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All Journal PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS (COMORBID) TRANSPUBLIKA INTERNATIONAL RESEARCH IN EXACT SCIENCES (TIRES) 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 Grimsa Journal of Science Engineering and Technology
Ghazi Mauer Idroes
Department Of Occupational Health And Safety, Faculty Of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Neuroscience 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.
EVALUATION OF COMPLIANCE WITH STANDARDS OF OPERATIONAL PROCEDURES FOR THE MEDICAL WASTE MANAGEMENT SYSTEM OF THE PUSKESMAS PANTE KUYUN, DISTRICT ACEH JAYA Ichsan, Muhammad; Karma, Taufiq; Idroes, Ghazi Mauer
TRANSPUBLIKA INTERNATIONAL RESEARCH IN EXACT SCIENCES Vol. 1 No. 4 (2022): OCTOBER
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/tires.v1i4.1119

Abstract

The public health center in Pante Kuyun, situated in the Setia Bakti district of Aceh Jaya regency, Southeast Aceh, generates various types of medical waste during its operations. Improper management of medical waste can have negative impacts on both the environment and human health. This research aims to assess the current medical waste management practices at the Pante Kuyun public health center. The study utilized a descriptive observation method and revealed that the medical waste at the facility is not being handled in accordance with standard operating procedures. Waste segregation by type is lacking, and there is a lack of plastic bag liners and proper labeling for waste disposal. The findings of this study underscore the importance of implementing proper medical waste management practices at the Pante Kuyun public health center to protect both the environment and human health. Recommendations for improvement include providing staff with training on proper waste handling procedures, ensuring the availability of necessary resources for waste management, and implementing regular monitoring and evaluation of waste management practices at the facility.
IDENTIFICATION OF DISEASE SYMPTOMS OF ARTISANAL GOLD MINERS IN TERMS OF LENGTH OF TIME WORKED (0-5 YEARS) IN THE WORKING AREA OF UPTD PUSKESMAS UJUNG PADANG RASIAN Lensoni, Lensoni; Idroes, Ghazi Mauer; Mustafa, Mustafa
TRANSPUBLIKA INTERNATIONAL RESEARCH IN EXACT SCIENCES Vol. 3 No. 2 (2024): APRIL
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/tires.v3i2.1210

Abstract

According to WHO, mercury is a naturally occurring metallic element and is divided into three groups: liquid and gaseous mercury, inorganic mercury, and organic mercury. Mercury is the only metal that is liquid under standard conditions and vaporizes on contact with air. This study aims to identify symptoms of illness due to mercury exposure in gold mine workers at the UPTD Puskesmas Ujung Padang Rasian, South Aceh District. The study involving 24 respondents was conducted in June 2023 using a questionnaire. The results showed that the most frequent acute clinical symptoms were headache (14 respondents), cough (11 respondents), pain during urination and nausea (10 respondents each), pelvic pain (9 respondents), vomiting and bloody urine (7 respondents), cloudy urine (5 respondents), abdominal pain and numbness in the mouth (3 respondents), loose teeth and swollen gums (2 respondents), and diarrhea and dark gums (1 respondent each). Symptoms of chronic toxicity included headache (6 respondents), muscle cramps (9 respondents), irritability (2 respondents), erythema, weight loss, anorexia, anxiety, depression, insomnia and memory loss (1 respondent each). In conclusion, the majority of respondents experienced symptoms due to mercury exposure such as headache (20 respondents), pelvic pain and painful urination (14 respondents), cough (13 respondents), and nausea (12 respondents).
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.
Characterizing the Size Distribution of Silver Nanoparticles Biofabricated Using Calotropis gigantea from Geothermal Zone Kemala, Pati; Khairan, Khairan; Ramli, Muliadi; Mauer Idroes, Ghazi; Mirda, Erisna; Setya Ningsih, Diana; Tallei, Trina Ekawati; 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.v1i2.21

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.
Evaluating Geothermal Power Plant Sites with Additive Ratio Assessment: Case Study of Mount Seulawah Agam, Indonesia Azhar, Fauzul; Misbullah, Alim; Lala, Andi; Idroes, Ghazi Mauer; Kusumo, Fitranto; Noviandy, Teuku Rizky; Irvanizam, Irvanizam; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 2 No. 1 (2024): March 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i1.158

