Articles
<![CDATA[AI-Driven Predictive Maintenance for Smart Manufacturing Systems: A Case Study Using Deep Learning on Sensor Data ]]>
<![CDATA[Nampira, Ardi Azhar]]>;
<![CDATA[Pangastuti, Nova]]>;
<![CDATA[Wiwit]]>;
<![CDATA[Taufik, Taufik]]>
<![CDATA[ Journal of Moeslim Research Technik Vol. 2 No. 3 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/technik.v2i3.2345
<![CDATA[The rapid advancement of Industry 4.0 has catalyzed the integration of artificial intelligence (AI) into smart manufacturing, with predictive maintenance emerging as a crucial application to reduce downtime and optimize operational efficiency. This study aims to develop and evaluate a deep learning-based predictive maintenance model by leveraging real-time sensor data from a smart factory environment. A convolutional neural network (CNN) architecture was implemented to detect anomalies and predict machinery failures in advance. The dataset, consisting of multivariate time-series signals from industrial sensors, was preprocessed and used to train, validate, and test the model’s predictive performance. Results indicate that the proposed deep learning model achieved a prediction accuracy of 94.6%, outperforming traditional statistical and machine learning methods in both precision and recall. The implementation of this AI-driven system enables proactive maintenance strategies, minimizing production losses and extending equipment lifespan. In conclusion, the research demonstrates the feasibility and effectiveness of deep learning in predictive maintenance applications for smart manufacturing systems and offers a scalable solution adaptable to diverse industrial settings. ]]>
<![CDATA[Hybrid Solar-Biomass Systems for Off-Grid Rural Electrification: Techno Economic and Environmental Assessment ]]>
<![CDATA[Nampira, Ardi Azhar]]>;
<![CDATA[Souza, Felipe]]>;
<![CDATA[Lima, Rafaela]]>
<![CDATA[ Journal of Moeslim Research Technik Vol. 2 No. 3 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/technik.v2i3.2354
<![CDATA[This study investigates the potential of a hybrid solar-biomass system to provide reliable and sustainable electricity to off-grid rural communities. The research background highlights the critical energy poverty prevalent in many rural areas, which lacks access to a stable power grid. While solar energy is a promising solution, its intermittent nature often limits its reliability. The primary objective is to conduct a comprehensive techno-economic and environmental assessment of a hybrid solar-biomass system. The study aims to design an optimized system configuration that can meet the energy demand of a typical rural village while minimizing the levelized cost of energy (LCOE) and reducing the system’s overall carbon footprint. The research seeks to demonstrate a viable and sustainable alternative to conventional fossil fuel-based generation. The research methodology involves creating a detailed energy model of a hybrid system using specialized software. The model integrates solar photovoltaic (PV) panels, a biomass gasifier, and a battery storage system.. The research findings demonstrate that the hybrid system is a technically and economically feasible solution for rural electrification. The optimized configuration achieved a low LCOE of $0.25/kWh, which is competitive with diesel-based generators. The environmental assessment projected a 75% reduction in GHG emissions. The conclusion is that hybrid solar-biomass systems provide a highly effective, cost-efficient, and environmentally sound approach to off-grid rural electrification. to both economic development and climate change mitigation goals.]]>
<![CDATA[Design Thinking in STEM Classrooms: A Mixed-Methods Study on Enhancing Student Creativity ]]>
<![CDATA[Rahayu, Ninik Sri]]>;
<![CDATA[Huda, Nurul]]>;
<![CDATA[Sari, Ira Wulan]]>;
<![CDATA[Nampira, Ardi Azhar]]>
<![CDATA[ Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 3 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/innovatsioon.v2i3.2355
<![CDATA[Fostering creativity within Science, Technology, Engineering, and Mathematics (STEM) education remains a critical challenge, as traditional pedagogies often prioritize convergent thinking over innovative problem-solving. This study investigates the impact of integrating design thinking methodologies into STEM classrooms to enhance student creativity. The primary objective was to quantitatively measure changes in students’ creative abilities and to qualitatively explore their experiences and perceptions of the design thinking process. This research employed a sequential explanatory mixed-methods design. Initially, 120 secondary school students participated in a quasi-experimental study, completing pre-and-post Torrance Tests of Creative Thinking (TTCT). Subsequently, semi-structured interviews were conducted with a purposive sample of 20 students to provide deeper insights into the quantitative results. The findings revealed a statistically significant increase in students’ TTCT scores, particularly in the dimensions of fluency and originality. In conclusion, the integration of design thinking presents a robust pedagogical framework for systematically nurturing creativity in STEM disciplines, equipping students with essential skills for future innovation. ]]>
