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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Yakub, Sy. Yuliani Wowor, Rivo Juicer Agustriawan, David Irmawati Kristiyanti, Dinar Ajeng Winarno, P.M. Hassan, Rosilah Mohamad, Roshayu Razzaq, Randy Zahran Universitas Multimedia Nusantara Department of Informatics Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Uin Sultan Maulana Hasanuddin Department of Informatics Banten Indonesia

ISBN: (纸本)9798331506995

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats. © 2025 IEEE.

关键词： Cyber attacks

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Deep Learning for Cyber Security: Deephoax Detection

Deep Learning for Cyber Security: Deephoax Detection

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： SY. Yuliani Yakub Rivo Juicer Wowor David Agustriawan Irmawati Dinar Ajeng Kristiyanti P.M. Winarno Rosilah Hassan Roshayu Mohamad Randy Zahran Razzaq Department of Informatics Universitas Multimedia Nusantara Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Center for Cyber Security (CYBER) Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Informatics UIN Sultan Maulana Hasanuddin Banten Indonesia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

关键词： Deep learning Measurement Training Surveys Adaptation models Technological innovation Accuracy computer security Fake news Videos

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Survival Analysis of Kasturi Tobacco Plants in Jember District using the Stratified Cox and Extended Cox Models

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MethodsX 2025年

作者： Angga Iryanto Pratama Dwi Rantini Ratih Ardiati Ningrum Arip Ramadan Fazidah Othman Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively

关键词： Productive Agriculture Cox Proportional Hazard Stratified Cox Extended Cox Kasturi Tobacco

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Improving University Community Service Communication with Kukerti's Fuzzy String Matching Chatbot

Improving University Community Service Communication with Ku...

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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Andriyani, Yanti Firyadi, Ricki Mahdiyah, Evfi Fitriansyah, Aidil Aminuddin, Al Meitarice, Sonya Niqotaini, Zatin University of Riau Information System Study Program Computer Science Department Pekanbaru Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Study Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350307306

Efficient communication plays a pivotal role in the success of university community service programs. This study highlights the innovative approach taken by the University of Riau to enhance communication within their community service initiative through the implementation of Kukerti, a cutting-edge chatbot system. Developed specifically for Bahasa Indonesia-speaking users, Kukerti utilizes advanced fuzzy string-matching algorithms to process user inputs and generate relevant responses. In this study, we delve into the deployment and impact of the Kukerti chatbot within the University of Riau's community service program. We explore its functionality in addressing the common challenge of delayed responses from web administrators, which had previously hindered efficient information retrieval for students and participants. Our research aims to explore how does the implementation of the Kukerti chatbot system with advanced fuzzy string-matching algorithms impact the efficiency of communication and information retrieval within the University of Riau's community service program. The result shows the implementation of fuzzy string-matching algorithms improves the communication process and the evaluation of the Kukerti chatbot showed that the implementation of fuzzy string-matching has an accuracy rate of 93.33% in providing the answers. By exploring the fuzzy string-matching implementation in the Kukerti chatbot, it is expected to provide further understanding regarding the implementation and encourage other chatbot developers to use it. © 2023 IEEE.

关键词： String searching algorithms

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Improved K-Means Algorithm on Home Industry Data Clustering in the Province of Bangka Belitung

Improved K-Means Algorithm on Home Industry Data Clustering ...

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2020 International Conference on Smart Technology and Applications, ICoSTA 2020

作者： Santoso, Hadi Magdalena, Hilyah Information System Study Program STMIK Atma Luhur Department of Computer Science Pangkalpinang Bangka Indonesia

ISBN: (纸本)9781728130835

The Government of Bangka Belitung Islands Province has not classified the home industry until now. Based on these problems, we propose a k-means algorithm for clustering home industry data. The k-means algorithm is widely used because it is straightforward and very suitable for grouping data. However, in its application, the k-means algorithm has a weakness in determining the starting point of the cluster center and, in its selection, is still carried out randomly. As a result, if the random value for initializing the initial centroid value is not right, then the grouping is less than optimal. Internal cluster validation is one way to determine the optimal cluster without knowing prior information from the data. This study aims to identify the optimal group by making improvements to the k-means algorithm and then to test it by applying an internal cluster, namely the Davies-Bouldin Index (DBI) and the Silhouette Index (SI) on the data of home industry in Bangka Belitung Island Province. The optimal cluster calculation results based on internal cluster validation both show that the Silhouette index and the DBI index with k = 3 on improved k-means algorithm. While the traditional k-means algorithm of internal cluster validation both show that the Silhouette index and the Davies-Bouldin Index with k = 2. The conclusion is k = 3 on the Davies-Bouldin Index of this research data gives good results for clustering home industry data in Bangka Belitung Islands Province. © 2020 IEEE.

关键词： K-means clustering

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Convolutional Neural Network Analysis on Handwriting Patterns and Its Relationship to Personality: A systematical Review

Convolutional Neural Network Analysis on Handwriting Pattern...

