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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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Matematika Musik dan Analisis Pola Harmoni sebagai Pendekatan Interdisipliner untuk Menjelaskan Hubungan Sains dan Seni dalam Kreativitas Manusia Eka Satria Wibawa; Dedy Arisjulyanto
Journal of New Trends in Sciences Vol. 1 No. 4 (2023): November : 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.v1i4.769

Abstract

This study investigates the intrinsic relationship between mathematics and classical music by analyzing harmonic patterns in compositions of renowned composers such as Bach, Mozart, and and Beethoven. The research employs an analytical quantitative approach, using statistical tools to map and identify numerical ratios within musical intervals and the relationships between chords. Data were collected from selected musical works to examine interval structures and harmonic progression patterns. Findings reveal that classical music exhibits consistent mathematical patterns, including the octave (2:1), perfect fifth (3:2), and other proportional relationships that form the basis of chord construction and harmonic coherence. These patterns demonstrate that mathematical principles are inherently embedded in musical compositions, allowing for an objective analysis of music structure without diminishing its aesthetic and emotional value. Furthermore, the study highlights the value of an interdisciplinary perspective, combining quantitative analysis with traditional music aesthetics, which provides new insights into understanding compositional techniques and creativity. The results also suggest that recognizing mathematical structures in music can contribute to a deeper understanding of music theory and education, enhancing how students and scholars approach musical analysis. Overall, this research emphasizes that integrating mathematical and artistic perspectives not only supports the scientific study of music but also encourages a holistic appreciation of the creative process, bridging the gap between art and science while offering practical implications for music pedagogy, theory, and performance studies.
Pengembangan Algoritma Kuantum Terinspirasi untuk Penyelesaian Masalah Optimasi Kompleks dalam Ilmu Komputasi Yona Eka Pratiwi; Renatalia Fika
Journal of New Trends in Sciences Vol. 1 No. 2 (2023): 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.v1i2.773

Abstract

Quantum-Inspired Algorithms (QIAs) combine principles of quantum computing with classical evolutionary strategies to address complex optimization problems. This research explores the potential of QIAs in improving optimization processes, particularly in combinatorial and multi-objective optimization scenarios. The study focuses on the application of Quantum-Inspired Genetic Algorithms (QIGAs) and Quantum-Inspired Evolutionary Algorithms (QIEAs), assessing their effectiveness in solving classical problems like the Traveling Salesman Problem (TSP) and Minimum Spanning Tree (MST). Through computational simulations, the research compares the time convergence and solution accuracy of QIAs against traditional classical algorithms. The findings demonstrate that QIAs achieve faster convergence rates and higher-quality solutions, with accuracy levels reaching 98-99% of the global optimal solutions, while significantly reducing computational time. These results underline the advantages of QIAs in solving large and complex optimization problems, making them a promising alternative to traditional algorithms. Additionally, QIAs excel in avoiding local minima, a common pitfall of classical methods, due to their ability to explore the solution space more efficiently through quantum principles like superposition and interference. The implications of this study suggest that QIAs can be a valuable tool for tackling real-world optimization challenges, with potential applications in fields such as finance, logistics, telecommunications, and energy management. The research also indicates the necessity for further improvements in quantum-inspired algorithms' scalability and hardware integration, particularly for larger, more intricate optimization problems, to fully realize their potential in practical industrial applications.
Analisis Genom untuk Identifikasi Penyakit Langka di Indonesia Dedy Arisjulyanto; Gerson Andrew Warnares
Journal of New Trends in Sciences Vol. 1 No. 2 (2023): 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.v1i2.774

