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Formosa Journal of Science and Technology (FJST)
Published by PT FORMOSA CENDEKIA GLOBAL
ISSN : -     EISSN : 29646804     DOI : https://doi.org/10.55927/fjst.v1i2
Core Subject : Humanities, Science, Education, Social, Engineering,
Humanities Computer Science & IT Education Industrial & Manufacturing Engineering Social Sciences
Formosa Journal of Science and Technology (FJST) is an open-access scientific journal that publishing full-length research papers and review articles covering subjects that fall under the wide spectrum of science and technology. FJST journal is dedicated towards dissemination of knowledge related to the advancement in science and technology. The prestigious interdisciplinary editorial board reflects the diversity of subjects covered in this journal. Under the realm of science and technology, the coverage includes environmental science, pure and applied mathematics, agricultural research and engineering, biology, biotechnology, bioinformatics, management science, business and economics, Healthcare sciences (including clinical medicine, preventive medicine & public health), physics, biophysics, computer science, chemistry and bioengineering, to name a few. Formosa Journal of Science and Technology is published by Formosa Publisher. The journal now brings out monthly publications. It supports the open access policy for making scientific research accessible to one and all.
Arjuna Subject : Umum - Umum
Articles 583 Documents
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AIPowered Trust and Security: Enhancing ECommerce with Blockchain and Machine Learning Ahmed, Nisher; Hossain, Md Emran; Hossain, Zakir; Kabir, Md Farhad; Hossain, Iffat Sania
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13680

Abstract

The ecommerce wave we saw over the years, not just added more opportunities, but added more challenges, especially in the area of trust and security. Fraud, data theft and a lack of transparency remain causes for concern for both businesses and consumers. This paper explores new possibilities of using blockchain and machine learning in designing a robust Artificial Intelligence (AI)based secure ecommerce ecosystem. The immutability of data, transparency, and decentralized control of blockchain act against counterfeit products, payment fraud, and integrity of supply chains. In parallel, machine learning algorithms provides realtime threat detection, predictive analytics, and personalized security measures to detect and counteract threats preemptively. The solution that is proposed leverages the benefits of these technologies to enhance trust among all parties involved, improve operational efficiency, and offer a more secure and trustworthy ecommerce environment. We address the underlying tech stack, realworld application, and next steps in leveraging the convergence of blockchain and ML technologies to transform ecommerce security for a clean and secure digital market.
Comparison of Students' Chemistry Learning Outcomes through Verification of Concept Maps and Mind Maps in Discovery Model Learning Auliah, Army; Islawati, Islawati
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13711

Abstract

Chemistry learning requires an understanding of complex abstract concepts. The Discovery Learning model has been applied to improve student understanding, but the verification stage in this model is often an obstacle in connecting the concepts found. This study compares the verification of concept maps and mind maps in improving student learning outcomes. The research method used is comparative descriptive, with two experimental groups each using concept maps and mind maps. The research instruments include learning outcome tests, assessment rubrics, and observations. The results showed that concept maps support systematic understanding more, while mind maps are more effective in developing flexibility of thinking. The verification process plays a role in correcting student misconceptions. These findings provide insight for educators to adjust learning methods to improve understanding of chemical concepts.
Analysis of Learning Outcome Indicator Completion through Verification of Concept Maps and Mind Maps in the Discovery Learning Model Yunus, Muhammad; Islawati, Islawati; Auliah, Army
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13712

Abstract

A less systematic conceptual understanding can hinder the completion of student learning outcomes. This study aims to compare the verification of concept maps and mind maps in improving student understanding in the Discovery Learning model. The method used is descriptive comparative, with two experimental groups each using concept maps and mind maps. The research process includes pretest, intervention with verification, posttest, and student reflection. Data collection was carried out through learning outcome tests, assessment rubrics, and observations. The results of the study showed that concept maps support more systematic understanding, while mind maps are more effective in developing flexibility of thinking. The verification process plays a role in correcting students' conceptual errors. These findings provide insight for educators to adjust learning methods to improve learning outcomes.

Page 59 of 59 | Total Record : 583

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