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7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023

作者： Hermawan, Hellik Mahardika, Fajar Darmayanti, Irma Sumantri, R. Bagus Bambang Saputra, Dhanar Intan Surya Aminuddin, Afrig Universitas Amikom Purwokerto Faculty of Computer Science Department of Informatics Jawa Tengah Banyumas Indonesia Institut Teknologi Dan Sains Nahdlatul Computer Technology Study Program Faculty of Science and Technology Ulama Pekalongan Indonesia Universitas Amikom Purwokerto Faculty of Computer Science Department of Information Technology Jawa Tengah Banyumas Indonesia Universitas Harapan Information Systems Study Program Faculty of Science and Technology Bangsa Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Sleman Indonesia

ISBN: (纸本)9798350382266

This systematic literature review (SLR) study aims to analyze the role of New Media as a Tool to Improve Creative Thinking, with relevant articles from 2018 to 2023 taken from reputable international journals. It uses three research questions (RQ) to explore New Media's relevance in stimulating creativity. The results indicate that interactive platforms such as social media and online collaboration tools positively influence creative thinking skills. Interaction through New Media allows individuals to share ideas, discuss, and be exposed to various points of view, all of which stimulate creative thinking. In addition, New Media also facilitates the creative process of solving problems and generating original ideas. The immersive experiences offered by this technology can enhance the exploration of ideas and increase the ability to think out-of-the-box. While New Media offers great potential to enhance creativity, there are also potential risks, such as false information, media addiction, and privacy concerns. Therefore, awareness of the wise use of New Media needs to be increased, especially in education and everyday life. Overall, this literature review provides in-depth insight into the role of New Media in enhancing creative thinking skills and driving innovation. With the right approach, New Media can be an effective tool in stimulating creative thinking and encouraging the creation of original ideas beneficial for future social and technological developments. © 2023 IEEE.

关键词： Creative Thinking New Media Original Ideas Systematic Literature Review The Effect of Using New Media

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Long Short Term Memory (LSTM) Improvement Accuracy Using FastText and Glove for Language Translation Batak - Indonesian 2

Long Short Term Memory (LSTM) Improvement Accuracy Using Fas...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Mandasari, Atikah Rahayu Nababan, Erna Budhiarti Mawengkang, Herman Informatics Program Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Faculty Of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Medan Indonesia

ISBN: (纸本)9798350351613

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages. © 2024 IEEE.

关键词： Semantics

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Speed Bump Detection Through Inertial Sensors and Deep Learning in a Multi-contextual Analysis

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SN computer science 2023年第1期4卷 18页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Department of Informatics and Statistics (INE) Federal University of Santa Catarina (UFSC)-Florianópolis Santa Catarina Brazil

Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the presence of speed bumps can affect travel time and fuel consumption, cause traffic jams, delay emergency vehicles, and cause vehicle damage or accidents when not properly signaled. Due to these factors, the availability of geolocation information for these obstacles can benefit several applications in Intelligent Transportation System (ITS), such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, allowing to trace more efficient routes or alert the driver of the presence of the obstacle ahead. Speed bump detection applications described in the literature employ cameras or inertial sensors, represented by accelerometers and gyroscopes. While camera-based solutions are mature with evaluation in different contextual conditions, those based on inertial sensors do not offer multi-contextual analyses, being mostly simple applications of proof of concept, not applicable in real-world scenarios. For this reason, in this work, we propose the development of a reliable speed bump detection model based on inertial sensors, capable of operating reliably in contextual variations: different vehicles, driving styles, and environments in which vehicles can travel to. For the model development and validation, we collect nine datasets with contextual variations, using three different vehicles, with three different drivers, in three different environments, in which there are three different surface types, in addition to variations in conservation state and the presence of obstacles and anomalies. The speed bumps are present in two different pavement types, asphalt and cobblestone. We use the collected data in experiments to evaluate aspects such as the influence of the placement of the sensors for vehicle data collection and the data window size. Afterwar

关键词： Autonomous vehicles Deep learning Inertial sensors Intelligent transport systems Road conditions Speed bump

