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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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Eksperimen Biolistrik pada Tanaman untuk Mengembangkan Sumber Energi Alternatif Berbasis Hayati Andi Arlina; Andi Syarifah Irmadani
Journal of New Trends in Sciences Vol. 2 No. 4 (2024): 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.v2i4.761

Abstract

This study explores the potential of water hyacinth (Eichhornia crassipes) as a bioelectricity source through Plant-Microbial Fuel Cell (PMFC) technology. The research aimed to evaluate the ability of water hyacinth to generate stable electrical energy when integrated with electrodes in a controlled laboratory setting. The method applied was an experimental laboratory design, where water hyacinths were placed in containers filled with water, equipped with anode and cathode electrodes, and connected to a voltmeter and ammeter for continuous monitoring. Observations were carried out for 72 hours with periodic recording of voltage and current values. The findings show that water hyacinth can generate measurable electricity, with voltage ranging from 0.25 V to 0.32 V and current between 0.18 mA and 0.24 mA, indicating the potential of this plant to produce renewable energy. Moreover, the results reveal that the generated electricity was relatively stable during the observation period, though variations occurred due to environmental conditions. The implications of this research suggest that water hyacinth, often considered a weed, can be utilized not only for energy production but also as part of ecological management programs. This dual function makes PMFC technology a promising alternative for sustainable energy development, especially in rural or remote areas where access to conventional electricity remains limited.
Studi Interdisiplin antara Fisika dan Biologi dalam Analisis Dinamika Jaringan Neuron Otak Dian Purnamasari; Hasriantirisna Hasriantirisna
Journal of New Trends in Sciences Vol. 2 No. 4 (2024): 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.v2i4.762

Abstract

The human brain is a highly complex system whose dynamics cannot be fully understood through a single disciplinary perspective. This study aims to examine the synchronization of neural networks by combining theoretical physics, neuroscience, and computational methods. The research employed two main approaches: computer simulations based on the Kuramoto oscillator model and empirical analysis of electroencephalography (EEG) data. The simulation modeled neural activity using a graph-theoretical framework, while EEG analysis provided time-series data of brainwave patterns. Both results were compared to validate the accuracy of the model. Findings show that synchronization levels from simulations closely resemble EEG data, with only minor differences across various frequency conditions. Notably, both results revealed a tendency for stronger synchronization at higher frequencies, indicating a collective mechanism of neural coordination. These results demonstrate that physics-based models can effectively represent biological phenomena, while empirical data ensures that the findings remain grounded in real neural dynamics. The integration of theoretical and empirical approaches highlights the importance of interdisciplinary collaboration in studying brain complexity. This research not only contributes to a deeper scientific understanding but also opens potential applications in neuroscience, clinical diagnostics, and computational modeling. Overall, the study reinforces that interdisciplinary frameworks are essential for bridging abstract theories with biological realities.
Aplikasi Nanoteknologi pada Energi Terbarukan untuk Peningkatan Efisiensi Sistem Penyimpanan Energi Agus Hariyanto; Rosiana Romadon
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.v1i2.763

