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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 International Journal of Hydrological and Environmental for Sustainability 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 Civil Engineering, Building, Construction & Architecture 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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The antinematicidal activity of vitamin E and its derivatives on Sterinernema feltieae Khairan, Khairan; Idroes, Rinaldi; Murniana, Murniana; Diah, Muhammad
Journal of Carbazon Vol 1, No 2 (2023): December 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jocarbazon.v2i1.35776

Abstract

Vitamin E is found naturally in some foods, and also available as a dietary supplement. "Vitamin E" is the fat-soluble compounds with distinctive antioxidant activities. The vitamin E acts as an antioxidant, which protects cell membranes. Sterinernema feltieae was selected as a test organism is because this organism is a complex organism and can be used as a model for whole organisms. The objective of this study is to evaluate the activity of vitamin E and its derivatives on Sterinernema feltieae. The results showed that the derivative of vitamin E (C37H55NO3) was most active against S. feltiae with the LD50 value was 207.77 M, followed by vitamin E (C29H50O2) with the LD50 value was 209.09 M. These results indicated that derivative of vitamin E (C37H55NO3) exerts more potent toxicity than vitamin E against Sterinernema feltieae.
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.
Assessing Anthropogenic Pressure through Biomonitoring: Aquatic Biota as Indicators of Water Quality in an Urban Lake Cundaningsih, Nurvita; Anwar, Haerul; Jasin, Faisal M; Hartono, Hartono; Nur, Adrian Rahmat; Idroes, Rinaldi
International Journal of Hydrological and Environmental for Sustainability Vol. 4 No. 3 (2025): International Journal of Hydrological and Environmental for Sustainability
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/ijhes.v4i3.846

Abstract

Urban areas in Jakarta face significant pressure on clean water resources due to increasing population and anthropogenic activities. This research aims to conduct biomonitoring of the Situ Bambon Ciracas lake ecosystem, East Jakarta, by analyzing water quality and the community structure of macrozoobenthos, phytoplankton, and zooplankton as bioindicators. A descriptive quantitative method was used, involving measurements of water physical-chemical parameters (TDS, TSS, pH, BOD, COD, Total-P) and identification of aquatic biota. The results indicate that the water quality of Situ Bambon Ciracas lake is lightly to moderately polluted, dominated by organic compounds. BOD (5−34.67 mg/L) and COD (17.05−193.56 mg/L) values consistently exceeded the Class 3 water quality standards, and TDS showed an increasing trend. The biota community structure reflects these conditions: macrozoobenthos showed moderate diversity (H′=1.2, E=0.6). Phytoplankton (H′=3.12−3.2, E=0.74−0.76) and zooplankton (H′=2.11−2.16, E=0.76−1.95) showed high diversity and evenness, but were dominated by bioindicator species tolerant to organic pollution (e.g., Oscillatoria sp., Nitzschia sp., Colpoda sp., Closterium sp.). The positive correlation between the abundance of these bioindicator species and high BOD and COD confirms organic waste as the main driver of ecological change. In conclusion, the Situ Bambon Ciracas lake ecosystem is under significant anthropogenic pressure. The dominance of pollution-tolerant species, despite existing diversity, highlights the urgency of comprehensive management and restoration efforts to maintain the sustainability of this urban lake.
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.
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 Claus Jacob Claus Jacob Claus Jacob, Claus Cundaningsih, Nurvita Deni Saputra Destiana, Khaerunisa Dharma, Aditia Dharma, Dian Budi Diah, Muhammad Dian Handayani Dian Lestari, Nova 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 Mauer Idroes Haerul Anwar Hakim, Rachmi F. Hanafiah, Olivia A. Harera, Cheariva Firsa Hartono Hartono 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 Jasin, Faisal M 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 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 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 Balqis Maulydia Nur, Adrian Rahmat Nurdjannah J. Niode Nurleila, Nurleila Nurul Khaira Oesman, Frida Patwekar, Faheem Patwekar, Mohsina 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 Zulfikar Thomas Schneider Thomas Schneider, Thomas Triana Hertiani Trina E. Tallei, Trina E. TRINA EKAWATI TALLEI Trina Ekawati Tallei Tuti Fadlilah Viona Diansari Zahraty, Ifrah Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulfiani, Utari Zulkarnain Jalil Zulkarnain Jalil