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All Journal HAYATI Journal of Biosciences Bulletin of Chemical Reaction Engineering & Catalysis INDONESIAN JOURNAL OF PHARMACY Indonesian Journal of Chemistry Unnes Journal of Public Health Narra J International Journal of Hydrological and Environmental for Sustainability Jurnal Natural Jurnal Kedokteran Hewan 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 International Journal of Renewable Energy Development Journal Of Carbazon JURNAL REKAYASA KIMIA & LINGKUNGAN
Rinaldi Idroes
Department Of Chemistry, Faculty Of Mathematics And Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Veterinary Other

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LC-HRMS-Based Metabolomics for Profiling the Metabolites in Different Plant Parts of Centella asiatica Rafi, Mohamad; Madya, Muhammad Miftahul; Karomah, Alfi Hudatul; Septaningsih, Dewi Anggraini; Ridwan, Taopik; Rohaeti, Eti; Aisyah, Siti; Idroes, Rinaldi
HAYATI Journal of Biosciences Vol. 31 No. 6 (2024): November 2024
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.31.6.1106-1115

Abstract

Centella asiatica, or pegagan in Indonesia, is a perennial plant used in Indonesian traditional medicine (jamu) and functional food with many biological activities. Those biological activities come from the bioactive metabolites present in C. asiatica. Differences in metabolite pathways in each part of the plant affect the accumulation of metabolites contained, thus impacting its biological activity. Therefore, this study aims to identify and evaluate differences in the distribution of metabolites in each part of C. asiatica, namely leaves, stems, stolons, and roots. Each plant part was extracted using methanol and sonicated for 30 minutes. The metabolites in the samples were separated and detected using UHPLC-Q-Orbitrap HRMS. Differences in the distribution of metabolites in each part of the plant were evaluated using chemometrics analysis. UHPLC-Q-Orbitrap HRMS analysis could positively identify 37 metabolites, most of which belong to the phenylpropanoid, triterpenoid, triterpenoid saponin, and flavonoid groups. Principal component analysis was able to clearly distinguish each part of the plant using the peak intensity of the overall chromatogram and the peak area of the identified metabolites. The different biosynthetic pathways of metabolites in plants could cause a difference in the distribution of metabolites in each plant.
Development of a Web-Based Educational Management System for a Technology Vocational High School in Banda Aceh, Indonesia Idroes, Rinaldi; Afidh, Razief Perucha Fauzie; Zahriah, Zahriah; Noviandy, Teuku Rizky; Sugara, Dimas Rendy; Ahsya, Yahdina; Amirah, Kelsy; Baihaqi, Baihaqi; Dharma, Aditia
Journal of Educational Management and Learning Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i2.230

Abstract

This study explores developing and implementing a web-based Management Information System (MIS) tailored for SMK Negeri 2 Banda Aceh, a vocational school in Indonesia. To enhance administrative efficiency and address unique challenges in vocational education, the system centralizes tasks such as attendance tracking, academic record management, and internship coordination. Employing the waterfall model, this project proceeded through structured phases, including requirements analysis, system design, development, and usability testing. A sample of 50 users, comprising students, teachers, and school operators, evaluated the system based on usability, interface design, and information clarity through a questionnaire, yielding high satisfaction scores. Reliability testing and correlation analysis revealed strong internal consistency across questionnaire items and identified critical factors influencing user satisfaction, such as interface appeal and effective error resolution. The results indicate that the system meets core user needs and contributes to a streamlined, user-friendly school management process. With implementation planning, user training, and ongoing maintenance, this MIS offers a sustainable solution that can serve as a model for vocational schools across Indonesia, showcasing the potential of digital solutions in advancing educational administration and supporting career readiness in vocational education.
Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Zahriah, Zahriah; Paristiowati, Maria; Emran, Talha Bin; Ilyas, Mukhlisuddin; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i2.233

Abstract

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As generative AI tools become more popular, there is growing interest in how AI can improve teaching, learning, and research. Higher education faces many challenges, such as meeting diverse learning needs and preparing students for fast-changing careers. Generative AI offers solutions by personalizing learning experiences, making education more engaging, and supporting skill development through adaptive content. It can also help researchers by automating tasks like data analysis and hypothesis generation, making research faster and more efficient. Moreover, generative AI can streamline administrative tasks, improving efficiency across institutions. However, using AI also raises concerns about privacy, bias, academic integrity, and equal access. To address these issues, institutions must establish clear ethical guidelines, ensure data security, and promote fairness in AI use. Training for faculty and AI literacy for students are essential to maximize benefits while minimizing risks. The paper suggests a strategic framework for integrating AI in higher education, focusing on infrastructure, ethical practices, and continuous learning. By adopting AI responsibly, higher education can become more inclusive, engaging, and practical, preparing students for the demands of a technology-driven world.
Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils Kurniadinur, Kurniadinur; Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Ahmad, Noor Atinah; Irvanizam, Irvanizam; Subianto, Muhammad; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.220

