Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Multidisciplinary Sustainability Asean Scientechno: Journal of Science and Technology
Aaraf Sharma
Indian Institute of Technology (IIT) Bombay
Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Other

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A Review of Corporate Social Responsibility (CSR) Programs for Community-Based Sustainable Development in Indonesia Edison Hatoguan Manurung; Aaraf Sharma; Rohan Kumar
Journal of Multidisciplinary Sustainability Asean Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v2i6.2933

Abstract

Background. Corporate Social Responsibility (CSR) programs play a vital role in promoting community-based sustainable development, particularly in developing countries like Indonesia. As industries expand, the need for companies to engage in socially responsible practices has become increasingly important to address environmental, social, and economic challenges. Purpose. This study aims to review CSR programs implemented by companies in Indonesia and assess their impact on community-based sustainable development. The research explores various CSR initiatives, focusing on their alignment with the Sustainable Development Goals (SDGs) and their contributions to local communities. Method. A comprehensive literature review and case study analysis were employed as the research methodology. Data was collected from academic articles, CSR reports, and case studies of companies that have implemented community-driven CSR programs in Indonesia. The study evaluates the types of CSR activities, the sectors involved, and the outcomes for community development. Results. The findings indicate that CSR programs in Indonesia have led to significant improvements in education, healthcare, and environmental sustainability in local communities. However, challenges such as limited stakeholder engagement, insufficient long-term planning, and a lack of monitoring mechanisms hinder the effectiveness of some initiatives. The study also reveals that successful CSR programs are those that actively involve local communities in decision-making and are aligned with their needs. Conclusion. In conclusion, CSR programs in Indonesia have the potential to contribute to sustainable community development, but they require stronger alignment with local priorities, better long-term strategies, and enhanced monitoring to maximize their impact.
DEEP LEARNING APPROACHES FOR PREDICTING DEFORESTATION PATTERNS AND BIODIVERSITY HOTSPOT LOSS IN SUMATRA Rithy Vann; Ming Kiri; Aaraf Sharma; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i1.2861

Abstract

Deforestation in Sumatra, Indonesia, represents a critical environmental challenge that has led to the degradation of biodiversity hotspots and poses serious threats to both local ecosystems and global climate stability, driven largely by rapid forest conversion into agricultural land, illegal logging, and extensive land-use changes, making accurate prediction of deforestation patterns essential for effective conservation planning. This study applies deep learning approaches to predict deforestation patterns in Sumatra while simultaneously assessing their impacts on biodiversity hotspots, with the objective of developing a model capable of identifying areas at high risk of deforestation and estimating potential biodiversity losses. The research employs deep learning algorithms, specifically Convolutional Neural Networks and Recurrent Neural Networks, to analyze satellite imagery, historical deforestation data, land-use changes, and biodiversity hotspot maps, enabling the model to capture both spatial and temporal trends in deforestation dynamics. The results demonstrate that the proposed deep learning model achieves a high prediction accuracy of 92 percent in identifying deforestation hotspots and successfully highlights key biodiversity-rich areas that are highly vulnerable to rapid forest loss, with agricultural expansion and infrastructure development emerging as the dominant drivers of deforestation in these regions. Overall, the findings confirm that deep learning provides a powerful and reliable tool for predicting deforestation patterns and assessing biodiversity hotspot degradation, offering valuable evidence-based insights for policymakers and conservation practitioners to prioritize protection efforts and design targeted interventions aimed at mitigating further environmental damage in Sumatra.
Co-Authors Manurung, Edison Hatoguan Ming Kiri Rithy Vann Rohan Kumar Rustiyana, Rustiyana