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检索条件"机构=Informatics Engineering study program"

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Real Market Basket Analysis using Apriori and Frequent Pattern Tree Algorithm

Real Market Basket Analysis using Apriori and Frequent Patte...

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2021 International Conference on Computer, Control, informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA

作者： Christian, Michael Albert Nathanael, Nathanael Mauliani, Annisa Indrawati, Ariani Manik, Lindung Parningotan Akbar, Zaenal Informatics Engineering Program Study Kwik Kian Gie School of Business Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9781450385244

Recently, data mining has been implemented in various fields, including business and telecommunications. Data mining is a technique for extracting and detecting patterns in massive data sets that combines machine learning, statistics, and database systems. One of the most important use-cases in data mining is finding the high-frequency patterns between the set of itemset called association rules. Association rule mining is a well-researched technique for finding some relations between variables in large databases. This paper aims to measure the performance of the Apriori and Frequent Pattern Tree algorithms by comparing them using several points of comparison. Then we compared the outputs, whether they produce the same or different rules, to find out whether the way the two algorithms work is similar or not. After that, we looked for the itemsets that best match the reality in the market by giving them to a user who had transaction data from his spare parts shop. © 2021 ACM.

关键词： Association rules

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Internet of Medical Things: A Bibliometric Analysis of Research Publications from 2018–2022 4th

Internet of Medical Things: A Bibliometric Analysis of Resea...

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4th International Conference on Electronics, Biomedical engineering, and Health informatics, ICEBEHI 2023

作者： Maulana, Fairuz Iqbal Lestari, Dian Adi, Puput Dani Prasetyo Arifin, Mohammad Nazir Purnomo, Agung Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9789819714629

The field of the Internet of Medical Things (IoMT) is experiencing significant growth as it involves the integration of medical devices and systems with the internet and various digital technologies. This study provides a bibliometric analysis of scholarly articles pertaining to the Internet of Medical Things (IoMT) during the time frame of 2018 to 2022. The analysis is conducted using data sourced from the Scopus database. A total of 1165 documents were identified and analyzed to gain insights into the current state of research in this field. The analysis includes trends in publication output, top authors and institutions, and key research topics. The results of the analysis indicate a steady increase in publication output on IoMT over the past five years, with the highest number of publications in 2022. The top authors in this field were Mohanty, S.P., Kougianos, E., and Pirbhulal, S., while the top institutions were King Saud University, Chinese Academy of Sciences, and University of North Texas. Co-citation analysis revealed that wearable devices, telemedicine, and data analytics were among the key research topics in IoMT. This bibliometric analysis offers useful insights into the present research landscape of the Internet of Medical Things (IoMT) spanning the years 2018 to 2022. The findings can be beneficial for researchers, practitioners, policymakers, and funding agencies in understanding the trends and priorities in this field. The top authors and institutions identified in this analysis can serve as valuable resources for collaboration and staying updated on the latest developments in IoMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Digital devices

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Hybrid Encryption Based on Fernet and Rivest Shamir Adleman (RSA)

Hybrid Encryption Based on Fernet and Rivest Shamir Adleman ...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Muhammad Dafi Hisbullah Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Chaerul Umam Folasade Isinkaye Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria

RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. This study uses Fernet and RSA which is the combination of symmetric and asymmetric encryption called hybrid encryption. In addition, the cipher text will be hidden inside an image using Stepic. Hybrid Encryption uses asymmetric encryption to encrypt the symmetric encryption secret key, it will secure the symmetric encryption. The result of this study is the lowest error that we got as the MSE is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche (AVA) with 79.00% and 42.34% in order. Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 46.48% for UACI and 99.86% for the NPCR.

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Augmented Reality Application for Book Detection with Google Colaboratory and SnapML Lens Studio

Augmented Reality Application for Book Detection with Google...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Galuh Saputri Joko Suwamo Achmad Lutfi Fuadi Agus Heri Yunial Program Study Informatics EngineeringFaculty of Engineering Universitas Pamulang Tangerang Selatan Banten Indonesia