Abstract

The Kovats retention index is a critical parameter in gas chromatography used for the identification of volatile compounds in essential oils. Traditional methods for determining the Kovats retention index are often labor-intensive, time-consuming, and prone to inaccuracies due to variations in experimental conditions. This study presents a novel approach combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) to predict the Kovats retention index of essential oil compounds more accurately and efficiently. The ANN-PSO hybrid model leverages the strengths of both techniques: the ANN's capacity to model complex nonlinear relationships and PSO's capability to optimize hyperparameters by finding the global optimum. The model was trained using a dataset of 340 essential oil compounds with molecular descriptors, with the performance evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results indicate that a simpler ANN configuration with one hidden neuron achieved the lowest RMSE (80.16) and MAPE (5.65%), suggesting that the relationship between the molecular descriptors and the Kovats retention index is not overly complex. This study demonstrates that the ANN-PSO model can serve as an effective tool for predictive modeling of the Kovats retention index, reducing the need for experimental procedures and improving analytical efficiency in essential oil research.
Advanced Anemia Classification Using Comprehensive Hematological Profiles and Explainable Machine Learning Approaches Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Suhendra, Rivansyah; Bakri, Tedy Kurniawan; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.237

Abstract

Anemia is a common health issue with serious clinical effects, making timely and accurate diagnosis essential to prevent complications. This study explores the use of machine learning (ML) methods to classify anemia and its subtypes using detailed hematological data. Six ML models were tested: Gradient Boosting, Random Forest, Naive Bayes, Logistic Regression, Support Vector Machine, and K-Nearest Neighbors. The dataset was preprocessed using feature standardization and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Gradient Boosting delivered the highest accuracy, sensitivity, and F1-score, establishing itself as the top-performing model. SHapley Additive exPlanations (SHAP) analysis was applied to enhance model interpretability, identifying key predictive features. This study highlights the potential of explainable ML to develop efficient, accurate, and scalable tools for anemia diagnosis, fostering improved healthcare outcomes globally.
Enhancing Early Detection of Alzheimer's Disease through MRI using Explainable Artificial Intelligence Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Purnawarman, Adi; Imran, Imran; Lestari, Nova Dian; Hastuti, Sri; Idroes, Rinaldi
Indonesian Journal of Case Reports Vol. 2 No. 2 (2024): December 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijcr.v2i2.255

Abstract

Alzheimer’s disease is a progressive brain disorder that causes memory loss and cognitive decline, affecting millions of people worldwide. Early detection is critical for slowing the disease's progression and improving patient outcomes. Magnetic Resonance Imaging (MRI) is widely used to identify brain changes associated with AD, but subtle abnormalities in the early stages are often difficult to detect using traditional methods. In this study, we used a deep learning approach with a model called ResNet-50 to analyze MRI scans and classify patients into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. The model was trained using MRI images, achieving an accuracy of 95.63%, with strong sensitivity, precision, and specificity. To make the model’s predictions understandable for healthcare professionals, we applied a technique called Grad-CAM, which highlights areas of the brain that influenced the model’s decisions. These visual explanations help clinicians see and trust the reasoning behind the AI's results. While the model performed well overall, misclassifications between adjacent disease stages were observed, likely due to class imbalance and subtle brain changes. This study demonstrates that explainable AI tools can improve early detection of Alzheimer’s disease, supporting clinicians in making accurate and timely diagnoses. Future work will focus on expanding the dataset and combining MRI with other clinical information to enhance the tool's reliability in real-world settings.
Ficus minahassae (Teijsm. & de Vriese) Miq.: A Fig Full of Health Benefits from North Sulawesi, Indonesia: A Mini Review Abas, Abdul Hawil; Tallei, Trina Ekawati; Idroes, Rinaldi; Fatimawali, Fatimawali
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.v1i1.24

