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All Journal HAYATI Journal of Biosciences INDONESIAN JOURNAL OF PHARMACY Indonesian Journal of Chemistry Unnes Journal of Public Health Narra J Jurnal Natural Jurnal Kedokteran Hewan Heca Journal of Applied Sciences Malacca Pharmaceutics Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Indonesian Journal of Case Reports Indatu Journal of Management and Accounting Grimsa Journal of Science Engineering and Technology International Journal of Renewable Energy Development Journal Of Carbazon JURNAL REKAYASA KIMIA & LINGKUNGAN
Rinaldi Idroes
Department Of Chemistry, Faculty Of Mathematics And Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Veterinary Other

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Garlic Mixed Olive Oil Cream Formulation and Its Activity Against A Clinical Isolate of Staphylococcus aureus Khairan, Khairan; Zahraty, Ifrah; Idroes, Rinaldi
Journal of Carbazon Vol 1, No 1 (2023): June 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jokarbazon.v1i1.32825

Abstract

Garlic or Allium sativum is known containing of organosulphur compounds. These compounds are known have potential as antimicrobial, antivirus, and anticancer. The purpose of this study is to determine the activity of garlic mixed olive oil (GMO2) cream against the clinical isolate of Staphylococcus aureus and its evaluation by observing the organoleptic, homogeneity, pH, spread ability, stickiness, and viscosity. The result showed that GMO2 was able to inhibit the growth of Staphylococcus aureus bacteria at concentrations of 25 and 50 mg/ml. Meanwhile, the formulation of GMO2 cream at concentration 25 mg/ml was done by poisoned food method showed no activity against clinical isolate of Staphylococcus aureus. In this study, vanishing cream was used as negative control. The evaluation of GMO2 cream at concentration of 25 mg/mL has stability in organoleptic and homogeneity after the cycling test. The pH value of GMO2 cream was approximately equal to vanishing cream. The results of spread ability and stickiness tests indicated that GMO2 cream had the spreading and sticking strength which satisfied with the standard cream in range 5-7 and 4 respectively. The viscosity value of GMO2 cream were decreased after the cycling test. The stability test result showed that of GMO2 cream was stable after the cycling test.
The Role of Study Habits, Parental Involvement, and School Environment in Predicting Student Achievement: A Machine Learning Perspective Noviandy, Teuku Rizky; Paristiowati, Maria; Isa, Illyas Md; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i2.350

Abstract

This study explores the application of machine learning techniques to predict student achievement based on study habits, parental involvement, and school environment. Using a dataset from Kaggle comprising academic, behavioral, and contextual variables, four machine learning algorithms, namely K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Random Forest, were implemented and evaluated. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curve, and Precision–Recall curves. Results show that all models effectively classified students into low- and high-achievement categories, with SVM achieving the highest accuracy (94.02%) and the strongest overall performance. The findings highlight the potential of machine learning-driven predictive analytics in educational settings, enabling early identification of at-risk students and supporting evidence-based interventions. By integrating diverse factors influencing academic performance, this study demonstrates how data-driven approaches can enhance educational management, inform policy, and promote equitable learning outcomes.
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.
A Systematic Review on the Transformation of Bone Waste into Valuable Dental Biomaterials Diansari, Viona; Idroes, Rinaldi; Sunarso, Sunarso; Fitriyani, Sri
Malacca Pharmaceutics Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v4i1.385

