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Journal Of World Science
Published by Riviera Publishing
ISSN : 28288726     EISSN : 28289307     DOI : https://doi.org/10.58344/jws.v1i1.1
Core Subject : Economy, Health, Science, Social, Engineering,
Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences
Journal Of World Science is a scientific journal in the form of research and can be accessed openly. This journal is published monthly by CV Riviera Publishing. Journal of World Science provides a means for ongoing discussion of relevant issues that fall within the focus and scope of the journal that can be empirically examined. The journal publishes research articles covering all aspects of social sciences, ranging from Management, Education, Economics, Culture, Law, Engineering sciences, Social and Sains that belong to the social context.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 2 Documents
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Search results for , issue "Vol. 5 No. 2 (2026): Journal of World Science" : 2 Documents clear
Treatment of Obesity with Diethylpropion as an Appetite Suppressant Liana Rahmawati, N Anna Sakinah; Abdullah, Rizky; Faridh, Izzah
Journal of World Science Vol. 5 No. 2 (2026): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v5i2.1626

Abstract

Obesity and the Use of Diethylpropion: A Review — Obesity is a medical condition characterized by excessive fat accumulation, elevated body weight, and increased waist circumference. It can manifest at any age and is influenced by various factors, including lifestyle, socioeconomic status, and behavioral patterns. Addressing obesity is critical due to its association with an increased risk of comorbidities. This review aims to evaluate the safety and efficacy of diethylpropion as a short-term pharmacotherapy for obesity. This article is based on a comprehensive literature review conducted across multiple databases, including PubMed, Scopus, Springer, Elsevier, NCBI, and Google Scholar, utilizing the search terms “diethylpropion” and “obesity.” The literature search included studies published between 2015 and 2025. Research indicates that diethylpropion has demonstrated significant reductions in both weight and waist circumference. It is considered safe for patients with a history of hypertension and is associated with minimal side effects. The medication’s effectiveness may be influenced by factors such as the timing of administration, genetic predisposition, and gender. In conclusion, diethylpropion represents a viable option for the short-term management of obesity in patients who have not achieved success through lifestyle modifications.
Learning Nonlinear Temporal Patterns in Ethereum Prices Via LSTM Networks Herdian, Cevi
Journal of World Science Vol. 5 No. 2 (2026): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v5i2.1630

Abstract

A Long Short-Term Memory (LSTM) neural network trained on hourly ETH/USDT market data from the Binance exchange is used in this study to examine short-term Ethereum price behavior. The proposed model emphasizes learning temporal dependencies and momentum-driven structures rather than relying on conventional linear forecasting assumptions, acknowledging the highly nonlinear and noise-dominated nature of cryptocurrency markets. The daily high price of Ethereum is selected as the target variable in the forecasting task, which is defined as a univariate regression problem. To ensure realistic predictive assessment, model performance is evaluated using a strictly out-of-sample testing methodology. Empirical findings demonstrate that the LSTM model achieves a strong statistical fit despite significant market volatility. The obtained results—RMSE of 127.33, MAE of 98.76, MSE of 16,213.76, MAPE of 2.73%, and an R² of 0.96—indicate that a substantial portion of short-term price volatility is effectively captured by the nonlinear architecture. Even in a noise-dominated market, the low MAPE and high coefficient of determination suggest robust predictive alignment. Forecasts over the next five days reveal a recurring short-term directional pattern accompanied by widening prediction intervals, which reflect increasing uncertainty as the forecast horizon extends. This pattern underscores the intrinsic difficulty of achieving accurate price-level forecasts in highly volatile cryptocurrency markets. Overall, when applied to short-term cryptocurrency price dynamics, the results indicate that LSTM models are well-suited for capturing trend persistence and regime-related signals, affirming their usefulness as risk-aware decision-support tools rather than deterministic forecasting systems.

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