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All Journal 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 Indatu Journal of Management and Accounting
Teuku Rizky Noviandy
Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

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QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms Teuku Rizky Noviandy; Aga Maulana; Talha Bin Emran; Ghazi Mauer Idroes; Rinaldi Idroes
Heca Journal of Applied Sciences Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i1.12

Abstract

This study focuses on the development of a machine learning ensemble approach for the classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity Relationship (QSAR) analysis. BACE1 is an enzyme linked to the production of amyloid beta peptide, a significant component of Alzheimer's disease plaques. The discovery of effective BACE1 inhibitors is difficult, but QSAR modeling offers a cost-effective alternative by predicting the activity of compounds based on their chemical structures. This study evaluates the performance of four machine learning models (Random Forest, AdaBoost, Gradient Boosting, and Extra Trees) in predicting BACE1 inhibitor activity. Random Forest achieved the highest performance, with a training accuracy of 98.65% and a testing accuracy of 82.53%. In addition, it exhibited superior precision, recall, and F1-score. Random Forest's superior performance was a result of its ability to capture a wide variety of patterns and its randomized ensemble approach. Overall, this study demonstrates the efficacy of ensemble machine learning models, specifically Random Forest, in predicting the activity of BACE1 inhibitors. The findings contribute to ongoing efforts in Alzheimer's disease drug discovery research by providing a cost-effective and efficient strategy for screening and prioritizing potential BACE1 inhibitors.
Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification Rivansyah Suhendra; Suryadi Suryadi; Noviana Husdayanti; Aga Maulana; Teuku Rizky Noviandy; Novi Reandy Sasmita; Muhammad Subianto; Nanda Earlia; Nurdjannah Jane Niode; Rinaldi Idroes
Heca Journal of Applied Sciences Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i2.85

Abstract

This study investigates the application of the Gradient Boosting machine learning technique to enhance the classification of Atopic Dermatitis (AD) skin disease images, reducing the potential for manual classification errors. AD, also known as eczema, is a common and chronic inflammatory skin condition characterized by pruritus (itching), erythema (redness), and often lichenification (thickening of the skin). AD affects individuals of all ages and significantly impacts their quality of life. Accurate and efficient diagnostic tools are crucial for the timely management of AD. To address this need, our research encompasses a multi-step approach involving data preprocessing, feature extraction using various color spaces and evaluating classification outcomes through Gradient Boosting. The results demonstrate an accuracy of 93.14%. This study contributes to the field of dermatology by providing a robust and reliable tool to support dermatologists in identifying AD skin disease, facilitating timely intervention and improved patient care.
Prediction of Pharmacokinetic Parameters from Ethanolic Extract Mane Leaves (Vitex pinnata L.) in Geothermal Manifestation of Seulawah Agam Ie-Seu’um, Aceh Nur Balqis Maulydia; Khairan Khairan; Teuku Rizky Noviandy
Malacca Pharmaceutics Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i1.33

Abstract

The Mane plant (Vitex pinnata L.) is traditionally used as medicine in Aceh Province, Indonesia. This study aimed to predict the pharmacokinetic parameters of compounds in the ethanolic extract of Mane leaf (EEML), including the absorption, distribution, metabolism, excretion, and toxicity (ADMET), by in-silico approach. The method used was to analyze the compounds using a web-predictor server and molecular docking. Gas chromatography-mass spectrometry (GCMS) analysis of EEML showed the presence of active compounds, including phytol (60.93%), acorenol (8.56%), n-hexadecanoic acid (4.89%), trans-Z-alpha-bisabolene epoxide (2.7%) and cedrane (2.03%). Lipinski's rule of five states that all compounds had a deviation of less than 2. Pharmacokinetic parameters suggested that phytol was moderately absorbed in the gastrointestinal tract and had a toxicity level of 5 with lethal doses (LD50) >5000 mg/kg. Molecular docking results showed that phytol could be used against the targeted enzyme Staphylococcus aureus. In conclusion, our study suggests that the active compounds of EEML may have potential as a drug candidate.
Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery Teuku Rizky Noviandy; Aga Maulana; Ghazi Mauer Idroes; Nur Balqis Maulydia; Mohsina Patwekar; Rivansyah Suhendra; Rinaldi Idroes
Malacca Pharmaceutics Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i2.60

Abstract

This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.
TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection Ghazi Mauer Idroes; Aga Maulana; Rivansyah Suhendra; Andi Lala; Taufiq Karma; Fitranto Kusumo; Yuni Tri Hewindati; Teuku Rizky Noviandy
Leuser Journal of Environmental Studies Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i1.42

