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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Teppap, Prasert Ponglangka, Wirot Luekhong, Prasert Rajamangala University of Technology Lanna Doctor of Engineering Program in Electrical Engineering Chiang Mai Thailand Rajamangala University of Technology Lanna Faculty of Engineering Chiang Rai Thailand College of Integrated Science and Technology Rajamangala University of Technology Lanna Chiang Mai Thailand

ISBN: (纸本)9798350383010

This study aims to categorize many 'actions' types presented in the comprehensive dataset, known as 'Internet Firewall Data.' We employed this dataset to train and assess several models, including KNN, NB, RF, DL, and DT, aiming to identify a highly accurate classification model for firewall log data. Through 10-fold cross-validation, we determined that the RF and DT models achieved robust performance, with accuracies of 99.86% and 99.85%, respectively. Moreover, the NB and DT models exhibited the quickest processing times, measured at 0.22 s and 0.35 s, respectively. These findings underscore the substantial impact of reducing the number of 'actions' on the model's capacity for discernment and precision. This paper offers insights and recommendations valuable to both cybersecurity professionals and researchers, advocating for the consolidation of firewall action groups to enhance manageability. Simplifying firewall configurations not only bolsters security but also diminishes operational complexities. © 2024 IEEE.

关键词： Cybersecurity

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Impact of High-k Materials and Tube Numbers on CNFET Gate's Performances 5

Impact of High-k Materials and Tube Numbers on CNFET Gate's ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Yalung, Christofer Tantraviwat, Doldet Uatrongjit, Sermsak Yamwong, Wittawat Doctor of Philosophy Program in Nanoscience and Nanotechnology International Program/Interdisciplinary Center of Excellence in Materials Science and Technology Faculty of Science Chiang Mai University Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand Chachoengsao Thailand

ISBN: (纸本)9781665474672

Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and contact materials. In this study, the dielectric constant and the number of carbon nanotubes (CNTs) were emphasized to analyze the propagation delay and average power dissipation of basic logic gates. Zirconium dioxide (ZrO2), hafnium oxide (HfO2), and silicon dioxide (SiO2) were used as dielectric materials. In this experiment, the Stanford CNFET model was utilized, and variable factors such as the number of CNTs and the dielectric constant (Kox) accumulated on the nanotube gate's surface were diversified. The correlation coefficients of the delay, the average power, and the CNT numbers were also presented. Furthermore, the multi-objective genetic algorithm and the Rmethod were utilized to find the optimal Kox and the number of CNTs for minimizing the average dissipation power and the propagation delay of some logic gates designed with CNFET. For all the gates considered, the optimal number of CNTs is 4 while the dielectric constant is within the ranges of 19 to 24. © 2023 IEEE.

关键词： Carbon nanotubes

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Machine Learning-Based Classification of Mental Health State Using the DASS-21 Profile 16

Machine Learning-Based Classification of Mental Health State...

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16th Biomedical engineering International Conference, BMEiCON 2024

作者： Duangchaemkarn, Khanita Khammarew, Patiphan Aramvith, Supavadee School of Pharmaceutical Sciences University of Phayao Department of Pharmacy Practice Phayao56000 Thailand School of Pharmaceutical Sciences University of Phayao Pharmacy Research Extern Under the Doctor of Pharmacy Program Phayao56000 Thailand Chulalongkorn University Multimedia Data Analytic and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9798331505431

This study aimed to determine the most effective machine learning model for classifying emotional states of depression, anxiety, and stress based on the DASS-21 profile. Six machine learning models were developed and tested using 2,602 records from the University of Phayao Counselling Center, collected between 23 July and 16 August 2020. The results demonstrate that the deep learning model provided the highest accuracy and precision, exceeding 90% for all three emotional categories, with an AUC greater than 99%. These findings introduce an innovative approach for classifying emotional states of depression, anxiety, and stress using the DASS-21 profile as a benchmark. Incorporating additional features into the model could further enhance its utility in clinical decision-making, improving the accuracy of mental health screening and diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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Cloud-Native Mobile Application Applied for Law and Regulations Harmonization 3

Cloud-Native Mobile Application Applied for Law and Regulati...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Al Qausar, Muhammad Juan Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Doctor of Computer Science Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Navigating the complex legal and regulatory landscape requires a sophisticated platform that is not only comprehensive but also user-friendly and enables seamless analysis and document comparison in the legal realm. To address this challenging requirement, this research is dedicated to the user experience (UX) design of state-of the-art cloud-native mobile applications specifically designed for legal and regulatory harmonization. The methodology used is based on design thinking principles and takes advantage of the benefits of cloud computing. These include, but are not limited to, scalability, reliability, and security. The result is a solution that is not only powerful but also cost-effective. The core of this application follows a user-centered design philosophy and involves potential users closely in the design process. A comprehensive evaluation is performed using the System Usability Scale (SUS) methodology to measure the effectiveness and usability of the application. The results of this comprehensive approach are convincing. The application has an impressive 95.5% usage rate, indicating strong user engagement. Furthermore, user satisfaction ratings are particularly high and transformative impact of this application in a complex environment of legal harmonization. Through its innovative design, seamless functionality, and user-centric approach, this application has proven to be not only a technical solution, but also a catalyst for positive change in the legal and regulatory harmonization process. © 2023 IEEE.