Abstract

Bone waste is a sustainable, calcium-rich resource for the production of hydroxyapatite (HA), a biomaterial widely used in dental and bone tissue engineering. This systematic review evaluates recent advances in the extraction, transformation, and biological performance of HA derived from bone waste. A total of 20 records were initially identified, of which 11 full-text articles met the eligibility criteria and were included in the qualitative synthesis. The reviewed studies demonstrate that bone waste can be effectively converted into HA through several routes, including thermal-based extraction (calcination, annealing, and sintering at 600–1000°C), alkaline hydrolysis, and hydrothermal or microwave-assisted methods, enabling the production of micro- and nano-sized HA with high purity. Post-extraction functionalization, such as ion doping (Mg²⁺, Na⁺, Co²⁺), drug loading, and composite formation, further enhances osteogenic, antimicrobial, and mechanical properties. Physicochemical characterization using XRD and FTIR consistently confirmed the formation of non-stoichiometric, ion-substituted HA with Ca/P ratios ranging from 1.6 to 1.9, closely resembling biogenic apatite. The presence of multiscale porosity (25–65%) and nano-scale features promotes protein adsorption, ion exchange, and cellular interactions. In vitro studies confirmed cytocompatibility, while ALP activity and mineralization assays demonstrated strong osteogenic potential. Overall, bone waste–derived HA offers biomimetic, functional, and environmentally sustainable alternatives for dental and maxillofacial applications.
QSAR Modeling of Beta-2 Adrenergic Receptor Ligands Using Molecular Descriptor–Based Machine Learning Noviandy, Teuku Rizky; Patwekar, Mohsina; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v4i1.394

Abstract

The Beta-2 Adrenergic Receptor (ADRB2) is a well-characterized G protein–coupled receptor widely studied in pharmacology and drug discovery. In this study, quantitative structure–activity relationship (QSAR) models were developed using molecular descriptor–based machine learning approaches to predict the activity of ADRB2 ligands. A curated dataset of 745 compounds with experimentally determined IC₅₀ values was obtained from the ChEMBL database. Two-dimensional molecular descriptors were calculated and preprocessed to remove low-variance and highly correlated features, resulting in a refined feature set for model development. The dataset was categorized into active and inactive compounds and divided into training and testing subsets. Four machine learning algorithms. Logistic Regression, Support Vector Machine, Gradient Boosting, and Random Forest were implemented and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Among the models, Random Forest achieved the best performance, with an accuracy of 89.26%, F1-score of 89.87%, and AUC of 0.926, followed by Gradient Boosting with an accuracy of 87.92% and AUC of 0.922. Analysis of physicochemical descriptors indicated that hydrogen-bond donor capacity (nHD) shows a statistically significant association with variations in compound activity toward ADRB2, while lipophilicity (LogP) and hydrogen-bond acceptor count (nHA) do not exhibit statistically significant differences between activity classes. Overall, the results demonstrate that molecular descriptor–based machine learning models, particularly ensemble methods, provide an effective framework for predicting ADRB2-related compound activity and support the prioritization of candidate molecules in computational drug discovery.
Clinical and oral microbiome pattern of halitosis patients with periodontitis and gingivitis Diana S. Ningsih; Rinaldi Idroes; Boy M. Bachtiar; Khairan Khairan; Trina E. Tallei; Pati Kemala; Nur B. Maulydia; Ghazi M. Idroes; Zuchra Helwani
Narra J Vol. 3 No. 2 (2023): August 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i2.163

