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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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Penyuluhan Penanggulangan Kebakaran Kompor Gas Menggunakan Alat Pemadam Api Tradisional Wildan Seni; Pasyamei Rembune Kala; Taufiq Karma; Putri Raisah; Hafni Zahara; Ghazi Mauer Idroes; Ali Bakri; Muhammad Ichsan; Siti Maulina Rukmana
Jurnal Pengabdian Masyarakat Bangsa Vol. 1 No. 6 (2023): Agustus
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v1i6.249

Abstract

Penyebab kebakaran selain karena faktor alam juga karena faktor manusia terutama kelalaian dan juga ketidaksiapan menghadapi kebakaran. Pelaksanaan Pengabdian Masyarakat ini dilatarbelakangi oleh seringnya terjadi kebakaran rumah yang berawal dari kompor terbakar atau meledak. Pelaksanaan kegiatan ini dilaksanakan oleh mahasiswa dan dosen prodi Keselamatan dan Kesehatan Kerja Universitas Abulyatama Aceh dengan jumlah peserta sebanyak 15 orang. Metode yang di lakukan adalah dengan memberikan pretest dan posttest kemudian menganalisis data dari lembar jawaban tersebut apakah peserta yang mengikuti penyuluhan tersebut mengalami peningkatan pemahaman yang signifikan atau tidak mengenai api, penyebab kebakaran dan cara penggunaan alat pemadam api tradisional. Sebelum diadakan kegiatan penyuluhan ini, para peserta kurang mengetahui tentang kebakaran dan cara menggunakan alat pemadam api tradisional. Kegiatan pelatihan ini dimulai dari pemaparan materi, praktek penggunaan karung basah, dan terakhir adalah tanya jawab. Dari hasil pelatihan terjadi peningkatan pemahaman sebelum dan sesudah pelatihan, diantaranya terjadi peningkatan pemahaman mengenai konsep segitiga api sebesar 73,3%, peningkatan pemahaman pengetahuan penyebab atau pemicu kebakaran sebesar 60%, dan pemahaman pengetahuan penggunaan alat pemadam api tradisional mengalami peningkatan sebesar 66,7%. Kegiatan ini sangat bermanfaat bagi semua peserta yang hadir karena ini merupakan bentuk edukasi tentang kejadian kebakaran yang memang sering di alami.
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.
A Review of the Ethno-dentistry Activities of Calotropis gigantea Diana Setya Ningsih; Ismail Celik; Abdul Hawil Abas; Boy Muhclis Bachtiar; Pati Kemala; Ghazi Mauer Idroes; Nur Balqis Maulydia
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.v1i1.31

Abstract

Calotropis gigantea is a medicinal herb that thrives in arid climates. All parts of this plant are rich in secondary metabolites, which are very beneficial for health. Phytochemicals of this plant include flavonoid, alkaloids, steroids, cardiac glycosides, and terpenoids, which have a wide range of pharmacological effects. The potential of metabolit compound from C. gigantea can be used in dental treatment. This review describes the potential use of C. gigantea in ethno-dentistry, specifically as anti-caries, soft tissue inflammation (periodontitis and gingivitis), degenerative diseases (tumor/cancer), and wound healing. This review provides general perspectives and basic literature on the use of C. gigantea in the field of etno-dentistry.
Antimicrobial Properties of Medicinal Plants in the Lower Area of Ie Seu-um Geothermal Outflow, Indonesia Fajar Fakri; Saima Putri Harahap; Akmal Muhni; Khairan Khairan; Yuni Tri Hewindati; Ghazi Mauer 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.v1i2.44

Abstract

The lower area of the Ie Seu-um manifestation, located in Ie Seu-um village, Aceh Besar District, harbors several medicinal plants that exhibit potential for the treatment of infectious diseases. This study aims to assess the secondary metabolite content and in vitro antimicrobial activity against Staphylococcus aureus, Escherichia coli, and Candida albicans of medicinal plants inhabiting the geothermal region. Medicinal plants, namely Pluchea indica (L.) Less., Acrostichum aureum L., Acacia mangium L., and Calotropis gigantea (L.) Dryand., were collected within a range of 100-150 meters from the hot springs in the lower area. Methanol extracts of these medicinal plants underwent phytochemical screening and were tested for antimicrobial activity using the Kirby-Bauer disc diffusion method at a concentration of 50%. The results of phytochemical screening demonstrated positive variations in alkaloids, flavonoids, saponins, steroids, triterpenoids, and tannins for each medicinal plant. The antimicrobial activity of the methanol extracts noticeably inhibited the growth of S. aureus compared to E. coli and C. albicans. The largest inhibition zones were observed for the leaf part of A. mangium (12.70 ± 2.30 mm) against S. aureus, the aerial part of A. aureum (11.57 ± 2.01 mm) against E. coli, and the aerial part of P. indica (9.89 ± 1.11 mm) against C. albicans. Based on the research findings, medicinal plants originating from the lower area of the Ie Seu-um manifestation exhibit potential as antimicrobial agents, particularly against gram-positive bacteria.
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.
Utilization of Drone with Thermal Camera in Mapping Digital Elevation Model for Ie Seu'um Geothermal Manifestation Exploration Security Ridzky Aulia Bahri; Teuku Rizky Noviandy; Rivansyah Suhendra; Ghazi Mauer Idroes; Muhammad Yanis; Erkata Yandri; Nizamuddin Nizamuddin; Irvanizam Irvanizam
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.v1i1.40

Abstract

Geothermal energy is a viable alternative energy source, particularly in Indonesia. This study was conducted at Ie Seu’um, Mount Seulawah Agam, which is a potential site for a geothermal power plant with an estimated electrical output of 150 megawatts. The objective of this study was to analyze and construct a digital elevation model (DEM) map of the geothermal manifestations. We analyzed water temperature, FLIR (Forward Looking Infrared) temperature, and temperature data from Landsat 8 satellite imagery. To map the heat signature of geothermal features, we utilized the DJI Phantom 4 Standard equipped with the FLIR One Gen 2 sensor. Additionally, we used the Milwaukee Mi306 to calculate the water temperature. Each test was conducted three times to obtain an optimal average level of accuracy. The DEM map was created to assess the level of safety in geothermal manifestation exploration. Elevation and slope values were analyzed to generate a 3D map display, providing a clearer image of the research site. In conclusion, drones prove to be an excellent method for ensuring the safety of exploration in geothermal manifestation areas.
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.
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