Abstract

Plants have been widely utilized as traditional medicine for an extended period of time. Numerous traditional remedies have demonstrated inherent anti-disease properties. Among the countries that extensively rely on traditional medicine, Indonesia stands out. Within the region of North Sulawesi, Indonesia, Ficus minahassae, an indigenous plant possessing several health benefits, is utilized by the local community as a traditional medicinal resource. This plant is employed for the treatment of various ailments such as rheumatism, physical discomfort, stimulation of lactation in breastfeeding women, bruises, relapse, fever, fatigue, migraines, bodily pain, headaches, convulsions, colds, coughs, influenza, and fractures. Typically, the leaves, roots, and stems of F. minahassae are boiled and consumed. Additionally, this plant has been reported to possess antibacterial and antioxidant properties. However, scientific investigations exploring the health advantages of F. minahassae are significantly limited in comparison to other traditional medicines. Consequently, it is highly recommended to conduct further research on the health benefits associated with this plant.
Antibacterial Activity of n-Hexane Dragon’s Blood Resin Extract (Daemonorops draco wild Blume) from Bener Meriah, Aceh Province, Indonesia Khairan, Khairan; Arini, Musfira; Idroes, Rinaldi; Awang, Khalijah; Jacob, Claus
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.v1i1.29

Abstract

The dragon’s blood resin (Daemonorops draco wild Blume) has been used in folk medicine for pharmacological activities such as antimicrobial, antivirus, anti-inflammation, gastrointestinal disorders, blood circulation dysfunctions, antitumor, and cancer. This study was designated to evaluate the antibacterial activity of n-Hexane dragon’s blood resin extract against Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Candida albicans 10231. The other purpose of this study was to determine the secondary metabolites compound of n-Hexane dragon’s blood resin extract. The antimicrobial activities of the n-Hexane dragon’s blood resin extract was determined using well diffusion method and the results showed that the extract at concentration of 15% exhibited antimicrobial activities against Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Candida albicans 10231 with the diameter inhibition of 13.20 mm; 21.3 mm; and 13.0 mm respectively. The phytochemicals screening showed that the extract contains secondary metabolites in the form of flavonoids. The GC-MS analysis showed that n-Hexane dragon’s blood resin extract contains 48 chemicals compounds, and the compound at RT 26 was indicated a Drachorhodin compound (C17H 18O3) with the mass ration of m/z was 270 g/mol. Overall, the n-Hexane dragon’s blood resin extract be a good choice for antimicrobial agent against bacteria and fungi.
Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Maulydia, Nur Balqis; Patwekar, Mohsina; Suhendra, Rivansyah; Idroes, Rinaldi
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.
Hybrid Handwash with Silver Nanoparticles from Calotropis gigantea Leaves and Patchouli Oil: Development and Properties Salsabila, Indah; Khairan, Khairan; Kemala, Pati; Idroes, Ghifari Maulana; Isnaini, Nadia; Maulydia, Nur Balqis; El-Shazly, Mohamed; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.206