Abstract

Rare diseases present a significant challenge in diagnosis due to their low prevalence and the limited awareness among healthcare professionals. The emergence of genomic technologies, particularly Next-Generation Sequencing (NGS), has revolutionized the diagnosis of rare diseases by enabling the identification of genetic variations associated with these conditions. This technology offers improved accuracy and speed compared to traditional clinical diagnostic methods, which are often time-consuming and insufficient for rare genetic conditions. This study explores the application of genomic technology in identifying rare diseases in Indonesia, highlighting its effectiveness, accuracy, and the challenges involved in its implementation. The research employed genomic testing techniques, including whole-genome sequencing (WGS), to identify genetic mutations associated with rare diseases in patients. The findings of the study demonstrate that genomic technology significantly reduces the time required for diagnosis, providing a more comprehensive understanding of the genetic conditions. Diseases such as Diphyllobothriasis and Sparganosis, which are rarely diagnosed through traditional clinical methods, were successfully identified using genomic technologies. However, challenges persist in the implementation of genomic technology in Indonesia, including limited infrastructure, high costs, and a lack of specialized training for healthcare professionals. Despite these barriers, the findings underscore the potential of genomic technologies to improve the diagnosis and management of rare diseases in Indonesia. The study concludes by recommending further investments in infrastructure, the training of healthcare professionals, and the development of supportive policies to facilitate the widespread adoption of genomic technologies in the healthcare system, particularly for the diagnosis of rare diseases.
Strategi Pendidikan STEM dalam Meningkatkan Keterampilan Pemecahan Masalah di Sekolah Menengah Greget Widhiati; Widya Aryani
Journal of New Trends in Sciences Vol. 1 No. 2 (2023): 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.v1i2.775

Abstract

This study examines the effectiveness of STEM (Science, Technology, Engineering, and Mathematics) education in enhancing problem-solving skills among high school students. With the increasing demand for critical thinking and problem-solving abilities in the workforce, STEM education has become a crucial approach to preparing students for future challenges. The study aims to evaluate how project-based STEM curricula impact students' creativity, analytical skills, and overall problem-solving capabilities. Using a quasi-experimental design with a control group, the research was conducted with high school students who participated in STEM-based lessons involving the 5E instructional model. Data collection involved pre- and post-tests, classroom observations, and semi-structured interviews to measure changes in student skills. The findings indicate that students exposed to STEM education demonstrated significant improvements in problem-solving, creativity, and analytical thinking compared to their peers in traditional lecture-based classes. The study highlights the advantages of project-based learning in promoting active student engagement and fostering skills necessary for addressing real-world problems. These results suggest that STEM education can be more effective than traditional methods in cultivating essential 21st-century skills. The study recommends the broader implementation of STEM strategies in secondary education and further exploration of the long-term impact of STEM learning on various skills required in the professional world.
Psikologi Kognitif dalam Pembelajaran Matematika Modern untuk Mengatasi Math Anxiety dan Meningkatkan Kreativitas Numerik Generasi Digital Adji Seputro; Ahmad Dwi Nurdiyanto
Journal of New Trends in Sciences Vol. 1 No. 3 (2023): Agustus: 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.v1i3.776

Abstract

Mathematics anxiety has long been recognized as a barrier to student achievement, particularly in an era where digital technologies are reshaping education. This study aims to investigate the role of cognitive psychology in modern mathematics learning as a means of reducing math anxiety and enhancing numerical creativity among digital generation learners. Employing an experimental classroom design informed by neuroeducation principles, the research explored how interventions rooted in cognitive regulation, emotional control, and cognitive load management influence student outcomes. Data were collected through pre-test and post-test instruments measuring levels of math anxiety and creativity in numerical problem-solving. The findings demonstrate a significant reduction in mathematics anxiety following the implementation of the neuroeducation-based intervention. Simultaneously, students exhibited marked improvements in creative approaches to numerical challenges, indicating that addressing psychological factors is essential to unlocking mathematical potential. These results highlight the importance of integrating cognitive psychology into mathematics instruction, moving beyond procedural learning toward a holistic approach that considers students’ emotional and cognitive states. The implications extend to educators, curriculum designers, and policymakers, suggesting that modern mathematics education should balance cognitive development, emotional well-being, and creative problem-solving skills to prepare students for the challenges of a rapidly evolving digital world.
Integrasi Bioakustik dan Sains Komputasi untuk Memetakan Komunikasi Mamalia Laut sebagai Upaya Konservasi Satwa Dilindungi di Samudera Tropis Aji Priyambodo; Hesti Ristanto
Journal of New Trends in Sciences Vol. 1 No. 3 (2023): Agustus: 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.v1i3.777