<![CDATA[The Role of AI in Personalized Learning: Enhancing Creative Thinking in Online Classrooms ]]>
<![CDATA[Judijanto, Loso]]>;
<![CDATA[Al-Momani, Ammar]]>;
<![CDATA[Hanim, Siti Aisyah]]>;
<![CDATA[Nampira, Ardi Azhar]]>
<![CDATA[ Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 4 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/innovatsioon.v2i4.2363
<![CDATA[The rapid integration of Artificial Intelligence (AI) into education has transformed the dynamics of online learning, creating opportunities for personalized learning pathways. However, the potential of AI to enhance creative thinking skills among students remains underexplored. This study aims to investigate the role of AI-driven personalized learning systems in fostering creative thinking in online classrooms. A quantitative research design with a quasi-experimental approach was applied, involving 240 university students enrolled in fully online courses. The experimental group used AI-powered adaptive learning platforms, while the control group participated in conventional online learning. Data were collected through creative thinking tests and analyzed using ANCOVA to measure the impact of the intervention. The findings indicate that AI-based personalization significantly improves students’ creative thinking, particularly in generating novel ideas, problem-solving flexibility, and originality of responses. The study concludes that AI can serve as an effective catalyst for promoting creativity in digital learning environments by tailoring content, pace, and feedback to individual learners. This research provides evidence for integrating AI tools in designing student-centered online learning models that emphasize creativity as a 21st-century skill. ]]>
<![CDATA[Application of Internet of Things (IoT) in Modern Livestock Management in New Zealand ]]>
<![CDATA[Destari, Dina]]>;
<![CDATA[Gomez, Raul]]>;
<![CDATA[Costa, Bruna]]>;
<![CDATA[Nampira, Ardi Azhar]]>
<![CDATA[ Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/agriculturae.v2i1.1991
<![CDATA[This study examines the application of Internet of Things (IoT) technology in modern livestock management in New Zealand. The background of this research is based on the need to increase productivity, efficiency, and sustainability in the increasingly competitive livestock sector. The purpose of the study is to explore the benefits of applying IoT in livestock health monitoring, feed management, as well as the impact of this technology on the environment and sustainability. The research method used is descriptive-qualitative with data collection through interviews, field observations, and secondary data analysis. The results show that the adoption of IoT in large farms increases productivity by up to 20% and reduces operational costs through more efficient feed management. The study also found that infrastructure challenges are a hindrance to IoT adoption in small and medium-sized farms. The conclusion of the study is that IoT has the potential to be a key solution to improve efficiency and sustainability in the livestock sector, but infrastructure support and training are urgently needed to accelerate its adoption across sectors.]]>
<![CDATA[Greenhouse Technology Innovations for Sustainable Agriculture in the United Kingdom ]]>
<![CDATA[Li, Zhang]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Yang, Liu]]>;
<![CDATA[Nampira, Ardi Azhar]]>
<![CDATA[ Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/agriculturae.v2i1.1993
<![CDATA[Greenhouse technology is an important innovation in facing the challenges of sustainable agriculture in the UK, especially in the face of climate change and increasing food needs. This research aims to explore the application of advanced technologies in greenhouses, such as automation sensors, hydroponics, aquaponics, and renewable energy, as well as their impact on agricultural productivity and sustainability. Descriptive-qualitative research methods are used to gain insights from farmers and experts in the field of agricultural technology, through interviews and direct observations. The results showed a significant improvement in resource use efficiency, with a reduction in water use of up to 50% and an increase in crop yields of up to 30%. The adoption of renewable energy in greenhouses also plays a role in reducing carbon emissions and operational costs. In conclusion, greenhouse technology innovation has the potential to be an important solution to achieving sustainable agriculture in the UK, but more research is needed to evaluate the long-term impact on the environment.]]>
<![CDATA[Plant Health Monitoring Technology with Artificial Intelligence in France ]]>
<![CDATA[Razak, Faisal]]>;
<![CDATA[Farah, Rina]]>;
<![CDATA[Idris, Haziq]]>;
<![CDATA[Nampira, Ardi Azhar]]>
<![CDATA[ Techno Agriculturae Studium of Research Vol. 2 No. 2 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/agriculturae.v2i2.1995
