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All Journal SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Mega Fitri Yani
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Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management

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Data Mining Clustering and Correlation Analysis of Marine Potential Insights from Capture Fisheries Coral Reef Quantity and Plankton Abundance Cindy Muhdiantini; Mega Fitri Yani; Ilham Auliya Rahman; Ati Maryati
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715318

Abstract

Indonesia, sebagai negara kepulauan dengan wilayah laut yang luas, memiliki potensi besar dalam memanfaatkan sumber daya laut, seperti terumbu karang, mangrove, budidaya, dan penangkapan ikan laut. Pemanfaatan yang berkelanjutan tidak hanya berdampak positif pada ekosistem laut tetapi juga meningkatkan kesejahteraan masyarakat. Untuk mendukung pengelolaan berkelanjutan, diperlukan analisis data mendalam guna mengidentifikasi pola dan hubungan yang relevan. Data mining menjadi alat efektif untuk menggali pola yang tersembunyi, terutama melalui teknik clustering. Analisis clustering dilakukan terhadap data perikanan tangkap, kuantitas terumbu karang, dan kelimpahan plankton guna menemukan kelompok homogen dalam dataset. Proses ini diawali dengan pemilihan data sesuai kriteria, dilanjutkan dengan preprocessing untuk menyaring data redundan. Hasilnya, terdapat tiga cluster utama: cluster 0 berfokus pada terumbu karang, cluster 1 pada jumlah ikan tangkap, dan cluster 2 pada kelimpahan plankton.
Risk Management in Financial Technology: A Systematic Literature Reviewto Support Sustainability and Security of Digital Financial Services Mega Fitri Yani; Cindy Muhdiantini; Syifa Nur Aini
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 1 (2025): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715318

Abstract

The rapid growth of the financial technology (FinTech) sector has revolutionized financial services by enhancing convenience, speed, and efficiency. However, this expansion also introduces significant risks, necessitating robust information technology (IT) risk management strategies. This study systematically reviews the existing literature on FinTech risk management, focusing on frameworks, algorithms, policies, and technical aspects that influence risk management practices. By integrating advanced algorithms such as Artificial Intelligence and Deep Forest with established frameworks like ISO 31000:2018 and NIST, the research provides a comprehensive perspective on managing risks in FinTech, bridging gaps not extensively covered in previous studies. A systematic literature review methodology identified and analyzed 17 key studies from an initial pool of 134 documents sourced from databases such as Scopus and Google Scholar. Findings highlight the critical role of advanced technologies and established frameworks in mitigating risks and underscore the need for continuous adaptation to evolving challenges. This research offers valuable insights for financial institutions and policymakers, promoting sustainable and secure digital financial services.
Co-Authors Ati Maryati Cindy Muhdiantini Ilham Auliya Rahman Syifa Nur Aini