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All Journal Narra J Jurnal Pengabdian Masyarakat Tjut Nyak Dhien JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Narra X Journal of Computing Theories and Applications 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 The International Journal of Financial Systems Jurnal Medika: Medika Grimsa Journal of Business and Economics Studies Journal of Future Artificial Intelligence and Technologies Artificial Intelligence Systems and Its Applications (AISA)
Teuku Rizky Noviandy
Department Of Information Systems, Faculty Of Engineering, Universitas Abulyatama, Aceh Besar 23372, Indonesia
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Penerapan Sistem Penyiraman Tanaman Secara Otomatis Berbasis Mikrokontroler Arduino Uno di Kecamatan Blang Bintang Anisah Anisah; Ryan Setiawan; Rahmat Sufri; Teuku Rizky Noviandy; Cut Rahmawati; Amri Amin; Haikal Azzuhry; Tajul Abrar
Jurnal Medika: Medika Vol. 4 No. 3 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jc3bec94

Abstract

Petani di Kabupaten Aceh besar dihadapkan pada permasalahan keterbatasan teknologi yang mampu memudahkan proses penyiraman tanaman secara otomatis dan efisien, khususnya dalam memantau kondisi tanah secara rutin. Oleh karena itu  dilakukan kegiatan pengabdian masyarakat ini yang bertujuan untuk meningkatkan kapasitas dan kemandirian petani di Desa Bung Pageu, Kecamatan Blang Bintang, Kabupaten Aceh Besar melalui penerapan sistem penyiraman tanaman otomatis berbasis Arduino. Sistem ini dirancang untuk meningkatkan efisiensi penggunaan air, mengurangi beban kerja petani, serta memastikan tanaman mendapatkan kebutuhan air yang optimal berdasarkan sensor kelembaban tanah. Kegiatan meliputi tahapan sosialisasi, pelatihan merakit dan mengoperasikan sistem, persiapan algoritma tertanam, perancangan jaringan pipa, pemasangan asesoris perangkat, serta evaluasi kinerja di lapangan. Pelaksanaan dilakukan pada tanggal 5 Juli 2025 dengan mitra utama pemilik lahan Desa Bung Pageu, Kecamatan Blang Bintang Kabupaten Aceh Besar. Hasil evaluasi menunjukkan bahwa sistem mampu bekerja secara otomatis sesuai parameter kelembaban tanah yang diatur, dengan debit air yang cukup untuk menyiram seluruh area lahan. Pelatihan langsung dan praktik lapangan meningkatkan keterampilan dan pengetahuan masyarakat dalam mengelola teknologi pertanian modern. Kegiatan ini telah menjadi model inovatif yang berkelanjutan untuk meningkatkan produktivitas dan keberlanjutan pertanian di kecamatan Blang Bintang, Kabupaten Aceh Besar.
Sosialisasi Tempat Sampah Otomatis Berbasis IoT dengan Arduino Uno Cut Rahmawati; Anisah Anisah; Ryan Setiawan; Muhammad Zardi; Teuku Rizky Noviandy; Rahmat Sufri; Amalia Amalia; Eva Herlina; Tety Sriana; Rusli Abdullah; Syafila Kamarudin; Teuku Nadhif Al Fath; Nurul Nazirah
Jurnal Medika: Medika Vol. 5 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/51305g35

Abstract

Pengelolaan sampah yang kurang efektif serta rendahnya kesadaran masyarakat dalam membuang sampah pada tempatnya masih menjadi permasalahan lingkungan yang nyata. Menjawab tantangan tersebut, tim pengabdian masyarakat melakukan inovasi dengan merancang tempat sampah otomatis berbasis Internet of Things (IoT) menggunakan Arduino Uno. Tujuan kegiatan ini adalah meningkatkan pemahaman santri dayah mengenai pentingnya pengelolaan sampah yang modern, efisien, dan ramah lingkungan, sekaligus memperkenalkan teknologi sederhana yang dapat diaplikasikan dalam kehidupan sehari-hari. Metode pelaksanaan meliputi sosialisasi yang dilakukan di Dayah Masyraf, Gampong Keuneu Eu, Kecamatan Peukan Bada, Kabupaten Aceh Besar. Selanjutnya dilakukan demonstrasi prototipe serta pendampingan kepada santri dalam memahami cara kerja dan manfaat teknologi IoT pada sistem pengelolaan sampah. Perangkat yang digunakan adalah arduino IDE, sensor, servo, modul IoT, breadboard, kabel jumper, dan power supply. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan dan antusiasme santri terhadap penerapan teknologi dalam mendukung kebersihan lingkungan. Dengan demikian, kegiatan ini diharapkan dapat mendorong terciptanya budaya membuang sampah pada tempatnya serta membuka peluang pengembangan inovasi serupa di masa depan.
Comparative Analysis of Ensemble Machine Learning Models for QSAR-Based Prediction of Anticoagulant Activity in Thrombotic Disorders Teuku Rizky Noviandy; Rahmat Sufri; Ryan Setiawan; Anisah Anisah
Heca Journal of Applied Sciences Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v4i1.393

