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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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Urban Air Quality Classification Using Machine Learning Approach to Enhance Environmental Monitoring Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Maulana, Aga; Zahriah, Zahriah; Suhendrayatna, Suhendrayatna; Suhartono, Eko; Khairan, Khairan; Kusumo, Fitranto; Helwani, Zuchra; Abd Rahman, Sunarti
Leuser Journal of Environmental Studies Vol. 1 No. 2 (2023): November 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i2.99

Abstract

Urban areas worldwide grapple with environmental challenges, notably air pollution. DKI Jakarta, Indonesia's capital city, is emblematic of this struggle, where rapid urbanization contributes to increased pollutants. This study employed the CatBoost machine learning algorithm, known for its resistance to overfitting and capability to handle missing data, to predict urban air quality based on pollutant levels from 2010 to 2021. The dataset, sourced from Jakarta's air quality monitoring stations, includes pollutants such as PM10, SO2, CO, O3, and NO2. After preprocessing, we used 80% of the data for training and 20% for testing. The model displayed high accuracy (0.9781), precision (0.9722), and recall (0.9728). The feature importance chart revealed O3 (Ozone) as the top influencer of air quality predictions, followed by PM10. Our findings highlight the dominant pollutants affecting urban air quality in Jakarta, Indonesia and emphasizing the need for targeted strategies to reduce their concentrations and ensure a cleaner and healthier urban environment.
Ensuring Accuracy: Critical Validation Techniques in Geochemical Analysis for Sustainable Geothermal Energy Development Idroes, Ghazi Mauer; Suhendrayatna, Suhendrayatna; Khairan, Khairan; Suhartono, Eko; Prasetio, Rasi; Riza, Medyan
Leuser Journal of Environmental Studies Vol. 2 No. 1 (2024): April 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v2i1.176

Abstract

Geochemical analysis is a critical tool in geothermal exploration, providing valuable insights into reservoir characteristics. However, obtaining accurate and reliable geochemical data requires rigorous validation techniques. This review examines key factors affecting the accuracy of geochemical data and discusses best practices for ensuring quality. Proper sampling methods, including selection of representative locations, use of appropriate equipment, and adherence to robust protocols for sample collection, filtration, preservation, and storage, are essential for maintaining integrity. Analytical techniques must be carefully selected, with regular calibration and standardization of instruments using certified reference materials. Implementing comprehensive quality assurance and quality control procedures, such as analyzing blanks, duplicates, and spike samples, helps monitor precision and accuracy. Data interpretation should consider the complexities of the geological and hydrological settings, integrating multiple lines of evidence. By following established guidelines and continuously updating methods based on emerging technologies and inter-laboratory comparisons, geothermal teams can optimize the reliability of their geochemical data. Accurate and precise geochemical information, when combined with geological, geophysical, and hydrological data, enables informed decision-making and enhances the success of geothermal projects. As geothermal energy gains importance in the transition to sustainable resources, ensuring the accuracy of geochemical analysis will be crucial for effective exploration and development.
Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Adam, Muhammad; Rusyana, Asep; Sofyan, Hizir
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.51

Abstract

This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.
Evaluating Extraction Methods for Caffeine Content in Gayo Arabica Coffee Oil through Gas Chromatography-Mass Spectroscopy Khairan, Khairan; Musvira, Intan; Lala, Andi; Diah, Muhammad; Maulana, Aga; Idroes, Ghazi Mauer; Awang, Khalijah
Grimsa Journal of Science Engineering and Technology Vol. 2 No. 1 (2024): April 2024
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v2i1.31

Abstract

This study aims to determine physicochemical properties, and caffeine analysis of green bean coffee essential oil (GBCEO) and roasted bean coffee essential oil (RBCEO) by maceration and soxhlet extraction methods. The results indicated that RBCEO by maceration method have higher percentage of yield compared to GBCEO. By the same to soxhlet extraction method, RBCEO also showed higher percentage of yield compared to GBCEO. The refractive index of the GBCEOm and GBCEOs have a lower acid value compared to RBCEOm and RBCEOs. The specific gravity obtained for GBCEOm, RBCEOm, GBCEOs, and RBCEOs ranged from 0.87 to 0.97. The results showed that GBCEOm has the highest saponification value followed by RBCEOs. GBCEOm has the highest iodine value followed by RBCEOs, while RBCEOm and GBCEOs have a similar iodine value. The peroxide value showed that GBCEOs, and RBCEOs by soxhlet extraction method have higher peroxide value. The GC-MS analysis revealed that GBCEOm has higher caffeine followed by GBCEOs with the percentages area of 9.31% and 7.36% respectively. Meanwhile RBCEOm has lower caffeine followed by RBCEOs with the percentages area of 7.36% and 4.28% respectively. This finding showed that GBCEO shows higher caffeine compound compared with RBCEO.
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.
Student Perspectives on the Role of Artificial Intelligence in Education: A Survey-Based Analysis Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Maulana, Aga; Irvanizam, Irvanizam; Jalil, Zulkarnain; Lensoni, Lensoni; Lala, Andi; Abas, Abdul Hawil; Tallei, Trina Ekawati; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.58

Abstract

Artificial intelligence (AI) has emerged as a powerful technology that has the potential to transform education. This study aims to comprehensively understand students' perspectives on using AI within educational settings to gain insights about the role of AI in education and investigate their perceptions regarding the advantages, challenges, and expectations associated with integrating AI into the learning process. We analyzed the student responses from a survey that targeted students from diverse academic backgrounds and educational levels. The results show that, in general, students have a positive perception of AI and believe AI is beneficial for education. However, they are still concerned about some of the drawbacks of using AI. Therefore, it is necessary to take steps to minimize the negative impact while continuing to take advantage of the advantages of AI in education.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Maulana, Aga; Idroes, Ghazi Mauer; Kemala, Pati; Maulydia, Nur Balqis; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Sofyan, Hizir; Rusyana, Asep
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.
Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm Maulana, Aga; Faisal, Farassa Rani; Noviandy, Teuku Rizky; Rizkia, Tatsa; Idroes, Ghazi Mauer; Tallei, Trina Ekawati; El-Shazly, Mohamed; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.72

Abstract

Diabetes is a chronic condition characterized by elevated blood glucose levels which leads to organ dysfunction and an increased risk of premature death. The global prevalence of diabetes has been rising, necessitating an accurate and timely diagnosis to achieve the most effective management. Recent advancements in the field of machine learning have opened new possibilities for improving diabetes detection and management. In this study, we propose a fine-tuned XGBoost model for diabetes detection. We use the Pima Indian Diabetes dataset and employ a random search for hyperparameter tuning. The fine-tuned XGBoost model is compared with six other popular machine learning models and achieves the highest performance in accuracy, precision, sensitivity, and F1-score. This study demonstrates the potential of the fine-tuned XGBoost model as a robust and efficient tool for diabetes detection. The insights of this study advance medical diagnostics for efficient and personalized management of diabetes.
ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography Idroes, Rinaldi; Noviandy, Teuku Rizky; Maulana, Aga; Suhendra, Rivansyah; Sasmita, Novi Reandy; Muslem, Muslem; Idroes, Ghazi Mauer; Jannah, Raudhatul; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.73

Abstract

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.
Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Emran, Talha Bin; Tallei, Trina Ekawati; Helwani, Zuchra; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.91

Abstract

This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.
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