Article Per Year (5 Year)
p-Index From 2021 - 2026
8.885
P-Index
This Author published in this journals
All Journal HAYATI Journal of Biosciences Bulletin of Chemical Reaction Engineering & Catalysis 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

Published : 86 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 86 Documents
Search

 5   6   7   8   9 
Explainable Deep Learning with Lightweight CNNs for Tuberculosis Classification Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Zulfikar, Teuku; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.305

Abstract

Tuberculosis (TB) remains a major global health threat, particularly in low-resource settings where timely diagnosis is critical yet often limited by the lack of radiological expertise. Chest X-rays (CXRs) are widely used for TB screening, but manual interpretation is prone to errors and variability. While deep learning has shown promise in automating CXR analysis, most existing models are computationally intensive and lack interpretability, limiting their deployment in real-world clinical environments. To address this gap, we evaluated three lightweight and explainable CNN architectures, ShuffleNetV2, SqueezeNet 1.1, and MobileNetV3, for binary TB classification using a locally sourced dataset of 3,008 CXR images. Using transfer learning and Grad-CAM for visual explanation, we show that MobileNetV3 and ShuffleNetV2 achieved perfect test performance with 100% accuracy, sensitivity, specificity, precision, and F1-score, along with AUC scores of 1.00 and inference times of 94.66 and 103.63 seconds, respectively. SqueezeNet performed moderately, with a lower F1-score of 82.98% and several misclassifications. These results demonstrate that lightweight CNNs can deliver high diagnostic accuracy and transparency, supporting their use in scalable, AI-assisted TB screening systems for underserved healthcare settings.
Inductive Biases in Feature Reduction for QSAR: SHAP vs. Autoencoders Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Lala, Andi; Helwani, Zuchra; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.306

Abstract

Machine learning models in drug discovery often depend on high-dimensional molecular descriptors, many of which may be redundant or irrelevant. Reducing these descriptors is essential for improving model performance, interpretability, and computational efficiency. This study compares two widely used reduction strategies: SHAP-based feature selection and autoencoder-based compression, within the context of Quantitative Structure-Activity Relationship (QSAR) classification. LightGBM is used as a consistent modeling framework to evaluate models trained on all descriptors, the top 50 and 100 SHAP-ranked descriptors, and a 64-dimensional autoencoder embedding. The results show that SHAP-based selection produces interpretable and stable models with minimal performance loss, particularly when using the top 100 descriptors. In contrast, the autoencoder achieves the highest test performance by capturing nonlinear patterns in a compact, low-dimensional representation, although this comes at the cost of interpretability and consistency across data splits. These findings reflect the differing inductive biases of each method. SHAP prioritizes sparsity and attribution, while autoencoders focus on reconstruction and continuity. The analysis emphasizes that descriptor reduction strategies are not interchangeable. SHAP-based selection is suitable for applications where interpretability and reliability are essential, such as in hypothesis-driven or regulatory settings. Autoencoders are more appropriate for performance-driven tasks, including virtual screening. The choice of reduction strategy should be guided not only by performance metrics but also by the specific modeling requirements and assumptions relevant to cheminformatics workflows.
Antibacterial Potential of Geothermal Plant Extracts from Jaboi Crater, Indonesia: A Thin Layer Chromatography-Bioautography Approach Khairan, Khairan; Mubaraq, Farhil; Maulydia, Nur Balqis; Awang, Khalijah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 2 (2025): September 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i2.312

Abstract

Antimicrobial resistance (AMR) poses an urgent global health concern, prompting the need for alternative therapeutic agents. This study evaluated the antimicrobial potential of ethyl acetate extracts from five medicinal plant species (Memecylon edule, Garcinia dioica, Syzygium sp., Memecylon caeruleum, and Aporosa octandra) collected from the geothermal Jaboi Crater in Aceh, Indonesia. Phytochemical profiling was performed using thin layer chromatography (TLC), and antimicrobial activity was assessed via TLC-bioautography against Escherichia coli, Staphylococcus aureus, and Candida albicans. The results revealed the presence of phenolic and terpenoid compounds, with antibacterial activity observed only against E. coli. No inhibition was detected against S. aureus or C. albicans. The study highlights the selective antimicrobial potential of geothermal plant extracts and underscores the relevance of bioautography as a rapid screening tool. While preliminary, these findings support further investigation into geothermal flora as a source of antibacterial compounds and call for advanced studies to isolate active constituents and explore their mechanisms of action.
Interpretable Machine Learning QSAR Models for Classification and Screening of VEGFR-2 Inhibitors in Anticancer Drug Discovery Noviandy, Teuku Rizky; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 2 (2025): September 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i2.339

