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All Journal Jurnal Pengabdian Masyarakat Tjut Nyak Dhien Journal of Computing Theories and Applications 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 The International Journal of Financial Systems Jurnal Medika: Medika Grimsa Journal of Business and Economics Studies Journal of Future Artificial Intelligence and Technologies
Teuku Rizky Noviandy
Interdisciplinary Innovation Research Unit, Graha Primera Saintifika, Aceh Besar 23771, Indonesia
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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A Deep Dive into Indonesia's CO2 Emissions: The Role of Energy Consumption, Economic Growth and Natural Disasters Idroes, Ghalieb Mutig; Hardi, Irsan; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Hilal, Iin Shabrina; Kusumo, Fitranto; Idroes, Rinaldi
Ekonomikalia Journal of Economics Vol. 1 No. 2 (2023): November 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i2.115

Abstract

This study examines the influence of non-renewable energy consumption, renewable energy consumption, economic growth, and natural disasters on Indonesia's carbon dioxide (CO2) emissions spanning from 1980 to 2021. The Autoregressive Distributed Lag (ARDL) model is employed, with supplementary robustness checks utilizing Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR). The findings reveal that economic growth, along with non-renewable and renewable energy consumption, significantly affects CO2 emissions in both the short and long term. Robustness checks confirm the positive impact of non-renewable energy consumption and economic growth, while renewable energy consumption has a negative effect on CO2 emissions. Moreover, natural disasters exhibit a positive short-term impact on CO2 emissions. Pairwise Granger causality results further underscore the intricate relationships between the variables. To mitigate climate change and curb CO2 emissions in Indonesia, the study recommends implementing policies that foster sustainable economic development, encourage the adoption of renewable energy, and enhance disaster resilience.
Optimizing University Admissions: A Machine Learning Perspective Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Paristiowati, Maria; Suhendra, Rivansyah; Yandri, Erkata; Satrio, Justinus; 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.v1i1.46

Abstract

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.
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.
Does Online Education Make Students Happy? Insights from Exploratory Data Analysis Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Emran, Talha Bin; Zahriah, Zahriah; Rahimah, Souvia; Lala, Andi; Idroes, Rinaldi
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.v1i2.124

Abstract

This study investigates the impact of online education on student happiness. Utilizing a dataset of 5715 students sourced from Bangladesh, we employed an exploratory data analysis to analyze the quantitative data. The key finding is that there is a prevalent trend of dissatisfaction with online education among Bangladeshi students, regardless of demographic factors like age, gender, education level, preferred device for access, or type of academic institution. The dissatisfaction trend highlights the need of continuous improvements and targeted interventions are essential to ensure online education not only enables academic success, but also supports the overall wellbeing and happiness of students in the context of a developing country.
Digital Transformations in Vocational High School: A Case Study of Management Information System Implementation in Banda Aceh, Indonesia Idroes, Rinaldi; Subianto, Muhammad; Zahriah, Zahriah; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Noviandy, Teuku Rizky; Sugara, Dimas Rendy; Mursyida, Waliam; Zhilalmuhana, Teuku; Idroes, Ghalieb Mutig; Maulana, Aga; Nurleila, Nurleila; Sufriani, Sufriani
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.v1i2.128

Abstract

This study examines the digital transformation in vocational education through the implementation of a Management Information System (MIS) in Banda Aceh, Indonesia. Focused on enhancing educational administration and decision-making, the study provides insightful analysis on the integration of MIS in State Vocational High School (SMK), specifically SMKN 1 and SMKN 3 in Banda Aceh. A purposive sampling method was employed for usability testing. The questionnaire-based usability test revealed high reliability and positive user responses across multiple indicators. Data analysis affirmed the system's high user satisfaction, effectiveness, and ease of use. Despite limitations, the study highlights the significant potential of well-designed MIS in improving operational efficiency and user satisfaction in educational settings. Future research directions include expanding the sample size, conducting longitudinal studies, incorporating qualitative methods, and exploring the impact on educational outcomes, to enhance the generalizability and depth of understanding regarding the role of MIS in education.
Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach Noviandy, Teuku Rizky; Zahriah, Zahriah; Yandri, Erkata; Jalil, Zulkarnain; Yusuf, Muhammad; Mohamed Yusof, Nur Intan Saidaah; Lala, Andi; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i1.191