Abstract

This study aims to analyze the communication of marine mammals, especially whales and dolphins, through a bioacoustic approach combined with computational science as an effort to support conservation in the Tropical Ocean region. The focus of the location is on the Banda Sea, the Seram Sea, and the tropical Pacific region which are important migration routes for marine mammals. Data were obtained from underwater sound recordings using hydrophones, accompanied by visual observations to validate the behavior and existence of species. The analysis is carried out through several stages, including signal pre-processing with noise filtering and sound segmentation, spectral analysis using Fast Fourier Transform (FFT), as well as the creation of a spectrogram to visualize vocalization patterns. Machine learning algorithms such as Support Vector Machine (SVM) are used to classify interspecies voices, while deep learning approaches are applied to identify more complex communication patterns, including dialect variations. The results showed that whales produced low-frequency vocalizations (20–200 Hz) for long-distance communication, while dolphins used high-frequency clicks and whistles (5–20 kHz) for echolocation and social interaction. The integration of bioacoustics and artificial intelligence improves the accuracy of sound classification by more than 90%. These findings confirm the effectiveness of computational-based non-invasive methods in monitoring the presence and behavior of marine mammals and provide a scientific basis for sustainable conservation.
Eksplorasi Linguistik Komputasional dalam Analisis Bahasa Alami untuk Mengungkap Evolusi Dialek Digital di Era Media Sosial Global Ari Putra WIbowo; Nurul Hidayat
Journal of New Trends in Sciences Vol. 1 No. 3 (2023): Agustus: 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.v1i3.778

Abstract

The development of digital technology and social media has driven a major transformation in the study of linguistics, particularly in understanding the evolution of global languages and the emergence of digital dialects. Interactions on platforms such as Twitter, Instagram, and TikTok accelerate the formation of new vocabulary, the use of slang, and the spread of cross-cultural expression. Antony and Tramboo (2023) highlight the digital metamorphosis in language, while Friedrich and De Figueiredo (2016) explain the birth of digital Englishes in a sociolinguistic perspective. Language research challenges on social media have also emerged, such as the limitations of dialect processing (Jørgensen et al., 2015) and the influence of digital linguistic ecology (Klushina, 2022). Advances in natural language processing (NLP) have also strengthened this study. Cambria and White (2014) and Skaria et al. (2024) show the development of NLP from sentiment analysis to its application in education (Khensous et al., 2023) and multilingual text analysis (Agüero-Torales et al., 2021). The systematic review of Sundaram et al. (2023) and Prihatini et al. (2023) reinforces the evidence that social media expands language skills and enriches contemporary discourse, while Sun et al. (2021) show an increasing trend of digital linguistic research through bibliometric analysis, including cultural gaps (Rani & Samjetsabam, 2024). Overall, language not only evolves naturally, but also through human interaction with technology and digital culture. Digital dialects, slang, and online communication patterns are part of the global linguistic evolution that opens up interdisciplinary research opportunities between linguistics, computing, and cultural studies.
Penerapan Komputasi Kuantum dalam Kriptografi Modern dan Sistem Keamanan Digital Rudolf Sinaga; Uswatun Kasanah
Journal of New Trends in Sciences Vol. 1 No. 4 (2023): November : 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.v1i4.779