ISBN: (纸本)9781665453905

Artificial intelligence is one of the technological breakthroughs in science, especially in the field of computers. Object detection and recognition presents a new challenge on how to make machines detect objects automatically. To detect an object, the previous system will take the classifier of an object and evaluate it at various locations and various scales in the image. Smartphones currently play an important role in everyday life, but the existence of smartphones can be one of the causes of the user's lack of interest in reading books. With this Augmented Reality-based object detector, it is hoped that users can experiment on smartphones with the Snapchat application installed to detect objects in books that are around them and the number of objects will be counted automatically, it is also expected to attract users' desire to read books. Image recognition identification is taken through the phone's camera which is recognized as a lens. Lens AR in the Lens Studio application is supported by the SnapML machine learning algorithm, this machine learning function is to display book information and the number of books detected. This research was conducted using Cloud Computing such as Google Collaboratory which uses the python programming language, the book object dataset is selected from the COCO class, the model is exported to Open Neural Network Exchange (.ONNX) and then imported into the Lens Studio application. Publish lenses in the Snapchat app. Finally, a model test was carried out to detect objects and the number of objects in books that had been made using a Smartphone. Open the Snapchat application, scan the Snapcode generated by Lens Studio, AR lenses can be used in real time. AR Book Detection has been tested by other users on Snapchat with 18,845 Lens Views, 18,737 Lens Plays and 16 Lens Shares.

关键词： Image recognition Machine learning algorithms Computational modeling Multimedia Web sites Neural networks Object detection Real-time systems

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Qori Alfatah Putra Christy Atika Sari Eko Hari Rachmawanto Nur Ryan Dwi Cahyo Edy Mulyanto Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science National University of Science and Technology DhiQar Iraq

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers.

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IoT: Monitoring Temperature, Water pH, and Automatic Water Drainage Systems for Gurami Cultivation

IoT: Monitoring Temperature, Water pH, and Automatic Water D...

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Networking, Electrical engineering, Computer Science, and Technology (IConNECT), International Conference on

作者： Heni Sulistiani Wijaya Saputra Dedi Darwis Auliya Rahman Isnain Ardy Rimanda Putra Yosi Khairunisa Study Program of Accounting Information System Universitas Teknokrat Indonesia Lampung Indonesia Study Program of Computer Engineering Universitas Teknokrat Indonesia Lampung Indonesia Study Program of Informatics Universitas Teknokrat Indonesia Lampung Indonesia Regional Research and Development Agency Lampung Province (Balitbangda) Lampung Indonesia

Gurami is a native fish from Indonesian waters that is much-loved by Indonesian people for daily food consumption. The gurami inhabits still waters such as swamps, lakes, and ponds. Besides that, carp cannot live in a water content that is too alkaline or acidic, the oxygen content in the water must be stable. Water quality is a very important factor and can affect the survival of cultivated fish. Several parameters must be considered in improving the development of carp, one of which is the temperature and pH of the water and automatic drainage of water. In this paper, we conduct IoT-based research that focuses on controlling water temperature and pH and automatically draining pool water. Water temperature and pH control such as a pH sensor temperature sensor and a water turbidity sensor will later be connected to the ESP 32 microcontroller which already has a wi-fi module. A system that is linked to electrical power and the internet then the sensor will read the water quality including the pH temperature value and water turbidity from inside the fish pond, after getting the data, the data will go to ESP 32. This research can help gurami cultivating farmers to make it easy to control the pH temperature and perform automatic pool water draining. The results of sensor testing with actual data are pretty accurate with the average error value of the ds18b20 sensor being 0.05% while the value of the pH sensor itself is 0.07%.

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Challenges on Cloud Computing Migration Strategy for Music Industry: A Systematic Literature Review

Challenges on Cloud Computing Migration Strategy for Music I...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Hijir Della Wirasti Henki Seta Deden Witarsyah Hamonagan K Prabu Ayu Azzahro Bayu Hananto Faculty of Engineering Industry Telkom University Bandung Indonesia Informatics Study Program Computer Science Faculty Universitas Pembangunan Nasional Veteran Jakarta

Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies.

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Design Enterprise Architecture Using the TOGAFADM Framework to Support Academic Information Systems at Nusa Nipa University

Design Enterprise Architecture Using the TOGAFADM Framework ...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Yohanes Brekmans M. Darkel Andi W. R. Emanuel Paulus Mudjihartono Faculty of Engineering Informatics Engineering Study Program Universitas Nusa Nipa Indonesia Departemen Informatika Fakultas Teknologi Industri Universitas Atma Jaya Yogyakarta Yogyakarta Indonesia

Information technology support is desperately needed for higher education. Universities must use information technology to effectively and efficiently manage potential resources to compete with other institutions. Utilization of IT in Higher Education Governance, particularly the academic information system at Nusa Nipa University, lacks a structured architecture. The current IT infrastructure is insufficient to respond promptly and precisely to application and business change solutions. This study aims to simplify the process of developing information system architecture by developing an architectural model and an implementation roadmap. This study's design uses the TOGAF ADM model, The organization's initial condition analysis stage is the preliminary requirement management phase; architectural vision; and business architecture. The enterprise architecture design begins with the second stage's information system architecture and technology architecture phases. The subsequent phases of opportunities and solutions, migration planning, and change management, recommendations for implementing enterprise architecture are presented in the third section. The consequences of this study are IT outlines for each engineering stage given the TOGAF ADM structure for business processes in new understudy affirmations frameworks, scholastic cycles, graduated classes, and libraries to help data frameworks at Nusa Nipa College. This research can create a blueprint for IT development suggestions for expected business techniques at Nusa Nipa College.