Abstract

This research explores the role of nanotechnology in enhancing energy storage systems for renewable energy applications. The study aims to evaluate the potential of nanomaterials, including carbon nanotubes, graphene, and fullerenes, in improving the performance of energy storage devices, specifically batteries, by focusing on their ability to enhance energy conversion efficiency, storage capacity, and battery lifespan. A case study was conducted using a prototype battery incorporating these nanomaterials, connected to a photovoltaic system to simulate real-world energy production conditions. The methodology included testing key parameters, such as energy conversion efficiency, charging time, and battery degradation through multiple charge-discharge cycles. The findings showed a significant improvement in energy conversion efficiency, with the nanomaterial-based batteries outperforming conventional batteries by up to 20%. Additionally, the nanomaterial-based batteries demonstrated an increased storage capacity and longer cycle life, maintaining performance after hundreds of charge-discharge cycles. Nanomaterials contributed to these improvements by enhancing electrical conductivity, reducing internal resistance, and preventing degradation that typically occurs in conventional batteries during prolonged use. Furthermore, nanostructured coatings on the electrodes were found to improve light trapping and anti-reflection capabilities, which directly contributed to the higher efficiency of the battery. This research highlights the transformative potential of nanotechnology in energy storage systems, offering a promising solution to the intermittency challenges of renewable energy sources such as solar and wind. The study also suggests that further research is needed to explore other nanomaterials, scale up the technology for industrial applications, and integrate smart energy management systems to optimize energy storage in renewable energy systems.
Analisis Fisika Gelombang Tsunami untuk Desain Sistem Peringatan Dini Berbasis Komputasi Cepat Marsiska Ariesta Putri; Ninik Dwi Atmin
Journal of New Trends in Sciences Vol. 2 No. 4 (2024): 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.v2i4.764

Abstract

The increasing frequency and severity of tsunamis in coastal areas underscore the urgent need for efficient Tsunami Early Warning Systems (TEWS). This research aims to optimize TEWS by integrating fast computational tsunami wave modeling to enhance prediction speed and accuracy. The study utilizes numerical simulations employing finite volume methods, along with GPU acceleration, to model tsunami wave propagation and its impact on coastal areas. Machine learning techniques, such as regression trees, are incorporated to analyze large datasets of pre-computed tsunami simulations for accurate forecasting. The results reveal that by applying rapid computational methods, detection time can be reduced by up to 7 minutes, particularly for near-field tsunamis. This significant time-saving enables more effective evacuation procedures and better disaster mitigation efforts. In comparison to conventional systems, the fast computation model also provides more accurate predictions, including tsunami heights and arrival times. The implications of these findings suggest that fast computational methods can substantially improve the current TEWS, allowing for quicker and more reliable tsunami warnings. Moreover, the integration of advanced machine learning techniques ensures the system's adaptability and robustness in predicting tsunami behaviors based on varying data inputs. The potential for implementing this model in tsunami-prone regions worldwide is considerable, offering an improved approach to tsunami disaster preparedness and response. By reducing detection time and enhancing prediction accuracy, the optimized TEWS can significantly minimize loss of life and infrastructure damage, making it a valuable tool for global disaster management strategies.  
Penggunaan Robot Mikroskopis untuk Menghantarkan Obat pada Sel Kanker secara Presisi Nur Aini; Endro Pramono
Journal of New Trends in Sciences Vol. 2 No. 4 (2024): 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.v2i4.765

Abstract

Cancer therapy has long faced the challenge of balancing treatment effectiveness with minimizing damage to healthy tissues. Conventional therapies such as chemotherapy and radiotherapy, although widely used, often lead to significant side effects due to their non-specific nature. This study aims to analyze the effectiveness of microscopic robots in delivering drugs precisely to cancer cells, thereby reducing collateral damage. The research employed simulations and in vitro preclinical tests using cancer cell cultures to evaluate both targeting accuracy and safety. Findings indicate that microscopic robots achieved 93% effectiveness in targeting cancer cells while limiting damage to healthy cells to only 10%. Compared to chemotherapy, radiotherapy, chemoradiotherapy, and nanoparticle-based drug delivery systems, microscopic robots demonstrated superior precision and efficiency. These results suggest that microscopic robots hold great potential as a breakthrough technology in precision cancer therapy, offering new possibilities for safer and more effective treatments. However, further research is required to address long-term biocompatibility, control mechanisms within the human body, and clinical validation. This study highlights the transformative potential of integrating microscopic robots into future cancer treatment strategies, contributing to the advancement of personalized medicine.
Analisis Dampak Implementasi Konsep Smart City terhadap Ekologi Perkotaan dan Kualitas Hidup Masyarakat dalam Perspektif Lingkungan dan Keberlanjutan Jangka Panjang Mia Kusmiati; Andi Ningrat
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.v1i2.766