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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Mair, Zaid Romegar Cholil, Widya Yulianti, Evi Marcelina, Dona Theresiawati Isnainiyah, Ika Nurlaili Computer and Natural Science Faculty Indo Global Mandiri University Informatics Study Program Indonesia Universitas Pembangunan Nasional Informatics Study Program Computer Science Faculty Jakarta Indonesia Indo Global Mandiri University Information System Study Program Computer and Natural Science Faculty Indonesia

ISBN: (纸本)9798350307306

It can be argued that the Convolutional Neural Network (CNN) used in this research is an efficient algorithm for classifying images based on the end prediction of the path taken. In every plot that is made, there is a similar process for achieving a prediction of the final result. The implementation for each of these procedures follows the steps that are summarized into a flow of analysis stages that can help to develop the application of an algorithm. The initial stage is to take the handwriting from the user which is then pre-processed the image, to eliminate existing noise, and sharpen the contrast, so that the image can be seen clearly. Images will be processed and analyzed using the Convolutional Neural Network model, training will be carried out, with an average training of a dataset of 100 epochs or about 7 to 10 minutes, and labeling on the trained dataset. The accuracy of the training reached 98.89%, as a proportion of the different characteristics of the handwriting sample. © 2023 IEEE.

关键词： Convolutional neural networks

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IoT-Based portable modules for energy consumption monitoring in smart home system 3

IoT-Based portable modules for energy consumption monitoring...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Bayu, J.P. Aryanto, K.A.A. Suweken, G. Aryanto, K.Y.E. Study Program of Computer Science Graduate Program Universitas Pendidikan Ganesha Singaraja-Bali Indonesia Department of Computer System Stikom Bali Denpasar-Bali Indonesia

Energy consumption has become one of the major problems in Indonesia. The use of recent technology is highly beneficial since various automation could be done even in simple devices. In this research, portable smart home modules based on the internet of things (IoT) technology to monitor the power consumption in household electrical devices were built. The module consisted of current sensors, voltage sensors, and IoT Wi-Fi Development Board. It communicated with the server, built using the Raspberry Pi, using the MQTT protocol. The server was equipped with web pages that allowed users to monitor the devices' electrical power usage. Therefore, all of the connected modules could be monitored to provide information regarding an electric household device's defect. The results had shown that the prototypes of the modules had been successfully built. It was shown that very slight differences were found between the system measurements compared to the manual one using the 60 seconds interval measurements. The power consumed by the module was very low, where the current sensor uses 0.125 Watt while the voltage sensor uses 0.001 Watt. The portable devices were developed in the shape of small boxes;therefore, it could be easy to move and install. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Smart monitoring apps for salvaging neolissochillus thienemanni sumateranus (batak heritage) from extinction

Smart monitoring apps for salvaging neolissochillus thienema...

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2017 International Conference on Electrical Engineering and computer Science, ICECOS 2017

作者： Harianja, Richad Pangaribuan, Nickholas Sinaga, Lambok Kisno, K. Siagian, Pandapotan Sembiring, Evita Rindani Institut Teknologi Del Computer Network Management Study Program Sitoluama22381 Indonesia Institut Teknologi Del Information System Study Program Del English Club-Writing Sitoluama22381 Indonesia

ISBN: (纸本)9781479976751

Ihan or Ikan Batak (Neolissochillus thienemanni sumateranus), the heritage of Batak tribe, is considered as vulnerable species of freshwater fish in Indonesia, especially at Lake Toba. This study aims to introduce SMAFIB application that is environment-friendly and helps the salvage of the fish from extinction. An application is built based on Django framework using Phyton programming language, which interacts with Raspberry pi through the internet to monitor the fish breeding and carries out all operations needed for fish conservation. A 14-day-experiment was carried out on 15 Ihan seed in a tailored aquarium equipped with the smart application. After two weeks, the result indicated that the success rate of breeding the endangered species using this system was 100% where all the fish put were survived. The implementation of the embedded system which is realized through mobile application has been successful in breeding the endangered species by considering and monitoring the essential aspects such as pH, temperature, feeding, water condition, and health risks. Time efficiency and yields are getting increased which reflect the increase in the economy for the farmers. SMAFIB application increases the efficiency of feeding time, monitoring time, and helps the salvage of the species in order to restore the custom and tradition of the Batak community as well as helps the improvement of the farmers' income without endangering the environment. © 2017 IEEE.

关键词： Embedded systems

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Online Handwritten Recognition for Alphabet Writing Practice 4

Online Handwritten Recognition for Alphabet Writing Practice

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4th International Conference on Cybernetics and Intelligent system, ICORIS 2022

作者： Santiari, Ni Putu Linda Rahayuda, I Gede Surya Institute of Technology and Business STIKOM Bali Information System Study Program Faculty of Informatics and Computer Denpasar Indonesia Informatics Study Program Faculty of Math and Natural Sciences Udayana University Mangupura Indonesia

ISBN: (纸本)9781665453950

Learning to write in children requires the child's habit of using a pencil to write on paper. The manual method has drawbacks such as the use of a lot of paper when children have to study harder to keep repeating the exercises and also cannot be done online remotely. In addition to this, writing exercises on the touchscreen can also train children to be more skilled at using the touchscreen. To overcome these problems, the author will discuss the use of handwriting recognition technology to practice writing capital letters (A-H), where the technology will be built on a web-based basis. The recognition algorithm used is K-NN. Testing the use of the website was carried out at Tunas Mekar Sari Kindergarten, Denpasar. The identification number and result will be stored on the web server. Based on the experiment, it was found that from 80 image data, the K-NN algorithm succeeded in providing accurate recognition results on 60 image data, and obtained an accuracy of 78.75%. © 2022 IEEE.

关键词： Websites

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