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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF SCIENCE AND SOCIAL RESEARCH International Journal of Advances in Data and Information Systems INFOKUM Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Minfo Polgan (JMP) Journal of Science Technology (JoSTec) Jurnal Pengabdian Masyarakat Nusantara Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Pengabdian kepada Masyarakat Jurnal Pustaka Dianmas Journal of Technology and Computer (JOTECHCOM) LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics JIPITI: Jurnal Pengabdian kepada Masyarakat International Journal of Applied Mathematics and Computing. International Journal of Educational Insights and Innovations (IJEDINS) Outline Journal of Community Development
M. Rhifky Wayahdi
Universitas Battuta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Artificial Intelligence-Based Hydroponic Plant Disease Detection System (Lactuca sativa) Wayahdi, M. Rhifky; Ruziq, Fahmi; Nurhajijah, Nurhajijah
Journal of Technology and Computer Vol. 2 No. 4 (2025): November 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Hydroponic cultivation of lettuce (Lactuca sativa) offers high water efficiency, yet productivity is frequently compromised by rapid disease spread and nutrient imbalances. Traditional manual monitoring is labor-intensive, time-consuming, and prone to subjective diagnostic errors, often leading to delayed interventions. This study aims to develop an automated, real-time disease detection system by integrating Deep Learning algorithms with an Internet of Things (IoT) architecture. The proposed method utilizes an optimized One-Stage Object Detector based on the YOLO framework, specifically designed for efficient deployment on edge computing devices. The model was trained and validated on a diverse dataset encompassing healthy plants, tip-burn, leaf spot, and nutrient deficiencies, employing rigorous data augmentation to ensure robustness against indoor lighting variability. Experimental results demonstrate that the system achieves a Mean Average Precision (mAP@0.5) of 94.8%, significantly outperforming conventional Support Vector Machine (SVM) approaches and standard detectors. The model maintains high detection accuracy even under complex background conditions. In conclusion, this research provides a viable, low-latency solution for precision agriculture, enabling growers to automate plant health monitoring and effectively minimize crop losses.
Real-Time Classification of Hydroponic Vegetable Types on Mobile Devices Using Lightweight Deep Learning Models Wayahdi, M. Rhifky; Ruziq, Fahmi; Nurhajijah, Nurhajijah
Journal of Technology and Computer Vol. 1 No. 4 (2024): November 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Hydroponic cultivation requires precise monitoring to ensure crop quality and productivity, yet manual identification of vegetable varieties and their growth status remains labor-intensive and prone to error. This study aims to develop a real-time, mobile-based classification system for hydroponic vegetables using lightweight Deep Learning models optimized for edge computing. The proposed method evaluates two distinct architectures, MobileNetV3 and YOLO-Nano, trained via transfer learning on a dataset comprising major hydroponic crops such as Lettuce, Pak Choy, Mustard Greens, and Cherry Tomatoes. Experimental results demonstrate that while YOLO-Nano offers superior inference speed (~55 FPS), MobileNetV3 achieves a significantly higher classification accuracy of 96.4% while maintaining a real-time performance of ~35 FPS on standard mobile hardware. The study concludes that MobileNetV3 provides the optimal balance between accuracy and computational efficiency for handheld agricultural applications. This research contributes a scalable, low-cost solution for smart farming, enabling producers to perform rapid, on-site digital inventory and quality assessment without reliance on internet connectivity.
Computational Simulation and Algorithm Analysis for Solving Combinatorial Optimization Problems in Graph Theory and Discrete Mathematics Dwi Oktaviana; M. Rhifky Wayahdi; Syed Hassan Ali
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.273

Abstract

Combinatorial optimization is a fundamental area in operations research and computer science, focusing on identifying optimal solutions from a finite set of possibilities. This study explores the integration of branch and bound methods with heuristic algorithms to address optimization problems in graph theory and discrete mathematics. Python was employed for algorithm implementation due to its flexibility and comprehensive computational libraries, enabling efficient data analysis and visualization. Several benchmark problems were examined, including the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and Graph Coloring. Simulations were conducted using datasets of varying sizes (small, medium, and large) to evaluate performance across different scales. The results demonstrate that the hybrid approach achieves a balance between solution quality and computational efficiency, outperforming brute-force methods in terms of speed while maintaining near-optimal accuracy. Tabulated results and graphical comparisons highlight the reduction in computation time and improved scalability of the proposed method. The findings suggest that combining systematic search strategies with heuristics offers a practical and effective framework for solving complex combinatorial optimization problems. Recommendations for future research include testing scalability with larger datasets, incorporating advanced metaheuristics, and applying the approach to real-world domains such as logistics and network design.
Penguatan Kompetensi Transformasi Digital Mahasiswa Universitas Battuta melalui Workshop Pemanfaatan Teknologi Cloud Computing Ginting, Subhan Hafiz Nanda; Wahyuni, Dewi; Sridewi, Nurmala; Wayahdi, M. Rhifky; Darma, Surya
Jurnal Pustaka Dianmas Vol 5, No 2 (2025)
Publisher : Universitas Prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/dianmas.v5i2.6764

Abstract

Digital transformation is a strategic necessity in facing the dynamics of the world of work and developments in the information technology industry. Students, as future professionals, are required to have relevant digital competencies, particularly in the use of cloud computing technology, which is increasingly being used in data management, application development, and network-based collaboration. This community service activity aims to strengthen the digital transformation competencies of Battuta University students through the implementation of structured and applicable cloud computing technology utilization workshops. The methods used in this activity include participant needs analysis, material preparation, interactive workshop implementation, practical guidance, and activity outcome evaluation. The workshop material covered an introduction to the basic concepts of cloud computing, an understanding of key cloud services, and practical use of cloud platforms for data storage, access management, and implementation of simple services. Evaluation was conducted through pre-tests, post-tests, and observation of participant engagement during the activity. The results of the activity showed an increase in students' conceptual understanding and technical abilities in utilizing cloud technology. This activity contributed positively to improving students' readiness to meet competency demands in the digital era and strengthening their technological literacy and adaptation.
Co-Authors Anzani, Nurul Baginda Harahap Davita, Davita Dewi Wahyuni Dewi Wahyuni Dinur Syahputra Dinur Syahputra Dinur Syahputra Ellanda Purwawijaya Ginting, Subhan Hafiz Nanda Guntur, Surya Hasibuan, Eka Hayana Hutasoit, Rutmian Mariama Manza, Yuke Mughnyanti, Mayang Nanda Ginting, Subhan Hafiz Nur Alisya, Siti Nurhajijah, Nurhajijah Ramadhan, Wisnu Ramadhani, Syafitri Rutmian Mariama Rutmian Mariama Hutasoit ruziq, fahmi Sheila Try Thania Sridewi, Nurmala Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Surya Darma Surya Guntur Syed Hassan Ali Thania, Sheila Try Zaki, Mohammed