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An Investigation of Ultrashort Pulses Propagation in Atmospheric Turbulence of FSO Communications 9

An Investigation of Ultrashort Pulses Propagation in Atmosph...

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9th International Conference on Electrical engineering, Computer Science and informatics, EECSI 2022

作者： Darusalam, Ucuk Priambodo, Purnomo Sidi Universitas Nasional Informatics Study Program Universitas Siber Asia of IEEE Photonics Society Informatics Faculty of Information and Communications Technology Jakarta Indonesia Universitas Indonesia IEEE Photonics Society Faculty Engineering Department of Electrical Engineering Depok Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

The atmospheric turbulence as the natural media of optical propagation for the free-space optical (FSO) communications is the great advantage to reach flexible connection and low-cost deployment but also is the major problem as well. Since, the phenomena of optical propagation in the atmosphere are random process, an optical signal is induced that produces severe effects to the performance. Thus, a method to reach best performance of FSO must be developed intensively to reduce the severe effects of atmospheric turbulence. One of the solutions to reduce those aforementioned effects is an ultrashort pulse implemented in FSO to modulate frequency carrier as the signal of optical propagation. In this paper, an investigation of an ultrashort pulse was performed through simulation. An ultrashort pulse has a great chance to be implemented in FSO since it has shorter width of pulse duration than a short pulse. An ultrashort pulse is generated using optical second harmonic pulse generator that produce a pulse at 50 fs of width. Based on the results of simulation, the ultrashort pulses showing better performance of FSO than short pulses have. © 2022 Institute of Advanced engineering and Science (IAES).

关键词： atmospheric turbulence free-space optical communications optical propagation optical scintillation temporal broadening turbulence effects ultrashort pulses

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Impact of Filter-Based Feature Selection on Prediction Model Performance for Early Detection of Stunting

Impact of Filter-Based Feature Selection on Prediction Model...

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International Conference on informatics and Computing (ICIC)

作者： Sri Handani Widiastuti Purwanto Adi Wibowo Ratih Wirapuspita Wisnuwardani Informatics Engineering Study Program Sekolah Tinggi Teknologi Bontang Indonesia Doctoral Program of Information System School of Postgraduate Studies Diponegoro Univercity Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro Univercity Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro Univercity Semarang Indonesia Faculty of Public Health Mulawarman Univercity Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study investigates the impact of feature selection results using the filter method on the performance of predictive models for the nutritional status of children aged 0-23 months. This study aimed to understand how correlation-based feature selection methods affect the efficiency and accuracy of predictive models. The dataset comes from the Basic Health Research survey, which consists of 84 variables and around 130,000 data, including anthropometric, family, socioeconomic, and environmental information. In this study, various filter-based feature selection methods were applied to evaluate and identify features that have significant relationships with children’s nutritional status, both linearly and non-linearly. The mutual information method was used to identify complex relationships between features and target variables, while the Pearson Correlation Coefficient and information gain were used to assess the relevance of numerical and categorical features. Of the 84 variables in the dataset, 37 features were selected after the selection process. The model performance evaluation was carried out using the Random Forest (RF) and Gradient Boosting (GB) algorithms, by comparing the results between the dataset that had been selected and without feature selection. The RF model without feature selection produces accuracy: 0,84600, precision: 0,83958, recall: 0,84600, F1-score: 0,78962, and a computation time of 9 seconds. The GB model without feature selection produces accuracy: 0,86330, precision: 0,84722, recall: 0,86330, F1-score: 0,84925, and a computation time of 2 minutes 50 seconds. After feature selection and cross-validation, the RF model shows accuracy: 0,85701, precision: 0,83599, recall: 0,85701, and F1 score: 0,83484. The GB model shows accuracy at 0,86124, precision at 0,84226, recall at 0,86130, and F1 score at 0,84120.

关键词： Radio frequency Pediatrics Accuracy Correlation Computational modeling Predictive models Aging Feature extraction Prediction algorithms Numerical models

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