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All Journal Malacca Pharmaceutics Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology
Patwekar, Mohsina
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Immunology & microbiology Medicine & Pharmacology Physics

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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.
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.
Fine-Tuning ChemBERTa for Predicting Activity of AXL Kinase Inhibitors in Oncogenic Target Modeling Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Patwekar, Mohsina; Idroes, Rinaldi
Grimsa Journal of Science Engineering and Technology Vol. 3 No. 2 (2025): October 2025
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v3i2.98

Abstract

The development of selective kinase inhibitors remains a key objective in cancer drug discovery, where predictive computational models can significantly accelerate the identification of leads. In this study, we investigate the fine-tuning strategies of the transformer-based ChemBERTa model for quantitative structure–activity relationship (QSAR) modeling of AXL receptor tyrosine kinase inhibitors, an important therapeutic target implicated in tumor progression and metastasis. A dataset of AXL inhibitors was curated from the ChEMBL database. Three fine-tuning configurations, namely baseline, full fine-tune, and aggressive, were implemented to examine the influence of learning rate, weight decay, and the number of frozen transformer layers on model performance. Models were evaluated using accuracy, precision, recall, F1-score, and calibration metrics. Results showed that both the full fine-tune and aggressive configurations outperformed the baseline model, achieving higher precision and F1-scores while maintaining robust recall. The aggressive configuration achieved the most balanced performance, with improved calibration and the lowest expected calibration error, indicating reliable probabilistic predictions. Overall, this study highlights that controlled fine-tuning of ChemBERTa significantly enhances predictive performance and confidence estimation in QSAR modeling, offering valuable insights for optimizing transformer-based chemical language models in kinase-targeted drug discovery.
An Interpretable Machine Learning Framework for Predicting Advanced Tumor Stages Noviandy, Teuku Rizky; Patwekar, Mohsina; Patwekar, Faheem; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i2.364

Abstract

Accurate identification of advanced tumor stages is essential for timely clinical decision-making and personalized treatment planning. This study proposes an explainable ensemble learning framework for predicting advanced tumor stage using a dataset containing 10,000 samples with 18 clinical and radiological features. Four machine learning models, namely Logistic Regression, Naïve Bayes, AdaBoost, and LightGBM, were evaluated using stratified train–test splits along with standard performance metrics. LightGBM achieved the highest performance, with an accuracy of 86.05% and an F1-score of 76.61%, outperforming linear and probabilistic classifiers. ROC–AUC and precision–recall analyses further confirmed the superior discriminative ability of ensemble methods. SHAP explainability techniques highlighted mitotic count, Ki-67 index, enhancement, and necrosis as the most influential predictors of advanced stage. The proposed framework demonstrates strong predictive capability and provides clinically interpretable insights, underscoring its potential as a decision-support tool in oncological diagnostics. Future work will involve external validation and integration of additional multimodal data to enhance generalizability.
Co-Authors Estevam, Ethiene Castellucci Ghazi Mauer Idroes Harera, Cheariva Firsa Kemala, Pati Khairan Khairan Maulana, Aga Maulydia, Nur Balqis Maysarah, Hilda Mohd Fauzi, Fazlin Patwekar, Faheem Rinaldi Idroes Suhendra, Rivansyah Teuku Rizky Noviandy TRINA EKAWATI TALLEI