Abstract

The concept of the smart city has emerged as a strategic response to rapid urbanization, increasing population density, and escalating environmental degradation. It is designed to create more efficient urban management through the integration of information and communication technologies, with the primary goal of enhancing public services, optimizing resource use, and improving citizens’ quality of life. One of its most significant promises is its potential to mitigate ecological challenges, particularly in monitoring and reducing air pollution levels. This study aims to analyze the impact of smart city implementation on urban ecology and quality of life, focusing on air quality as a key indicator. A quantitative comparative approach was applied, using both primary data from air quality measurements PM2.5, CO₂, NO₂, and AQI and secondary data from government reports, environmental agencies, and academic publications. The research was conducted in five major Asian cities recognized for adopting smart city initiatives. The results reveal varied outcomes: Tokyo and Kuala Lumpur demonstrated substantial improvements in air quality and livability, while Beijing and New Delhi continued to struggle with severe pollution. Jakarta showed partial progress, though improvements remain limited. These findings indicate that while technology is critical, it alone does not ensure ecological sustainability. Effective governance, public participation, and context-based strategies are equally crucial. This study highlights the need for smart city policies to prioritize ecological resilience and human well-being as central elements of sustainable urban development.
Peran Matematis Fraktal dalam Analisis Pola Pertumbuhan Tanaman Tropis dan Aplikasinya untuk Optimasi Pertanian Presisi di Era Modern Sutikni Sutikni; Ida Martini
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.v1i3.767

Abstract

The role of fractal geometry in analyzing growth patterns of tropical plants and its application in precision agriculture has become an emerging interdisciplinary topic in the modern era. Tropical plants often exhibit complex and irregular structures that cannot be fully described by conventional Euclidean geometry. This study aims to examine fractal-based mathematical models to identify self-similar patterns in tropical leaves and to explore their potential for optimizing precision farming practices. The methodology employs image-based mathematical analysis, using digital images of tropical plants to measure fractal dimensions and quantify growth complexity. The findings reveal that consistent fractal patterns can be observed across different species of tropical plants, particularly in leaf venation and branching structures, indicating a universal growth principle. Such patterns demonstrate high predictive potential for estimating biomass, monitoring plant health, and assessing responses to environmental changes. Furthermore, the study highlights how fractal-based approaches, when combined with precision agriculture technologies, can improve resource efficiency by supporting accurate irrigation scheduling, soil quality monitoring, and yield forecasting. The implications extend to sustainable agricultural development, as fractal analysis provides a scientific foundation for balancing productivity with environmental preservation. In conclusion, this research underscores the significance of fractals not only as mathematical concepts but also as powerful analytical tools with practical benefits, offering new pathways to advance digital farming, ecological monitoring, and sustainable food security in the modern era.
Interaksi Budaya Lokal dan Perubahan Iklim dalam Perspektif Ilmu Lingkungan untuk Memahami Adaptasi Sosial Ekologis Masyarakat Pesisir Nusantara Dwi Rahayu; Siti Hidayah
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.v1i3.770

