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  • The Impact Of The Russia-Ukraine War On The European Union Economy

    The Impact Of The Russia-Ukraine War On The European Union Economy

    Author: Andrias Darmayadi ; Nikolay Megits

    DOI: 10.15549/jeecar.v10i1.1079

    Abstract

    This study aims to see the impact of the Russo-Ukrainian War on the European Union economy. It uses a qualitative descriptive method. This method answers research questions requiring an explanation and understanding of social activities due to the war. In this descriptive research, we evaluate aimed to evaluate the impact of the Russo-Ukrainian War on the European Union Economy. The results of this study indicate that the European Union’s economy was greatly affected by this war, starting from the disruption of trade relations between Russia and the European Union in all fields, especially in the energy sector, to the creation of high inflation rates in developing countries. With this, of course, the economy and stability in the European Union will be interrupted and causing an economic crisis in many European countries. © 2023, Institute of Eastern Europe and Central Asia. All rights reserved.

    Author keywords

    economy; European Union; Russia; Ukraine; war

    This article can be accessed at: https://www.scopus.com/pages/publications/85146896294

  • Enhancing Startup Business Performance Through Iterative Strategies and Lean Programs: Insights from Capital Cities in Indonesia to Unlock Central Asia’s Potential

    Author: Muhammad Fahreza Aulia ;Dini Turipanam Alamanda ; Ubaidillah Zuhdi ; Grisna Anggadwita ; Dinar Mariam Kurniati ; Eddy Soeryanto SOegoto

    DOI: 10.14453/aabfj.v18i4.13

    Abstract

    This study investigates the influence of the startup ecosystem on startup business performance in Capital Cities (Jakarta, Bogor, Depok, Tangerang, and Bekasi), emphasizing the roles of iterative incremental strategies and lean startup programs. This study also examines the similarities between Central Asian countries like Kazakhstan, Uzbekistan, and the Indonesia region. Central Asian countries face unique challenges, including less mature ecosystems, regulatory hurdles, and cultural attitudes toward entrepreneurship. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) on data from startups aged 1-10 years, the research reveals that these strategies significantly mediate the relationship between ecosystem support and startup success, enhancing market optimization, innovation, and sustainability. Startups achieve better financial and nonfinancial outcomes by effectively leveraging ecosystem structures through agile methodologies. By drawing on capital city experiences, Central Asian policymakers can improve startup environments through robust support structures, regulatory clarity, and education on agile methodologies. This research offers a model for enhancing startup business performance via ecosystem support and strategic agility, informing policy and program development in other regions. © 2024, University of Wollongong. All rights reserved.

    Author keywords

    Iterative Strategies; Lean Startup Programs; Startup Business Performance; Startup Ecosystem

  • Comparative Study of K-Means and Mean Shift Clustering Algorithms for Waste Data in West Java Province

    Comparative Study of K-Means and Mean Shift Clustering Algorithms for Waste Data in West Java Province

    Author: Rony Setyawan ; Geraldi Catur Pamuji

    Abstract

    This study presents a comprehensive comparative analysis of the k-Means and Mean Shift clustering algorithms, utilizing waste data collected from West Java Province’s final disposal site spanning 2016 to 2021, with the primary objective of evaluating their performance and applicability for waste management practices; the analysis encompasses several critical parameters, including the number of clusters generated, variable uniformity, evaluation metrics employed, divergence measures, and processing time efficiency, revealing that k-Means, which formed three clusters, excels in rapid processing and provides finer cluster division, while Mean Shift, yielding two clusters, offers nuanced insights into data patterns, leading to the recommendation that the choice between the two algorithms should be driven by specific project requirements and considerations such as urgency of waste management needs and the depth of understanding desired for effective decision-making, thereby offering a tailored approach to waste data organization that optimizes categorization and contributes to more efficient and sustainable waste disposal practices in the future. © School of Engineering, Taylor’s University.

    Author keywords

    k-means clustering; Mean shift clustering; Waste data

  • The Role of Environmental Policy in Promoting Green Transportation Technologies

    The Role of Environmental Policy in Promoting Green Transportation Technologies

    Authors: Suthakavatin, Siphimvadee ; Shatila, Khodor ; Aydinyan, Karine ; Santy, Raeni Dwi

    DOI: 10.1051/bioconf/202414506013

    Abstract:

    This study explores the role of environmental policy in promoting the adoption of green transportation technologies, focusing on the effects of financial incentives, regulatory frameworks, and public investment in infrastructure across key Asian urban centres. The research highlights how technological innovation acts as a crucial mediator between policy measures and the adoption of sustainable transportation options such as electric vehicles (EVs) and hydrogen-powered vehicles. Using Structural Equation Modelling (SEM), the study examines the relationships between these factors, demonstrating that higher levels of financial incentives, supportive regulations, and strategic public investment lead to significant advancements in technological innovation, which in turn drives the adoption of green transportation technologies. The findings contribute to the broader understanding of sustainable urban mobility solutions and offer important insights for policymakers and industry stakeholders in regions experiencing rapid urbanization. The study also discusses the dual role of these policy drivers, both in directly influencing adoption and in fostering technological advancement. © The Authors, published by EDP Sciences.