关键词： Cost effectiveness

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty 6

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Andriadi, Kus Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Yogyakarta55281 Indonesia

ISBN: (纸本)9798350358346

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large. © 2023 IEEE.

关键词： Generative adversarial networks

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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Development of Buffer Zone into Living Lab-Garuda Smart City Framework: Systematic Review 10

Development of Buffer Zone into Living Lab-Garuda Smart City...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Emmanuel, Gunadi Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Bina Nusantara University Doctor of Computer Science Jakarta Indonesia Univ. Katolik Musi Charitas Fakultas Sains Dan Teknologi Palembang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a living lab require consideration of the right location for a living lab. This study wants to show that suburban areas as city buffer zones can become living laboratories for smart city development. The diversity of situations in cities and regencies across Indonesia, the potential for resources, and the problems faced are the challenges of developing a living lab-Garuda Smart City Framework. This research uses the method of reviewing the literature of research publications for the last five years (2019-2023) to obtain information on Smart City development in Indonesia. We collected selected articles from databases in Google Scholar, IEEE, and Scopus using the Publish and Perish 8. The search keywords used were garuda AND Smart city AND Framework. The findings show the potential and dynamics of buffer zones to become appropriate living laboratories. Smart city regional planning can more holistically involve neighborhoods and address urban issues. © 2023 IEEE.

关键词： Smart city

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Dynamic Surrogate Optimization of Vertically Stacked Nanosheet FET Based on Gaussian Process Regression

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IEEE Access 2025年 13卷 95730-95738页

作者： Christofer N. Yalung Doldet Tantraviwat Doctor of Philosophy Program in Nanoscience and Nanotechnology (International Program/Interdisciplinary) Materials Science Research Center Faculty of Science Chiang Mai University Chiang Mai Thailand Department of Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Center of Excellence in Materials Science and Technology Faculty of Science Chiang Mai University Chiang Mai Thailand

While speed improvements are pivotal for advancing modern transistor technologies and ensuring faster computational performance, the tradeoffs with other critical electrical properties, such as threshold voltage ( $V_{T}$ ) and off-current ( $I_{OFF}$ ), are often overlooked. These tradeoffs can significantly impact the overall device performance. The design of vertically stacked n-type and p-type nanosheet FETs (NSFETs) was applied using a dynamic surrogate optimization-gaussian process regression (DSO-GPR) with batch assessment and halting conditions. A vertically stacked silicon-based system is created and refined. Source extension ( $S_{ext}$ ), drain extension ( $D_{ex\mathrm {t}}$ ), gate length ( $L_{g}$ ), nanosheet height (NSh), nanosheet width (NSw), and number of fin (nfin) make up the input variables. In addition to applying normalization to make the weight assignment to the goal functions easier, a penalty component was incorporated to manage the $V_{T}$ . This study demonstrates the effectiveness of the DSO-GPR framework in optimizing NSFET-based inverter designs by balancing speed, $I_{OFF}$ and $V_{T}$ . Among the three evaluated cases, case 1 achieved the fastest propagation delay (1.05 ps) due to higher $I_{ON}$ and lower $V_{T}$ , albeit with increased $I_{OFF}$ and $V_{T}$ mismatch. Case 2 provided a balanced trade-off, significantly reducing $I_{OFF}$ while maintaining a competitive delay of 1.25 ps and achieving $V_{T}$ matching at 0.11 V for both n-type and p-type NSFETs. The optimum parameters for n-type are ${L} _{g}$ = 5 nm, $N_{Sh} = 9$ nm, $N_{Sw} = 42$ nm, $S_{ext} = 3$ nm and $D_{ext} = 4$ nm, nfin = 5; for p-type, $L_{g} = 7$ nm, $N_{Sh} = 10$ nm, $N_{Sw} = 42$ nm, $S_{ext} = 3$ nm and $D_{ext} = 3$ nm, nfin = 5. Case 3 minimized leakage at the cost of delay, highlighting the trade-off between power and performance. These results validate the framework’s adaptability and automation, with performance-based stopping criteria that balance

关键词： Optimization Logic gates Ions Silicon Performance evaluation Linear programming Gaussian processes Field effect transistors Accuracy Machine learning

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Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers 5

Sentiment Analysis of Cooking Oil using Bidirectional Encode...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Widianto, Mochammad Haldi Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Department Binus Graduate Program - Doctor of Computer Science Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Departement of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as;pre-processing, data quantity, and data variation. © 2022 IEEE.

关键词： Sentiment analysis

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