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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Lestari, Nina Purnama, Trisna Risnanto, Slamet Fitriadi, Budi Yusuf, Ach Maulana Habibi Prihatmanto, Ary Setijadi Institut Teknologi Bandung Doctoral Program of School of Electrical Engineering and Informatics Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Electrical Engineering Department Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Bandung Indonesia Universitas Komputer Indonesia Bandung Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350309164

The Internet of Things (IoT) and Artificial Intelligence (AI) have reshaped educational technology, increasing the chances of developing more effective and engaging learning environments. This study proposes an architecture for asynchronous data communication with real-Time face recognition, a virtual and remote labs confluence. The architecture uses IoT hardware and software to comprise critical components such as IoT cameras, message brokers, FTP servers, and AI processing servers. The aim is to optimize resource usage and maximize response time efficiency. The architecture employs message queuing to manage high volumes of face image data, facilitating immediate verification and rapid access. Our findings demonstrate that the system can achieve face recognition with 91.7% accuracy and an average system communication latency of 2.631 seconds, effectively leveraging asynchronous communication to optimize resource usage and computational efficiency. The research comprehensively measures the data communication metrics and face recognition metrics. It also explains how asynchronous data communication can be strategically deployed in a hybrid lab environment to enhance educational and operational efficacy. © 2023 IEEE.

关键词： Computational efficiency

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural Computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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Classification Of Ornamental Fish Types Using CNN Method With VGG-16 And U-NET Architecture

Classification Of Ornamental Fish Types Using CNN Method Wit...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ni Luh Wiwik Sri Rahayu Ginantra Christina Purnama Yati I Gusti Ayu Agung Diatri Indradewi I Komang Agus Yudiawan Technology and Informatics Informatics Engineering Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia Department of Informatics Engineering Information Systems Study Program Faculty of Engineering and Vocational Studies Universitas Pendidikan Ganesha (UNDIKSHA) Denpasar Indonesia

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Ornamental fish are a type of fish that live in freshwater or seawater habitats and are typically maintained for aesthetic purposes, often in ornamental ponds or as household pets. The diversity of colors, shapes, and patterns observed in ornamental fish presents a challenge in their classification. Therefore, a method capable of accurately recognizing the visual characteristics of these fish is required. The objective of this study is to evaluate the performance of two Convolutional Neural Network (CNN) architectures, specifically VGG-16 and U-Net, using five distinct types of ornamental fish, namely Angel Fish, Betta Fish, Clown Fish, Gold Fish, and Guppy Fish. The dataset comprises 3.240 images that have been processed using TensorFlow and Keras on Google Collaboratory. The results demonstrated that U-Net demonstrated superior performance in segmentation, with an Average Intersection over Union (IoU) of 58.91 % and an Average Dice Coefficient of 73.72%. In contrast, VGG-16 exhibited signs of overfitting, as evidenced by lower evaluation metrics. To enhance the efficacy of the transfer learning model, further optimization is recommended.

关键词： Measurement Image segmentation Visualization Shape Transfer learning Fish Internet Convolutional neural networks Optimization Overfitting

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Analyzing the use of SEO and Google Analytic on the Website of Vocational High School of Information Technology Annajiyah 3

Analyzing the use of SEO and Google Analytic on the Website ...

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3rd International Conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2020

作者： Supraba, L.D. Jati, H. Electrical and Informatics Engineering Education Study Program Faculty of Engineering Yogyakarta State University Indonesia

The number of Islamic boarding school teaching both Information Technology (IT) Education and Religious Education is still limited. Vocational High School of Information Technology Annajiyah located in Tambakberas, Jombang, East Java is a school having both subjects taught. The existence of Vocational High School of Information Technology Annajiyah has not been widely known by outside and inside society of Jombang Regency. Therefore, Therefore, analysis of the use of SEO is necessary to determine the potential of website visit and school promotion. Previously, Vocational High School of Information Technology Annajiyah promoted school by using brochures only. This method was considered able to introduce the school through traditional way. Analysis result of improving SEO can be seen in Google Analytic recorded a number of site visits amounted 403 in June, where 82.2% of active users are dominated by mobile phone users and fluctuation of the number of visits. This increase is affected by both the implementation of SEO and online PPDB process during the COVID pandemic. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Websites

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Improvement of PID Based on Fuzzy Logic Controller for Controlling Attitude of a Quadcopter 1

Improvement of PID Based on Fuzzy Logic Controller for Contr...