Abstract

Indonesia, a country rich in geothermal resources, has yet to fully exploit its potential, particularly in volcanic regions like Mount Seulawah Agam. This study investigates the application of the Additive Ratio Assessment (ARAS) method for the site selection of Geothermal Power Plants (GPP) in Indonesia. The ARAS method provides a systematic approach to evaluating and prioritizing geothermal development sites by integrating multiple criteria, including geological, environmental, and socio-economic factors. The study collects data from various sources and weights criteria using the Ordinal Priority Approach (OPA), incorporating expert opinions. The findings demonstrate the effectiveness of the ARAS method in identifying optimal locations for GPP development, ensuring sustainability and feasibility. The study also tests the ARAS method in existing GPP locations in Jaboi, Sabang, Indonesia, to investigate alignment with the results and validate the approach. Furthermore, the study presents recommendations for GPP site selection. This research emphasizes the significance of multi-criteria decision-making techniques in facilitating renewable energy projects. It promotes a more systematic and informed approach to geothermal energy development in Indonesia and other geothermal-rich regions.
Explainable Deep Learning Approach for Mpox Skin Lesion Detection with Grad-CAM Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Emran, Talha Bin; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.216

Abstract

Mpox is a viral zoonotic disease that presents with skin lesions similar to other conditions like chickenpox, measles, and hand-foot-mouth disease, making accurate diagnosis challenging. Early and precise detection of mpox is critical for effective treatment and outbreak control, particularly in resource-limited settings where traditional diagnostic methods are often unavailable. While deep learning models have been applied successfully in medical imaging, their use in mpox detection remains underexplored. To address this gap, we developed a deep learning-based approach using the ResNet50v2 model to classify mpox lesions alongside five other skin conditions. We also incorporated Grad-CAM (Gradient-weighted Class Activation Mapping) to enhance model interpretability. The results show that the ResNet50v2 model achieved an accuracy of 99.33%, precision of 99.34%, sensitivity of 99.33%, and an F1-score of 99.32% on a dataset of 1,594 images. Grad-CAM visualizations confirmed that the model focused on relevant lesion areas for its predictions. While the model performed exceptionally well overall, it struggled with misclassifications between visually similar diseases, such as chickenpox and mpox. These results demonstrate that AI-based diagnostic tools can provide reliable, interpretable support for clinicians, particularly in settings with limited access to specialized diagnostics. However, future work should focus on expanding datasets and improving the model's capacity to distinguish between similar conditions.
Utilization of Drone with Thermal Camera in Mapping Digital Elevation Model for Ie Seu'um Geothermal Manifestation Exploration Security Bahri, Ridzky Aulia; Noviandy, Teuku Rizky; Suhendra, Rivansyah; Idroes, Ghazi Mauer; Yanis, Muhammad; Yandri, Erkata; 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 Idroes, Ghazi Mauer; Maulana, Aga; Suhendra , Rivansyah; Lala, Andi; Karma, Taufiq; Kusumo, Fitranto; Hewindati, Yuni Tri; Noviandy, Teuku Rizky
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; Maulana, Aga; Noviandy, Teuku Rizky; Lala, Andi; Yusuf, Muhammad; Marwan, Marwan; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Nizamuddin, Nizamuddin; Idroes, Ghazi Mauer
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.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Ahmad, Noor Atinah Akmal Muhni Alfizar Alfizar Ali Bakri Anggi, Tiara Aprianto . Arkadinata, Teguh Asep Rusyana Azhar, Fauzul Bachtiar, Boy Muhclis Bahri, Ridzky Aulia Bako, Winanda Celik, Ismail Diah, Muhammad Diana Setya Ningsih, Diana Diana Setya Ningsih, Diana Setya Diki, Diki Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Faisal, Farassa Rani Fajar Fakri Fauziah, Niken Fazli, Qalbin Salim Hafni Zahara Harahap, Saima Putri Harera, Cheariva Firsa Hewindati, Yuni Tri Hizir Sofyan Idroes, Ghalieb Mutig Ifandi, Ilham Imelda, Eva Irvanizam, Irvanizam Irwana, Salman Jainury, Aldi Jauna, Jauna Kemala, Pati Khairan Khairan Khalijah Awang Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lukman Hakim Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Maysarah, Hilda Medyan Riza Mirda, Erisna Mirja, Mirja Misbullah, Alim Muhammad Adam, Muhammad Muhammad Ichsan Muhammad Ichsan Muhammad Sabri Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mukhlisuddin Ilyas Muliadi Ramli Muslem Muslem, Muslem Musvira, Intan Natasya Natasya Nizamuddin Nizamuddin Nova Yanti Pasyamei Rembune Kala Patwekar, Mohsina Prasetio, Rasi Purnama, M. Risky Putri Raisah Raisah, Putri Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Rizkia, Tatsa Sasmita, Novi Reandy Shofi, Shofi Siti Maulina Rukmana Souvia Rahimah Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Surna, Muhammad Ipan Susanna Susanna Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar Tjut Chamzurni TRINA EKAWATI TALLEI Wahyuni, Srie Wangi, Putri Ayu Sekar Wildan Seni, Wildan Wiwik Handayani Yandri, Erkata Yustiana Yustiana, Yustiana Zahriah, Zahriah Zuchra Helwani, Zuchra Zulkarnain Jalil