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All Journal International Journal of Sharia Business Management Journal of Indonesian Management
Muhammad Hanif Anshari
Universitas Teknologi Yogyakarta
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Sistem Pendukung Keputusan untuk Menentukan Penerima Bantuan Corporate Social Responsibility (CSR) di CV Jogja Horti Lestari Menggunakan Metode Analytical Hierarchy Process (AHP) Muhammad Hanif Anshari; Sri Handayani
Journal of Indonesian Management Vol. 5 No. 4 (2025): December
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jim.v5i4.3955

Abstract

Penentuan prioritas program Corporate Social Responsibility (CSR) merupakan tantangan strategis bagi perusahaan hortikultura dalam menyelaraskan keterbatasan sumber daya dengan kebutuhan masyarakat yang beragam. Penelitian ini bertujuan untuk menerapkan Sistem Pendukung Keputusan (SPK) berbasis metode Analytical Hierarchy Process (AHP) dalam menentukan prioritas program CSR di CV Jogja Horti Lestari. Metode penelitian yang digunakan adalah deskriptif kuantitatif dengan pendekatan Decision Support System, di mana data diperoleh melalui wawancara terstruktur dengan pihak manajemen perusahaan. Kriteria penilaian mencakup dampak sosial (people), dampak lingkungan (planet), dampak ekonomi (profit), dan kontribusi terhadap branding perusahaan. Hasil penelitian menunjukkan bahwa kriteria kontribusi terhadap branding memiliki bobot kepentingan tertinggi sebesar 0,5281. Berdasarkan sintesis akhir AHP, alternatif pemberdayaan petani dan UMKM menempati peringkat pertama dengan nilai prioritas 0,4739, diikuti oleh edukasi pertanian berkelanjutan (0,3113), penghijauan lingkungan (0,1491), dan bantuan sosial serta donasi hasil panen (0,0657). Nilai Consistency Ratio (CR) sebesar 0,0465 menunjukkan bahwa hasil pengambilan keputusan bersifat konsisten dan valid. Implementasi SPK ini memberikan landasan objektif bagi perusahaan untuk mengalokasikan bantuan secara strategis, sehingga program CSR tidak hanya berdampak sosial namun juga memperkuat citra merek secara berkelanjutan.
Implementation of Artificial Intelligence in Operations Management: Opportunities and Challenges for Business Muhammad Hanif Anshari
International Journal of Sharia Business Management Vol 5 No 2 (2026): IJSBM: International Journal of Sharia Business Management
Publisher : Sharia Business Management Department of STEBIS Bina Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to examine the implementation of Artificial Intelligence (AI) in operations management by identifying the opportunities it provides for improving operational efficiency, decision-making, productivity, and business performance, as well as the challenges organizations face during its implementation. This study employs a qualitative approach using a literature review method. Relevant scientific literature on Artificial Intelligence, operations management, business processes, automation, and organizational transformation was reviewed and analyzed thematically to identify key patterns regarding the opportunities and challenges associated with AI implementation in business operations. The findings indicate that AI implementation provides significant opportunities for businesses, particularly in process automation, demand forecasting, inventory management, resource optimization, operational decision-making, and customer service. AI can also support organizations in improving efficiency and responding more rapidly to changes in market conditions. However, its implementation is accompanied by several challenges, including high implementation costs, data quality and availability, cybersecurity and privacy risks, employee resistance, lack of AI-related skills, and ethical concerns. The findings suggest that successful AI implementation in operations management requires not only technological readiness but also organizational preparedness, employee competence, effective data governance, and appropriate managerial strategies.
Co-Authors Sri Handayani