Article Per Year (5 Year)
p-Index From 2021 - 2026
3.416
P-Index
This Author published in this journals
All Journal Narra J 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
Maulana, Aga
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Neuroscience Physics Public Health Social Sciences Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : heca journal of applied sciences

 1 
QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms Noviandy, Teuku Rizky; Maulana, Aga; Emran, Talha Bin; Idroes, Ghazi Mauer; Idroes, Rinaldi
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 Suhendra, Rivansyah; Suryadi, Suryadi; Husdayanti, Noviana; Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Subianto, Muhammad; Earlia, Nanda; Niode, Nurdjannah Jane; Idroes, Rinaldi
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.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Aprianto . Asep Rusyana Dharma, Dian Budi Diah, Muhammad Earlia, Nanda Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Enitan, Seyi Samson Erkata Yandri Faisal, Farassa Rani Ghazi Mauer Idroes Hewindati, Yuni Tri Hizir Sofyan Husdayanti, Noviana Idroes, Ghadamfar Muflih Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Irsan Hardi Irvanizam, Irvanizam Kairupan, Tara S. Kemala, Pati Khairan Khairan Khairul, Mhd Khalijah Awang Kusumo, Fitranto Lala, Andi Maria Paristiowati Marwan Marwan Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Misbullah, Alim Muhammad Adam, Muhammad Muhammad Subianto Muhammad Yusuf Mukhlisuddin Ilyas Muksalmina Muksalmina Mursyida, Waliam Muslem Muslem Musvira, Intan Niode, Nurdjannah Jane Nizamuddin Nizamuddin Noviandy, Teuku R. Nurdjannah J. Niode Nurleila, Nurleila Patwekar, Mohsina Prakoeswa, Cita RS. Rahman, Isra Farliadi Ramadeska, Siti Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Ringga, Edi Saputra Rizkia, Tatsa Sasmita, Novi Reandy Satrio, Justinus Souvia Rahimah sufriani, sufriani Sugara, Dimas Rendy Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Suryadi Suryadi Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar TRINA EKAWATI TALLEI Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma