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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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A Sentiment Analysis of Hate Speech in Philippine Election-Related Posts Using BERT Combined with Convolutional Neural Networks and Model Variations Incorporating Hashtags and ALL-CAPS Mendoza, Micah Collette O.; Nadurata, Wayne Gabriel S.; Oritz, Mark Gabriel E.; Padlan, Joshua Mari L.; Ponay, Charmaine S.
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.v4i2.491

Abstract

As the number of people who use X continually increases, the same thing is true for hate speech. A pressing need exists for automatic detection of posts that promote hate speech. The datasets gathered and validated from the base study were used to categorize posts as either hate or non-hate and classify them as positive, negative, or neutral using Conventional Neural Networks. The partitioning of the labeled data into training and testing sets adhered to a ratio scheme: 70%-30%, 80%-20%, and 90%-10%. The model of this study, BERT-CNN, had an overall better performance than the base study, fastText CNN. Notably, among the three splits, the BERT-CNN model for binary classification without the features of Hashtags and ALL-CAPS with the 90:10 split achieved the best performance with an accuracy of 93.55%, precision of 93.59%, and F1-score of 93.55%. For multi-label classification, the BERT-CNN model demonstrated its optimal performance when incorporating hashtags, specifically with the 90:10 split, achieving an accuracy of 69.14%, precision of 68.44%, recall of 68.40%, and an F1-score of 67.41%. The innovative use of BERT word embeddings paired with CNN proved to excel in classifying Philippine election-related posts as hate or non-hate.
Radiation Performance Comparison and Analysis of Ku-band Microstrip Antennas with Diamond, Octagonal, and Circular Array Configurations Adriansyah, Muhammad Athallah; Wahdiyat, Aditya Inzani; 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.v4i2.502

Abstract

Phased array antennas are essential in modern communication systems, particularly within the Ku-band, which is widely used for satellite communications and radar applications due to its high data rate capabilities. This paper explores the radiation characteristics of Ku-band microstrip antennas arranged in diamond, octagonal, and circular arrays, focusing on uniform excitation to ensure consistency across evaluations. Using CST Microwave Studio 2024 for simulations, the study found that the rectangular array provides the highest gain and narrowest beamwidth, making it suitable for applications where directional accuracy is critical. However, this configuration also resulted in higher sidelobe levels, which can be problematic in environments where minimal interference is required. The diamond array, while exhibiting lower gain, achieved superior sidelobe suppression, making it ideal for scenarios where reducing interference is prioritized over maximizing directivity. The octagonal and circular arrays provided balanced performance across all metrics, offering versatile options for various operational needs. These results provide valuable guidance for optimizing phased array designs to meet specific requirements in Ku-band applications.
Artificial Neural Network for Benchmarking the Dimensional Accuracy of the PLA Fused Flament Fabrication Process Setiawan, Kevin Stephen; Tanaji, Irvantara Pradmaputra; Permana, Ari; Akbar, Hafizh Naufaly; Prihatmaja, Dhonadio Aurell Azhar; Normasari, Nur Mayke Eka; Rifai, Achmad Pratama; Pamungkasari, Panca Dewi
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.v4i2.522

Abstract

Fused Deposition Modeling (FDM) is an additive manufacturing technique that uses a 3D printer to extrude molten filament through a nozzle, which moves along the X, Y, and Z axes to create parts with the desired geometry. FDM offers numerous advantages, especially for producing parts with complex shapes, due to its ability to enable rapid and cost-effective manufacturing compared to traditional methods. This study implemented an Artificial Neural Network (ANN) to optimize process parameters aimed at minimizing dimensional inaccuracies in the FDM process. Key parameters considered for optimization included the number of shells, infill percentage, and nozzle temperature. The ANN utilized three algorithms: Scaled Conjugate Gradient, Bayesian Regularization, and Levenberg-Marquardt. Model performance was evaluated based on dimensional deviations along the X and Y axes, with a hidden layer of 25 neurons. Among the algorithms, Scaled Conjugate Gradient provided the most accurate results in minimizing dimensional errors.
Literature Review: Biomedical Information of Animal Treadmill Speed Control Using Proportional Integral Derivative Controller Nurbadriani, Cut Nanda; Melinda, Melinda; Roslidar, Roslidar
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.v4i2.526

Abstract

The use of treadmill exercise in cardiovascular research played a vital role in assessing heart health and determining appropriate exercise regimens for patients. Before applying these regimens to humans, experiments on animals, such as white rats or mice, were conducted to simulate human cardiovascular responses. A specialized treadmill designed for experimental animals was required to determine exercise doses based on individual abilities. This process involved controlling the treadmill speed, which was generated by a conveyor driven by a DC motor. The motor speed was regulated through PID (Proportional Integral Derivative) control, while encoder sensors monitored the motor’s rotation speed, and limit switch sensors determined the exercise duration. This article reviewed the design and implementation of treadmill systems used for animal-based cardiovascular research, focusing on the control of DC motor speed using PID controllers. Previous studies that contributed to the development of such systems were discussed, with an emphasis on the precise control mechanisms required to simulate exercise conditions tailored to the subject's abilities. The treadmill system also incorporated sensors to accurately adjust motor speed and track exercise duration, ensuring alignment with the physiological capabilities of the test subjects. Furthermore, this review explored the potential for advancing research on treadmill control systems, offering insights into how this technology could support medical experts in determining optimal exercise regimens for white rats. These developments helped bridge the gap between animal-based studies and human applications, facilitating improved cardiovascular research outcomes.
Design of IoT-Based Battery Monitoring for DC Backup Yunidar, Yunidar; Fathurrahman, Fathurrahman; Melinda, Melinda; Azra, Ery; Malahayati, M.; Elizar, Elizar
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.v4i2.528

Abstract

The battery monitoring process for the DC backup power supply at the Banda Aceh Main Substation was previously performed manually using a multimeter, leading to inefficiencies. This study aimed to develop an automated battery monitoring system based on the Internet of Things (IoT) to enhance operational efficiency. The proposed system integrated a DC voltage sensor (voltage divider) connected to the battery and an INA219 sensor to measure current flow during battery usage. A NodeMCU ESP8266 microcontroller, programmed with the Arduino IDE, served as the main data processor and internet interface. Monitoring data was transmitted to officers via an IoT-based cloud server on the Blynk platform. The system was tested using eight NiCd 1.2 V battery cells arranged to simulate the substation setup. The resulting prototype automated daily battery monitoring, significantly improving the efficiency and effectiveness of the monitoring process.
Spam and Phishing Whatsapp Message Filtering Application Using TF - IDF and Machine Learning Methods Manurung, Ferdinand Aprillian; Munawir; Pradeka, Deden
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.551

Abstract

The rapid development of communication technology has led to an increase in the number of unwanted messages, such as spam and phishing attempts. However, this progress has not been accompanied by sufficient user awareness of the basics of technology use. Additionally, the enforcement of laws regarding internet-based crimes remains unclear, further increasing the risk for users of internet technology to fall victim to such crimes. As one of the media prone to spam and phishing, WhatsApp is the focus of this research, which aims to develop an application capable of filtering spam and phishing messages. The application employs the TF-IDF (Term Frequency-Inverse Document Frequency) method and machine learning using the Random Forest model. It is developed using the MVVM (Model-View-ViewModel) architecture, enabling the separation of business logic from the user interface, thereby improving development and maintenance efficiency. The research findings demonstrate that the combination of TF-IDF and Random Forest achieves high accuracy in classifying spam and phishing messages. Performance evaluation using a confusion matrix reveals an accuracy rate of 92%. For the safe message class, the precision, recall, and F1 scores are 89%, 95%, and 92%, respectively, while for the dangerous message class, the scores are 95%, 88%, and 92%, respectively. Furthermore, the integration of the model and application performed exceptionally well, as evidenced by black-box testing results. All test scenarios were met, successfully detecting test messages with 98% accuracy. Therefore, the developed application provides enhanced protection for WhatsApp users against digital threats.
Design of A Braille Printer Based on ESP32 Microcontroller with Voice Input Beatrix, Maria; Wahab, Wahidin; Wulandari, Meirista
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.592