Abstract

Cancer remains a major global health burden, with angiogenesis playing a central role in tumor growth and progression. Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a key mediator of angiogenesis and an attractive therapeutic target, but existing inhibitors are limited by reduced efficacy, toxicity, and resistance, creating a need for more effective predictive models in drug discovery. In this study, an interpretable machine learning based QSAR approach was developed using a curated dataset of 10,221 VEGFR-2 inhibitors from ChEMBL represented by 164 molecular descriptors. Four algorithms, kNN, AdaBoost, Random Forest, and XGBoost, were compared, and XGBoost achieved the best results with an accuracy of 83.67 percent, sensitivity of 91.38 percent, specificity of 71.73 percent, F1-score of 87.17 percent, and AUC of 0.9009. Model interpretation with LIME identified molecular descriptors related to hydrogen bonding, electrostatics, and lipophilicity as key contributors to activity. These results indicate that interpretable ensemble models can combine strong predictive performance with mechanistic insights, supporting rational design and optimization of novel VEGFR-2 inhibitors for anticancer therapy.
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.
The Potent Antimicrobial Spectrum of Patchouli: Systematic Review of Its Antifungal, Antibacterial, and Antiviral Properties Kemala, Pati; Idroes, Rinaldi; Khairan, Khairan; Ramli, Muliadi; Tallei, Trina Ekawati; Helwani, Zuchra; Rahman, Sunarti Abd
Malacca Pharmaceutics Vol. 2 No. 1 (2024): March 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i1.156

Abstract

ntention towards natural essential oils from medicinal plants has increased rapidly over the past decade as these oils have antimicrobial and antioxidant properties against various chronic diseases. One essential oil source with antimicrobial properties is the essential oil from Pogostemon cablin (Blanco) Benth. This review aims to provide information on using patchouli oil as an antimicrobial against bacterial, fungal, and viral pathogens in the last five years. There were 37 articles found in the PUBMED database by June 15, 2023. After searching, 6 of them were duplicates. A total of 2 papers were inaccessible, 4 were not research articles, and five were excluded because they were irrelevant to the scope of this study. This review shows that research related to patchouli as an antimicrobial in the last five years involves Pogostemon cablin leaf samples as silver nanoparticle bioreductors. Patchouli oil is used in membrane, nanocomposite film, and starch hydrogel manufacturing. Patchouli oil is a prestigious antimicrobial agent because it can fight numerous pathogenic microbes from bacteria, fungi, and viruses.
Exploring the Medicinal Potential of Blumea balsamifera: Insights from Molecular Docking and Molecular Dynamics Simulations Analyses Maulydia, Nur Balqis; Khairan, Khairan; Tallei, Trina Ekawati; Salaswati, Salaswati; Musdalifah, Annisa; Nabila, Fiki Farah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 1 (2024): March 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i1.168

Abstract

Blumea balsamifera from the Ie-Jue geothermal area in Aceh Province, Indonesia, has been reported to have a variety of secondary metabolites. However, there is limited information about the activity of these chemical metabolites from B. balsamifera. The aim of this study is to evaluate the therapeutic potential of these compounds using molecular docking and molecular dynamics simulations. Six selective compounds were thoroughly evaluated using molecular docking techniques for their inhibitory effects on both Coronavirus protease and human interleukin receptors. Additionally, druglikeness assessments based on the Lipinski rule of five were performed to evaluate these six ligands. Our results show that stigmasterol, a key component of B. balsamifera, has demonstrated low binding free energy values across four receptors. Furthermore, molecular dynamics simulations confirmed the stability of the top ligand-receptor complex, particularly stigmasterol-1IRA, based on five parameters, indicating its stability as an inhibitor. This research highlights the potential of stigmasterol as a therapeutic agent derived from medicinal plants of B. balsamifera and underscores the value of our molecular approach in identifying opportunities for pharmaceutical development.
AKTIVITAS BEBERAPA SENYAWA TURUNAN BENZOPIRAN (CHROMONES) DAN BENZOFURANON (COUMARANONES) TERHADAP Steinernema feltiae (The Activities of Benzopyran (Chromones) and Benzofuranones (Coumaranones) Derivatives against Steinernema feltiae) Khairan, Khairan; Bahi, Muhammad; Jacob, Claus; Idroes, Rinaldi
Jurnal Kedokteran Hewan Vol 10, No 1 (2016): March
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21157/j.ked.hewan.v10i1.3368