  • Geospatial Intelligence Framework for BTS Infrastructure Planning Toward Universal Internet Access Target in Indonesia

    Geospatial Intelligence Framework for BTS Infrastructure Planning Toward Universal Internet Access Target in Indonesia

    Authors: Sakti, Anjar Dimara ; Ayu Andani, I Gusti ; Putri, Anissa Dicky ; Zakiar, Muhammad Rizky ; Faruqi, Ismail Al; Santoso, Cokro ; Caraka, Rezzy Eko ; Rohayani, Pitri ; Pramudya, Fabian Surya ; Wijayanto, Arie Wahyu ; Setiyadi, Angga ; Shalannanda, Wervyan

    DOI: 10.1016/j.jag.2024.104274

    Abstract:

    Equitable internet coverage has emerged as a key global priority, which is essential for promoting inclusive and sustainable development. The Indonesian government aims to provide universal internet access by 2024, particularly in remote regions. This study introduces a novel machine-learning-based approach to identify the priority areas for deploying Base Transceiver Station (BTS) towers, which are crucial for achieving the internet access targets of the government. A BTS Network Priority Index was developed by integrating the internet demand estimates with a BTS suitability index derived from key predictors: proximity to fiber optic stations, physical–environmental suitability, and infrastructure–economic readiness. The model identified areas with high internet demand and high BTS suitability as the most critical for immediate development, covering 20 km2. Additionally, future BTS development should target areas with high demand but medium suitability (900 km2) and medium demand but high suitability (280 km2). To validate the methodology, the Random Forest model was employed, which achieved an area under the curve value of 0.7315, indicating strong predictive performance. For the BTS Deployment Suitability parameter, the median was 0.65, with the lower and upper quartiles at 0.44 and 0.85, respectively, confirming that most proposed locations are highly suitable for development. This systematic approach provides data-driven insights for the equitable distribution of BTS towers to ensure efficient internet infrastructure expansion across Indonesia. Furthermore, the study offers a framework that can be adapted by other countries aiming to improve their digital infrastructure and achieve comprehensive, equitable internet access. © 2024 The Author(s)

    Author Keywords:

    Internet accessibility; Machine learning; Remote sensing; Spatial assessment

    Indexed Keywords:

    Regional Index

    Indonesia

    GEOBASE Subject Index

    infrastructure planning; Internet; machine learning; remote sensing; sustainable development

  • Advancing Language Education in Indonesia:

    Integrating Technology and Innovations

    Authors: Luckyardi, Senny ; Munawaroh, Silvi ; Abduh, Amirullah; Rosmaladewi R. ; Hufad, Achmad ; Haristian, Nuria

    DOI: 10.17509/ajse.v4i3.79471

    Abstract:

    Language learning in Indonesia’s educational environment is accelerating change due to the introduction of novel technologies and teaching methods. This paper analyzed the essential functions of digital tools, the internet, and contemporary educational approaches to enhancing Indonesian language learning results. When considering the challenges of low penetration in remote areas and fluctuating technological maturity, opportunities exist to explore the potential of technology to bridge the language acquisition gap. Drawing on a review of several projects (i.e., mobile applications, e-learning platforms, and artificial intelligence (AI)-driven language support), the current study outlines their success in fostering multilingualism, enhancing engagement rates, and developing mother tongue and second language skills. The outcome suggests that education through language for Indonesian learners will be successful if there is a balance between traditional teaching advantages, such as face-to-face teaching, and modern technology’s power and freedom. Policies, pedagogical, and institutional ideas are proposed to build a sustainable, inclusive, and dynamic model for a language education framework based on the sake of its citizenry. © 2024 Universitas Pendidikan Indonesia.

