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Methods in Science and Technology Studies
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31234232     DOI : https://doi.org/10.64539/msts
Core Subject : Engineering,
Engineering
The Methods in Science and Technology Studies (MSTS) (e-ISSN: 3123-4232) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal publishes research that focuses on methods, models, analytical approaches, and systematic studies in science, technology, and science- and technology-based education. It aims to support the development and application of scientific and technological methods in addressing research problems and practical challenges. The journal accepts original research articles and review papers that present methodological frameworks, experimental and analytical methods, computational models, and applied studies in science, technology, and education, including interdisciplinary and applied perspectives. Scope includes: Natural and applied sciences Engineering and technology studies Computational, mathematical, and data-driven methods Machine learning, artificial intelligence, and information technology Decision-making, optimization, and forecasting methods Science and technology–based education studies Legal and regulatory studies related to science and technology The journal provides a focused platform for methodological and applied studies in science, technology, education, and related regulatory contexts.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2025): December" : 5 Documents clear
Machine Learning Approach for Heart Failure Patient Classification Using K-Nearest Neighbors Algorithm Masitha, Alya; Lonang, Syahrani; Reski, Julia Mega
Methods in Science and Technology Studies Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i2.2025.44

Abstract

Heart failure is a cardiovascular disease with a high mortality rate and tends to increase every year. Therefore, a method is needed that can help the process of classifying heart failure quickly and accurately. This study aims to design and implement a heart failure classification system using the K-Nearest Neighbor (K-NN) machine learning method. The dataset used consists of 918 patient data with eleven input variables and two output classes, namely patients diagnosed with heart failure and patients not diagnosed with heart failure. The research stages include data loading, dividing training data and test data, implementing the K-NN algorithm with various K values, and evaluating model performance using accuracy, precision, recall, and F1-score metrics. The test results show that variations in the K value have a significant effect on the performance of the classification model. The K value = 9 produces the best performance with an accuracy of 93.48%, a recall of 96.36%, and an F1-score of 94.64%, which indicates a good balance between precision and recall. Based on these results, the K-NN method with a value of K = 9 is recommended as the optimal configuration in the classification of heart failure disease in this study.
The Impact of FinTech on Financial Sustainability and Digital Transformation in Emerging Economies: A Comparative Analysis Across Regions Hassouna, Mohamed; Mohammed, Sara
Methods in Science and Technology Studies Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i2.2025.336

Abstract

Financial Technology (FinTech) has been a revolutionary force in the last ten years, especially in emerging economies that are working to expedite digital transformation and attain financial sustainability. This study examines how FinTech development affects financial sustainability, using institutional preparedness as a moderating variable and digital transformation as a mediating factor. The study uses fixed-effects panel regression analysis to investigate cross-regional dynamics using a panel dataset of 18 rising economies in Africa, the Middle East, and Southeast Asia from 2015 to 2025. FinTech development considerably improves financial sustainability, according to empirical studies (β = 0.42, p < 0.001), with digital transformation processes mediating about 38% of this benefit. This association is further strengthened by institutional preparedness, suggesting that regulatory frameworks and governance quality are crucial for maintaining FinTech-driven growth. Southeast Asia has the strongest correlation between FinTech adoption and sustainability, according to regional studies, whereas Sub-Saharan Africa's influence is still limited by policy and infrastructure constraints. The results highlight how FinTech may promote equitable and sustainable financial systems when it is backed by strong digital governance. It is recommended that policymakers advance financial literacy, improve digital infrastructure, and include ESG principles into FinTech regulations. This study advances our theoretical and practical knowledge of how FinTech may promote digital inclusion, economic resilience, and sustainable financial growth in developing nations.
Thin-Film Deposition for ZnO-Based Semiconductors: Advantages, Challenges, and Future Directions Haque, Md Najmul; Khan, Md Yakub Ali
Methods in Science and Technology Studies Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i2.2025.342

