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All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Artificial Intelligence (ITALIC)
Oliver Sauntos
Pandawan Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science

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Enhancing Social Value through Orange Technology Adoption in Creative Industry Micro Enterprises Ninda Lutfiani; Hindriyanto Dwi Purnomo; Heru Riza Chakim; Syahrul Mu’Arif Wahid; Oliver Sauntos
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1353

Abstract

Social media–based digital transformation has become an essential strategy for MSMEs to expand their market reach and strengthen consumer loyalty in the digital economy era. This article presents a conceptual review and empirical synthesis of digital business transformation strategies in MSMEs that utilize social media as the core channel for marketing and customer service. By integrating the Technology Acceptance Model (TAM), Customer Engagement theory, and the Resource-Based View, this paper proposes a strategic framework consisting of (1) digital capabilities, (2) content and engagement, (3) digital after-sales services, and (4) a collaborative ecosystem (platforms and micro-influencers). The literature synthesis indicates that interactive social media activities and responsive services are consistently associated with increased customer engagement and brand loyalty among MSMEs. Practical recommendations and future research directions are provided to support MSMEs in implementing loyalty-oriented digital transformation.
Artificial Intelligence for Optimizing Renewable Energy Systems in Sustainable Power Generation Ageng Setiani Rafika; Dendy Jonas; Muchlisina Madani; Oliver Sauntos
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1098

Abstract

The rapid expansion of renewable energy adoption has increased the need for intelligent energy management, as conventional rule based dispatch systems of ten struggle with the dynamic, nonlinear, and uncertain operating conditions of high-penetration renewable grids. Traditional controllers show limited energy utilization efficiency and frequent frequency-standard violations under variable wind and solar conditions. This study proposes and evaluates an integrated Artificial Intelligence (AI) framework combining a Long Short-Term Memory (LSTM) neural network for 24-hour energy demand and generation forecasting with Particle Swarm Optimization (PSO) for real-time dispatch optimization. The framework is tested against a conventional rule-based baseline using three benchmark datasets from the UCI Machine Learning Repository, the National Renewable Energy Laboratory (NREL), and Open Power System Data, covering 36 months of hourly solar and wind observations. The objective is to design and experimentally validate an AI-based optimization framework that improves energy efficiency, reduces operational losses, and enhances grid stability in renewable energy systems. The proposed LSTM-PSO framework reduces Mean Absolute Error (MAE) by 50.7% and Root Mean Square Error (RMSE) by 44.3%. Energy efficiency increases from 76.2% to 91.4%, while energy losses decrease from 20.7% to 9.6%, equivalent to approximately 5,800 tonnes of CO2 equivalent avoided annually at a 100 MW grid scale. The integrated LSTM PSO architecture provides a reliable and scalable basis for AI-driven renewable energy optimization, supporting SDG 7, SDG 9, SDG 11, and SDG 13.
Co-Authors Dendy Jonas Heru Riza Chakim Hindriyanto Dwi Purnomo Muchlisina Madani Ninda Lutfiani Rafika, Ageng Setiani Syahrul Mu’Arif Wahid