Abstract

Halitosis is caused by a bacterial proteolytic process that induces the production of volatile sulfur compounds, odor-causing gases. The aim of this study was to determine the clinical oral hygiene state and oral microbiome pattern of halitosis patients with periodontitis and gingivitis. The oral hygiene state of halitosis patients with periodontitis and gingivitis was assessed using the oral hygiene index simplified (OHI-S), decay missing filled teeth (DMFT), and tongue biofilm. The dorsum of the tongue and subgingival swabs were cultured for bacteria, and bacterial morphology was evaluated using Gram staining. Evaluation of the bacterial genus using the Bergey's systematic bacteriology diagram as a guide. A total of ten patients with periodontitis and gingivitis were included. Our data indicated that the scores of OHI-S and DMFT were different significantly between halitosis patients with periodontitis and gingivitis (both had p<0.001) while tongue biofilm score was not different between groups. On the dorsum of the tongue, periodontitis patients had a significant higher oral microbiome population (85.65x106 CFU/mL) compared to those with gingivitis (0.047x106 CFU/mL) with p=0.002. In contrast, the number of microbiomes in the subgingival had no significant different between periodontitis and gingivitis. On the dorsum of the tongue, six bacterial genera were isolated from periodontitis cases and seven genera were detected from gingivitis patients. On subgingival, 10 and 15 genera were identified from periodontitis and gingivitis, respectively. Fusobacterium, Propionibacterium, Eubacterium and Lactobacillus were the most prevalent among periodontitis cases while Porphyromonas was the most prevalent in gingivitis patients. In conclusion, although OHI-S and DMFT are different between periodontitis and gingivitis, overlapping of bacterial genera was detected between periodontitis and gingivitis cases.
Fine-Tuning ChemBERTa for Predicting Activity of AXL Kinase Inhibitors in Oncogenic Target Modeling Teuku Rizky Noviandy; Ghazi Mauer Idroes; Mohsina Patwekar; Rinaldi Idroes
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.
Sustainable Plant-Assisted Production of Silver Nanoparticle Hybrids for Antimicrobial Use: Insights from Chromolaena odorata and Patchouli Oil Pebriani, Liska Nova; Kemala, Pati; Idroes, Ghazi Mauer; Fatriasari, Widya; Khairan, Khairan; Idroes, Rinaldi
Grimsa Journal of Science Engineering and Technology Vol. 4 No. 1 (2026): April 2026
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v4i1.117

