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Studi Aplikasi Metode WASPAS dalam Pengambilan Keputusan Strategi Pemasaran Biji Kopi Premium Fahreza, Muhammad Rizqi
Journal of Knowledge and Collaboration Vol. 1 No. 7 (2024): Journal of Knowledge and Collaboration
Publisher : Arbain Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59613/1qxfbn35

Abstract

Strategi pemasaran yang efektif menjadi kunci utama bagi produsen biji kopi premium untuk dapat bersaing di pasar yang semakin kompetitif. Penelitian ini bertujuan untuk mengeksplorasi aplikasi metode WASPAS (Weighted Aggregated Sum Product Assessment) dalam proses pengambilan keputusan strategi pemasaran biji kopi premium. Menggunakan pendekatan kualitatif dengan studi literatur dan penelitian pustaka, penelitian ini menganalisis konsep dan penerapan metode WASPAS sebagai salah satu teknik pengambilan keputusan multi-kriteria. Metode WASPAS mengombinasikan model penjumlahan berbobot dan produk berbobot untuk menghasilkan penilaian yang lebih komprehensif dalam pemilihan strategi pemasaran terbaik berdasarkan beberapa kriteria, seperti kualitas produk, harga, promosi, dan distribusi. Hasil penelitian menunjukkan bahwa metode WASPAS dapat membantu produsen biji kopi premium dalam menilai dan memprioritaskan strategi pemasaran yang paling sesuai dengan kondisi pasar dan tujuan bisnis. Temuan ini memberikan kontribusi penting dalam membantu perusahaan untuk mengambil keputusan strategis yang lebih baik, khususnya dalam merancang strategi pemasaran yang efektif untuk produk biji kopi premium. Dengan demikian, penelitian ini menambah literatur terkait penerapan metode pengambilan keputusan multi-kriteria dalam konteks pemasaran produk, dan memberikan wawasan praktis bagi pelaku industri kopi premium yang ingin meningkatkan daya saingnya.
The Role of Data Governance and Ethical AI in Strengthening Reliability of Healthcare Information Systems Subekthi, Errysa; Fahreza, Muhammad Rizqi
Jurnal Ar Ro'is Mandalika (Armada) Vol. 6 No. 1 (2026): JURNAL AR RO'IS MANDALIKA (ARMADA)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59613/armada.v6i1.5458

Abstract

The increasing reliance on digital technologies in healthcare has underscored the importance of robust information systems for ensuring the delivery of high-quality patient care. As healthcare systems adopt artificial intelligence (AI) to support decision-making, the role of data governance and ethical AI has become increasingly vital in maintaining the reliability and trustworthiness of these systems. Data governance involves the frameworks, processes, and standards for managing the quality, integrity, and security of healthcare data, while ethical AI ensures that the algorithms driving healthcare decisions are transparent, fair, and free from biases. This paper explores the crucial role that data governance and ethical AI play in enhancing the reliability of healthcare information systems. It highlights key challenges such as data privacy concerns, algorithmic biases, and accountability, and discusses strategies for improving transparency, data stewardship, and decision-making frameworks. The research further examines the potential impacts of poor data governance and unethical AI practices on patient outcomes, trust in healthcare systems, and overall system efficiency. Through a review of current trends and best practices, this study aims to provide actionable insights for healthcare providers, policymakers, and technology developers in strengthening the integrity and effectiveness of healthcare information systems.
Optimization of Biofiltration Techniques for Reducing Heavy Metal Contamination in Urban Wastewater Susetyaningsih, Retno; Muyasaroh, Nurul; Ayuningtyas, Endah; Jumiati, Jumiati; Fahreza, Muhammad Rizqi
Jurnal Riset Teknologi Pencegahan Pencemaran Industri Vol. 16 No. 2 (2025): November
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Pencegahan Pencemaran Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21771/jrtppi.2025.v16.no2.p90-97

Abstract

This study focuses on optimizing biofiltration techniques to mitigate heavy metal contamination in urban wastewater. The increasing presence of heavy metals in wastewater, particularly in urban environments, poses a significant threat to both human health and ecosystem stability. Biofiltration, a natural remediation process utilizing living organisms, has gained attention as an effective method for removing heavy metals from contaminated water. The research combines experimental analysis with a comprehensive literature review to evaluate and enhance the performance of various biofiltration systems. By examining the influence of different variables, such as plant species, soil composition, flow rate, and pollutant concentration, on the efficiency of heavy metal removal, this study provides a broad perspective on the potential applications of biofiltration. Experimental analysis of biofilter setups demonstrated that specific plant-microbe interactions and substrate types significantly enhance the absorption and accumulation of toxic metals. The literature review further supports these findings by summarizing past studies and providing insights into existing biofiltration techniques, their effectiveness, and limitations. The study's findings indicate that optimized biofiltration can serve as a sustainable and cost-effective solution for urban wastewater management. By providing a detailed understanding of how biofilters can be adapted and scaled for urban applications, the research contributes to the development of environmentally friendly wastewater treatment technologies. The results underscore the importance of integrating biofiltration systems into urban water management strategies for improving water quality and reducing environmental pollution.