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All Journal Heca Journal of Applied Sciences Malacca Pharmaceutics Journal of Educational Management and Learning
Pati Kemala
Graduate School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Earth & Planetary Sciences Education Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Other

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Characterizing the Size Distribution of Silver Nanoparticles Biofabricated Using Calotropis gigantea from Geothermal Zone Pati Kemala; Khairan Khairan; Muliadi Ramli; Ghazi Mauer Idroes; Erisna Mirda; Diana Setya Ningsih; Trina Ekawati Tallei; Rinaldi Idroes
Heca Journal of Applied Sciences Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i2.21

Abstract

This research aims to synthesize silver nanoparticles (AgNPs) using an aqueous leaf extract of Calotropis gigantea obtained from the geothermal manifestation Ie Seu-Um, Aceh Besar, Aceh Province, Indonesia. The C. gigantea leaf extract was mixed with AgNO3 solutions at concentrations of 2, 5, and 9 mM, respectively. The mixture was stirred at 80 rpm by a magnetic stirrer for 48 hours in the dark. The change in solution color indicated the reduction of Ag+ to Ag0. The resulting AgNPs synthesized using C. gigantea leaf extract (AgNPs-LCg) exhibited cloudy grey, reddish dark brown, and light brown colors when synthesized with AgNO3 concentrations of 2, 5, and 9 mM, respectively. The particle sizes of AgNPs-LCg had maximum frequencies at 246.98 nm (synthesized using AgNO3 2 mM), 93.02 nm (synthesized using AgNO3 5 mM), and 171.25 nm (synthesized using AgNO3 9 mM). The zeta potential values of AgNPs-LCg using 2, 5, and 9 mM AgNO3 were -41.9, -40.1, and -31.4 mV, respectively. Based on the solution color, nanoparticle size, and stability value of AgNPs, it can be concluded that the use of AgNO3 at 5 mM is optimal for the green synthesis process of AgNPs-LCg.
A Review of the Ethno-dentistry Activities of Calotropis gigantea Diana Setya Ningsih; Ismail Celik; Abdul Hawil Abas; Boy Muhclis Bachtiar; Pati Kemala; Ghazi Mauer Idroes; Nur Balqis Maulydia
Malacca Pharmaceutics Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i1.31

Abstract

Calotropis gigantea is a medicinal herb that thrives in arid climates. All parts of this plant are rich in secondary metabolites, which are very beneficial for health. Phytochemicals of this plant include flavonoid, alkaloids, steroids, cardiac glycosides, and terpenoids, which have a wide range of pharmacological effects. The potential of metabolit compound from C. gigantea can be used in dental treatment. This review describes the potential use of C. gigantea in ethno-dentistry, specifically as anti-caries, soft tissue inflammation (periodontitis and gingivitis), degenerative diseases (tumor/cancer), and wound healing. This review provides general perspectives and basic literature on the use of C. gigantea in the field of etno-dentistry.
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 Abdul Hawil Abas Aga Maulana Asep Rusyana Boy Muhclis Bachtiar Diana Setya Ningsih Diana Setya Ningsih Erisna Mirda Ghazi Mauer Idroes Hizir Sofyan Ismail Celik Khairan Khairan Muliadi Ramli Novi Reandy Sasmita Nur Balqis Maulydia Rinaldi Idroes Trina Ekawati Tallei Trina Ekawati Tallei