Abstract

This study developed a green synthesis approach for silver nanoparticles (AgNPs) using ethanolic extracts of Chromolaena odorata leaves (LCo) collected from geothermal areas, followed by post-synthesis incorporation of patchouli oil (PO) to improve antimicrobial performance. The synthesis was optimized using Response Surface Methodology (RSM) based on AgNO₃ concentration and pH, with surface plasmon resonance (SPR) as the response indicator. Successful formation of AgNPs was confirmed by characteristic SPR absorption in the visible region. Structural and morphological analyses indicated the involvement of plant-derived functional groups in nanoparticle stabilization, with predominantly spherical particles and some aggregation observed. Antimicrobial testing against Staphylococcus aureus, Escherichia coli, and Candida albicans showed that the PO-AgNPs-LCo system exhibited a slightly higher inhibition zone compared to AgNPs-LCo alone, indicating a marginal enhancement in antimicrobial activity. These results suggest that geothermal-derived plant extracts can be effectively utilized for AgNPs synthesis, while post-synthesis incorporation of natural oils may provide additional functional modification. However, the observed enhancement remains limited, indicating the need for further optimization and mechanistic studies. Overall, this work highlights a simple and eco-friendly route for developing plant-based antimicrobial nanomaterials.
Co-Authors - Fakhrurrazi - Mahmud Abas, Abdul Hawil Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Agus Winarsih Ahmad, Khairunnas Ahmad, Noor Atinah Ahsya, Yahdina Akyuni, Qurrata Amirah, Kelsy Andri Yadi Paembonan Arini, Musfira Asep Rusyana Azhar, Fauzul Azharuddin Azharuddin BAKRI, TEDY KURNIAWAN Binawati Ginting Boy M Bachtiar Boy M. Bachtiar Claus Jacob Claus Jacob Claus Jacob, Claus Deni Saputra Destiana, Khaerunisa Dharma, Aditia Dharma, Dian Budi Diah, Muhammad Dian Handayani Dian Lestari, Nova Diana S. Ningsih Diana Setya Ningsih, Diana Earlia, Nanda Eka Safitri Eka Safitri EKA SAFITRI El-Shazly, Mohamed Elisa Purwaendah Emran, Talha Bin Enitan, Seyi Samson Erkata Yandri Essy Harnelly Estevam, Ethiene Castellucci Ethiene Castellucci Estevam Eti Rohaeti Evi Yufita Ezzat, Abdelrahman O. Faddillah, Vira Faisal Abdullah Faisal, Farassa Rani Faradilla Faradilla FARADILLA, FARADILLA Farnida Farnida Fatimawali . Fauzi, Fazlin M. Fauzi, Fazlin Mohd Fazlin Mohd Fauzi Firaihanil Jannah Ghalieb Mutig Idroes Ghani, Azman Abdul Ghazi M. Idroes Ghazi Mauer Idroes Hakim, Rachmi F. Hanafiah, Olivia A. Harera, Cheariva Firsa Hesti Meilina Hizir Sofyan Husdayanti, Noviana Ida Zahrina Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Idroes, Ghifari Maulana Iin Shabrina Hilal Ilham Maulana Ilham Maulana Imelda, Eva Imran Imran Ira Maya Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Ismail Ismail Isnaini, Nadia Isra Firmansyah, Isra Jannah, Firaihanil Jannah, Rizka Auliatul Kairupan, Tara S. Karl Herbert Schaefer Karl Herbert Schaefer, Karl Herbert Karomah, Alfi Hudatul Kemala, Pati Khairan . Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan KHAIRI SUHUD Khairi Suhud Khalijah Awang Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lelifajri Lelifajri Lelifajri Lelifajri Lubis, Vanizra F. M. Rafi M. Yogi Riyantama Isjoni Madya, Muhammad Miftahul Mahmudi Mahmudi Maimun Syukri, Maimun Malahayati Malahayati MARIA BINTANG Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur B. Maulydia, Nur Balqis Maysarah, Hilda Md Sani, Nor Diyana Mikyal Bulqiah, Mikyal Mirda, Erisna Misbullah, Alim Misrahanum Misrahanum Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Mohsina Patwekar Mubaraq, Farhil Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Diah Muhammad Ridha Adhari, Muhammad Ridha Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mukhlisuddin Ilyas Muliadi Ramli Munawar, Agus Murniana Murniana Mursal Mursal Mursyida, Waliam Musdalifah, Annisa Muslem Muslem Muslem Muslem, Muslem Muzakir N. Nazaruddin Nabila, Fiki Farah Nainggolan, Sarah Ika Nanda Earlia Nasrullah Idris Nasrullah Idris Nazaruddin Nazaruddin NAZARUDDIN NAZARUDDIN Neonufa, Godlief Frederick Ningsih, Diana S. Niode, Nurdjannah Jane Nor Diyana Md Sani Novi Reandy Sasmita Noviandy, Teuku R. Nugraha, Gartika Nur B. Maulydia Nur Balqis Maulydia Nurdjannah J. Niode Nurleila, Nurleila Nurul Khaira Oesman, Frida Pati Kemala Patwekar, Faheem Patwekar, Mohsina Pebriani, Liska Nova Prakoeswa, Cita RS. Purwaendah, Elisa Putra, Noviandi I. Qurrata Akyuni Rahmadi Rahmadi Rahmadi Rahmadi Rahman, Isra Farliadi Rahman, Sunarti Abd Raihan Raihan Raihan Raihan, Raihan Raudhatul Jannah Razief Perucha Fauzie Afidh Ringga, Edi Saputra Rizka Auliatul Jannah Rizkia, Tatsa Romadhoni, Yenni Rusdi Andid Safhadi, Aulia Al-Jihad Saiful . Saiful Saiful Salaswati, Salaswati Salsabila, Indah Sasmita, Novi Reandy Satrio, Justinus Septaningsih, Dewi Anggraini Shafira, Ghina A. Siti Aisyah Solly Aryza Souvia Rahimah Sri Fitriyani Sufriadi, Elly sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Suhud, Khairi Sunarso Sunarso Supriatno Supriatno Supriatno Suryadi Suryadi Suryawati Suryawati Taopik Ridwan Taufik Ridwan Taufiq Karma Teuku Rizky Noviandy Teuku Rizky Noviandy Teuku Zulfikar Thomas Schneider Thomas Schneider, Thomas Triana Hertiani Trina E. Tallei Trina E. Tallei, Trina E. Trina Ekawati Tallei TRINA EKAWATI TALLEI Tuti Fadlilah Viona Diansari Widya FATRIASARI Zahraty, Ifrah Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani Zuchra Helwani, Zuchra Zulfiani, Utari Zulkarnain Jalil Zulkarnain Jalil