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muhammad Muhajir
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INDONESIA
Enthusiastic : International Journal of Applied Statistics and Data Science
Published by Universitas Islam Indonesia
ISSN : 2798253X     EISSN : 27983153     DOI : 10.20885
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics
ENTHUSIASTIC is an international journal published by the Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia. ENTHUSIASTIC publishes original research articles or review articles on all aspects of the statistics and data science field which should be written in English. ENTHUSIASTIC has the vision to become a reputable journal and publish good quality papers. We aim to provide lecturers, researchers both academic and industry, and students worldwide with unlimited access to be published in our journal. Specifically, these scopes of the ENTHUSIASTIC journal are: 1. Statistical Disaster Management 2. Actuarial Science 3. Data Science 4. Statistics of Social and Business 5. Statistics of Industry
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 2 Documents
 1 
Search results for , issue "Volume 6 Issue 1, April 2026" : 2 Documents clear
Sharpe Ratio-Based Dynamic Crypto Asset Allocation with Trend Filtering Using SMA
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art1

Abstract

This paper proposes a dynamic cryptocurrency asset allocation strategy that combines Sharpe Ratio-based weighting with trend filtering using the Simple Moving Average (SMA) of Bitcoin (BTC). The model reallocates capital among a portfolio of seven major cryptocurrencies (BTC, ETH, BNB, SOL, TON, TRX, XRP) every three days, conditional on BTC trading above its respective SMA threshold (50-day, 100-day, or 200-day). When BTC trends below the SMA, the strategy shifts fully to USDT to minimize downside risk. Using historical data from January 1, 2024, to January 1, 2025, the study evaluates performance across three SMA configurations and benchmarks against a buy-and-hold baseline. Results show that the SMA-50 strategy achieved the highest cumulative return (+231.51%) and Sharpe Ratio (2.51), significantly outperforming both the longer SMA-based models and the baseline average return (+132.14%). Risk analysis indicates that shorter SMA windows allow more responsive exposure during market uptrends but increase short-term volatility. Overall, the findings support the use of hybrid strategies combining trend-following filters and risk-adjusted allocation for managing crypto portfolios in volatile environments.
Determination Premiums Motor Vehicle Insurance Using Bonus-Malus Optimal
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art2

Abstract

The increasing number of motor vehicles in Sumatera has heightened accident risks, emphasizing the need for motor vehicle insurance to distribute risk between policyholders and insurers. Determining fair and risk-based premium requires consideration of each policyholder’s claim history. This study aimed to determine motor vehicle insurance premiums using the optimal bonus-malus system based on claim data for the minibus category with comprehensive coverage in Sumatera during 2022. The proposed model extended the Bayesian bonus-malus framework by incorporating the trust region reflective (TRR) method for estimating claim severity and the Newton-Raphson method for estimating claim frequency, thereby enhancing parameter estimation accuracy and numerical stability. This approach offers a more equitable and precise premium adjustment mechanism aligned with individual risk levels, contributing to improved risk-based pricing, reduced underwriting losses, and greater transparency for policyholders. The results showed that the claim frequency followed the Poisson-Lindley distribution, while claim severity followed the lognormal-gamma distribution. Based on these models, the premium was computed by multiplying the basic premium by the relative value of the subsequent year and dividing it by the base relative value. Premium decrease in the absence of claims and increase when claims occur.

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