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The Improvement of Personal Service in the MSMEs Marketplace Using a Rank-Based Collaborative Filtering Approach

The Improvement of Personal Service in the MSMEs Marketplace...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Pratama, Yan Aditiya Informatics Engineering Study Program Faculty of Computer Science Lampung Bandar Indonesia Management Study Program Faculty of Business Economics Lampung Bandar Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Indonesian MSMEs in 2022 will begin to enter the transformational recovery phase. Various preparations must be made in order to adapt. One that has been done is to increase the number of MSMEs entering the digital ecosystem, such as the MSME marketplace. The MSME Marketplace is used for marketing various MSME products online. The MSME Marketplace must be supported by methods that are able to provide good and fast personal services, considering the types and variety of products offered are increasing in number and varying from time to time. Therefore, this study proposes the Porat_Rank method to produce product recommendations that match user interests quickly so that users are served well. The experimental results show that the Porat_Rank method is superior to the Copelan Score method both in producing recommendations and in processing speed. © 2022 IEEE.

关键词： Collaborative filtering

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Advanced Design Pattern Selection Methodology through Weighted Semantic Ontologies for Microservices

Advanced Design Pattern Selection Methodology through Weight...

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International Conference on computer and Applications (ICCA)

作者： Rabab Atef Attia Samy Ghoniemy Abeer Hamdy Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Computer Science and Engineering Program Galala University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Seamless communication between authorities, people, and smart devices is crucial in today's globally interconnected world. Unprecedented demands on software design result from the advent of ubiquitous connectivity, which allows connections at any time and from any location. By structuring applications as separate services that can communicate both internally inside a platform and externally across platforms, microservices (MS) architecture satisfies these requirements. One of the most difficult parts, though, is still controlling communication between MS services, which calls for knowledge of the best design patterns. In order to solve this, we suggest a context-aware recommender system that is adapted to MS design patterns. This system not only makes recommendations for appropriate patterns, but it also gives statistical scores that show how relevant each solution is to the particular issue at hand. Other patterns with ratings indicating their possible feasibility are also displayed by our algorithm. On the other hand, sentence transformers handle intricate, human-like problem descriptions, while our method uses MS Domain Ontologies to distinguish pattern applications, constraints, and best practices. Our methodology provides a refined examination of solution suitability and recommendations through the use of weighing and correlation algorithms. Extensively tested with more than 237 developer-style scenarios, our framework offers qualitative and quantitative insights into its performance and efficacy, showing notable gains over current solutions in terms of offering pattern-specific, contextually accurate guidance for MS communication challenges.

关键词： Hands Adaptation models Accuracy Correlation Knowledge based systems Semantics Microservice architectures Ontologies Reliability Testing

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Fetal Health Risk Classification Using Important Feature Selection and Cart Model on Cardiotocography Data

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Prabandari, Jasmine Iswarini Dyah Sari, Christy Atika Rachmawanto, Eko Hari Irawan, Candra Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant. © 2023 IEEE.

关键词： Convolutional neural networks

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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Improved IP lookup technology for trie-based data structures

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Journal of computer and System sciences 2023年 133卷 41-55页

作者： Lin, Yen-Heng Hsieh, Sun-Yuan Master Degree Program on Artificial Intelligence National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Institute of Manufacturing Information Systems Institute of Medical Informatics National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Puli Township54561 Taiwan

Many Internet protocol (IP) lookup algorithms have been formulated to improve network performance. This study reviewed and experimentally evaluated technologies for trie-based methods that reduce memory access, memory consumption and IP lookup time. Experiments involving binary tries (for simplicity) were conducted on four real-world data sets, two of which were on IPv4 router tables (855,997 and 876,489 active prefixes) and two of which were on IPv6 router tables (155,310 and 157,579 active prefixes). Technologies designed to reduce time taken (at the expense of memory consumption) worked well for IPv4, and those designed to reduce memory consumption worked well for IPv6. Various combinations of these technologies were applied together in the experiments. © 2022 Elsevier Inc.

关键词： Table lookup

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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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