Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Engineering, Science and Information Technology International Journal of Cyber and IT Service Management (IJCITSM)
Muhammad Farhan Kamil
University of Raharja
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
The Impact of War on The Cryptocurrency Economy from a Management Perspective Suryari Purnama; Bayu Laksma Pradana; Gautam Khanna; Suhandi Suhandi; Agung Rizky; Ihsan Nuril Hikam; Muhammad Farhan Kamil
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.169

Abstract

Armed conflicts and wars are increasingly shaping the global economic landscape, impacting both traditional markets and the burgeoning cryptocurrency economy. Cryptocurrencies, underpinned by blockchain technology, hold revolutionary potential for transactions, investments, and trading. However, their decentralized and global nature leaves them vulnerable to external shifts, particularly geopolitical events like war. This research explores the influence of war on cryptocurrency from a management perspective, analyzing how conflict impacts regulation, investment patterns, and technology adoption within the cryptocurrency ecosystem. By employing a literature based approach, this study aims to elucidate how global political and security shifts affect the cryptocurrency market. The findings indicate high reliability in the observed variables Investor, Crypto Market, and War with Cronbach alpha values ranging from 0.832 to 0.878, and rhoA values between 0.860 and 0.881. Additionally, composite reliability scores are robust, ranging from 0.860 to 0.882, demonstrating strong measurement reliability. The Average Variance Extracted (AVE) values, between 0.603 and 0.673, confirm that these measurement variables significantly explain the variance of the latent constructs. These results underscore the efficacy of the developed model in analyzing the interplay between war and cryptocurrency markets, contributing valuable insights into the sector resilience and adaptability amid geopolitical conflicts.
The Impact of War on The Cryptocurrency Economy from a Management Perspective Suryari Purnama; Bayu Laksma Pradana; Gautam Khanna; Suhandi Suhandi; Agung Rizky; Ihsan Nuril Hikam; Muhammad Farhan Kamil
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.169

Abstract

Armed conflicts and wars are increasingly shaping the global economic landscape, impacting both traditional markets and the burgeoning cryptocurrency economy. Cryptocurrencies, underpinned by blockchain technology, hold revolutionary potential for transactions, investments, and trading. However, their decentralized and global nature leaves them vulnerable to external shifts, particularly geopolitical events like war. This research explores the influence of war on cryptocurrency from a management perspective, analyzing how conflict impacts regulation, investment patterns, and technology adoption within the cryptocurrency ecosystem. By employing a literature based approach, this study aims to elucidate how global political and security shifts affect the cryptocurrency market. The findings indicate high reliability in the observed variables Investor, Crypto Market, and War with Cronbach alpha values ranging from 0.832 to 0.878, and rhoA values between 0.860 and 0.881. Additionally, composite reliability scores are robust, ranging from 0.860 to 0.882, demonstrating strong measurement reliability. The Average Variance Extracted (AVE) values, between 0.603 and 0.673, confirm that these measurement variables significantly explain the variance of the latent constructs. These results underscore the efficacy of the developed model in analyzing the interplay between war and cryptocurrency markets, contributing valuable insights into the sector resilience and adaptability amid geopolitical conflicts.
Machine Learning-Based Heart Failure Worsening Prediction Model to Build Self-Monitoring Prototype as an Effort to Prevent Readmissions and Maintain Quality of Life Untung Rahardja; Kristoko Dwi Hartomo; Indrajani Sutedja; Ardi Kho; Muhammad Farhan Kamil
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1467

Abstract

Heart failure is a long-term condition of great concern which calls for health care services in cycles. This significantly hampers quality of life for patients and increases costs for the healthcare systems. If the worsening of heart failure could be detected early, the intervention to prevent readmission could be employed, such that readmission would be avoided, enhancing the quality of life for the patient. Accordingly, the paper explains how such a model to predict the worsening of heart failure in patients who are at high risk of this condition has been developed. The model uses information gathered from the Electronic Health Records (EHRs) (Clinical Variables, Vitals, Test Results, and Demographics) to make accurate predictions on patients. As an effective and efficient approach towards achieving this goal, comparison of different algorithms such as random forests, support vector machines and gradient boosting has been employed towards the building of the final model. At this stage, the model is embedded into a user-friendly self-monitoring device, allowing the chronic heart failure patients to assess health indices on the fly with the help of the mobile app and wearable devices. This secondary prevention strategy makes patients more responsible for their health and decreases the number of patients readmitted to the hospital by increasing their functioning and well-being. The paper further projects the future development of other forms of treatment for chronic heart failure, especially at the first line, focusing primarily on the timing and succession.
Co-Authors Agung Rizky Ardi Kho Bayu Laksma Pradana Gautam Khanna Ihsan Nuril Hikam Indrajani Sutedja Kristoko Dwi Hartomo Suhandi Suhandi Suryari Purnama Untung Rahardja