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Adam Mudinillah
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INDONESIA
Scientechno: Journal of Science and Technology
Published by Yayasan Adra Karima Hubbi
ISSN : 29864887     EISSN : 29637481     DOI : 10.70177/Scientechno
Core Subject :
The journal provides a platform for the publication of original qualitative and quantitative research on education and instruction, compilations based on critical evaluation of current literature, and meta-analysis studies. The Scientechno: Journal of Science and Technology also aims to provide a platform where multiple educational disciplines can contribute and share educational insights, innovative approaches and practices. In this respect, Scientechno: Journal of Science and Technology publishes research in an attempt to present a reliable and respectable information source for the researchers.
Arjuna Subject : -
Articles 75 Documents
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AUTONOMOUS SYSTEMS IN INDUSTRY 5.0: ENHANCING HUMAN ROBOT COLLABORATION AND SAFETY IN INDONESIAN MANUFACTURING Lucas Lima; Tiago Costa; Li Wei; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i2.2890

Abstract

Industry 5.0 represents a fundamental shift toward a more human-centric paradigm in manufacturing by emphasizing enhanced collaboration between humans and robots, where autonomous systems are designed not only to optimize efficiency but also to improve safety and support workers in performing more complex and value-added tasks. In the Indonesian manufacturing context, the adoption of autonomous technologies is accelerating as industries seek to remain competitive; however, empirical evidence regarding their effectiveness in improving human-robot collaboration and workplace safety remains limited. This study addresses this gap by exploring the role of autonomous systems in Industry 5.0 and examining how integrated safety protocols and collaboration strategies can enhance both operational efficiency and occupational safety. Employing a mixed-methods approach, the research combines qualitative insights from interviews with industry experts and quantitative data derived from experimental implementations of autonomous robotic systems in Indonesian manufacturing environments. The findings demonstrate that the deployment of adaptive safety systems significantly strengthens human-robot collaboration, resulting in a 30% reduction in workplace accidents and a 20% increase in production efficiency. These results indicate that well-designed autonomous systems can effectively minimize risks while enabling workers to interact more confidently and productively with robots, thereby supporting the conclusion that Industry 5.0 technologies hold substantial potential for improving safety standards and overall performance in Indonesian manufacturing settings.
INTEGRATING DIGITAL TWINS AND SYSTEMIC AI FOR PREDICTIVE MAINTENANCE OF NATIONAL CRITICAL INFRASTRUCTURE Lucas Wong; Sofia Lim; Rohan Kumar; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i3.2891

Abstract

National critical infrastructure, including energy, transportation, and communication systems, plays a vital role in sustaining modern society, yet failures within these systems can trigger severe economic, environmental, and security consequences. Conventional maintenance approaches often lack the capability to anticipate failures in complex and large-scale infrastructures. Recent advancements in Digital Twin technology and Artificial Intelligence (AI) provide innovative opportunities to enhance predictive maintenance and infrastructure resilience. This study aims to integrate Digital Twins with systemic AI to optimize predictive maintenance strategies for national critical infrastructure by leveraging real-time data and intelligent prediction mechanisms. The research employs a combined framework in which sensor-generated data from infrastructure components are continuously synchronized with Digital Twin models and analyzed using machine learning algorithms to monitor system conditions, simulate operational behavior, and predict potential failures. The proposed framework was implemented in a case study of a national energy grid to evaluate its effectiveness. The results indicate that the integrated system significantly improved predictive maintenance performance, achieving a 30% reduction in unplanned downtime and a 25% decrease in maintenance costs through accurate failure prediction and timely intervention. These findings demonstrate that the integration of Digital Twins and systemic AI offers a robust, scalable, and efficient solution for enhancing reliability, resilience, and sustainability in the management of national critical infrastructure.
A SYSTEMIC AI AND CYBER-PHYSICAL FRAMEWORK FOR REAL TIME REMOTE PATIENT MONITORING IN INDONESIAN RURAL HEALTH CLINICS (PUSKESMAS) Ethan Tan; Ava Lee; Li Wei; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i2.2894