Abstract

This study aims to analyze the interaction of local culture and climate change from an environmental science perspective, with a focus on the socio-ecological adaptation strategies of coastal communities in the Indonesian archipelago. Climate change has had significant impacts on coastal areas, ranging from increased tidal flooding and coastal erosion to decreased fisheries productivity. In this context, coastal communities rely not only on technical approaches but also utilize local wisdom that has been passed down through generations. The research method used is a qualitative study with an environmental ethnography approach, which allows researchers to explore the relationship between culture, ecology, and environmental adaptation. Data were collected through participant observation, in-depth interviews with traditional leaders, fishermen, and local communities, and analysis of cultural documents covering rituals, customary rules, and traditional ecological practices. Data analysis was conducted thematically to identify adaptation patterns, then compared with technical approaches in environmental studies. The results show that coastal communities in the Indonesian archipelago have various forms of culturally based adaptation that are relevant to the challenges of climate change. For example, the practice of marine sasi in Maluku serves to preserve fishery resources, traditional rituals in Bali support collective ecological awareness, and mutual cooperation (gotong royong) on ​​the coast of Java helps mitigate tidal flooding. These practices demonstrate that local wisdom serves a dual purpose: strengthening social cohesion and sustainably protecting the environment. The study's conclusions confirm that adaptation strategies will be more effective if they integrate local wisdom with science-based technical approaches. This integration not only strengthens ecological resilience but also ensures the socio-cultural sustainability of coastal communities.
Dinamika Fraktal dalam Pertumbuhan Kristal Logam untuk Inovasi Material Baru Taufiq Dalming; Asyari Al Hutama Azis
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.v2i1.754

Abstract

Fractal structures play a crucial role in improving material properties due to their unique self-similar geometries. These geometrical patterns exhibit repetition at multiple scales, which can enhance surface area, strength, and other mechanical characteristics. This study investigates the influence of fractal patterns on the crystal growth and mechanical performance of metallic materials, focusing on strength, toughness, and hardness. Using a combination of computer simulations and laboratory experiments, the research models metal crystal formation under controlled conditions, where fractal characteristics are introduced through diffusion-limited aggregation (DLA) and electrodeposition methods. The findings reveal that embedding fractal patterns into the crystal growth process can increase material strength by approximately 20% compared to conventionally structured metals. This improvement is attributed to the efficient stress distribution within the fractal geometry, which minimizes stress concentration points and enhances resistance to fracture. Additionally, materials with fractal-based microstructures exhibit better toughness and deformation resistance, improving durability under mechanical load. The study also examines the underlying mechanisms of these effects, emphasizing the role of fractal-induced microstructural control in optimizing material integrity. These results demonstrate the significant potential of fractal-based material design in engineering stronger, lighter, and more flexible metallic components. The research contributes to the broader understanding of how geometric complexity can be harnessed to develop advanced materials for applications in construction, automotive manufacturing, and flexible electronics, thereby supporting the development of next-generation high-performance materials.
Integrasi Bioinformatika dan Farmakogenomik untuk Merancang Terapi Individualisasi pada Pasien dengan Resistensi Obat Tuberkulosis Dharmika Pranidhi; Dhanan Abimanto
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.v1i4.768

Abstract

Drug-resistant tuberculosis (TB) is an escalating global health issue, particularly with the rise of multidrug-resistant (MDR-TB) and extensively drug-resistant TB (XDR-TB), which complicate treatment and control efforts. Resistance to both first-line and second-line drugs weakens the effectiveness of standard WHO-recommended therapies, while alternative drugs can cause severe side effects and reduce patient adherence. This study aims to explore the integration of bioinformatics and pharmacogenomics in supporting personalized TB treatment to improve therapeutic success and reduce the risk of further resistance. The research employed a laboratory-based experimental design with a bioinformatics approach, involving TB patients with clinical evidence of drug resistance. Clinical samples were analyzed through whole genome sequencing to identify gene mutations associated with resistance, followed by pharmacogenomic mapping to predict pharmacological responses based on patients’ genetic variations. The results revealed several specific gene mutations consistently linked to resistance and produced individualized therapeutic recommendations that were more targeted than standard protocols. Effectiveness evaluation demonstrated that genome-based personalized therapy yielded higher treatment success rates, faster recovery times, and lower rates of subsequent resistance. These findings highlight the significant potential of precision medicine in TB management, particularly for resistant cases that are difficult to treat with conventional approaches. In conclusion, the integration of bioinformatics and pharmacogenomics plays an essential role in strengthening TB treatment strategies through a personalized, adaptive, effective, and sustainable approach. Nevertheless, its implementation still faces challenges such as high costs, limited infrastructure, and the need for clear regulations regarding the use of patients’ genomic data.

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