Abstract

Quantum computing has emerged as a revolutionary paradigm, holding immense potential to solve complex problems that classical computing struggles to address. This study explores the application of quantum computing in cryptography, with a specific focus on two major quantum algorithms: Shor’s algorithm for large number factorization and Grover’s algorithm for unstructured database searching. The main objective of this research is to compare the performance of these quantum algorithms with classical cryptographic methods in terms of computational efficiency and time. Shor’s algorithm, which can factorize large numbers in polynomial time, presents a significant threat to the security of public-key cryptosystems such as RSA, which rely on the difficulty of factoring large numbers. On the other hand, Grover’s algorithm offers a quadratic speedup for searching unstructured databases, making it highly relevant for symmetric key cryptography systems like AES. In this study, simulations of both algorithms were conducted using quantum simulators to assess their speed and effectiveness in solving cryptographic challenges. The results demonstrate that quantum algorithms significantly reduce the computation time compared to classical methods, with Shor’s algorithm efficiently solving factorization problems and Grover’s algorithm accelerating key searching processes. However, while these quantum algorithms show promise in improving cryptographic systems, the implementation of large-scale quantum computers remains a challenge. This research highlights the potential of quantum computing to revolutionize data security and underscores the need for further development in quantum algorithms and the transition to quantum-resistant cryptographic systems to safeguard against the threat posed by quantum computers.
Penerapan Teknologi Digital Twin untuk Pemodelan Sistem Industri Otomatis dalam Meningkatkan Efisiensi Produksi dan Keamanan Operasional Siswanto Siswanto; Maya Utami Dewi
Journal of New Trends in Sciences Vol. 1 No. 4 (2023): November : 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.v1i4.780

Abstract

The advancement of Industry 4.0 demands production systems to operate more efficiently, adaptively, and securely in facing global challenges. One promising technology that addresses these needs is the Digital Twin (DT), a digital representation of physical systems that enables integration between the real and virtual environments. Through DT, production processes can be modeled, monitored, and tested in real time, allowing for evaluation and optimization before implementation in actual systems. This study aims to explore the effectiveness of DT in modeling automated industrial systems, particularly in relation to improving production efficiency, quality control, energy savings, and operational safety. The research employed an experimental approach based on simulation within a robotic production line consisting of machines, sensors, actuators, and conveyors. The research stages included identifying system components and workflows, developing a DT model that integrates physical and virtual layers with Internet of Things–based data connectivity, and conducting simulations representing diverse operational scenarios. The findings indicate that DT implementation enhances operational efficiency, reduces production errors, and optimizes energy utilization. Furthermore, DT proves effective in strengthening safety aspects by enabling early detection of potential disruptions and providing preventive recommendations before significant impacts occur. Compared to conventional simulations, DT offers a more realistic, adaptive, and relevant approach to the needs of modern industry. The implications of this study highlight DT’s strong potential to become a new standard in the development and control of automation-based production systems, driving the creation of smarter, more efficient, and sustainable industries.
Penerapan Metode Monte Carlo Simulation untuk Estimasi Risiko Portofolio Saham pada Pasar Modal Indonesia Sunarmi Sunarmi; Siti Kholifah
Journal of New Trends in Sciences Vol. 1 No. 4 (2023): November : 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.v1i4.782

Abstract

This research explores the application of Monte Carlo Simulation in estimating portfolio risk in the Indonesian stock market. The primary objective is to assess the effectiveness of this method in predicting portfolio return distribution and managing risk compared to traditional methods like Value at Risk (VaR). Data from the Indonesia Stock Exchange (IDX) were used to analyze stock returns, focusing on sectors such as telecommunications and property. Monte Carlo Simulation was applied to generate multiple scenarios of stock returns based on historical data and probabilistic distributions. The findings show that Monte Carlo Simulation provides a more comprehensive risk estimation, especially for stocks with high volatility and small market capitalization. Unlike VaR, which assumes a normal distribution, Monte Carlo Simulation accounts for extreme risk events and market uncertainties. The study also highlights the importance of diversification, as portfolios with a mix of high and low volatility stocks demonstrate a more stable risk profile. The results suggest that Monte Carlo Simulation is an effective tool for investors looking to manage risk in dynamic market conditions, providing more accurate and reliable estimations compared to traditional risk assessment methods. This research recommends further exploration of Monte Carlo Simulation in other sectors or with varied data for broader applications in risk management.

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