Abstract

Braille is a tactile phonetic alphabet system invented by Louis Braille, a blind teacher from France, in the 1800s. The Braille system was recognized as "a vital language of communication, as valid as all other languages in the world" in 2005. There are other alternatives, such as touch-based methods, to convey information that is generally obtained through sight. One of them is the use of Braille letters for reading, writing, and improving welfare by increasing insight. However, only 52 special schools in Indonesia have printers for Braille books. Limited access to Braille printing facilities in Indonesia is due to high costs. The cost of a printer machine, approximately 50 million per school, poses a challenge in providing learning facilities. This research proposes a compact and cost-effective Braille printer using an ESP32 microcontroller with both speech and mechanical switch inputs. The mechanical switch is used for typing text to be printed, while the microphone captures sound input in the form of audio, as it is easier to use. Audio input is processed using speech-to-text technology. The speech-to-text process is carried out with speech recognition, which listens to the words spoken by the user and matches them with the data in the module to execute specific commands. This Braille printer is designed to print Braille letters based on data received directly from individuals with and without disabilities. The printer accepts input in the form of speech or text, which is then sent to the processing module, the ESP32 microcontroller. Once all data is processed, the Braille printer module controls axis movements using a stepper motor. Braille prints are embossed to create raised dots on paper. Experimental results demonstrate 100% accuracy for both speech and typing inputs, along with reliable printing performance on standard HVS paper. Compared to previous solutions, the proposed design is more versatile, affordable, and portable. This study presents a practical solution for increasing access to Braille education and information.
Twitter Sentiment Analysis of Mental Health Issues Post COVID-19 Pamungkasari, Panca Dewi; Ningsih, Sari; Rifai, Achmad Pratama; Nandila, Alisyafira Sayyidina; Nguyen, Huu Tho; Penchala, Sathish Kumar
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.588

Abstract

The Coronavirus Disease 2019 (COVID-19) impacted many aspects of daily life, including mental health, as some individuals struggled to adjust to the rapid changes brought on by the pandemic. This paper investigated sentiment analysis of Twitter data following the COVID-19 pandemic. Specifically, we analyzed a large corpus of tweets to understand public sentiment and its implications for mental health in the post-pandemic context. The Naïve Bayes and Support Vector Machine (SVM) classifiers were used to categorize tweets into positive, negative, and neutral sentiments. The collected tweet data samples showed that 38.35% were neutral, 32.56% were positive, and 29.09% were negative. Results using the SVM method showed an accuracy of 84%, while Naïve Bayes achieved 80% accuracy.
Comparison Of Feature Extraction Techniques For Long Short-Term Memory Models In Indonesian Automatic Speech Recognition Armaisya, Dimas Dwi; Pamungkasari, Panca Dewi; Rifai, Achmad Pratama; Sholihati, Ira Diana; Gopal Sakarkar
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.605

Abstract

Automatic Speech Recognition (ASR) faced challenges in accuracy and noise robustness, particularly in Bahasa Indonesia. This research addressed the limitations of single feature extraction methods, such as Mel-Frequency Cepstral Coefficients (MFCC), which were sensitive to noise, and Relative Spectral Transform - Perceptual Linear Predictive (RASTA-PLP), which was less effective in frequency representation, by proposing a hybrid approach that combined both techniques using Long Short-Term Memory (LSTM) models. MFCC enhanced spectral accuracy, while RASTA-PLP improved noise robustness, resulting in a more adaptive and informative acoustic representation. The evaluation demonstrated that the hybrid method outperformed single and non-extraction approaches, achieving a Character Error Rate (CER) of 0.5245 on clean data and 0.8811 on noisy data, as well as a Word Error Rate (WER) of 0.9229 on clean data and 1.0015 on noisy data. Although the hybrid approach required longer training times and higher memory usage, it remained stable and effective in reducing transcription errors. These findings suggested that the hybrid method was an optimal solution for Indonesian speech recognition in various acoustic conditions.
A Benchmark Study of DeepLabV3+, U-Net++, and Attention U-Net for Blood Cell Segmentation Angelina, Clara Lavita; Rospawan, Ali
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.607

Abstract

Cell segmentation is a critical process in biomedical image analysis. This study evaluated the performance of three state-of-the-art deep learning models—DeepLabV3+, U-Net++, and Attention U-Net—using the Blood Cell Count and Detection (BCCD) dataset, which contains annotated images of blood cells. The models were rigorously analyzed through qualitative and quantitative evaluations, employing accuracy, precision, recall, and F1 score metrics. The results demonstrated that all three models achieved high segmentation performance, with U-Net++ excelling in accuracy (0.9740), precision (0.9511), and F1 score (0.9576), Attention U-Net achieving the highest recall (0.9692), and DeepLabV3+ providing a balanced performance across all metrics. Qualitative analyses revealed that U-Net++ delivered superior segmentation of complex and overlapping cell structures, while Attention U-Net exhibited exceptional sensitivity to dense cell clusters. Training and validation curves of the models confirmed their stability and generalizability, indicating efficient convergence without overfitting. By highlighting the unique strengths of each model, this study emphasized the importance of selecting architectures tailored to specific tasks. Future research will expand the application of these models to diverse biomedical datasets to further advance automated image analysis and its impact on healthcare outcomes.

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