Abstract

When washing hands, handwashing is one way to prevent diseases caused by bacteria such as Staphylococcus aureus and Escherichia coli, the most common bacteria that can cause infections. The production of handwash utilizing silver nanoparticles as an active antibacterial agent remains a relatively infrequent practice. The synthesis of silver nanoparticles from the leaves of Calotropis gigantea, which grows in the geothermal area of Ie Seu-um Aceh Besar, has been carried out using the green synthesis method and hybrid green synthesis with patchouli oil. Handwash with active ingredients such as silver nanoparticles was successfully formulated, evaluated, and tested against S. aureus and E. coli. The organoleptic characteristics, pH, viscosity, foam height measurements, density, irritation, and antibacterial activity against S. aureus and E. coli were evaluated. The results showed that the organoleptic properties of the handwash with silver nanoparticles were not changed during a 30-day storage period, with pH values in the range of 9.7-10.3, and did not cause irritation upon using silver nanoparticle handwash. The best formula for handwashing with silver nanoparticles in inhibiting the growth of S. aureus and E. coli bacteria was F2, with inhibition zones of 12.9 ± 2.85 mm and 10.95 ± 0.8 mm, respectively. The formulated handwash with silver nanoparticles met the requirements of good liquid soap according to the Indonesian National Standard (SNI) with potent antibacterial activity.
Co-Authors - Fakhrurrazi - Mahmud Abas, Abdul Hawil Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Agus Winarsih Ahmad, Khairunnas Ahmad, Noor Atinah Ahsya, Yahdina Akyuni, Qurrata Amirah, Kelsy Andri Yadi Paembonan Arini, Musfira Asep Rusyana Azhar, Fauzul Azharuddin Azharuddin BAKRI, TEDY KURNIAWAN Binawati Ginting Boy M. Bachtiar Claus Jacob Claus Jacob Claus Jacob, Claus Cundaningsih, Nurvita Deni Saputra Destiana, Khaerunisa Dharma, Aditia Dharma, Dian Budi Diah, Muhammad Dian Handayani Dian Lestari, Nova Diana Setya Ningsih, Diana Earlia, Nanda Eka Safitri EKA SAFITRI Eka Safitri El-Shazly, Mohamed Elisa Purwaendah Emran, Talha Bin Enitan, Seyi Samson Essy Harnelly Estevam, Ethiene Castellucci Ethiene Castellucci Estevam Eti Rohaeti Evi Yufita Ezzat, Abdelrahman O. Faddillah, Vira Faisal Abdullah Faisal, Farassa Rani Faradilla Faradilla FARADILLA, FARADILLA Farnida Farnida Fatimawali . Fauzi, Fazlin M. Fauzi, Fazlin Mohd Fazlin Mohd Fauzi Firaihanil Jannah Ghalieb Mutig Idroes Ghani, Azman Abdul Ghazi Mauer Idroes Haerul Anwar Hakim, Rachmi F. Hanafiah, Olivia A. Harera, Cheariva Firsa Hartono Hartono Hesti Meilina Hizir Sofyan Husdayanti, Noviana Ida Zahrina Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Idroes, Ghifari Maulana Iin Shabrina Hilal Ilham Maulana Ilham Maulana Imelda, Eva Imran Imran Ira Maya Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Ismail Ismail Isnaini, Nadia Isra Firmansyah, Isra Jannah, Firaihanil Jannah, Rizka Auliatul Jasin, Faisal M Kairupan, Tara S. Karl Herbert Schaefer Karl Herbert Schaefer, Karl Herbert Karomah, Alfi Hudatul Kemala, Pati Khairan . Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan KHAIRI SUHUD Khairi Suhud Khalijah Awang Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lelifajri Lelifajri Lelifajri Lelifajri Lubis, Vanizra F. M. Rafi Madya, Muhammad Miftahul Mahmudi Mahmudi Maimun Syukri, Maimun Malahayati Malahayati MARIA BINTANG Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur B. Maulydia, Nur Balqis Maysarah, Hilda Md Sani, Nor Diyana Mikyal Bulqiah, Mikyal Mirda, Erisna Misbullah, Alim Misrahanum Misrahanum Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Mohsina Patwekar Mubaraq, Farhil Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Diah Muhammad Ridha Adhari, Muhammad Ridha Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mukhlisuddin Ilyas Muliadi Ramli Munawar, Agus Murniana Murniana Mursal Mursal Mursyida, Waliam Musdalifah, Annisa Muslem Muslem, Muslem Muzakir N. Nazaruddin Nabila, Fiki Farah Nainggolan, Sarah Ika Nanda Earlia Nasrullah Idris Nasrullah Idris NAZARUDDIN NAZARUDDIN Nazaruddin Nazaruddin Neonufa, Godlief Frederick Ningsih, Diana S. Niode, Nurdjannah Jane Nor Diyana Md Sani Novi Reandy Sasmita Noviandy, Teuku R. Nugraha, Gartika Nur Balqis Maulydia Nur, Adrian Rahmat Nurdjannah J. Niode Nurleila, Nurleila Nurul Khaira Oesman, Frida Patwekar, Faheem Patwekar, Mohsina Prakoeswa, Cita RS. Purwaendah, Elisa Putra, Noviandi I. Qurrata Akyuni Rahmadi Rahmadi Rahmadi Rahmadi Rahman, Isra Farliadi Rahman, Sunarti Abd Raihan Raihan Raihan Raihan, Raihan Raudhatul Jannah Razief Perucha Fauzie Afidh Ringga, Edi Saputra Rizka Auliatul Jannah Rizkia, Tatsa Romadhoni, Yenni Rusdi Andid Safhadi, Aulia Al-Jihad Saiful . Saiful Saiful Saiful Saiful Salaswati, Salaswati Salsabila, Indah Sasmita, Novi Reandy Satrio, Justinus Septaningsih, Dewi Anggraini Shafira, Ghina A. Siti Aisyah Solly Aryza Souvia Rahimah Sufriadi, Elly sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Suhud, Khairi Supriatno Supriatno Supriatno Suryadi Suryadi Suryawati Suryawati Taopik Ridwan Taufik Ridwan Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar Thomas Schneider Thomas Schneider, Thomas Triana Hertiani Trina E. Tallei, Trina E. Trina Ekawati Tallei TRINA EKAWATI TALLEI Tuti Fadlilah Yandri, Erkata Zahraty, Ifrah Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulfiani, Utari Zulkarnain Jalil Zulkarnain Jalil