Articles
Quantum Computing and Complexity Theory
Sucipto, Purwo Agus;
Judijanto, Loso;
Qudah, Nasser
Journal of Tecnologia Quantica Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/quantica.v2i1.1967
The background of this research is driven by the rapid development of quantum computing which has the potential to change the paradigm in complexity theory and computational algorithms. The purpose of this study is to explore the advantages and limitations of quantum algorithms in solving problems with high complexity, as well as to understand their role in complexity theory. The research method used involves quantum computer simulations to analyze the performance of Shor and Grover's algorithms in solving cryptographic problems and large database searches, as well as comparing them with classical algorithms. The results show that quantum algorithms have significant advantages in solving certain problems, although there are technical obstacles in quantum hardware that affect overall performance. Quantum computing has great potential in the fields of cryptography and big data processing, but challenges such as quantum errors and decoherence still have to be overcome. The conclusion of this study confirms the importance of further research in improving quantum hardware and developing more efficient algorithms, as well as opening up new opportunities for the application of quantum computing in various industries.
The Role of AI in Personalized Learning: Enhancing Creative Thinking in Online Classrooms
Judijanto, Loso;
Al-Momani, Ammar;
Hanim, Siti Aisyah;
Nampira, Ardi Azhar
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/innovatsioon.v2i4.2363
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.
The Precision Agriculture Revolution in the United States: The Utilization of Drone Technology and Big Data
Judijanto, Loso;
Zaki, Amin;
Razak, Faisal
Techno Agriculturae Studium of Research Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/agriculturae.v1i3.1589
Precision agriculture is an innovative approach that is increasingly being applied in the United States to increase agricultural productivity through the use of cutting-edge technology. The background of this research focuses on the importance of adopting modern technologies, such as drones and big data, in addressing global challenges such as climate change, resource limitations, and increasing food needs. The purpose of this study is to explore how the integration of drone technology and big data can revolutionize the agricultural sector in the United States, specifically in improving efficiency, reducing waste, and increasing crop yields. This study uses a qualitative method with a case study approach to several agricultural companies that have adopted drone technology and big data. Data collection was carried out through in-depth interviews with farmers, document analysis, and field observations regarding the implementation of the technology. The data collected was then analyzed using thematic analysis methods to identify patterns and impacts that arise from the adoption of this technology. The results show that the use of drones allows for real-time monitoring of land, early detection of plant diseases, and more efficient resource management, while big data helps in more accurate data-driven decision-making. These two technologies significantly improve the productivity and sustainability of agriculture in the study area. The conclusion of this study confirms that the integration of drone technology and big data can make a great contribution in facing the challenges of modern agriculture. Precision agriculture, with the support of technology, has the potential to be a long-term solution to improve the efficiency and yield of agricultural production.
Innovation in Vertical Agriculture in Dutch Cities: Sustainable Solutions with Modern Hydroponics
Judijanto, Loso;
Chai, Nong;
Tubangsa, Ian
Techno Agriculturae Studium of Research Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/agriculturae.v1i3.1590
Vertical agriculture has emerged as an innovative solution to the challenge of food security in densely populated urban areas such as those in the Netherlands. The background of this research is driven by the need to find more efficient and sustainable agricultural methods amid the limitations of conventional agricultural land. The purpose of this study is to examine the potential application of modern hydroponic technology in vertical farming systems in urban Netherlands, as well as to analyze its impact on environmental and economic sustainability. This study uses a qualitative method with a case study approach, where data is collected through interviews with agricultural experts, literature analysis, and direct observation on vertical farming facilities using hydroponic technology. The results show that vertical farming systems with hydroponic technology are able to reduce water use by up to 90% compared to traditional agriculture, as well as increase crop production up to 3 times on the same land area. In conclusion, hydroponic vertical agriculture innovations not only provide solutions to urban land limitations, but also support more environmentally friendly and sustainable agricultural practices. However, challenges in terms of initial costs and technology are still the main obstacles that need to be overcome for wider implementation.
Urban Agricultural Revolution in Japan With Verticulture Technology
Judijanto, Loso;
Tan, Ethan;
Lee, Ava
Techno Agriculturae Studium of Research Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/agriculturae.v1i4.1596
Japan's urban agricultural revolution with vertical culture technology emerged as a solution to overcome the limitations of agricultural land due to rapid urbanization. Vertical culture technology allows the cultivation of plants in urban environments by utilizing vertical spaces, thereby increasing food security and reducing environmental impact. This study aims to analyze the development and impact of the application of verticulture technology in major Japanese cities, as well as identify the factors that support its success. The research method used is qualitative descriptive with data collection through in-depth interviews, direct observation, and secondary data analysis from government reports and scientific publications. The results show that the number of vertical culture installations in Japan has increased significantly in the last five years, supported by government policies and local community initiatives. In addition to increasing food production in urban areas, this technology also helps create green spaces and improve environmental quality in densely populated areas. The conclusion of this study is that verticulture can be a sustainable solution for urban food security, but further research is needed to evaluate the long-term impact on the environment and energy efficiency.
