-time analysis software, thereby supporting preventive maintenance, extending infrastructure lifespan, and enhancing transportation safety.
Pengembangan Algoritma Deteksi Emosi Melalui Analisis Suara untuk Aplikasi Konseling Digital Ari Putra Wibowo; gunawan Prayitno
Journal of New Trends in Sciences Vol. 3 No. 2 (2025): Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v3i2.792

Abstract

Mental health issues such as depression, anxiety, and stress continue to increase globally and are recognized as critical factors that influence social functioning, productivity, and overall quality of life. Conventional mental health services are often limited by barriers including high cost, geographical distance, and persistent stigma that discourage individuals from seeking timely help. The digital era provides an alternative through the integration of technology into mental health counseling, offering greater accessibility, flexibility, and anonymity. Nevertheless, a key limitation of many digital counseling platforms lies in their inability to fully capture and respond to the emotional nuances of users during interactions. This study aims to address that gap by developing a speech-based emotion detection framework designed to be integrated into digital counseling environments. The proposed methodology includes the collection and preprocessing of speech samples, feature extraction using acoustic parameters, and training machine learning models to classify emotions in real time. Experimental results demonstrate that this approach significantly improves the accuracy of emotion detection, enabling digital counseling systems to provide more adaptive and personalized support. Beyond counseling, the research highlights the broader applicability of speech emotion recognition in education, telemedicine, and interactive digital assistants, all of which benefit from improved sensitivity to human emotions. These findings underscore the potential of artificial intelligence to strengthen digital mental health interventions, ensuring services that are not only more efficient and inclusive but also capable of fostering long-term emotional well-being in diverse populations.
Pemanfaatan CRISPR dalam Pengembangan Vaksin Generasi Baru terhadap Virus Tropis Bagus Kusuma Ardi; Abdul Muchlis
Journal of New Trends in Sciences Vol. 3 No. 2 (2025): Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v3i2.793

Abstract

The development of vaccines for tropical viruses such as dengue and Zika presents a significant challenge in global health. These viruses not only cause serious health complications but also impact health systems and economies in tropical countries. CRISPR/Cas9 technology offers an innovative solution to accelerate vaccine development by enabling precise gene editing. This study aims to explore the potential of CRISPR in accelerating the design and production of vaccines for tropical viruses. The method used in this study is a laboratory-based experimental design involving genetic engineering, with dengue and Zika virus genome models. The first step involves identifying virus target genes using the CRISPR/Cas9 system, which allows the detection of specific genes involved in pathogenesis and immune responses. Subsequently, genetic constructs are designed to generate vaccine candidates that can efficiently and precisely target pathogens. The resulting vaccines are tested in vitro in cell cultures to observe immune responses and their effectiveness against virus infections. The results show that CRISPR not only accelerates the process of identifying and engineering vaccine genes but also significantly improves vaccine production efficiency. CRISPR-based vaccines demonstrate higher immunogenicity compared to conventional methods, thus holding potential as the foundation for developing faster and safer next-generation vaccines. However, this study also identifies challenges related to off-target effects and the efficient delivery of CRISPR components, which require further research to ensure safety and efficacy in human clinical applications.
Simulasi Kuantum untuk Optimasi Algoritma Kriptografi pada Era Komputasi Modern Firdaus Firdaus; Teguh Arifianto
Journal of New Trends in Sciences Vol. 2 No. 2 (2024): Mei : Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i2.794

Abstract

The rapid advancement of quantum computing has significantly impacted data security, as classical cryptographic algorithms such as RSA and ECC are increasingly vulnerable to quantum attacks. This study aims to evaluate the performance of classical and post-quantum cryptographic algorithms in a quantum simulation environment, focusing on stability, efficiency, and computational time. The research method employed experimental simulations using Qiskit, where cryptographic algorithms were modeled into quantum circuits and tested across varying qubit sizes of 128, 256, 512, and 1024. The simulation results indicate that classical algorithms face substantial limitations, with exponentially increasing computational time and drastically reduced stability beyond 512 qubits. In contrast, post-quantum algorithms demonstrated superior performance, maintaining high stability up to 1024 qubits, achieving greater quantum efficiency, and showing resilience against quantum attacks such as Shor’s and Grover’s algorithms. These findings highlight the urgent need to transition toward post-quantum cryptography as a more adaptive and reliable approach to safeguarding data in the quantum era. Although post-quantum algorithms still face certain challenges, such as larger key sizes and slightly higher computational costs at smaller scales, their overall benefits are far more significant in ensuring sustainable information security. Therefore, adopting post-quantum cryptography represents a strategic step that must be prioritized to address the evolving risks posed by quantum computing technologies.

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