<![CDATA[This study explores the role of artificial intelligence (AI)-based plant health monitoring technology in France, which is expected to improve the efficiency of early detection of plant diseases and optimize the use of agricultural resources. The background of this research is based on the urgent need to increase agricultural productivity and reduce negative impacts on the environment. The purpose of this study is to test the effectiveness of AI in detecting plant health problems and provide data-driven recommendations for farmers. This study uses a mixed approach, with quantitative data from farmer surveys and qualitative data from interviews and case studies in major agricultural regions in France. The results showed that 80% of farmers reported an increase in early detection of diseases, and 75% reported a reduction in pesticide use. In conclusion, AI is playing an important role in supporting sustainable agriculture in France, although challenges in access to technology still need to be addressed. Further research is needed to explore ways to expand the adoption of this technology among smallholders.]]>
<![CDATA[Utilization of Renewable Energy in Modern Agriculture in Denmark ]]>
<![CDATA[Ramos, Nathaniel]]>;
<![CDATA[Martinez, Isabel]]>;
<![CDATA[Fernandez, Carloz]]>;
<![CDATA[Nampira, Ardi Azhar]]>
<![CDATA[ Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/agriculturae.v2i1.1997
<![CDATA[Renewable energy has become an important component in efforts to create sustainable and efficient agriculture in Denmark. The background of this research is the need to improve energy efficiency and agricultural productivity, as well as reduce the environmental impact of fossil energy use. This study aims to evaluate the impact of renewable energy on operational efficiency, agricultural yields, and energy cost reduction. The study used a mixed approach, involving 150 farmers in different regions of Denmark through in-depth surveys and interviews. The results show that renewable energy increases energy efficiency by up to 30%, increases crop yields by 20%, and reduces energy costs by up to 25%. In conclusion, renewable energy not only supports environmental sustainability, but also increases the productivity and profitability of the agricultural sector in Denmark. More research is needed to identify the long-term impacts and more equitable access to renewable energy technologies.]]>
<![CDATA[Real-Time Sensing of Airborne Pollutants Using IoT-Integrated Electrochemical Sensors ]]>
<![CDATA[Nampira, Ardi Azhar]]>;
<![CDATA[Pong, Ming]]>;
<![CDATA[Lek, Siri]]>
<![CDATA[ Research of Scientia Naturalis Vol. 2 No. 5 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v2i5.2383
<![CDATA[Air pollution poses a significant threat to public health, demanding effective real-time monitoring solutions. Traditional monitoring systems are often costly and sparsely located, limiting their spatial-temporal resolution. This study aimed to develop and validate a low-cost, IoT-integrated electrochemical sensor system for the real-time detection of key airborne pollutants. We fabricated electrochemical sensors for nitrogen dioxide (NO?), sulfur dioxide (SO?), and volatile organic compounds (VOCs), which were then integrated with a microcontroller and a wireless communication module. The system was calibrated and validated against reference instruments in both laboratory and field conditions. The developed sensors exhibited high sensitivity, good selectivity, and rapid response times (<60s). Field data demonstrated a strong correlation (R² > 0.92) with co-located reference-grade analyzers, and the IoT platform successfully provided continuous data visualization via a cloud dashboard. This study confirms that IoT-integrated electrochemical sensors provide a scalable and cost-effective solution for building dense, real-time air quality monitoring networks, offering significant potential for urban environmental management.]]>
<![CDATA[Comparative Analysis of Smart Catalysts for CO? Reduction: From Molecular Design to Lab-Scale Performance ]]>
<![CDATA[Nampira, Ardi Azhar]]>;
<![CDATA[Mendes, Clara]]>;
<![CDATA[Costa, Tiago]]>
<![CDATA[ Research of Scientia Naturalis Vol. 2 No. 4 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v2i4.2388
<![CDATA[The electrochemical reduction of carbon dioxide (CO?) is a critical strategy for mitigating climate change and producing value-added chemicals, yet the development of highly selective catalysts remains a primary challenge. This study aimed to conduct a rigorous comparative analysis of three distinct classes of "smart" catalysts—a molecular cobalt complex, a metal-organic framework (MOF), and a single-atom copper catalyst (Cu-SAC)—to elucidate the relationship between molecular design and lab-scale performance. The catalysts were synthesized, characterized via XRD and XAS, and evaluated for electrocatalytic CO? reduction in a flow cell reactor. The results showed that the Cu-SAC exhibited superior performance, achieving a Faradaic efficiency for ethylene (C?H?) exceeding 70% at a low cell voltage, significantly outperforming the MOF and molecular catalysts, which primarily produced CO and formate. This high selectivity was directly correlated with the optimized coordination environment of the isolated Cu sites. This comparative analysis confirms that rational design at the atomic level is a highly effective strategy for steering reaction pathways towards valuable multi-carbon products, providing a crucial benchmark for future catalyst development. ]]>
