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M Rhifky Wayahdi
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INDONESIA
Journal of Technology and Computer (JOTECHCOM)
Published by PT. Technology Laboratories Indonesia
ISSN : -     EISSN : 30480477     DOI : -
Core Subject : Science,
Computer Science & IT
The Journal of Technology and Computer (JOTECHCOM) brings together researchers, academics (faculty and students), and industry practitioners to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote cross-disciplinary and cross-domain collaboration. JOTECHCOM aims to integrate all scientific disciplines, such as computer science, information systems, informatics, information technology, data science, databases, artificial intelligence, data mining, decision support systems, expert systems, and other related disciplines. This journal is published by PT. Technology Laboratories Indonesia (TechnoLabs) Publisher division. Accepted papers will be available online (free open access).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 73 Documents
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Implementation of Security on Library System Login Using a Combination of AES and RSA Cryptography Siregar, M. Zaki Musaid
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

The development of information technology has encouraged library systems to shift from manual methods to digital-based platforms. While this transformation improves efficiency, it also raises security risks in the login process, which handles sensitive user data. This research aims to implement login security using a combination of the Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms. AES-128 is used to encrypt passwords, while RSA is used to encrypt the AES output before transmission to the server. The research method includes requirements analysis, security architecture design, algorithm implementation, and testing through login simulations and traffic analysis using Wireshark. The results show that the combination of AES and RSA effectively protects user credentials and prevents unauthorized access to the transmitted data. The conclusion of this study is that the combined cryptographic approach provides dual protection for library system authentication. Further development is suggested by integrating secure communication protocols such as HTTPS and exploring modern public-key cryptographic algorithms.
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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Abstract

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.

Page 8 of 8 | Total Record : 73

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