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All Journal Aptisi Transactions on Management Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Kallas, Evelin
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Optimasi Kinerja Sistem Informasi Manajemen Kampus Menggunakan Teknik Data Mining: Optimizing the Performance of Campus Management Information Systems Using Data Mining Techniques Noyari, Jihan Asa; Aprillia, Ariesya; Munthe, Rusli Ginting; Sutarman, Asep; Kallas, Evelin
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 3 No 1 (2024): September
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v3i1.613

Abstract

Penelitian ini bertujuan mengoptimalkan kinerja Sistem Informasi Manajemen Kampus dengan menggunakan data mining untuk meningkatkan efisiensi dan kualitas pengambilan keputusan. Masalah utama yang dihadapi adalah volume data besar, kompleksitas tinggi, dan kebutuhan analisis cepat dan akurat, yang menghambat performa sistem. Penelitian ini menggunakan teknik clustering, classification, dan association rule mining untuk mengekstraksi pola dari data. Metodologi campuran diterapkan melalui wawancara, Focus Group Discussion (FGD), serta analisis data dari database. Hasil menunjukkan peningkatan kecepatan akses data 30%, pengurangan redundansi 25%, dan akurasi prediksi hingga 85%, membuktikan efektivitas data mining dalam meningkatkan kinerja sistem. Studi kasus menunjukkan peningkatan kepuasan pengguna dan efisiensi operasional, membuktikan data mining efektif untuk meningkatkan kinerja sistem. Penelitian ini berkontribusi signifikan dalam bidang manajemen informasi kampus, dan menunjukkan bahwa data mining adalah solusi efektif untuk meningkatkan kinerja Sistem Informasi Manajemen Kampus.
International Business Expansion Strategies: A Data-Driven Approach with IBM SPSS Williams, Tane; Kallas, Evelin; Garcia, Emily; Fitzroy, Arabella; Sithole, Precious
APTISI Transactions on Management (ATM) Vol 8 No 2 (2024): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i2.2275

Abstract

This paper presents a structural framework to enhance time management proficiency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The methodology involves the application of the Eisenhower Matrix, Pareto Principle, and time-blocking techniques, supported by case studies in diverse professional settings. Results indicate a 20% improvement in project completion times, a 25% reduction in project turnaround time, and a 30% increase in project visibility. These findings underscore the framework’s effectiveness in enhancing time management and achieving long-term success. Implications include recommendations for continuous refinement and integration of emerging technologies.
The Effectiveness of Using Blockchain Technology as A Machine Learning Program Sutarman, Asep; Kallas, Evelin; Jayanagara, Oscar
Blockchain Frontier Technology Vol. 4 No. 1 (2024): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v4i1.576

Abstract

 Several successful papers and applications have demonstrated machine learning's limitless potential. However, when we immerse ourselves in powerful machine learning-based systems or applications, two critical research issues arise: how to ensure that the searched results of a machine learning system are not tampered with by anyone and how to prevent other users in the same network environment from easily obtaining our private data. This predicament is similar to that of other modern information systems that face security and privacy concerns. The introduction of blockchain technology presents us with an alternate method of addressing these two challenges. As a result, recent research has sought to construct machine learning systems using blockchain technology or to apply machine learning methods to blockchain systems. In this study, we provided a parallel framework to identify acceptable deep learning hyperparameters in a blockchain context using a metaheuristic algorithm to demonstrate what the combination of blockchain and machine learning is capable of. The suggested system also takes communication costs into consideration by restricting the amount of information transfers between miners and blockchain.
Enhancing Cybersecurity Risk Management Strategies in Financial Institutions: A Comprehensive Analysis of Threats and Mitigation Approaches Kristian, Agus; Az-Zahra, Achani Rahmania; Hidayat, Farhan; Yadi Fauzi, Ahmad; Kallas, Evelin
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.31

Abstract

This study investigates the cybersecurity risks faced by financial institutions, with a particular focus on identifying common threats, evaluating their impact, and assessing the effectiveness of risk management strategies. Utilizing a mixed-methods approach, data were collected from both primary and secondary sources, including expert interviews, surveys, and a review of academic and industry literature. The results highlight that phishing, ransomware, and malware are among the most prevalent threats, with email and websites being the primary attack vectors. The study also examines the significant financial and reputational impacts these threats pose. A case study of XYZ Bank demonstrates how a layered approach to cybersecurity, involving prevention, detection, response, and recovery strategies, can substantially reduce the frequency of cyber incidents. The findings emphasize the importance of continuous updates to security policies, regular employee training, and investment in advanced security technologies. The study concludes with recommendations for financial institutions to enhance their cybersecurity posture through comprehensive risk management strategies.
Co-Authors Aprillia, Ariesya Az-Zahra, Achani Rahmania Fitzroy, Arabella Garcia, Emily Hidayat, Farhan Jayanagara, Oscar Kristian, Agus Noyari, Jihan Asa Rusli Ginting Munthe, Rusli Ginting Sithole, Precious Sutarman, Asep Williams, Tane Yadi Fauzi, Ahmad