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All Journal PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS (COMORBID) TRANSPUBLIKA INTERNATIONAL RESEARCH IN EXACT SCIENCES (TIRES) Jurnal Pengabdian Masyarakat Bangsa 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 Grimsa Journal of Science Engineering and Technology
Ghazi Mauer Idroes
Department Of Occupational Health And Safety, Faculty Of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma Noviandy, Teuku Rizky; Imelda, Eva; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i1.271

Abstract

Glaucoma is a leading cause of irreversible blindness, primarily managed by lowering intraocular pressure (IOP). Carbonic Anhydrase-II (CA-II) inhibitors play a crucial role in this treatment by reducing aqueous humor production. However, existing CA-II inhibitors often suffer from poor selectivity, side effects, and limited bioavailability, highlighting the need for more efficient and targeted drug discovery approaches. This study uses machine learning-driven Quantitative Structure-Activity Relationship (QSAR) modeling to predict CA-II inhibition based on molecular descriptors, significantly enhancing screening efficiency over traditional experimental methods. By evaluating multiple machine learning models, including Support Vector Machine, Gradient Boosting, and Random Forest, we identify SVM as the most effective classifier, achieving the highest accuracy (83.70%) and F1-score (89.36%). Class imbalance remains challenging despite high sensitivity, necessitating further improvements through resampling and hyperparameter optimization. Our findings underscore the potential of machine learning-based virtual screening in accelerating CA-II inhibitor identification and advocate for integrating AI-driven approaches with traditional drug discovery techniques. Future directions include deep learning enhancements and hybrid machine learning-docking frameworks to improve prediction accuracy and facilitate the development of more potent and selective glaucoma treatments.
Influence of Elevational and Environmental Factors on Parasitic Nematode Distribution in Arabica Coffee in the Gayo Highlands, Indonesia Surna, Muhammad Ipan; Fazli, Qalbin Salim; Chamzurni, Tjut; Susanna, Susanna; Idroes, Ghazi Mauer
Leuser Journal of Environmental Studies Vol. 3 No. 2 (2025): October 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v3i2.293

Abstract

Highland agricultural landscapes are sensitive to environmental variation, particularly in regions like the Gayo Highlands of Aceh, Indonesia, where Arabica coffee (Coffea arabica L.) is a major crop. While parasitic nematodes are known to affect crop health and soil ecosystems, little is known about how their abundance and distribution vary with elevation in tropical coffee systems. The Gayo Highlands, despite their significant contribution to national coffee production, have been understudied in terms of soil biodiversity and nematode-related threats. To address this knowledge gap, we assessed the composition and abundance of parasitic nematodes in coffee plantations across three elevation zones: 800–1000 m, 1001–1200 m, and 1201–1400 m above sea level. We collected soil and root samples from symptomatic coffee plants, extracted nematodes using the Baermann funnel method, and identified them to the genus level. The study found three genera: Pratylenchus, Meloidogyne, and Rotylenchus. Pratylenchus was the most abundant, particularly at 800–1,000 m (34 individuals/10 ml), while the highest total nematode abundance occurred at 1,001–1,200 m (7.2 ± 1.44 individuals/10 ml). Statistical analysis showed significant differences in nematode abundance between elevation zones. These results indicate that elevation influences nematode populations, likely through environmental factors such as temperature, soil moisture, and pH. Understanding these patterns is important for developing site-specific strategies for pest management and maintaining soil health in highland coffee systems.
Resilience and Adaptation: Plant Ecology in Indonesia’s Geothermal Environments Idroes, Ghazi Mauer; Khairan, Khairan; Suhartono, Eko; Prasetio, Rasi; Idroes, Ghalieb Mutig; Suhendrayatna, Suhendrayatna
Leuser Journal of Environmental Studies Vol. 3 No. 1 (2025): April 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v3i1.294

Abstract

Geothermal ecosystems are defined by extreme environmental conditions, such as elevated temperatures, high concentrations of toxic chemicals, and fluctuations in abiotic stressors, which shape plant survival and adaptation. These unique ecosystems, found across various geothermal regions globally, support specialized plant communities that have developed distinctive morphological, physiological, and ecological adaptations. Indonesia, located on the Pacific Ring of Fire, is one of the world’s richest geothermal nations, offering an important yet underexplored context for studying vegetation in geothermal zones. This review examines the environmental conditions of geothermal ecosystems, the adaptive strategies of vegetation, and patterns of plant diversity within Indonesian geothermal fields. It also explores ecological succession, community dynamics, and the potential use of geothermal vegetation as environmental indicators for biomonitoring. Despite growing interest, significant research gaps remain, particularly in long-term monitoring and the integration of molecular-level studies. Addressing these gaps is essential for enhancing scientific understanding and informing conservation and sustainable geothermal energy development in tropical regions. This review highlights the ecological significance of geothermal vegetation and underscores the need for interdisciplinary research to support both biodiversity preservation and responsible energy exploitation.
Techniques and Tools in Learning Analytics and Educational Data Mining: A Review Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Paristiowati, Maria; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i1.308