Abstract

Thrombotic disorders remain a major cause of global morbidity and mortality, with dysregulation of blood coagulation pathways playing a central role in disease progression. In particular, Thrombin is a key therapeutic target for anticoagulant drug development, making accurate prediction of inhibitory activity highly relevant for accelerating discovery efforts. Despite advances in computational drug discovery, there is still a need for systematic evaluation of machine learning approaches for QSAR-based prediction of anticoagulant activity. Many existing studies focus on single models or lack consistent comparison frameworks, limiting insights into the relative performance of different ensemble techniques. To address this gap, this study explores the application of multiple ensemble machine learning methods, including Random Forest, XGBoost, Gradient Boosting, and Extra Trees, combined with hyperparameter optimization using random search. The main objective of this work is to conduct a comparative analysis of these ensemble models to predict pIC50 values for thrombin inhibitors using molecular descriptors derived from chemical structures. The results show that the Extra Trees model achieved the best overall performance, with an R2 of 0.697, RMSE of 0.851, and MAE of 0.615 after tuning. Additionally, Gradient Boosting and XGBoost demonstrated significant improvement following hyperparameter optimization, highlighting the importance of model tuning in QSAR tasks. Overall, the study confirms that ensemble learning methods yield reliable, accurate predictions of anticoagulant activity, with Extra Trees emerging as the most effective approach for this dataset.
A Data-Driven Classification of Student Productivity Based on Academic Performance, Lifestyle Patterns, and Digital Habits Teuku Rizky Noviandy; Hizir Sofyan; Yosza Dasril; Rinaldi Idroes
Journal of Educational Management and Learning Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v4i1.433

Abstract

Student productivity is influenced by various factors, including academic habits, lifestyle characteristics, and digital distraction behaviors. The increasing use of digital technologies, such as smartphones, social media, and online gaming, has created new challenges for maintaining student focus and academic performance. Therefore, understanding and predicting student productivity levels is important for supporting effective educational management and student success. This study aims to classify student productivity levels using machine learning techniques based on academic, behavioral, and digital distraction variables. The study utilized the Student Productivity & Digital Distraction Dataset obtained from Kaggle, consisting of 20,000 student records. The productivity score was transformed into five productivity categories, namely very low, low, medium, high, and very high productivity. Four machine learning algorithms, including Decision Tree (DT), and K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Random Forest (RF) were evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results showed that RF achieved the best performance with an accuracy of 81.15%, precision of 81.35%, recall of 81.15%, and F1-score of 81.23%, outperforming GB, DT, and KNN. The findings indicate that ensemble learning methods are more effective in modeling the complex relationships among academic habits, lifestyle factors, digital distraction, and student productivity. Furthermore, the study demonstrates the potential of machine learning as a decision-support tool for educational management, enabling the identification of students with different productivity levels and supporting data-driven interventions to improve academic outcomes.
Online Gaming, Digital Entertainment, and Academic Performance among Students: A Review Teuku Rizky Noviandy; Rinaldi Idroes
Journal of Educational Management and Learning Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v4i1.434

Abstract

Online gaming and digital entertainment have become increasingly integrated into students’ daily lives through smartphones, computers, gaming platforms, video streaming, and social media. This review paper examines the relationship between online gaming, digital entertainment, and academic performance among students. The discussion highlights that digital entertainment can produce both positive and negative effects depending on duration, timing, content type, self-regulation, and learning context. Moderate and purposeful use may support cognitive skills, social interaction, motivation, and digital literacy. In contrast, excessive or problematic use may contribute to distraction, reduced study time, poor sleep quality, lower learning engagement, and weaker academic outcomes. Quantitative findings from previous studies show that many students engage frequently in online gaming and digital entertainment, including long weekly gaming time, mobile gaming as a major source of entertainment, and frequent social media checking. Overall, the reviewed literature suggests that the relationship between digital entertainment and academic performance is conditional rather than absolute. Healthy digital habits, effective time management, and guidance from parents, teachers, and educational institutions are important to help students balance entertainment and academic responsibilities.
QSAR Modeling of Beta-2 Adrenergic Receptor Ligands Using Molecular Descriptor–Based Machine Learning Teuku Rizky Noviandy; Mohsina Patwekar; Rinaldi Idroes
Malacca Pharmaceutics Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v4i1.394

Abstract

The Beta-2 Adrenergic Receptor (ADRB2) is a well-characterized G protein–coupled receptor widely studied in pharmacology and drug discovery. In this study, quantitative structure–activity relationship (QSAR) models were developed using molecular descriptor–based machine learning approaches to predict the activity of ADRB2 ligands. A curated dataset of 745 compounds with experimentally determined IC₅₀ values was obtained from the ChEMBL database. Two-dimensional molecular descriptors were calculated and preprocessed to remove low-variance and highly correlated features, resulting in a refined feature set for model development. The dataset was categorized into active and inactive compounds and divided into training and testing subsets. Four machine learning algorithms. Logistic Regression, Support Vector Machine, Gradient Boosting, and Random Forest were implemented and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Among the models, Random Forest achieved the best performance, with an accuracy of 89.26%, F1-score of 89.87%, and AUC of 0.926, followed by Gradient Boosting with an accuracy of 87.92% and AUC of 0.922. Analysis of physicochemical descriptors indicated that hydrogen-bond donor capacity (nHD) shows a statistically significant association with variations in compound activity toward ADRB2, while lipophilicity (LogP) and hydrogen-bond acceptor count (nHA) do not exhibit statistically significant differences between activity classes. Overall, the results demonstrate that molecular descriptor–based machine learning models, particularly ensemble methods, provide an effective framework for predicting ADRB2-related compound activity and support the prioritization of candidate molecules in computational drug discovery.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Afjal, Mohd Ahmad Watsiq Maula Ahmad, Khairunnas Ahmad, Noor Atinah Ahsya, Yahdina Al-Gunaid , Hala T. Alfharijy, Muhammad Daffa Amalia Amalia Amalina, Faizah Amirah, Kelsy Amri Amin Anisah Anisah Anisah Anisah Anisah Anisah Anisah Aprianto . Apriliansyah, Feby Asep Rusyana Azhar, Fauzul Baehaqi Bahri, Ridzky Aulia BAKRI, TEDY KURNIAWAN Cut Rahmawati Cut Rahmawati Dahlawy, Arriz Dharma, Aditia Dian Handayani Dian Lestari, Nova Dimas Chaerul Ekty Saputra Duta, Teuku F. Earlia, Nanda Effendy, Amalia Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Enitan, Seyi Samson Erkata Yandri Essy Harnelly Eva Herlina Faisal, Farassa Rani Fajri, Irfan Fatani, Muhammad Fauzi, Fazlin Mohd Furqan, Nurul Ghalieb Mutig Idroes Ghazi Mauer Idroes Hafizah, Iffah Haikal Azzuhry Hamoud, Lama MA. Hardia, Natasha Athira Keisha Hewindati, Yuni Tri Hidayatullah, Ferdy Hizir Sofyan Hizir Sofyan Husdayanti, Noviana Idroes , Ghalieb M. Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Idroes, Ghifari Maulana Iin Shabrina Hilal Imelda, Eva Imran Imran Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Isra Firmansyah, Isra Kadri, Mirzatul Kairupan, Tara S. Kemala, Pati Khairan Khairan Khairul, Mhd Khairul, Moh Khairun Nisa Kruba, Rumaisa Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lindawati Lindawati Lubis, Vanizra F. Maimun Syukri, Maimun Mardalena, Selvi Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Misbullah, Alim Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Mohsina Patwekar Muhammad Adam, Muhammad Muhammad Faisal Muhammad Subianto Muhammad Yanis Muhammad Yusuf Muhammad Zardi Muhibbul Tibri Muhtadin Muhtadin Mukhlisuddin Ilyas Muksalmina Muksalmina Muliadi Muliadi Ramli Mursyida, Waliam Muslem Muslem Mutaqin, Raihan Nainggolan, Sarah Ika Nizamuddin Nizamuddin Novi Reandy Sasmita Nurdjannah J. Niode Nurleila, Nurleila Nurul Nazirah Patwekar, Faheem Patwekar, Mohsina Prakoeswa, Cita RS. Rahmat Sufri Rahmat Sufri Rahmat Sufri Rahmat Sufri Rahmawati, Cut Raihan Raihan, Raihan Ramadeska, Siti Raudhatul Jannah Ray, Samrat Razief Perucha Fauzie Afidh Rinaldi Idroes Ringga, Edi Saputra Rizkia, Tatsa Rusli Abdullah Ryan Setiawan Ryan Setiawan Ryan Setiawan Safhadi, Aulia Al-Jihad Sasmita, Novi Reandy Satrio, Justinus Sofyan, Rahmi Solly Aryza Souvia Rahimah Sufri, Rahmat sufriani, sufriani Sugara, Dimas Rendy Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Suryadi Suryadi Syafila Kamarudin Syahyana, Ahmad Tajul Abrar Taufiq Karma Tety Sriana Teuku Nadhif Al Fath Teuku Zulfikar TRINA EKAWATI TALLEI Utami, Resty Tamara Yosza Dasril Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma