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All Journal 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
Ghazi Mauer Idroes
Department Of Occupational Health And Safety, Faculty Of Health Sciences, Universitas Abulyatama
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology 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 Nursing Physics Public Health Social Sciences Other

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Journal : Heca Journal of Applied Sciences

 1 
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.
Characterizing the Size Distribution of Silver Nanoparticles Biofabricated Using Calotropis gigantea from Geothermal Zone Pati Kemala; Khairan Khairan; Muliadi Ramli; Ghazi Mauer Idroes; Erisna Mirda; Diana Setya Ningsih; Trina Ekawati Tallei; 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.v1i2.21

Abstract

This research aims to synthesize silver nanoparticles (AgNPs) using an aqueous leaf extract of Calotropis gigantea obtained from the geothermal manifestation Ie Seu-Um, Aceh Besar, Aceh Province, Indonesia. The C. gigantea leaf extract was mixed with AgNO3 solutions at concentrations of 2, 5, and 9 mM, respectively. The mixture was stirred at 80 rpm by a magnetic stirrer for 48 hours in the dark. The change in solution color indicated the reduction of Ag+ to Ag0. The resulting AgNPs synthesized using C. gigantea leaf extract (AgNPs-LCg) exhibited cloudy grey, reddish dark brown, and light brown colors when synthesized with AgNO3 concentrations of 2, 5, and 9 mM, respectively. The particle sizes of AgNPs-LCg had maximum frequencies at 246.98 nm (synthesized using AgNO3 2 mM), 93.02 nm (synthesized using AgNO3 5 mM), and 171.25 nm (synthesized using AgNO3 9 mM). The zeta potential values of AgNPs-LCg using 2, 5, and 9 mM AgNO3 were -41.9, -40.1, and -31.4 mV, respectively. Based on the solution color, nanoparticle size, and stability value of AgNPs, it can be concluded that the use of AgNO3 at 5 mM is optimal for the green synthesis process of AgNPs-LCg.
Co-Authors Abdul Hawil Abas Aga Maulana Akmal Muhni Ali Bakri Andi Lala Andi Lala Aprianto Aprianto Asep Rusyana Boy Muhclis Bachtiar Diana Setya Ningsih Diana Setya Ningsih Eko Suhartono Erisna Mirda Erkata Yandri Fajar Fakri Farassa Rani Faisal Fitranto Kusumo Hafni Zahara Hizir Sofyan Irvanizam Zamanhuri Ismail Celik Khairan Khairan Khairan Khairan Khairan Khairan Lensoni Marwan Marwan Mohamed El-Shazly Mohsina Patwekar Muhammad Adam Muhammad Ichsan Muhammad Yanis Muhammad Yusuf Muliadi Ramli Muslem Muslem Nizamuddin Nizamuddin Nizamuddin Nizamuddin Novi Reandy Sasmita Nur Balqis Maulydia Pasyamei Rembune Kala Pati Kemala Putri Raisah Raudhatul Jannah Razief Perucha Fauzie Afidh Ridzky Aulia Bahri Rinaldi Idroes Rivansyah Suhendra Saima Putri Harahap Siti Maulina Rukmana Suhendrayatna Suhendrayatna Sunarti Abd Rahman Talha Bin Emran Tatsa Rizkia Taufiq Karma Teuku Rizky Noviandy Teuku Rizky Noviandy Trina Ekawati Tallei Trina Ekawati Tallei Wildan Seni, Wildan Yuni Tri Hewindati Yuni Tri Hewindati Zahriah Zahriah Zuchra Helwani Zuchra Helwani Zulkarnain Jalil