Article Per Year (5 Year)
p-Index From 2021 - 2026
2.036
P-Index
This Author published in this journals
All Journal Indonesian Journal of Chemistry Heca Journal of Applied Sciences Malacca Pharmaceutics Leuser Journal of Environmental Studies Journal of Educational Management and Learning Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology
Maulydia, Nur Balqis
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Reduction of Microbial Content (Escherichia coli) in Well Water Using Various Processes: Microfiltration Membranes, Aeration and Bentonite Adsorption Lala, Andi; Marlina, Marlina; Yusuf, Muhammad; Rivansyah Suhendra; Maulydia, Nur Balqis; Muslem, Muslem
Heca Journal of Applied Sciences Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i1.17

Abstract

Water is a basic need for living things. This research aims to know the reduction of microbe content (Escherichia coli) in well water by using microfiltration membrane, adsorption using bentonite and aeration. The capability of those three methods in reducing E. coli was examined on the variety of time contact: 30, 60, 90, 120,180 and 300 minutes. The result of the research shows that using those methods has shown that the optimum percentage of E. coli reduction by using microfiltration membrane with 23 Most Probable Number (MPN)/100 mL of E. coli initial concentration in well water and became 0 MPN/100 mL by 100 % of E. coli reduction. Adsorption using bentonite resulted in a 78% reduction in E. coli and reduction by using aeration, the reduction of E. coli by 21%. This study shows that microfiltration has the best ability compared to other methods.
Characterization of Geochemical and Isotopic Profiles in the Southern Zone Geothermal Systems of Mount Seulawah Agam, Aceh Province, Indonesia Lala, Andi; Yusuf, Muhammad; Suhendra, Rivansyah; Maulydia, Nur Balqis; Dharma, Dian Budi; Saiful, Saiful; Idroes, Rinaldi
Leuser Journal of Environmental Studies Vol. 2 No. 1 (2024): April 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v2i1.172

Abstract

The Seulawah Agam geothermal area exhibits significant potential as a source of energy for power generation, with an estimated capacity of 130 MW. Geological and geochemical investigations indicate that the Seulawah Agam geothermal system is part of the extensive Sumatra Fault. Analysis of the geochemical composition of geothermal water at the South Zone manifestation location of Mount Seulawah Agam, Aceh Province-Indonesia, involves examining cation (K+, Na+, Ca2+, and Mg2+), anion (Cl-, HCO3-, and SO42-), and isotope (δD and δ18O) contents. This data aids in estimating reservoir temperatures using geothermometer equations. Surface characteristics of the South Zone manifestation reveal neutral to alkaline pH values (6.02 to 8.68), relative temperatures (29.97 to 42.57 ºC), conductivity (49.8 to 100.7 mV), and TDS (Total Dissolved Solids) ranging from 352.6 to 497.0 mg/L. The dominant water composition is sodium–calcium–bicarbonate (Ca–Na–HCO3), indicating a bicarbonate water type. Average temperature depths in the South Zone manifestation of Mount Seulawah Agam are estimated as follows: Alue Ie Seu’um around 288.84 ± 2.19 ºC, Alue Ie Masam around 304.17 ± 20.9 ºC, Alue PU around 290.02 ± 6.85ºC, and Alue Teungku around 265±11.39 ºC. Isotope data (δD and δ18O) suggest meteoric water as the source for this manifestation. Fluid geochemical analysis indicates the potential for utilizing the geothermal manifestations of the South Zone of Mount Seulawah Agam for geothermal development or the construction of a geothermal power plant, given its high enthalpy system with an average temperature exceeding 225 ºC. Further research, including data drilling, is essential to gather precise subsurface data. Additionally, the Aceh Provincial Government should formulate policies to identify strategic areas for geothermal development, leveraging the existing exploitable potential.
GC-MS Analysis Reveals Unique Chemical Composition of Blumea balsamifera (L.) DC in Ie-Jue Geothermal Area Maulydia, Nur Balqis; Khairan, Khairan; Tallei, Trina Ekawati; Estevam, Ethiene Castellucci; Patwekar, Mohsina; Mohd Fauzi, Fazlin; Idroes, Rinaldi
Grimsa Journal of Science Engineering and Technology Vol. 1 No. 1 (2023): October 2023
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v1i1.6

Abstract

Blumea balsamifera (L.) DC. or Sembung is a flowering plant belonging to the genus Blumea of the family Asteraceae. Many pharmacological activities of this plant show potential in human therapy. In this study, an investigation was conducted on the ethanolic extract of B. balsamifera collected from a geothermal area known as Ie-Jue, in Aceh Province, Indonesia. The results showed that the ethanolic extract of B. balsamifera contained secondary metabolites of flavonoids and tannins. Chemical constituents of ethanolic extracts B. balsamifera further analysis using gas chromatography-mass spectrometry (GC-MS) show that active compounds from this plant was Proximadiol (C15H28O2) with relative area 41.76%. This research underscores the compelling potential of the Ie-Jue geothermal area as a promising reservoir of flora owing to the plant's adaptability to geothermal extremities.
Geothermal Flora and AgNPs Synergy: A Study on the Efficacy of Lantana camara and Acrostichum aureum-Infused Hand Sanitizers Harera, Cheariva Firsa; Maysarah, Hilda; Kemala, Pati; Idroes, Ghazi Mauer; Maulydia, Nur Balqis; Patwekar, Mohsina; Idroes, Rinaldi
Grimsa Journal of Science Engineering and Technology Vol. 2 No. 2 (2024): October 2024
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v2i2.38

Abstract

Hand hygiene is an important factor that needs to be observed in controlling the spread of diseases transmitted through hand-to-hand contact. Synthesis of silver nanoparticles from tembelekan (Lantana camara) and paku laut (Acrostichum aureum) using the green synthesis method has good antibacterial activity against Staphylococcus aureus and Escherichia coli bacteria. Therefore, a preparation formulation was made, namely hand sanitizer, which is still rarely used. Formulations that have successfully entered the evaluation stage include organoleptic tests, homogeneity tests, spreadability tests, adhesion tests, viscosity tests, pH tests, accelerated stability tests, and irritation tests. Antibacterial activity was evaluated against bacteria Staphylococcus aureus and Escherichia coli. The hand sanitizer is formulated to contain 5% tembelekan AgNPs (F1); paku laut AgNPs 5% (F2); and a combination of 2.5% paku laut AgNPs and 2.5% tembelekan AgNPs. The resulting hand sanitizer has good organoleptic characteristics, except for the color of the preparation, which changed during the accelerated stability test. Test results for pH, adhesion, spreadability, viscosity, and homogeneity of hand sanitizer meet the requirements of a good test. Irritation tests on ten volunteers showed no irritation reaction. Antibacterial tests show that hand sanitizer has bacterial antibacterial activity with an average ± standard deviation of the inhibition zone Staphylococcus aureus is 6.605±0.459(F1); 6.665±0.615(F2); 6.380±0.282(F3) dan Escherichia coli namely 6.575 ± 0.219 (F1); 6.860 ± 0.155 (F2); 6.810 ± 0.056 (F3). Making hand sanitizer AgNPs-based ingredients from plants can be used as hand sanitizer, but stabilizers are required to prevent color changes during storage.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Maulana, Aga; Idroes, Ghazi Mauer; Kemala, Pati; Maulydia, Nur Balqis; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Sofyan, Hizir; Rusyana, Asep
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.
Predicting Obesity Levels with High Accuracy: Insights from a CatBoost Machine Learning Model Maulana, Aga; Afidh, Razief Perucha Fauzie; Maulydia, Nur Balqis; Idroes, Ghazi Mauer; Rahimah, Souvia
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.195

Abstract

This study aims to develop a machine learning model using the CatBoost algorithm to predict obesity based on demographic, lifestyle, and health-related features and compare its performance with other machine learning algorithms. The dataset used in this study, containing information on 2,111 individuals from Mexico, Peru, and Colombia, was used to train and evaluate the CatBoost model. The dataset included gender, age, height, weight, eating habits, physical activity levels, and family history of obesity. The model's performance was assessed using accuracy, precision, recall, and F1-score and compared to logistic regression, K-nearest neighbors (KNN), random forest, and naive Bayes algorithms. Feature importance analysis was conducted to identify the most influential factors in predicting obesity levels. The results indicate that the CatBoost model achieved the highest accuracy at 95.98%, surpassing other models. Furthermore, the CatBoost model demonstrated superior precision (96.08%), recall (95.98%), and F1-score (96.00%). The confusion matrix revealed that the model accurately predicted the majority of instances in each obesity level category. Feature importance analysis identified weight, height, and gender as the most influential factors in predicting obesity levels, followed by dietary habits, physical activity, and family history of overweight. The model's high accuracy, precision, recall, and F1-score and ability to handle categorical variables effectively make it a valuable tool for obesity risk assessment and classification. The insights gained from the feature importance analysis can guide the development of targeted obesity prevention and management strategies, focusing on modifiable risk factors such as diet and physical activity. While further validation on diverse populations is necessary, the CatBoost model's results demonstrate its potential to support clinical decision-making and inform public health initiatives in the fight against the global obesity epidemic.
A Review of the Ethno-dentistry Activities of Calotropis gigantea Ningsih, Diana Setya; Celik, Ismail; Abas, Abdul Hawil; Bachtiar, Boy Muhclis; Kemala, Pati; Idroes, Ghazi Mauer; Maulydia, Nur Balqis
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.
Prediction of Pharmacokinetic Parameters from Ethanolic Extract Mane Leaves (Vitex pinnata L.) in Geothermal Manifestation of Seulawah Agam Ie-Seu’um, Aceh Maulydia, Nur Balqis; Khairan, Khairan; Noviandy, Teuku Rizky
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.v1i1.33

