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Adam Mudinillah
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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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UTILIZATION OF THE MICROBIOME TO INCREASE FOOD SECURITY THROUGHT SUSTAINABLE BIOTECHNOLOGY Muhammad Hazmi; Seo Jiwon; Ruby Kingh
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.v4i3.2116

Abstract

Food security remains a critical global challenge, requiring innovative and sustainable solutions to meet the growing demand for nutritious food. One promising approach is the utilization of microbiomes in sustainable biotechnology to enhance agricultural productivity, improve soil health, and increase food production efficiency. This study aims to explore the potential of microbiome-based biotechnological applications in strengthening food security through sustainable agricultural practices. A qualitative research methodology was employed, involving an extensive literature review and analysis of case studies related to microbiome utilization in agriculture. The findings indicate that microbiomes play a significant role in improving crop resilience, enhancing nutrient absorption, and reducing the need for chemical fertilizers and pesticides. Furthermore, microbiome-based biotechnology contributes to environmental sustainability by promoting soil biodiversity and reducing greenhouse gas emissions. The study concludes that integrating microbiome technology into agricultural systems can significantly enhance food security while ensuring ecological balance. Future research should focus on optimizing microbiome applications and developing scalable implementation strategies for various agricultural settings.
PATTERN RECOGNITION SYSTEM FOR AUTOMATING MEDICAL DIAGNOSIS BASED ON IMAGE DATA Evi Irianti; Nina Anis; Saifiullah Aziz
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.v4i3.2126

Abstract

The increasing volume and complexity of medical image data have presented significant challenges for healthcare professionals in delivering timely and accurate diagnoses. Traditional diagnostic processes are often time-consuming and prone to human error, underscoring the need for automated solutions. This study aims to develop a pattern recognition system to automate medical diagnosis using image data, thereby improving diagnostic accuracy and efficiency. A hybrid methodology was employed, combining image preprocessing, feature extraction using convolutional neural networks (CNNs), and classification through deep learning algorithms. The system was trained and validated using publicly available medical image datasets across various disease types. The results demonstrate high diagnostic accuracy, with the system achieving over 92% precision in identifying disease patterns from image inputs. Furthermore, the model exhibited robustness across different imaging modalities, such as X-rays, MRIs, and CT scans. These findings suggest that the proposed pattern recognition system can serve as a reliable support tool for medical practitioners. In conclusion, the integration of image-based pattern recognition in medical diagnostics holds significant promise in enhancing clinical decision-making processes and reducing diagnostic errors.
ANALYSIS OF THE EFFECT OF ADDING GLASS POWDER WASTE AS A CEMENT SUBSTITUTION AND THE USE OF PUMICE AGGREGATE ON THE COMPRESSIVE STRENGTH OF LIGHT CONCRETE Galing Wira Buana; Nuni Khoirinnisa Hudaya Taher; Tira Roesdiana
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.v4i2.2336

Abstract

Along with the increasing need for building materials and high cement prices, innovation in the use of alternative materials that are environmentally friendly and economical is needed. Glass powder waste, which is difficult to decompose and has the potential to pollute the environment, has the potential as a cement substitute because of its supportive physical and chemical properties. Additionally, pumice, as a lightweight aggregate, can reduce the dead load of the structure and enhance the efficiency of construction execution. This study aims to analyze the effect of the addition of glass powder waste as a cement substitution and the use of pumice aggregate on the compressive strength of light concrete. Glass waste, which comes from industrial and household waste, is used as a cement substitute with variations of 0%, 5%, 10%, and 15%. In contrast, pumice is used as a partial substitute for coarse aggregate. The method used is experimental, with laboratory testing including compressive strength tests at 14 and 28 days of age. The results showed that the addition of glass powder at a percentage of 5% gave the highest compressive strength values of 10.98 MPa (14 days) and 12.40 MPa (28 days), compared to concrete without glass powder, which only reached 8.14 MPa (14 days) and 11.11 MPa (28 days). This suggests that the combination of glass powder and pumice stone can significantly increase the compressive strength of light concrete, although the efficiency of the mixture decreases at higher percentages. This research provides an alternative to the use of local waste and aggregates in the development of environmentally friendly and economically efficient concrete.
AN AI-BASED ADAPTIVE LEARNING PLATFORM FOR PERSONALIZED STEM EDUCATION IN INDONESIAN HIGH SCHOOLS Nong Chai; Shahinur Rahman; Tasnia Islam; Ruhul Amin
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.v5i1.2639

Abstract

In recent years, the demand for personalized education has increased, particularly in Science, Technology, Engineering, and Mathematics (STEM) fields, where students’ learning needs vary significantly. In Indonesia, traditional classroom-based teaching methods struggle to accommodate the diverse learning styles and paces of students. With the rise of Artificial Intelligence (AI), there is a growing opportunity to design adaptive learning platforms that provide personalized learning experiences. However, such platforms are still underutilized in Indonesian high schools, especially in STEM education. This study aims to develop and evaluate an AI-based adaptive learning platform tailored to personalized STEM education for high school students in Indonesia. The platform’s primary goal is to enhance student engagement and academic performance by adapting learning materials and strategies based on individual student progress and preferences. The research utilized a design-based methodology, developing the platform using AI algorithms to monitor and adjust content delivery according to the student’s learning pace, strengths, and weaknesses. A quasi-experimental design was employed, with pre- and post-assessments conducted to evaluate the effectiveness of the platform in a sample of 200 high school students across four Indonesian schools. The platform significantly improved student engagement, with a 15% increase in STEM learning outcomes. Students demonstrated higher retention rates and improved problem-solving abilities, especially in mathematics and science. The AI-based adaptive learning platform proves to be a promising tool for personalized STEM education, enhancing both student learning experiences and academic performance in Indonesian high schools.
TOWARDS INDUSTRY 5.0: A HUMAN-CENTRIC CYBER-PHYSICAL PRODUCTION SYSTEM FOR INDONESIA’S BATIK SMES Mochammad Isa Anshori; Chai Pao; Siri Lek; Andy Rachman
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.v4i2.2640

Abstract

Industry 4.0 has revolutionized manufacturing through automation and data exchange, but the transition towards Industry 5.0 emphasizes human-centric approaches, integrating human expertise with advanced technologies like cyber-physical systems (CPS). In Indonesia, Small and Medium Enterprises (SMEs), particularly in traditional sectors such as batik production, face challenges in adapting to these technological advancements. The batik industry, while rich in cultural heritage, has yet to fully embrace automation or digitalization, resulting in inefficiencies and limited scalability. This study aims to explore the potential of Industry 5.0 by developing a Human-Centric Cyber-Physical Production System (HCPPS) tailored to Indonesia’s batik SMEs. The goal is to enhance production efficiency while preserving traditional craftsmanship through the integration of smart technologies. The research employed a mixed-methods approach, combining qualitative interviews with batik producers and quantitative analysis using data from pilot implementations of a CPS model. A prototype of a human-centric cyber-physical system was developed, integrating Internet of Things (IoT) devices, augmented reality (AR), and robotics to assist batik artisans. The implementation of the HCPPS prototype resulted in a 25% increase in production efficiency, while artisans reported higher job satisfaction due to enhanced skill integration with technology. The system enabled greater customization, faster production cycles, and reduced errors. The study demonstrates that Industry 5.0’s human-centric approach can significantly improve productivity in traditional sectors like batik, providing a path for Indonesian SMEs to modernize while maintaining their cultural identity.  
USING MACHINE LEARNING TO PREDICT DENGUE FEVER OUTBREAKS IN INDONESIAN URBAN CENTERS BASED ON CLIMATE AND MOBILITY DATA Som Chai; Shahram Rahimov; Dilshod Tursunuv; Gulbahor Alimova
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.v5i1.2641

Abstract

Dengue fever remains a critical public-health threat in Indonesia’s densely populated urban centers, where climatic fluctuations and human mobility accelerate transmission dynamics. This study aims to develop a predictive model for dengue outbreaks using machine-learning techniques that integrate multi-source climate indicators (temperature, rainfall, humidity) and population-mobility data. A quantitative research design employing supervised learning algorithms including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks—was applied to historical datasets from 2015–2023 across six major Indonesian cities. Model performance was evaluated using accuracy, precision, recall, and AUC metrics. Results indicate that the LSTM model achieved the highest predictive accuracy (92.3%) and superior temporal sensitivity to climatic shifts and mobility surges compared with traditional regression models. These findings demonstrate that machine-learning-based early-warning systems can identify outbreak hotspots up to four weeks in advance, providing actionable insights for urban health authorities. The study concludes that integrating climate and mobility analytics enhances the effectiveness of public-health surveillance and supports proactive dengue-control interventions in rapidly urbanizing environments.
MODELING THE IMPACT OF SEA-LEVEL RISE ON COASTAL VULNERABILITY IN JAKARTA USING AN INTEGRATED DATA SCIENCE FRAMEWORK Nofirman Nofirman; Dulguun Amarsaikhan; Munkhzul Ganbat; Tugsuu Jargalsaikhan
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.v4i3.2669

Abstract

Jakarta, the capital city of Indonesia, is highly vulnerable to the impacts of sea-level rise due to its coastal location, rapid urbanization, and subsidence, making it crucial to understand how climate change-driven increases in sea levels affect the city’s coastal areas for effective adaptation planning. This study aims to model the impact of sea-level rise on the vulnerability of Jakarta’s coastal zones by using an integrated data science framework to assess potential risks such as flooding, land loss, and other environmental consequences under various sea-level rise scenarios. Employing a combination of geographic information systems (GIS), remote sensing data, and machine learning models, the analysis integrates sea-level rise projections with land elevation, population density, and infrastructure data to evaluate potential impacts, while algorithms such as Random Forest and Support Vector Machine (SVM) are utilized to predict vulnerability levels. The results indicate that Jakarta’s coastal areas face high vulnerability, with substantial portions of land projected to be inundated under higher sea-level scenarios, particularly in low-lying and densely populated regions at heightened risk of flooding and infrastructure damage. Overall, this research offers valuable insights into future coastal vulnerability in Jakarta and demonstrates how an integrated data science approach can support urban planning and climate adaptation strategies aimed at reducing the risks associated with rising sea levels.
AI-DRIVEN SIMULATION OF DENGUE FEVER OUTBREAKS IN URBAN JAVA BASED ON CLIMATE VARIABILITY AND HUMAN MOBILITY DATA Vann Sok; Sokha Dara; Thabo Mokoena; Rustiyana Rustiyana
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.v4i1.2860

Abstract

Dengue fever outbreaks in urban areas of Java, Indonesia, have become a significant public health concern, with increasing frequency due to climate variability and human mobility patterns, where the spread of dengue is influenced by environmental conditions such as temperature and rainfall as well as human movement within urban centers, making an understanding of these factors crucial for effective disease control and prevention. This study aims to simulate and predict dengue fever outbreaks in urban Java using AI-driven models based on climate variability and human mobility data, with the research seeking to identify the key factors that contribute to the transmission dynamics of dengue fever in urban settings and to evaluate the effectiveness of predictive models in managing potential outbreaks. The study employs machine learning techniques, including Random Forest and Artificial Neural Networks, to analyze climate data consisting of temperature, rainfall, and humidity alongside human mobility data collected from mobile phone tracking and demographic information, where the data is processed to create a simulation model of dengue fever transmission that is validated against historical outbreak data. The results show that the AI-driven model successfully simulated dengue fever outbreaks, demonstrating a high correlation between climate conditions, human mobility, and the spread of the disease, and indicating that increased mobility during the rainy season significantly amplified the risk of outbreaks in high-density urban areas. Overall, the findings conclude that AI-driven simulations offer a promising approach to understanding and predicting dengue fever outbreaks in urban Java, and by incorporating climate and mobility data, the model provides valuable insights for early warning systems and targeted public health interventions.
DEEP LEARNING APPROACHES FOR PREDICTING DEFORESTATION PATTERNS AND BIODIVERSITY HOTSPOT LOSS IN SUMATRA Rithy Vann; Ming Kiri; Aaraf Sharma; Rustiyana Rustiyana
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.v4i1.2861

Abstract

Deforestation in Sumatra, Indonesia, represents a critical environmental challenge that has led to the degradation of biodiversity hotspots and poses serious threats to both local ecosystems and global climate stability, driven largely by rapid forest conversion into agricultural land, illegal logging, and extensive land-use changes, making accurate prediction of deforestation patterns essential for effective conservation planning. This study applies deep learning approaches to predict deforestation patterns in Sumatra while simultaneously assessing their impacts on biodiversity hotspots, with the objective of developing a model capable of identifying areas at high risk of deforestation and estimating potential biodiversity losses. The research employs deep learning algorithms, specifically Convolutional Neural Networks and Recurrent Neural Networks, to analyze satellite imagery, historical deforestation data, land-use changes, and biodiversity hotspot maps, enabling the model to capture both spatial and temporal trends in deforestation dynamics. The results demonstrate that the proposed deep learning model achieves a high prediction accuracy of 92 percent in identifying deforestation hotspots and successfully highlights key biodiversity-rich areas that are highly vulnerable to rapid forest loss, with agricultural expansion and infrastructure development emerging as the dominant drivers of deforestation in these regions. Overall, the findings confirm that deep learning provides a powerful and reliable tool for predicting deforestation patterns and assessing biodiversity hotspot degradation, offering valuable evidence-based insights for policymakers and conservation practitioners to prioritize protection efforts and design targeted interventions aimed at mitigating further environmental damage in Sumatra.
ADAPTIVE AND RESILIENT LEARNING TECHNOLOGIES IN FORMAL AND INFORMAL EDUCATION: A SYSTEMATIC LITERATURE REVIEW Riska Meisyi Putri; Sarinah Sarinah; Fatimah Al-Rashid
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.v4i3.2879

Abstract

This study reviews the use of Mobile-Assisted Language Learning (MALL) and digital storytelling in multilingual education, focusing on research trends, teaching strategies, and their impact on inclusive and effective language learning. A systematic literature review was conducted using peer-reviewed studies published between 2020 and 2025 from major academic databases. The studies were analyzed using thematic coding and comparison. The findings show that MALL and digital storytelling improve learner engagement, motivation, intercultural competence, and language proficiency in diverse linguistic and cultural contexts. These approaches also support personalized learning, collaboration, and the development of critical literacy related to social justice and educational equity. The novelty of this review is its integrated view of mobile learning and digital storytelling as connected teaching approaches in multilingual and multicultural settings. By summarizing recent research, this study shows how technology-based learning supports learner autonomy, flexible learning processes, and inclusive teaching practices. The findings offer practical guidance for educators, policymakers, and instructional designers in developing curricula and professional training suited to multilingual classrooms.

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