Abstract

Learning analytics and educational data mining are rapidly evolving fields that leverage data-driven methods to enhance teaching, learning, and institutional decision-making. This review provides a comprehensive overview of the key analytical techniques and tools employed in learning analytics and educational data mining, including classification, clustering, regression, association rule mining, and data visualization. It also highlights the integration of advanced methods such as deep learning and adaptive systems for personalized education. The paper examines various platforms and technologies, including learning management systems, open-source tools, and AI/ML libraries, to evaluate their capabilities, scalability, and practical adoption. Key application areas, such as dropout prediction, engagement analysis, personalized learning, and curriculum design, are examined through selected case studies spanning K–12 and higher education. The review emphasizes the growing importance of ethical considerations, interpretability, and usability in the application of educational analytics. By synthesizing current practices and trends, this work aims to inform educators, researchers, and developers seeking to harness educational data for improved learning outcomes and strategic planning.
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.
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.
Environmental Influence of Altitude on Coffee Leaf Rust Severity in Arabica Coffee of Aceh Tengah, Indonesia Arkadinata, Teguh; Fazli, Qalbin Salim; Alfizar, Alfizar; Hakim, Lukman; Idroes, Ghazi Mauer
Leuser Journal of Environmental Studies Vol. 3 No. 2 (2025): October 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v3i2.344

Abstract

Coffee leaf rust (CLR), caused by Hemileia vastatrix, remains one of the most damaging diseases affecting Arabica coffee worldwide. Understanding how environmental gradients influence CLR development is critical for sustainable management in tropical highland systems. This study examined the influence of altitude on CLR incidence and severity across five elevation ranges (800–1800 masl) in Arabica coffee plantations of Aceh Tengah, Indonesia. Field assessments were conducted on 25 farms using a standardized sampling layout and severity scoring scale. Analysis of variance (ANOVA) revealed that altitude had no significant effect on disease incidence (F = 0.14 < F0.05 = 3.01), which remained uniformly high across all sites (>75%), but significantly affected disease severity (F = 3.34 > F0.05 = 3.01). The highest mean severity (51.88%) occurred at 1600–1800 masl, differing significantly from lower elevations. These findings suggest that while CLR infection frequency is widespread, environmental conditions at higher altitudes favor greater lesion expansion and disease development. The results highlight the importance of considering local microclimatic variability in disease risk assessment and adaptive management. Further studies integrating microclimatic and agronomic measurements are needed to strengthen causal understanding and support environmentally based strategies for sustainable Arabica coffee production.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Ahmad, Noor Atinah Akmal Muhni Alfizar Alfizar Ali Bakri Anggi, Tiara Aprianto . Arkadinata, Teguh Asep Rusyana Azhar, Fauzul Bachtiar, Boy Muhclis Bahri, Ridzky Aulia Bako, Winanda Celik, Ismail Diah, Muhammad Diana Setya Ningsih, Diana Diana Setya Ningsih, Diana Setya Diki, Diki Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Faisal, Farassa Rani Fajar Fakri Fauziah, Niken Fazli, Qalbin Salim Hafni Zahara Harahap, Saima Putri Harera, Cheariva Firsa Hewindati, Yuni Tri Hizir Sofyan Idroes, Ghalieb Mutig Ifandi, Ilham Imelda, Eva Irvanizam, Irvanizam Irwana, Salman Jainury, Aldi Jauna, Jauna Kemala, Pati Khairan Khairan Khalijah Awang Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lukman Hakim Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Maysarah, Hilda Medyan Riza Mirda, Erisna Mirja, Mirja Misbullah, Alim Muhammad Adam, Muhammad Muhammad Ichsan Muhammad Ichsan Muhammad Sabri Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mukhlisuddin Ilyas Muliadi Ramli Muslem Muslem, Muslem Musvira, Intan Natasya Natasya Nizamuddin Nizamuddin Nova Yanti Pasyamei Rembune Kala Patwekar, Mohsina Prasetio, Rasi Purnama, M. Risky Putri Raisah Raisah, Putri Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Rizkia, Tatsa Sasmita, Novi Reandy Shofi, Shofi Siti Maulina Rukmana Souvia Rahimah Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Surna, Muhammad Ipan Susanna Susanna Syamsiar, Syamsiar Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar Tjut Chamzurni TRINA EKAWATI TALLEI Wahyuni, Srie Wangi, Putri Ayu Sekar Wildan Seni, Wildan Wiwik Handayani Yandri, Erkata Yustiana Yustiana, Yustiana Zahriah, Zahriah Zuchra Helwani, Zuchra Zulkarnain Jalil