Abstract

Education is important for societal advancement and individual empowerment, providing opportunities, developing essential skills, and breaking cycles of poverty. Nonetheless, the path to educational success is marred by challenges such as achieving academic excellence and preventing student dropouts. Early identification of students at risk of dropping out or those likely to excel academically can significantly enhance educational outcomes through tailored interventions. Traditional methods often fall short in precision and foresight for effective early detection. While previous studies have utilized machine learning to predict student performance, the potential for more sophisticated ensemble methods, such as stacked classifiers, remains largely untapped in educational contexts. This study develops a stacked classifier integrating the predictive strengths of LightGBM, Random Forest, and logistic regression. The model achieved an accuracy of 80.23%, with precision, recall, and F1-score of 79.09%, 80.23%, and 79.20%, respectively, surpassing the performance of the individual models tested. These results underscore the stacked classifier's enhanced predictive capability and transformative potential in educational settings. By accurately identifying students at risk and those likely to achieve academic excellence early, educational institutions can better allocate resources and design targeted interventions. This approach optimizes educational outcomes and supports informed policymaking, fostering environments conducive to student success.
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.
Maternal Health Risk Detection Using Light Gradient Boosting Machine Approach Noviandy, Teuku Rizky; Nainggolan, Sarah Ika; Raihan, Raihan; Firmansyah, Isra; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.123

Abstract

Maternal health risk detection is crucial for reducing morbidity and mortality among pregnant women. In this study, we employed the Light Gradient Boosting Machine (LightGBM) model to identify risk levels using data from rural healthcare facilities. The dataset included key health indicators aligned with the United Nations Sustainable Development Goals. The LightGBM model underwent rigorous optimization through hyperparameter tuning and 10-fold cross-validation. Its predictive performance was benchmarked against other algorithms using accuracy, precision, recall, and F1-score, with feature importance assessed to identify critical risk predictors. The LightGBM model demonstrating the highest performance across all metrics. The results underscore the value of advanced machine learning techniques in public health. Future research directions include expanding the demographic scope, incorporating temporal data, and enhancing model transparency. This study highlights the transformative potential of machine learning in maternal healthcare, providing a foundation for improved risk detection and proactive healthcare interventions.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Abdullah, Rusli Abrar , Tajul Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Afjal, Mohd Ahmad Watsiq Maula Ahmad, Noor Atinah Ahsya, Yahdina Alfharijy, Muhammad Daffa Amalia Amalia Amalina, Faizah Amirah, Kelsy Amri Amin Anisah Aprianto . Apriliansyah, Feby Asep Rusyana Azhar, Fauzul Azzuhry , Haikal Baehaqi Bahri, Ridzky Aulia BAKRI, TEDY KURNIAWAN Dahlawy, Arriz Dharma, Aditia Dian Handayani Dian Lestari, Nova Dimas Chaerul Ekty Saputra Earlia, Nanda Effendy, Amalia Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Enitan, Seyi Samson Erkata Yandri Essy Harnelly Eva Herlina Faisal, Farassa Rani Fajri, Irfan Fatani, Muhammad Fath, Teuku Nadhif Al Fauzi, Fazlin Mohd Furqan, Nurul Ghazi Mauer Idroes Hafizah, Iffah Hardia, Natasha Athira Keisha Hewindati, Yuni Tri Hidayatullah, Ferdy Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Idroes, Ghifari Maulana Iin Shabrina Hilal Imelda, Eva Imran Imran Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Isra Firmansyah, Isra Kadri, Mirzatul Kamarudin, Syafila Khairan Khairan Khairul, Mhd Khairul, Moh Khairun Nisa Kruba, Rumaisa Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lindawati Lindawati Maimun Syukri, Maimun Mardalena, Selvi Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Misbullah, Alim Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Muhammad Adam, Muhammad Muhammad Faisal Muhammad Subianto Muhammad Yanis Muhammad Yusuf Muhtadin Muhtadin Mukhlisuddin Ilyas Muksalmina Muksalmina Muliadi Mursyida, Waliam Muslem Muslem, Muslem Mutaqin, Raihan Nainggolan, Sarah Ika Nazirah, Nurul Niode, Nurdjannah Jane Nizamuddin Nizamuddin Nurleila, Nurleila Patwekar, Faheem Patwekar, Mohsina Rahmawati, Cut Raihan Raihan, Raihan Ramadeska, Siti Raudhatul Jannah Ray, Samrat Razief Perucha Fauzie Afidh Rinaldi Idroes Ringga, Edi Saputra Rizkia, Tatsa Ryan Setiawan Safhadi, Aulia Al-Jihad Sasmita, Novi Reandy Satrio, Justinus Sofyan, Rahmi Solly Aryza Souvia Rahimah Sufri, Rahmat sufriani, sufriani Sugara, Dimas Rendy Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Suryadi Suryadi Syahyana, Ahmad Taufiq Karma Tety Sriana Teuku Zulfikar TRINA EKAWATI TALLEI Utami, Resty Tamara Zahriah, Zahriah Zardi, Muhammad Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma