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2025 International Conference on Advancement in Data Science, E-learning and information system, ICADEIS 2025

作者： Pratama, I. Putu Agus Eka Widyantara, I. Made Oka Linawati Gunantara, Nyoman Suakanto, Sinung Irawan, Eka Tresna Udayana University Doctoral Program of Engineering Sciences Faculty of Engineering Bali Badung Indonesia Udayana University Dept. of Electrical Engineering Faculty of Engineering Bali Badung Indonesia Telkom University Dept. of Information System West Java Bandung Indonesia Mayar International Pte. Ltd. Singapore

ISBN: (纸本)9798331513320

The increasing use of information Technology (IT) in all areas of life, followed by increasing cybersecurity threats on the user side, makes cybersecurity awareness among users an obligation that must be met. Several solutions currently available (guides, discussion forums, workshops) have not been able to provide good results and are lacking in terms of user engagement, adaptability, and optimizing available resources, so technology-based learning is needed, especially AI. This paper introduces the design and prototype of an AI-based learning system, especially with the Naive Retrieval-Augmented Generation (Naive RAG) method with three steps, including Retrieval, Augmented, and Generation. The system combines contextual user questions and answers with internal and domain knowledge systems to explain cybersecurity awareness to users dynamically and personally. The prototype that has been developed is then tested using the Black Box Testing method to evaluate functionality and usability. The test results show that the functionality of the system built has run well with a percentage of success of 100%, so it can be used as a technologybased learning medium for education and learning of user security awareness. © 2025 IEEE.

关键词： Adversarial machine learning

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Technology-Enhanced Learning for User Security Awareness Using AI-based Naive RAG: A Design and Prototype

Technology-Enhanced Learning for User Security Awareness Usi...

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Advancement in Data Science, E-learning and information systems (ICADEIS), International Conference on

作者： I Putu Agus Eka Pratama I Made Oka Widyantara Linawati Nyoman Gunantara Sinung Suakanto Eka Tresna Irawan Doctoral Program of Engineering Sciences Faculty of Engineering Udayana University Badung Bali Indonesia dept. of Electrical Engineering Faculty of Engineering Udayana University Badung Bali Indonesia dept. of Information System Telkom University Bandung West Java Indonesia Mayar International Pte. Ltd. Singapore

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

关键词： Electronic learning Retrieval augmented generation Knowledge based systems Prototypes Learning (artificial intelligence) Computer security Usability information technology Testing information systems

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Comparison of Feature Selection Methods on Medical Record Data

Comparison of Feature Selection Methods on Medical Record Da...

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2023 International Conference on Modeling and E-information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Astuti, Lastri Widya Ermatita Rini, Dian Palupi Informatic Engineering Doctoral Program Sriwijaya University Palembang Indonesia Sriwijaya University Information System Palembang Indonesia Sriwijaya University Informatic Engineering Palembang Indonesia

ISBN: (纸本)9798350369359

Medical record data are an essential part of medicine, but the effective use of data has become difficult due to the rapid increase in data. Determining the selected features allows researchers to extract the information from medical data. Feature selection in classification provides the advantage of reduced time and storage. In addition, feature selection methods can improve performance by removing redundant or irrelevant data based on their weights. The benefits of feature selection are that it speeds up the training time, simplifies the model, makes interpretation easier, and improves generalization by reducing overfitting. Feature selection in this research uses two methods: Binary Whale Optimization (BWOA), Particle Swarm Optimization, and Hybrid with Gray Wolf Optimization (PSOGWO). Two feature selection methods were compared to determine the most optimal method for the classification process. A comparison of the two feature selection methods shows that the classification process with many attributes produces an optimal classification process, where the number of representative variables in the breast cancer dataset in the BWOA method has five features, and the PSOGWO method has 11 attributes. Meanwhile, the highest accuracy results in breast cancer classification using the BWOA average of 95.90% and PSOGWO Method average of 97.64% © 2023 IEEE.

关键词： Feature Selection

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Classification of Paddy Leaf Disease Using MobileNet Model 8

Classification of Paddy Leaf Disease Using MobileNet Model

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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Masykur, Fauzan Adi, Kusworo Nurhayati, Oky Dwi Diponegoro University Doctoral Program of Information System School of Postgraduate Studies Semarang Indonesia Muhammadiyah University of Ponorogo Department of Informatics Engineering Ponorogo Indonesia

ISBN: (纸本)9781665453899

Paddy is a rice-producing plant as a staple food raw material for most of the population in Indonesia. The rice planting process cannot be separated from pests and diseases that can cause crop failure and impact on rice production stocks. Currently, the process of monitoring the development of rice plants is done conventionally by direct observation by farmers. Meanwhile, in this paper, a classification model and determination of object detection in rice plants infected with pests and diseases is proposed based on leaf color. Leaf classification applies convolution conditional network algorithm with mobilenet as the architecture. The leaf image dataset was taken from public data from *** by involving 4 classes, namely healthy leaf images, brownspot, hispa and leafblast images. In each class the leaf images are grouped into 2 groups, namely 70% training and 30% validation. On the mobilenet backbone, it produces 97% accuracy and 22% loss with the time required for 195s 19s/step in 1 epoch round. © 2022 IEEE.