Abstract

Access to healthcare in rural Indonesia remains a significant challenge due to limited medical resources and healthcare personnel, leading to delayed diagnosis and suboptimal patient care. Remote patient monitoring offers a potential solution by enabling real-time health assessments and reducing the need for long-distance travel to healthcare facilities. This study aims to design and implement a systemic Artificial Intelligence and Cyber-Physical Systems framework for real-time remote patient monitoring in rural primary health clinics in Indonesia to enhance patient care, support early disease detection, and optimize healthcare resource allocation. The research employed a hybrid AI–CPS approach that integrated wearable health devices, Internet of Things sensors, and cloud computing infrastructure to continuously monitor patient vital signs. Artificial Intelligence algorithms were utilized to analyze health data and identify early signs of potential health anomalies. Data were collected from multiple rural Puskesmas where remote monitoring devices were installed, and system performance was evaluated using metrics including data accuracy, response time, and user satisfaction. The results indicated that the system achieved a high level of accuracy, with a 92 percent success rate in predicting potential health anomalies, while feedback from healthcare workers and patients demonstrated positive perceptions, particularly in terms of convenience, efficiency, and time savings. Overall, the findings confirm that the AI and Cyber-Physical Systems-based remote patient monitoring framework is effective in improving healthcare delivery in rural Indonesian clinics and holds strong potential as a scalable solution to enhance accessibility and quality of rural healthcare services.
DEVELOPMENT OF LOW-CARBON GEOPOLYMER CONCRETE USING FLY ASH AND INDUSTRIAL SLAG AS SUSTAINABLE MATERIAL ENGINEERING Edward Ngii; Dulguun Amarsaikhan; Fatima Al-Said; Khaled Al-Sharqi
Scientechno: Journal of Science and Technology Vol. 4 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i2.2954

Abstract

The construction industry is a major contributor to global carbon emissions, with traditional Portland cement production accounting for approximately 8% of total CO? output. The development of low-carbon alternatives is essential to achieving sustainability goals and reducing the environmental footprint of infrastructure. This research focuses on developing geopolymer concrete using fly ash and industrial slag as sustainable raw materials, offering a viable substitute for ordinary Portland cement. The objective of the study is to evaluate the mechanical performance, durability, and carbon footprint reduction potential of geopolymer concrete mixtures under varied proportions of fly ash and slag. A quantitative experimental method was employed, involving the synthesis of multiple mix designs with differing binder ratios, followed by compressive strength testing, microstructural analysis, and lifecycle assessment (LCA). The results indicate that the optimal blend of 60% fly ash and 40% slag achieved a 42% reduction in carbon emissions compared to conventional concrete while maintaining a compressive strength exceeding 45 MPa after 28 days of curing. The inclusion of slag significantly enhanced early strength development and chemical stability due to calcium enrichment, while the use of fly ash contributed to long-term durability. The study concludes that fly ash–slag–based geopolymer concrete represents a promising low-carbon alternative, combining industrial waste valorization with superior structural performance. Future applications could advance sustainable material engineering practices in both civil and environmental infrastructure sectors.
INTEGRATING DEEP LEARNING AND CLIMATE MODELLING: PREDICTING REGIONAL BIODIVERSITU LOSS UNDER EXTREME WEATHER SCENARIOS Nofirman Nofirman; Chak Sothy; Ming Kiri
Scientechno: Journal of Science and Technology Vol. 4 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i1.3299