Abstract

The Mane plant (Vitex pinnata L.) is traditionally used as medicine in Aceh Province, Indonesia. This study aimed to predict the pharmacokinetic parameters of compounds in the ethanolic extract of Mane leaf (EEML), including the absorption, distribution, metabolism, excretion, and toxicity (ADMET), by in-silico approach. The method used was to analyze the compounds using a web-predictor server and molecular docking. Gas chromatography-mass spectrometry (GCMS) analysis of EEML showed the presence of active compounds, including phytol (60.93%), acorenol (8.56%), n-hexadecanoic acid (4.89%), trans-Z-alpha-bisabolene epoxide (2.7%) and cedrane (2.03%). Lipinski's rule of five states that all compounds had a deviation of less than 2. Pharmacokinetic parameters suggested that phytol was moderately absorbed in the gastrointestinal tract and had a toxicity level of 5 with lethal doses (LD50) >5000 mg/kg. Molecular docking results showed that phytol could be used against the targeted enzyme Staphylococcus aureus. In conclusion, our study suggests that the active compounds of EEML may have potential as a drug candidate.
Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Maulydia, Nur Balqis; Patwekar, Mohsina; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i2.60

Abstract

This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.
Hybrid Handwash with Silver Nanoparticles from Calotropis gigantea Leaves and Patchouli Oil: Development and Properties Salsabila, Indah; Khairan, Khairan; Kemala, Pati; Idroes, Ghifari Maulana; Isnaini, Nadia; Maulydia, Nur Balqis; El-Shazly, Mohamed; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.206

Abstract

When washing hands, handwashing is one way to prevent diseases caused by bacteria such as Staphylococcus aureus and Escherichia coli, the most common bacteria that can cause infections. The production of handwash utilizing silver nanoparticles as an active antibacterial agent remains a relatively infrequent practice. The synthesis of silver nanoparticles from the leaves of Calotropis gigantea, which grows in the geothermal area of Ie Seu-um Aceh Besar, has been carried out using the green synthesis method and hybrid green synthesis with patchouli oil. Handwash with active ingredients such as silver nanoparticles was successfully formulated, evaluated, and tested against S. aureus and E. coli. The organoleptic characteristics, pH, viscosity, foam height measurements, density, irritation, and antibacterial activity against S. aureus and E. coli were evaluated. The results showed that the organoleptic properties of the handwash with silver nanoparticles were not changed during a 30-day storage period, with pH values in the range of 9.7-10.3, and did not cause irritation upon using silver nanoparticle handwash. The best formula for handwashing with silver nanoparticles in inhibiting the growth of S. aureus and E. coli bacteria was F2, with inhibition zones of 12.9 ± 2.85 mm and 10.95 ± 0.8 mm, respectively. The formulated handwash with silver nanoparticles met the requirements of good liquid soap according to the Indonesian National Standard (SNI) with potent antibacterial activity.
Co-Authors . Marianne Abas, Abdul Hawil Asep Rusyana Azura, Rafna Bachtiar, Boy Muhclis Bastian, Reza Akbar Celik, Ismail Dharma, Dian Budi Diana Setya Ningsih, Diana Setya El-Shazly, Mohamed Estevam, Ethiene Castellucci Ghazi Mauer Idroes Harera, Cheariva Firsa Hilda, Michelia Mutiara Hizir Sofyan Idroes, Ghifari Maulana Isnaini, Nadia Kemala, Pati Khairan Khairan Khalijah Awang Lala, Andi Marlina Marlina Maulana, Aga Maysarah, Hilda Mohd Fauzi, Fazlin Mubaraq, Farhil Muhammad Bahi Muhammad Yusuf Musdalifah, Annisa Muslem Muslem, Muslem Nabila, Fiki Farah Patwekar, Mohsina Razief Perucha Fauzie Afidh Rinaldi Idroes Rivansyah Suhendra Rosnani Nasution Saiful Saiful Salaswati, Salaswati Salsabila, Indah Sasmita, Novi Reandy Souvia Rahimah Suhendra, Rivansyah Teuku Rizky Noviandy TRINA EKAWATI TALLEI