Article Per Year (5 Year)
p-Index From 2019 - 2024
0.562
P-Index
This Author published in this journals
All Journal Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning
Asep Rusyana
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Languange, Linguistic, Communication & Media Mathematics Other

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

Found 3 Documents
Search

 1 
Biocomposite Innovation: Assessing Tensile and Flexural Performance with Maleated Natural Rubber Additives Warman Fatra; Kaspul Anuar; Febri Dwi Oktriyono; Rivo Fernando; Zuchra Helwani; Asep Rusyana; Said Zul Amraini
Leuser Journal of Environmental Studies Vol. 1 No. 2 (2023): November 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i2.98

Abstract

Fiberglass is the most common reinforcing fiber used in composites, with polymer matrices having high tensile strength and chemical resistance, including an excellent insulating property; however, they are non-degradable. Natural fiber reinforced polymer composites have advantageous properties such as lower density and price, when compared to synthetic composite products. In addition, hybrid composites may be obtained depending on various properties such as the fibers' length, structure, content and orientation, matrix bonding and arrangement. This study was carried out to determine the effect of adding Maleated Natural Rubber (MNR) from natural rubber as a coupling agent, in order to produce the highest tensile and flexural strength. The hand lay-up and vacuum bag methods with the Response Surface Method-Central Composite Design (RSM). -CCD) were used. The composite arrangement pattern was E-glass/OPEFB/E-glass, the volume fraction of OPEFB (oil palm empty fruit bunches):E-glass was 40:60, 50:50 and 60:40, the fraction volume of OPEFB + E-glass:matrix was 40:60, 50: 50, 60: 40 and the coupling agent were added by 9, 10 and 11% of the total epoxy resin used. Furthermore, the composite mold was made of glass with dimensions of 200mm x 50mm x 50mm. The results showed that the composite product obtained from both methods had a tensile strength value, which was influenced by the variable OPEFB fiber and epoxy resin. Meanwhile, the flexural strength was influenced by the OPEFB fiber and the quadratic factor of the epoxy-MNR resin.
Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet Teuku Rizky Noviandy; Aga Maulana; Ghazi Mauer Idroes; Rivansyah Suhendra; Muhammad Adam; Asep Rusyana; Hizir Sofyan
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.51

Abstract

This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Aga Maulana; Ghazi Mauer Idroes; Pati Kemala; Nur Balqis Maulydia; Novi Reandy Sasmita; Trina Ekawati Tallei; Hizir Sofyan; Asep Rusyana
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.
Co-Authors Aga Maulana Febri Dwi Oktriyono Ghazi Mauer Idroes Hizir Sofyan Kaspul Anuar Muhammad Adam Novi Reandy Sasmita Nur Balqis Maulydia Pati Kemala Rivansyah Suhendra Rivo Fernando Teuku Rizky Noviandy Trina Ekawati Tallei Warman Fatra Zuchra Helwani