Abstract

Accelerating climate change has intensified the frequency and severity of extreme weather events, posing substantial threats to regional biodiversity. Conventional climate-biodiversity models often struggle to capture non-linear interactions between climatic variables and ecological responses, limiting their predictive accuracy under extreme scenarios. This study aims to integrate deep learning techniques with climate modelling to improve the prediction of regional biodiversity loss under extreme weather conditions. We employed a hybrid modelling framework that combines high-resolution regional climate model outputs with deep learning architectures, specifically convolutional and recurrent neural networks. The model was trained using multi-decadal climate data, species distribution records, and ecological indicators across selected regions. Extreme weather scenarios were simulated based on projected temperature anomalies, precipitation extremes, and drought indices. Model performance was evaluated using cross-validation and comparative benchmarks against traditional statistical models. The integrated deep learning–climate model demonstrated significantly higher predictive accuracy and robustness in identifying biodiversity loss hotspots under extreme weather scenarios. Results reveal pronounced spatial heterogeneity, with ecosystems exposed to compound extremes showing disproportionately higher vulnerability. Integrating deep learning with climate modelling offers a powerful approach for anticipating regional biodiversity loss under extreme climate events, providing valuable insights for adaptive conservation planning and climate-resilient biodiversity management.
Bionspired Photocatalysts for Green Hydrogrn Production: Toward Scalable Eco-Enengy Solutions Ardi Azhar Nampira; Lucas Lima; Thiago Rocha
Scientechno: Journal of Science and Technology Vol. 3 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v3i3.3402

Abstract

In the pursuit of sustainable energy solutions, the development of green hydrogen production technologies has garnered significant attention. Photocatalytic water splitting is one of the most promising methods to generate hydrogen using solar energy. This study focuses on bioinspired photocatalysts for green hydrogen production, aiming to enhance the efficiency and scalability of photocatalytic processes. The research explores the principles behind bioinspired photocatalysts, which mimic the natural processes of photosynthesis in plants, and their potential to provide eco-friendly energy solutions. The primary objective of this research is to investigate novel bioinspired photocatalysts for efficient hydrogen production under solar irradiation. A combination of experimental methods, including synthesis, characterization, and performance evaluation of photocatalysts, was used. The study employs various techniques, such as X-ray diffraction, UV-Vis absorption spectroscopy, and electrochemical tests, to assess the photocatalytic performance under simulated sunlight. The results reveal that the bioinspired photocatalysts exhibit significantly enhanced hydrogen production rates compared to traditional catalysts. Notably, the integration of natural materials such as plant-derived components improves the photocatalytic efficiency and stability. In conclusion, bioinspired photocatalysts hold great promise for large-scale green hydrogen production, offering a sustainable and cost-effective alternative to conventional energy solutions. Future research will focus on optimizing these catalysts for industrial applications.
THE INTERPLAY BETWEEN DATA MINING MATURITY AND STRATEGIC DECISION MAKING: ASSESSING THE MEDIATING ROLE OF PREDICTIVE ANALYTICS IN KNOWLEDGE INTENSIVE FIRMS Muchamad Sobri Sungkar; Zhou Hui; Sun Wei
Scientechno: Journal of Science and Technology Vol. 5 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i3.3767

Abstract

In knowledge-intensive firms, the ability to make informed and timely strategic decisions is increasingly reliant on data-driven insights. Data mining maturity, defined as the level of sophistication in an organization's data mining capabilities, plays a critical role in shaping decision-making processes. Predictive analytics, which utilizes data to forecast future trends, has been proposed as a key mediator in this relationship, enhancing the ability of firms to make accurate and efficient decisions. This study explores the interplay between data mining maturity and strategic decision-making, specifically assessing the mediating role of predictive analytics. The research uses a mixed-methods approach, combining quantitative surveys and structural equation modeling (SEM) to examine data from 200 knowledge-intensive firms. The results reveal a significant positive relationship between data mining maturity and decision-making outcomes, with predictive analytics mediating this relationship and accounting for 45% of the variance in decision quality and 38% in decision speed. The study concludes that firms with higher data mining maturity, coupled with effective use of predictive analytics, make faster and more accurate strategic decisions. These findings provide empirical evidence that integrating predictive analytics into decision-making processes significantly enhances the effectiveness of data mining systems.
CULTIVATING RESILIENCE: A MULTI-LEVEL ANALYSIS OF ORGANIZATIONAL CULTURE AND BUREAUCRATIC AGILITY IN THE FACE OF SYSTEMIC CRISES Mulyaningsih Mulyaningsih
Scientechno: Journal of Science and Technology Vol. 5 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i1.3806