关键词： Object detection

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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

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Potential Classification Prediction of Solar and Wind Energy in Indonesia using Machine Learning with Random Forest Algorithm 5

Potential Classification Prediction of Solar and Wind Energy...

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5th IEEE International Conference on Computer and Informatics engineering, IC2IE 2022

作者： Ardiansyah, Deden Mantoro, Teddy Syafei, Wahyul Amien System School of Postgraduate Diponegoro University Doctoral Program of Information Bogor Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta Indonesia Diponegoro University Faculty of Engineering Semarang Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

The realization of the mass use of renewable energy needs to be supported by various parties, including Indonesia, which is a country that has great potential in the use of renewable energy. The community, government, and private parties who become developers in this regard need to realize the main potential of regions in Indonesia. This study aims to determine the prediction results of renewable energy in Indonesia, especially the potential for solar and wind energy. The method used in this research is machine learning with a random forest algorithm. Random forest classification prediction modeling was carried out using R software. The data samples used were data on maximum temperature, minimum temperature, average temperature, average humidity, duration of sunshine, and average wind speed from 34 provinces in Indonesia. Based on the prediction results with random forest, the average accuracy of the random forest classification model ranges from 81-92%. So, it can be said that the model can provide reasonable predictions. Based on random forest predictions, the classification of the largest level of solar energy potential is in 3 main areas, namely the provinces of East N usa Tenggara, Bali, and West Nusa Tenggara. While the areas with the greatest wind energy potential are in 3 provinces, namely Kep. Bangka Belitung, East Nusa Tenggara, and East Java. The province of East Nusa Tenggara has a high potential for solar and wind energy. The potential duration of solar radiation reaches 10.6 hours per day, while the average wind speed reaches 4 m/s per day. The results of this study are expected to be input for parties who play a role in developing renewable energy in Indonesia to support the renewable energy mix target of 23 % by 2025. © 2022 IEEE.

关键词： Forecasting

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Simulation studies on dynamic and distributed domain-based mobile IPv6 mobility management

Simulation studies on dynamic and distributed domain-based m...

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11th IEEE International Conference on Networks, ICON 2003

作者： Pyo, Chang Woo Li, Jie Kameda, Hisao Doctoral Program System and Information Engineering University of Tsukuba Tsokuba Science City Ibaraki 305-8573 Japan Institute of Information Science and Elelctronics University of Tsukuba Tsukuba Science City Ibaraki 305-8573 Japan

ISBN: (纸本)0780377885

We address mobility management for the Internet Protocol version 6 in mobile networks, namely Mobile IPv6 (MIPv6). To correctly transfer IPv6 packets to a mobile node (MN), the MN has to update its binding to the home network while roaming away from its home network. Mobile IP networks suffer high binding update traffic from high rates of handoffs and the long packet delivery delay from the large distance to the home network. In this paper, we introduce a dynamic and distributed mobility management protocol for MIPv6 to reduce the binding update and packet delivery delay and traffic. We describe the basic protocol operations, and examine the performance improvement of our mobility management against the previous Mobile IP mobility management methods. We show that the dynamic and distributed mobility management method outperforms other previous methods, and the dynamic approach provides the optimal mobility solution for MIPv6 and the distributed approach can decentralize the network load. ©2003 IEEE.

关键词： Home networks

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

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Privacy Preserving Electronic Health Record with Consortium Blockchain 6

Privacy Preserving Electronic Health Record with Consortium ...

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6th International Conference on information Technology, information systems and Electrical engineering, ICITISEE 2022

作者： Gautama, Adri Rochim, Adian Fatchur Bayuaji, Luhur Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Computer Science Budi Luhur University Jakarta Indonesia

ISBN: (纸本)9798350399615

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, Science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance. © 2022 IEEE.

关键词： Consortium Blockchain e-health EHR Privacy

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Bibliometric Analysis of Trends and Directions in Machine Learning Applied to FinTech 4

Bibliometric Analysis of Trends and Directions in Machine Le...

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4th International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2024

作者： Arif, Erman Suherman, Suherman Widodo, Aris Puji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350378368

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. The study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. The methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. The results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. This analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services. © 2024 IEEE.

关键词： Adversarial machine learning

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