Abstract

This study was purposed to inquire the activities of benzopyran (chromones) and benzofuranones (coumaranones) derivatives against Steinernema feltiae (S. feltiae). The toxicity assay against S. feltiae showed that benzopyran derivatives 2, 3, 4, 6, and 9 have the highest activity on S. feltiae with viabilities percentage of 50%. The compound 9 demostrated the highest activity with LD 50 and LD values, 7.2 and 52.2 M, respectively. The activities of compound 7 and 10 showed the lowest toxicity. Interestingly, the activity of benzofuranone derivatives showed significant activities against S. feltiae. Compare to benzopyran derivatives, the benzofuranone derivatives has the highest toxicity, in particular compound 13 with LD 5.45 M. The nematicidal assay showed that benzofuranones (coumaranones) derivatives revealed higher activities than benzopyran (chromones) derivatives.Key words: chromones, benzopyran, coumaranones, benzofuranone, and Steinernema feltiae
AKTIVITAS SULFUR DAN SELENIUM NANOPARTIKEL TERHADAP CACING Steinerma feltiae DAN PERBANDINGAN TOKSISITASNYA TERHADAP SEL NEUROBLASTOMA (NEURO 2A CELL LINES) Khairan, Khairan; Idroes, Rinaldi; Bahi, Muhammad; Schaefer, Karl Herbert; Schneider, Thomas; Jacob, Claus
Jurnal Kedokteran Hewan Vol 9, No 1 (2015): March
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21157/j.ked.hewan.v9i1.2798

Abstract

Penelitian ini bertujuan mengetahui aktivitas sulfur dan selenium nanopartikel terhadap cacing Steinernema feltiae (S. feltiae) dan perbandingan toksisitasnya terhadap sel neuroblastoma (neuro 2A). Sulfur dan selenium nanopartikel aqueous nanoparticles dikarakterisasimenggunakan Nano Zetasizer pada pH 7 dan suhu 25 C. Dalam penelitian ini uji nematoda dilakukan terhadap S. feltiae. Hasil penelitian menunjukkan bahwa sulfur nanopartikel mempunyai aktivitas yang sangat tinggi terhadap S. feltiae dengan lethal dose 50 (LD50) berkisar pada 6,99 g/ml setelah 24 jam inkubasi. Sementara itu, live and dead assay dilakukan terhadap neuroblastoma sel (Neuro 2A cell lines). Hasil penelitian menujukkan bahwa aqueous sulfur nanopartikel (NPS) menunjukkan aktivitas yang lebih baik dibandingkan dengan aqueous selenium nanopartikel (NPSe) terhadap sel neuroblastoma (neuro 2A cell lines) dengan IC50 1 g/ml.
ISOLASI ANTIBIOTIK REDUKTIOMISIN DARI BAKTERI TERRESTRIAL Streptomyces sp Bahi, Muhammad; Idroes, Rinaldi
Jurnal Kedokteran Hewan Vol 7, No 2 (2013): September
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21157/j.ked.hewan.v7i2.925

Abstract

Penelitian ini bertujuan melakukan isolasi dan penentuan aktivitas mikrobial senyawa bioaktif reduktiomisin dari bakteri Streptomyces sp. Ank181 daratan (terrestrial). Penelitian ini dilaksanakan di laboratorium kimia organik, Institute of Organic and Biomoleculare Chemistry, University of Goettingen, Germany. Isolat dan subkultur agar bakteri Streptomyces sp. Ank181 diperoleh dari koleksi sampel genus Streptomyces Professor Dr. H. Anke, Institute for Biotechnology and Drug Research, Kaiserslautern, Germany. Struktur reduktiomisin dalam penelitian ini dielusidasi berdasarkan data spektroskopi dan spektrometri massa. Tiga senyawa metabolit sekunder telah berhasil diisolasi dan diidentifikasi dari bakteri tanah genus Streptomyces sp. Ank181, yaitu reduktiomisin, asam 2,3-dihidroksibenzoat, dan asam indole-3-karboksilat. Hasil uji antimikroba menunjukkan bahwa reduktiomisin bersifat bioaktif terhadap bakteri, jamur dan sitotoksik terhadap Artemia salina.
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 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 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 Saiful Saiful Salaswati, Salaswati Salsabila, Indah Sasmita, Novi Reandy Satrio, Justinus Septaningsih, Dewi Anggraini Shafira, Ghina A. Siti Aisyah Solly Aryza Souvia Rahimah Sufriadi, Elly sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Suhud, Khairi 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 Yandri, Erkata Zahraty, Ifrah Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulfiani, Utari Zulkarnain Jalil Zulkarnain Jalil