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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 47 Documents
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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.
Internet of Things and Web-App-Based Data Accessibility and Management System for Chromameter Sensor Database Faisal Samsuri; Joni Welman Simatupang
Green Intelligent Systems and Applications Volume 4 - Issue 1 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i1.342

Abstract

Information technology, an integral part of most industrial activities, is essential for supporting the rapid and substantial processes of the industrial sector. A key component of this technology is the Internet of Things (IoT), which is extensively integrated into these systems. At PT Sugity Creatives, an analysis revealed impractical methods in the production process, such as manual data recording and input, as well as the use of stickers on the rear side of product bumpers. These stickers can be detached from the main body (car) for verification purposes. To improve these processes, a data accessibility and management system were incorporated into a Raspberry Pi-based chromameter sensor prototype. This integrated system is designed to collect and store data in a database and uniquely identify data using QR Codes. The system takes an average of 7.97 seconds to store data and generate a QR Code per entry. This includes a module processing time of 7.25 seconds per data point and a rapid transmission rate of 0.72 seconds, covering data recording and QR Code transmission from the chromameter prototype, with data sizes ranging between 700 to 750 bytes.
Durian Species Classification Using Deep Learning Method Boon Chen Teo; Huong Yong Ting; Abdulwahab Funsho Atanda
Green Intelligent Systems and Applications Volume 4 - Issue 1 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i1.352

Abstract

Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy.
Enhancing Supply Chain Traceability through Blockchain and IoT Integration: A Comprehensive Review Elton Kee Sheng Wong; Huong Yong Ting; Abdulwahab Funsho Atanda
Green Intelligent Systems and Applications Volume 4 - Issue 1 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i1.355

Abstract

Supply chain traceability is essential for ensuring safety, preventing counterfeit goods, and improving efficiency. The integration of blockchain technology and the Internet of Things (IoT) has emerged as a transformative approach to enhance supply chain traceability by creating a secure, transparent, and efficient way to track the movement of goods and materials. This comprehensive literature review examines how the integration of blockchain and the Internet of Things can enhance supply chain traceability, utilizing a systematic literature search to identify and analyze all relevant studies. Recent and related articles selected from the Scopus database were reviewed. Our analysis underscores the potential for blockchain and IoT integration to provide end-to-end visibility, secure data sharing, and real-time monitoring across the supply chain ecosystem. It also identifies Machine Learning (ML) as another key component that enhances the security challenges of the Internet of Things while simultaneously serving as an analytical tool in Supply Chain Management (SCM). The review concludes that the integration of blockchain, the Internet of Things, and ML has the potential to transform supply chain traceability. By providing a secure, transparent, and efficient way to track the movement of goods and materials, businesses can improve their operations and offer better products and services to their customers. However, these findings do not impact the results of this research work. Additional research and a more extensive examination of the literature could offer a more comprehensive insight into the subject matter.
Transcribing Handwritten Medical Prescription using Convolutional Neural Network AlexNet Architecture and Canny Edge Detection Benitez, Ralph Andrei A.; Acula, Donata D.; Bondoc, Anton Oliver M.; Hizon, Angelo Louis L.; Santos, Aaron Joseph D.
Green Intelligent Systems and Applications Volume 4 - Issue 1 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i1.417

Abstract

Misinterpreted medical prescriptions had led to casualties due to the illegible cursive handwriting of medical practitioners. Many studies focused on this problem. However, the accuracy was unsatisfactory and needed improvement. The study evaluated the performance of the Canny edge detection with other preprocessing methods, including RGB to Grayscale Conversion, Binarization, and Inversion, which was used to process the images of cursive handwritten medical prescriptions using Alexnet Convolutional Recurrent Neural Network (ACoRNN). The CRNN model developed by previous researchers was used as the basis for comparison, and the researchers created a faster and more accurate model. The best combination of preprocessing methods for ACoRNN was with RGB to Grayscale Conversion, Binarization, Canny edge detection, and Inversion. The researchers’ model had faster preprocessing and testing time and achieved 90.76% average accuracy through five trials.
Comparative Study of Base Transceiver Stations Infrastructure Planning Using Machine Learning for Under Construction Area: A Case Study of Ibu Kota Nusantara Yustin, Alfiyah Shaldzabila; Apriono, Catur
Green Intelligent Systems and Applications Volume 4 - Issue 2 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i2.457

Abstract

Communication is a fundamental human need that occurs directly or through technologies like telephones and signal transmitters such as BTS and satellites. Satellites, including Starlink, serve as additional solutions for internet access needs, particularly in remote areas, albeit higher costs remain a factor necessitating conventional BTS infrastructure development. Telecommunication operators face challenges in constructing BTS in areas with limited access and complex financial considerations due to low demand in rural areas, requiring careful planning. This study utilizes several supporting variables with the aid of machine learning techniques such as Linear Regression, SVR, Random Forest, and Gradient Boosting to forecast BTS requirements. Comparative analysis shows that the random forest machine learning method provides the best modeling results compared to linear regression, Gradient Boosting, and SVR methods. Despite the superior performance of the random forest method, further fine-tuning is still needed through parameter adjustments and evaluation of variables used to achieve an even better model. The modeling results can be utilized to predict the BTS infrastructure needs in IKN, estimated at 61,135 units. In BTS development planning, mobile operators can collaborate both among themselves and with Internet Service Providers (ISPs) utilizing satellite media. Utilizing shared towers can be an option for cost-efficient BTS infrastructure development.

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