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p-Index From 2021 - 2026
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All Journal Engineering Science Letter Bincang Sains dan Teknologi Journal of Educational Technology Innovation and Applications
Franklin Ore Areche
National University of Huancavelica
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Transportation Other

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Service Desk in Perspective of Information Technology Infrastructure Library 3rd-Version Dimas Mahardika; Tawar; Saima Ahmed Rahin; Franklin Ore Areche; Ari Fajar Santoso; Ahmad Suryan
Engineering Science Letter Vol. 1 No. 02 (2022): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (736.746 KB) | DOI: 10.56741/esl.v1i02.142

Abstract

ICT services are an essential element in modern organizations. Understanding the workings of service systems with specific frameworks helps manage services well. One of the services in this management is the Service Desk or Helpdesk. This study aims to explain the Service Desk using the ITIL V.3 framework. The method used is descriptive qualitative using relevant references. The results of this study indicate that ITIL V.3 can be used to describe the management of the Service Desk accurately.
Scoping Review of AI-Enabled Predictive Analytics and Decision Support in Agricultural Education: Current Trends, Tools, and Pedagogical Implications Daniela Laura; Nancy V. Quispe Cordova; Franklin Ore Areche
Journal of Educational Technology Innovation and Applications Vol. 2 No. 01 (2026): Journal of Educational Technology Innovation and Applications
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.jetia.001994

Abstract

Artificial Intelligence (AI) and predictive analytics are increasingly reshaping contemporary agricultural systems by enabling data-driven decision support, early pest and disease detection, and optimized crop management. As these technologies become embedded in agricultural practice, agricultural education faces growing pressure to prepare learners with analytical reasoning and decision-making competencies aligned with AI-enabled environments. This scoping review maps peer-reviewed literature published between 2020 and 2025 on AI-enabled predictive analytics, crop advisory systems, and pest and disease detection technologies, with a specific focus on their educational implications. Following PRISMA-ScR guidelines, 18 studies were systematically identified and synthesized through thematic analysis. The results indicate rapid growth in technically oriented AI applications for precision agriculture, contrasted with limited empirical research on curriculum integration and competency-based learning outcomes. While project-based learning, simulations, and decision support tools are frequently proposed as pedagogical strategies, explicit assessment of learners’ analytical and decision-making competencies remains scarce. This review highlights critical gaps between AI innovation and educational research, and underscores the need for interdisciplinary approaches, curriculum redesign, and competency frameworks that support responsible and effective AI use in agricultural education.
From Data Assets to Value Creation: Competitive Advantage in the AI Age Franklin Ore Areche; Silvia Hypatia Sarabia Cansaya; Daniela Laura
Bincang Sains dan Teknologi Vol. 4 No. 03 (2025): Bincang Sains dan Teknologi
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/bst.v4i03.1890

Abstract

The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed how organizations create and sustain competitive advantage. In contemporary business environments, data is no longer treated merely as a supporting asset but has emerged as a primary source of value creation. This article examines how data-driven approaches, enabled by AI technologies, generate competitive advantage through enhanced decision-making, personalization, and operational efficiency. Using a conceptual research method, this study synthesizes findings from international peer-reviewed journals to analyze the mechanisms through which data becomes economic value. The Results and Discussion section elaborates on three core dimensions: the transformation of data into value generators, the economic characteristics of data in AI-driven markets, and the strategic logic of value creation through data–algorithm–context alignment. The findings indicate that data-driven competitive advantage is contingent not only on data volume but also on data quality, governance, and organizational capability. This article contributes to the growing body of literature on AI-enabled strategy by offering an integrated framework for understanding data as a strategic source of competitive advantage.
Co-Authors Ahmad Suryan Ari Fajar Santoso Daniela Laura Dimas Mahardika Nancy V. Quispe Cordova Saima Ahmed Rahin Silvia Hypatia Sarabia Cansaya Tawar