Application of Robotics in Large-Scale Agriculture in Australia
Judijanto, Loso;
Takahashi, Haruto;
Nakamura, Yui
Techno Agriculturae Studium of Research Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/agriculturae.v1i4.1597
Large-scale agriculture in Australia faces various challenges, such as labor shortages, land management efficiency, and suboptimal use of resources. Robotic technology offers innovative solutions to address these problems by automating agricultural processes, such as planting, fertilizing, and harvesting. This study aims to evaluate the impact of the application of robotics technology in large-scale agriculture in Australia, including its impact on productivity, resource use efficiency, and environmental sustainability. The research uses a combined qualitative and quantitative approach. Quantitative data was collected through surveys of farmers in different regions of Australia, while qualitative data was obtained from in-depth interviews with farmers and agronomists. The data collected was analyzed to understand the impact of robotics technology on productivity and resource use. The results show that the use of robotic technology increases productivity by 20% in the wheat and cotton sectors. In addition, the use of sensor-based automated irrigation systems reduces water consumption by up to 30%, while drones for pesticide applications help reduce chemical use by up to 25%. Robotics technology has contributed significantly to improving the efficiency of large-scale agriculture in Australia, both in terms of increasing crop yields and reducing resource use. These findings suggest that robotics can be a sustainable solution for modern agriculture, although more research is needed to evaluate its long-term impact on the environment.
Gene Expression Analysis to Predict Patient Response to Chemotherapy
Judijanto, Loso;
Amin, Rafiullah;
Zahir, Roya
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/jbtn.v2i1.2020
Chemotherapy is a cornerstone of cancer treatment, yet patient responses vary significantly. Understanding the molecular basis of this variability is crucial for optimizing therapy. To predict patient response to chemotherapy using gene expression analysis, aiming to improve personalized treatment strategies. A prospective cohort study involving high-throughput RNA sequencing and microarray analysis was conducted on tumor biopsies and blood samples from patients undergoing chemotherapy. Differentially expressed genes were identified and correlated with treatment outcomes. Gene expression profiles of ABCB1, TP53, BRCA1, ERBB2, and BCL2 were found to significantly predict chemotherapy response. Patients with high expression of these genes showed better treatment outcomes. In vitro and in vivo models validated these findings, confirming the predictive power of these gene signatures. Gene expression analysis provides valuable insights into predicting chemotherapy response, facilitating personalized cancer treatment. Further clinical trials are necessary to validate these biomarkers and develop accessible diagnostic platforms.
Development of a Recombinant Vaccine to Prevent Influenza Virus Infection
Judijanto, Loso;
Myint, Aung;
Hlaing, Nandar;
Muntasir, Muntasir
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/jbtn.v2i2.2025
Influenza virus remains a significant global health challenge, causing seasonal epidemics and potential pandemics with high morbidity and mortality rates. This study aims to develop a recombinant vaccine as a safer and more effective alternative to traditional influenza vaccines, which often suffer from limited efficacy and lengthy production timelines. Utilizing a recombinant DNA technology approach, this research employed the baculovirus expression system to produce hemagglutinin (HA) antigens derived from the influenza A virus. Experimental methods included antigen purification, immunogenicity assays in murine models, and neutralizing antibody titration. Results revealed that the recombinant HA vaccine elicited a robust immune response, with a significant increase in hemagglutination inhibition titers compared to control groups. Furthermore, the vaccinated subjects exhibited substantial protection against viral challenge, evidenced by reduced viral load and minimized lung pathology. The findings suggest that recombinant vaccine platforms offer promising avenues for rapid and scalable vaccine development. This study underscores the potential of recombinant influenza vaccines in mitigating future influenza outbreaks with improved safety, efficacy, and production agility.
Hybrid Nanozyme-Enabled Biosensors for Real-Time Detection of Multi-Disease Biomarkers
Judijanto, Loso;
Rahman, Rashid;
Anis, Nina
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/jbtn.v2i3.2378
The early and accurate detection of disease biomarkers is fundamental to timely diagnosis and effective treatment, yet conventional laboratory methods are often slow, costly, and require complex instrumentation. Nanozymes—nanomaterials with intrinsic enzyme-like properties—offer a promising alternative for developing robust biosensors. This study aimed to design, synthesize, and validate a novel hybrid nanozyme-enabled biosensor platform capable of the sensitive, selective, and real-time multiplexed detection of biomarkers for different diseases from a single sample. A hybrid nanozyme was synthesized by integrating platinum nanoparticles with metal-organic frameworks (MOFs) to create a material with superior catalytic activity. This hybrid nanozyme was then immobilized onto a multi-channel electrochemical sensor chip. Each channel was functionalized with specific aptamers targeting three distinct biomarkers: cardiac troponin I (a cardiac marker), prostate-specific antigen (a cancer marker), and glucose (a metabolic marker). The detection was based on the catalytic signal amplification upon biomarker binding. The platform showed excellent selectivity with negligible cross-reactivity between channels and achieved a rapid detection time of under 15 minutes. The multiplexed assay successfully and accurately quantified all three biomarkers simultaneously in complex serum samples. The hybrid nanozyme-enabled electrochemical biosensor represents a significant advancement in diagnostic technology.
AI-Powered Digital Histopathology: Predicting Immunotherapy Response Using Deep Learning
Judijanto, Loso;
Chai, Som;
Pong, Ming;
Justam, Justam;
Nampira, Ardi Azhar
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi
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DOI: 10.70177/jbtn.v2i3.2379
Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers. Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.