Abstract

Organizational resilience has become an essential factor for navigating systemic crises, yet the interplay between organizational culture and bureaucratic agility remains underexplored. This study investigates how these two elements collectively contribute to an organization’s ability to respond to and recover from crises. The main objective of the research is to examine the relationship between organizational culture, bureaucratic agility, and resilience, particularly during systemic crises. A multi-level analysis is conducted through a comparative study across public, private, and non-profit sectors, utilizing both qualitative (interviews and focus groups) and quantitative (surveys) methods. The results show that organizations with a strong, adaptable culture and flexible bureaucratic systems exhibit higher levels of resilience, enabling quicker adaptation and more effective crisis management. The private sector, with its emphasis on innovation and agility, demonstrated superior resilience compared to the more rigid structures found in the public and non-profit sectors. This study concludes that both organizational culture and bureaucratic agility are crucial in cultivating resilience, with their combined influence offering a more effective approach to crisis preparedness and response. The findings underscore the need for organizations to integrate cultural and structural flexibility to enhance resilience in the face of uncertainty.
THE ETHICS OF TRANSPARENCY: EVALUATING THE IMPACT OF OPEN GOVERNMENT DATA ON PUBLIC TRUST AND BUREAUCRATIC ACCOUNTABILITY IN EMERGING ECONOMIES Mulyaningsih Mulyaningsih; Nadiah Ismail; Ahmad Zainal
Scientechno: Journal of Science and Technology Vol. 5 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i2.3807

Abstract

Open Government Data (OGD) has gained significant attention as a tool for promoting transparency, enhancing public trust, and improving bureaucratic accountability. However, the impact of OGD on these factors, particularly in emerging economies, remains underexplored. This research investigates the ethical implications and effectiveness of OGD in fostering trust and accountability within the public sector in emerging economies. The primary objective is to evaluate how OGD influences public trust in government and the accountability of bureaucratic systems. A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews across five emerging economies. The findings suggest that while OGD can improve public trust and bureaucratic accountability, its effectiveness is highly contingent on the strength of the institutional context. Countries with stronger democratic frameworks and institutional capacity experienced greater improvements in trust and accountability, while weaker institutions limited OGD’s impact. The study concludes that OGD alone does not automatically lead to increased transparency; instead, institutional readiness and capacity play critical roles in determining its success. This research contributes to the literature by offering a nuanced understanding of OGD’s role in enhancing governance in emerging economies, highlighting both its potential and its limitations.
DEVELOPMENT OF EDUCATIONAL COMICS BASED ON SCIENTIFIC APPROACH TO IMPROVE UNDERSTANDING OF THE CONCEPT OF THE DIGESTIVE SYSTEM IN GRADE VIII STUDENTS OF MTs DARUL HIKAM Intan Miladia Solihah; Sugeng Kurniawan; Yurnalisma Dewi
Scientechno: Journal of Science and Technology Vol. 5 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i3.3836

Abstract

This study aims to develop educational comics based on a scientific approach on digestive system materials, assess their feasibility, test their effectiveness in improving understanding of concepts, and describe the response of grade VIII MTs Darul Hikam students to their use in science learning. The method used is research and development with the ADDIE model which includes the analysis, design, development, implementation, and evaluation stages, with test subjects as many as 25 grade VIII students as well as instruments in the form of expert validation sheets, pre-test and post-test tests, and student response questionnaires. The results of the study showed that the developed media obtained a very feasible category based on the validation of 100% and 96% media experts, 88% of material experts, and 95% and 95% of teacher practicality, while its effectiveness was shown by an increase in the average score from 43.6 in the pre-test to 89.6 in the post-test with an N-gain of 0.80 in the high category. The novelty of this research lies in the systematic integration of scientific approaches into the narrative flow of educational comics that are adapted to the context of MTs students and digestive system materials. The implications of this study show that educational comics can be an alternative science learning media that is contextual, interesting, and supports active learning to strengthen students' understanding of concepts and help teachers implement student-centered learning according to the demands of the Independent Curriculum in the tsanawiyah madrasah environment more effectively and relevant to learning needs.

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