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Green Intelligent Systems and Applications
Published by Tecno Scientifica
ISSN : -     EISSN : 28091116     DOI : https://doi.org/10.53623/gisa.v2i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The journal is intended to provide a platform for research communities from different disciplines to disseminate, exchange and communicate all aspects of green technologies and intelligent systems. The topics of this journal include, but are not limited to: Green communication systems: 5G and 6G communication systems, power harvesting, cognitive radio, cognitive networks, signal processing for communication, delay tolerant networks, smart grid communications, power-line communications, antenna and wave propagation, THz technology. Green computing: high performance cloud computing, computing for sustainability, CPSS, computer vision, distributed computing, software engineering, bioinformatics, semantics web. Cyber security: cryptography, digital forensics, mobile security, cloud security. Internet of Things (IoT): sensors, nanotechnology applications, Agriculture 5.0, Society 5.0. Intelligent systems: artificial intelligence, machine learning, deep learning, big data analytics, neural networks. Smart grid: distributed grid, renewable energy in smart grid, optimized power delivery, artificial intelligence in smart grid, smart grid control and operation.
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 13 Documents
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Search results for , issue "volume 6 - issue 1 - 2026" : 13 Documents clear
Preliminary Study of ResNet-Based Facial Identification for Access Control Systems Andre Kurniawan; Ery Hartati
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1197

Abstract

Facial recognition is an important application of artificial intelligence (AI) and computer vision in modern security systems, particularly for automated access control. This study aimed to implement and evaluate a Residual Network (ResNet)-based facial identification system for door access control applications. A publicly available facial image dataset was used and was divided into training (70%), validation (20%), and testing (10%) subsets. The proposed methodology consisted of data preprocessing, ResNet-based model training, and performance evaluation using accuracy and loss metrics. The model was trained for 10 epochs to assess its initial learning capability. The experimental results showed relatively low performance, with training accuracy ranging from 3.6% to 3.8% and validation accuracy of approximately 3.6%, while loss values remained high throughout the training process. These findings indicated that the model was unable to effectively learn discriminative facial features from the dataset and exhibited signs of underfitting. The limited performance was likely associated with insufficient dataset diversity, suboptimal preprocessing procedures, and non-optimized training parameters. The study highlighted the challenges of implementing ResNet-based facial recognition systems under constrained training conditions. Future work should focus on expanding the dataset, applying data augmentation techniques, optimizing hyperparameters, and utilizing pretrained models to improve recognition performance and system reliability.
Implementation of a Centralized Cloud-Based Hotspot Voucher Management System and Network Traffic Monitoring Using MikroTik Virtual Cristovani Ari Wibowo Lohonauman; Christopel Hamonangan Simanjuntak; Maksy Sendiang; Herry Setiawan Langi; Venny Vita Ponggawa
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1215

Abstract

Hotspot voucher services became one of the most widely adopted solutions among local Internet Service Providers (ISPs) because they facilitated user access control, usage duration management, and bandwidth allocation. In the existing network environment, hotspot voucher sales were operated at two different locations. However, the management of these locations was still performed independently using separate MikroTik routers. This condition created several challenges, including decentralized voucher data, standalone user authentication processes, inefficient voucher administration, and the inability to perform comprehensive network traffic monitoring. This study implemented a centralized hotspot voucher management system using a MikroTik Cloud Hosted Router (CHR) to integrate two hotspot voucher sales locations into a unified management platform. The MikroTik CHR was deployed in a cloud computing environment and functioned as the central server for voucher management, user authentication, and network traffic monitoring. Each MikroTik router at the voucher sales locations was connected to the MikroTik CHR through a VPN tunnel. Furthermore, a web-based application was developed as a management interface and was integrated with the MikroTik CHR through an Application Programming Interface (API). The application enabled administrators to generate vouchers, monitor voucher status, manage users, and observe network traffic through a centralized dashboard. The research adopted the Network Development Life Cycle (NDLC) methodology, which consisted of the stages of analysis, design, simulation, implementation, monitoring, and management. The implementation results demonstrated that the centralized system successfully integrated voucher management across two different locations, simplified administrative processes, supported centralized user authentication, and provided unified network traffic monitoring through a single platform. Therefore, the implementation of MikroTik CHR in a centralized hotspot voucher system improved management efficiency and supported the expansion and sustainability of hotspot services across multiple locations.
Classification of Leaf Diseases in Guava Plants Based on Images Using the MobileNetV3 Model Rikky; Ery Hartati
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1239

Abstract

Guava cultivation is often threatened by leaf diseases that disrupted plant growth and reduced agricultural productivity. Early and accurate disease identification was crucial but relied heavily on slow and subjective visual inspections by human experts. This study proposed an automated, accurate, and efficient solution by comparing lightweight deep learning models. A total of 656 augmented guava leaf images representing four classes (Algal Leaf Spot, Insects Eaten, Red Rust, and Healthy Leaf) were evaluated. Using a transfer learning approach, the hyperparameters were systematically tuned for MobileNetV3-Small as the proposed model and compared with MobileNetV2 as the baseline architecture. The experimental results demonstrated that MobileNetV3-Small achieved a superior test accuracy of 91.00%, outperforming MobileNetV2, which achieved 87.00%. The integration of Squeeze-and-Excitation (SE) modules and the h-swish activation function in MobileNetV3-Small significantly improved the identification of subtle visual symptoms, particularly for the Healthy Leaf and Insects Eaten classes. However, MobileNetV2 maintained a slight advantage in real-time processing speed (54.86 FPS versus 50.30 FPS) because of memory-bound bottlenecks associated with the SE modules. Overall, MobileNetV3-Small provided superior diagnostic accuracy, whereas MobileNetV2 remained a highly viable option for latency-critical deployment on low-end devices.

Page 2 of 2 | Total Record : 13

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