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Risky Ayu Kristanti
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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 5 Documents
 1 
Search results for , issue "Volume 3 - Issue 2 - 2023" : 5 Documents clear
Android Based College App Using Flutter Dart Kavitha Marimuthu; Arunkumar Panneerselvam; Senthilkumar Selvaraj; Lakshmi Praba Venkatesan; Vetriselvi Sivaganesan
Green Intelligent Systems and Applications Volume 3 - Issue 2 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i2.269

Abstract

In today's world, communication and information sharing between teachers and students have increasingly shifted to online platforms such as Google Classroom, Gmail, Google Forms, WhatsApp, and more. To address the diverse needs of educational institutions, we developed an app that supports all devices, including mobile phones, laptops, and tablets. The Android app for mobile and tablet websites supports all devices seamlessly. This app provides comprehensive information on attendance, examination schedules, lecture notes, fee details, event notifications, and online tests, catering to all the requirements of the institution. We developed this app using the latest technology, including Flutter and Dart, with Firebase integration. Additionally, we created a web application that is easily accessible via desktops. This website, along with the app, is connected to the same Firebase server, ensuring synchronized data access. The institute has taken a step further by developing its own Android application and website to enhance efficient communication with its students. These platforms are exclusively accessible and available to authorized users associated with the institute, ensuring privacy and security.
Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern Melinda Melinda; Yunidar Yunidar; Nur Afny Catur Andryani
Green Intelligent Systems and Applications Volume 3 - Issue 2 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i2.270

Abstract

In the acquisition of amplitude data, the inaccuracy of a signal with the occurrence of an unstable peak value of the amplitude in the data is called a fluctuation. This study uses High-High Fluctuation (HHF) signal data from the acquisition of Multi-Spectral Capacitive Sensors (MSCS) with Hydrogen Dioxide (H2O) and Hydrogen Dioxide (H2O) objects mixed with Sodium Hydroxide (NaOH) that have been organized into a matrix form. The data acquisition results in previous studies have several parts that are difficult to distinguish with the naked eye. The method used in this study applies the CNN method for image recognition of signal fluctuations of type HHF from H2O and H2O mixed with NaOH, using the architecture known as AlexNet. Then, the H2O and H2O data groups mixed with NaOH were grouped into training data groups of 280 image data for each data type, and 70 image data for test data for both groups. During the training stage, the number of epochs used is 20. However, by the time the number of epochs reaches 15, the accuracy rate is already high, reaching 98%. Furthermore, at the testing stage, the CNN program can correctly recognize the entire 70 image data for both materials, achieving perfect recognition for the total amount of the two materials.
The Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection Suhas Kakade; Rohan Kulkarni; Somesh Dhawale; Muhammed Fasil C
Green Intelligent Systems and Applications Volume 3 - Issue 2 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i2.313

Abstract

Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.
IoT-based Heart Signal Processing System for Driver Drowsiness Detection Yunidar Yunidar; Melinda Melinda; Khairani Khairani; Muhammad Irhamsyah; Nurlida Basir
Green Intelligent Systems and Applications Volume 3 - Issue 2 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i2.323

Abstract

Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers.
Enhanced IoT Solution System for Smart Agriculture in Indonesia Hugeng Hugeng; Dedi Trisnawarman; Axel Irving Yoshua Huntarso
Green Intelligent Systems and Applications Volume 3 - Issue 2 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i2.325

Abstract

This innovative solution encompasses an IoT-based smart agricultural system. The system includes a solar panel power supply, a weather station (monitoring temperature, humidity, air pressure, wind speed and direction, raindrop), an air quality monitoring module (measuring NH4, CO2, and PM2.5 levels), a soil quality measurement module, a microcontroller, a GSM cellular module for internet connectivity, and an automated relay actuator for a water pump. The water pump's operation is contingent upon the soil moisture levels, ensuring efficient irrigation. The utilization of an IoT-driven smart agricultural system enables real-time monitoring of weather conditions, air quality, and agricultural soil conditions. Additionally, it facilitates the remote control of automated water pumps via smartphones—an aspect that remains unattainable within the confines of traditional Indonesian agriculture. Leveraging an Android application on smartphones, this system delivers detailed insights. To present the collected sensor data in accordance with prevailing environmental and soil states, a dedicated Android application has been developed. Moreover, this application facilitates the control of the water pump to irrigate arid soil as required. The data is transmitted via the internet to a cloud server, serving as the intermediary that receives data from the IoT system's sensors positioned at the farm.

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