    Author Keywords:

    Education; Indonesia; Language; Technology

  • A Knowledge Capabilities Financial Model Based on the Performance

    of Fashion Enterprises: Micro, Small, and Medium

    Authors: Novianti, Windi ; Narimawati, Umi ; Ahiase, Godwin ; Pramuditha, Panji

    DOI: 10.14453/AABFJ.V19I2.05

    Abstract:

    This study investigates the relationship between financial knowledge capability and MSME performance in West Java, Indonesia, and explores the mediating role of digital marketing and intelligent financial technology. Structural equation modeling was utilized to analyze 375 MSMEs, revealing a significant impact of Financial Knowledge Capability on MSME’s performance. Moreover, the study finds that digital marketing and intelligent financial technology mediate this relationship positively, with digital marketing awareness of utilizing intelligent financial technology identified as a key determinant of MSME performance. This research shows that with efforts to improve the role of digital marketing effectively, MSMEs can increase their competitiveness in the market and grow their business significantly. Intelligent Financial Technology allows MSMEs to access financial services such as loans, payments, and investments more easily and quickly. It concludes that specific strategies can be implemented to maximize these contributions. © 2025, University of Wollongong. All rights reserved.

    Author Keywords:

    Digital Marketing; Intelligent Financial Technology; Knowledge Capabilities Financial; MSMEs Performance

  • Governing AI-Driven Agriculture: Policy, Ethics, and

    the Role of Language in Knowledge Transfer

    Authors: Zangana, Hewa Majeed ; Amelia, Pratiwi ; Luckyardi, Senny ; Mustafa, Firas Mahmood ; Li, Shuai 

    DOI: 10.4018/979-8-3373-4862-9.ch011

    Abstract:

    Artificial intelligence (AI) is transforming agriculture by enabling precision farming, predictive analytics, and automated decision-making, thereby enhancing productivity and sustainability. However, this rapid technological advancement raises complex policy and ethical challenges, particularly regarding data governance, equitable access, environmental impact, and transparency. This chapter explores the regulatory frameworks shaping AI-driven agricultural practices and emphasizes the critical role of language in facilitating effective knowledge transfer among diverse stakeholders, including farmers, policymakers, and researchers. By analyzing interdisciplinary approaches, the chapter highlights how linguistic clarity and ethical considerations underpin responsible AI governance in agriculture, ensuring innovations serve public interest while mitigating risks. Ultimately, it advocates for comprehensive policies that balance innovation with accountability and inclusivity in the evolving landscape of agri-tech. © 2025, IGI Global Scientific Publishing.

    Indexed Keywords:

    Engineering controlled terms

    Agribusiness; Agriculture; Artificial intelligence; Decision making; Environmental impact; Ethical technology; Knowledge management; Knowledge transfer; Smart agriculture; Sustainable agriculture; Sustainable development

    Engineering uncontrolled terms

    Agricultural practices; Automated decision making; Data governances; Environmental transparency; Equitable access; Knowledge transfer; Policy makers; Precision-farming; Regulatory frameworks; Technological advancement

    Engineering main heading

  • Contributing Factors to Greenhouse Gas Emissions in Agriculture for Supporting Sustainable Development Goals (SDGs): Insights from a Systematic Literature Review Completed by Computational Bibliometric Analysis

    Contributing Factors to Greenhouse Gas Emissions in Agriculture for Supporting Sustainable Development Goals (SDGs): Insights from a Systematic Literature Review Completed by Computational Bibliometric Analysis

    Authors: Soegoto, Herman S. ; Pohan, Muhammad Aria Rajasa ; Luckyardi, Senny ; Supatmi, Sri ; Amasawa, Eri ; Phithakkitnukoon, Santi ; Hasibuan, Zainal Arifina

    DOI: 10.17509/ajse.v5i2.83667

    Abstract:

    Agricultural production contributes significantly to greenhouse gas (GHG) emissions and global climate change. This study conducts a systematic literature review to examine the evolution and drivers of agricultural GHG emissions. We analyzed Web of Science, Scopus, and Google Scholar data using bibliometric and thematic methods. Our analysis identified emission sources such as energy use, soil management, fertilizer application, and livestock management. It also discussed mitigation measures such as sustainable practices, precision agriculture, and renewable energy. The findings showed that crop cultivation, livestock activities, and land-use change remained key sources of emissions. Technological innovations and policy-driven strategies are reshaping the research landscape. This study provides a framework for understanding agricultural GHG emissions and supporting interventions to reduce the sector’s carbon footprint as well as Sustainable Development Goals (SDGs). © 2025 Kantor Jurnal dan Publikasi UPI.

    Author Keywords:

    Agricultural production; Emission sources; Greenhouse gas emissions; Mitigation strategies; Sustainable development goals (SDGs); Sustainable practices; Systematic literature review