Abstract

Forest fires have emerged as a significant threat to the environment, wildlife, and human lives, necessitating the development of effective early detection systems for firefighting and mitigation efforts. In this study, we introduce TeutongNet, a modified ResNet50V2 model designed to detect forest fires accurately. The model is trained on a curated dataset and evaluated using various metrics. Results show that TeutongNet achieves high accuracy (98.68%) with low false positive and false negative rates. The model's performance is further supported by the ROC curve analysis, which indicates a high degree of accuracy in classifying fire and non-fire images. TeutongNet demonstrates its effectiveness in reliable forest fire detection, providing valuable insights for improved fire management strategies.
Exploring Geothermal Manifestations in Ie Jue, Indonesia: Enhancing Safety with Unmanned Aerial Vehicle Aprianto Aprianto; Aga Maulana; Teuku Rizky Noviandy; Andi Lala; Muhammad Yusuf; Marwan Marwan; Razief Perucha Fauzie Afidh; Irvanizam Irvanizam; Nizamuddin Nizamuddin; Ghazi Mauer Idroes
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.v1i2.75

Abstract

Geothermal energy is a renewable resource derived from the Earth's interior that provides an environmentally friendly alternative. Indonesia is at the forefront of geothermal potential, possessing ample resources primarily concentrated in places like Sumatra. However, there is a requirement for greater exploitation of this potential. This research utilizes unmanned aerial vehicles (UAVs) and thermal imaging to detect geothermal indications in the Ie Jue region of Sumatra within the province of Aceh, Indonesia. The analysis focuses on three main manifestation locations using FLIR One thermal camera and water temperature gauges. The study leverages satellite imagery for comparative purposes. Temperature data highlights variations among distinct manifestations, underscoring the necessity for thorough exploration. Moreover, the study devises a secure pathway for researchers to access the site. This investigation contributes to comprehending geothermal activity and its possible role in sustainable energy and other domains.
Urban Air Quality Classification Using Machine Learning Approach to Enhance Environmental Monitoring Ghazi Mauer Idroes; Teuku Rizky Noviandy; Aga Maulana; Zahriah Zahriah; Suhendrayatna Suhendrayatna; Eko Suhartono; Khairan Khairan; Fitranto Kusumo; Zuchra Helwani; Sunarti Abd Rahman
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.
Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet Teuku Rizky Noviandy; Aga Maulana; Ghazi Mauer Idroes; Rivansyah Suhendra; Muhammad Adam; Asep Rusyana; Hizir Sofyan
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.
A Deep Dive into Indonesia's CO2 Emissions: The Role of Energy Consumption, Economic Growth and Natural Disasters Ghalieb Mutig Idroes; Irsan Hardi; Teuku Rizky Noviandy; Novi Reandy Sasmita; Iin Shabrina Hilal; Fitranto Kusumo; Rinaldi Idroes
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 Aga Maulana; Teuku Rizky Noviandy; Novi Reandy Sasmita; Maria Paristiowati; Rivansyah Suhendra; Erkata Yandri; Justinus Satrio; Rinaldi Idroes
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.
Co-Authors Abdul Hawil Abas Aga Maulana Ahmad Watsiq Maula Andi Lala Andi Lala Aprianto Aprianto Arriz Dahlawy Asep Rusyana Dimas Chaerul Ekty Saputra Dimas Rendy Sugara Edi Saputra Ringga Eko Suhartono Erkata Yandri Farassa Rani Faisal Feby Apriliansyah Fitranto Kusumo Ghalieb Mutig Idroes Ghalieb Mutig Idroes Ghazi Mauer Idroes Hizir Sofyan Hizir Sofyan Iin Shabrina Hilal Irfan Fajri Irsan Hardi Irvanizam Zamanhuri Isra Firmansyah Justinus Satrio Khairan Khairan Khairan Khairan Lensoni Maria Paristiowati Marwan Marwan Moh Khairul Mohamed El-Shazly Mohsina Patwekar Muhammad Adam Muhammad Subianto Muhammad Yusuf Muliadi Muliadi Muslem Muslem Nanda Earlia Natasha Athira Keisha Hardia Nizamuddin Nizamuddin Novi Reandy Sasmita Novi Reandy Sasmita Novi Reandy Sasmita Novi Reandy Sasmita Noviana Husdayanti Nur Balqis Maulydia Nurdjannah Jane Niode Nurleila Nurleila Nurul Furqan Raihan Raihan Raudhatul Jannah Razief Perucha Fauzie Afidh Resty Tamara Utami Rinaldi Idroes Rivansyah Suhendra Rumaisa Kruba Sarah Ika Nainggolan Selvi Mardalena Souvia Rahimah Sufriani Sufriani Suhendrayatna Suhendrayatna Sunarti Abd Rahman Suryadi Suryadi Talha Bin Emran Tatsa Rizkia Taufiq Karma Teuku Zhilalmuhana Trina Ekawati Tallei Waliam Mursyida Yuni Tri Hewindati Zahriah Zahriah Zuchra Helwani Zuchra Helwani Zulkarnain Jalil