Abstract

This research dives into the field of thin film deposition, with a particular emphasis on its application in ZnO-based semiconductors. The examination tries to illustrate the array of benefits, intricate challenges, and bright prospects inherent in this field through an in-depth case study. Notably, the work emphasizes the benefits of thin film deposition techniques in ZnO-based semiconductor production, such as precise control over film thickness, improved material use, and the ability to modify electrical properties. It does, how-ever, recognize the difficulties in assuring uniformity and quality control in deposition, dealing with complex deposition processes, addressing interface effects to maximize device performance, and navigating material compatibility constraints. In terms of the future, the study sees significant potential in the development of advanced materials to augment ZnO-based semiconductor functionalities, the incorporation of nanotechnology to boost performance, and the emergence of novel monitoring strategies for real-time quality assurance during deposition. Sustainable deposition methods are also being considered considering environmental concerns. The study continues by emphasizing the revolutionary significance of ZnO-based semiconductors in many applications and emphasizing the importance of interdisciplinary collaboration to unlock the full spectrum of benefits and overcome hurdles in this dynamic field. This research provides a comprehensive look at the complex domain of thin film deposition in ZnO-based semiconductor environments, shedding light on its potential to transform technological landscapes and inspire creative solutions.
An Iterative Modeling and Validation Study of a Low-Cost Thyristor-Based Controlled Half-Wave Rectifier Emon, Asif Eakball; Tabassum, Anika
Methods in Science and Technology Studies Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i2.2025.354

Abstract

The effective teaching of power electronics is critical for developing engineers capable of addressing global energy challenges, yet a persistent gap exists between idealized theoretical models and the non-ideal behavior of physical systems. This gap undermines both technical proficiency and conceptual understanding in engineering education. To address this, our study implemented and evaluated an iterative research and development methodology focused on a fundamental power conversion circuit: the controlled half-wave rectifier. The primary objective was to quantify the simulation-reality discrepancy and to assess whether a cyclical process of modeling, simulation, physical deployment, and data-driven refinement could serve as an effective pedagogical framework. Our key findings reveal a quantifiable performance gap, with a consistent 1.67V forward voltage drop in the silicon-controlled rectifier (SCR) leading to output deviations of up to 38% from theoretical predictions at low firing angles, as rigorously analyzed using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Crucially, this technical investigation was seamlessly integrated with experiential learning. The iterative methodology resulted in a measurable 40% average improvement in student troubleshooting skills and conceptual mastery, while the entire prototype was realized for under USD 12, demonstrating a commitment to accessible and sustainable design. The implications of this work are twofold: it provides educators with a validated, replicable blueprint for a hands-on curriculum that bridges theoretical and practical knowledge, and it offers engineers a model for cost-effective prototyping that acknowledges and integrates component non-idealities from the outset. This research confirms that closing the simulation-reality gap is not merely a technical necessity but a foundational element of responsible and effective engineering education.
Regularization-Based Solutions to Overfitting in Modern Predictive Models: A Review Nwakeze, Osita Miracle
Methods in Science and Technology Studies Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i2.2025.370

Abstract

One of the most serious issues in the current predictive modelling is overfitting, especially with the increase in the complexity and size of machine learning systems. This research proposes regularisation-based methods as critical methods of enhancing generalisation and avoiding noise memorization in training data by models. The systematic literature review of the study that includes 2006-2025 research provides the synthesis of classical regularization methods, including L1/L2 penalties, Elastic Net, dropout, and early stopping, and emerging methods, including probabilistic dropout variants, Bayesian regularization, adaptive regularizers, and hybrid frameworks. The review points to the importance of the regularisation in the context of increasing the performance of generalisation, enhancing robustness to noisy or finite-sized datasets, stabilising optimization dynamics, and interpretability in high dimensional computations. It also determines the major shortcomings in the extant research such as, lack of comprehension on implicit regularisation, the cross domain comparative assessment and the requirement of adaptive and automatic strategies of regularisation. The paper ends with the research recommendations and open directions of research that are meant to enhance the theory, diagnostic tools, and towards the practitioners to effective regularisation configurations under different data regimes. Altogether, this paper gives an integrative and holistic approach to regularisation as a core building block of constructing credible, robust and general predictive models.

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