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1st International Conference of Adisutjipto on Aerospace Electrical engineering and informatics, ICAAEEI 2024

作者： Rizki Dalimunthe, Ernando Sibuea, Horas Nico L. P. Rama Setiadi, Dirga Jayadi, Akhmad Persada Sembiring, Jaka Pranita, Elka Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia Politenik Negeri Lampung Informatic Management Study Program Bandar Lampung Indonesia

ISBN: (纸本)9798331507930

The quadcopter has a fairly simple mechanical design because it does not require other drives to perform various movements such as roll, pitch, and yaw. The simplicity of this mechanical design has a considerable impact on the complexity of its control. Therefore, it is necessary to have a good control method to maintain the stability of the quadcopter. The research aims to maintain stability of quadcopter by using a combination of the Fuzzy Logic Controller (FLC) with the Proportional-Integral-Derivative (PID) method, where FLC is used to initialize the input of the PID method to find the quadcopter stability point. The application of fuzzy methods for the initial tuning of PID parameters has proven effective in enhancing the system's fundamental performance, such as overshoot, rise time, and settling time, compared to the conventional PID approach. The proposed method can reduce overshoot by over 20%, with reductions reaching up to 88% in yaw motion. Additionally, the Fuzzy-PID approach significantly improves stability, shortening the time to achieve steady-state by less than 1 seconds for all movements while PID did more than 2s for pitch and roll motions and over 1s for yaw motion. © 2024 IEEE.

关键词： Two term control systems

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Enhancing Search Efficiency: Analyzing Learning Mechanisms in Metaheuristics

Enhancing Search Efficiency: Analyzing Learning Mechanisms i...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Nurcahyadi, Teddy Blum, Christian Universitas Muhammadiyah Yogyakarta Mechanical Engineering Study Program Engineering Faculty Yogyakarta Indonesia Institut d'Investigació en Inielligència Artificial Consejo Superior de Investigaciones Científicas Barcelona Spain

ISBN: (纸本)9798350379839

Metaheuristics have evolved significantly in the last few decades. Nowadays, more complex problems and essential applications are emerging, requiring more powerful metaheuristics to handle them. This article analyzes the characteristics and performance of existing metaheuristics, with particular emphasis on their learning mechanisms. Several critical aspects for improvement are identified, and several promising future research areas are recommended. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-Normalization Residual Graph Convolutional Network for 3D Human Pose Estimation

Multi-Normalization Residual Graph Convolutional Network for...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Pramono, Andy Chang, I-Cheng Puspasari, Betty Dewi Animation Study Program Universitas Negeri Malang Jawa Timur Malang Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Informatics Engineering Institut Teknologi Nasional Malang Jawa Timur Malang Indonesia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nodes and reliance on skeleton-based graphs that may miss complex motion *** address these issues, we introduce a Multi-Normalization Residual Graph Convolutional Network that fine-tunes the graph structure through affinity multi-normalization and activation, allowing the representation of additional connections beyond the *** extensive ablation study shows that this approach enhances performance with minimal overhead while maintaining the same model size, consistently outperforming state-of-the-art techniques on two benchmark datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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Measurement of key performance indicator Green Supply Chain Management (GSCM) in palm industry with green SCOR model

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Materials Today: Proceedings 2022年 63卷 S326-S332页

作者： Rosyidah, Masayu Khoirunnisa, Ninin Rofiatin, Umi Asnah, Asnah Andiyan, Andiyan Sari, Dyana Industrial Engineering Study Program Universitas Muhammadiyah South Sumatra Palembang 30263 Indonesia Agribusiness Study Program Faculty of Agriculture University of Tribhuwana Tunggadewi East Java Malang 65144 Indonesia Master of Agricultural Economics Postgraduate School of University of Tribhuwana Tunggadewi East Java Malang 65144 Indonesia Architecture Study Program Faculty of Science and Engineering Universitas Faletehan West Java Bandung 40266 Indonesia

SCOR (Supply Chain Operations Reference) model is a tool for diagnosing Supply Chain Management (SCM) that allows users to understand all processes in a business organization. This study aimed to analyze activities related to Green Supply Chain Management (GSCM), especially in the palm oil sector, and to determine the performance of Green Supply Chain Management (GSCM) at PT. Sutopo Lestari Jaya using the green score model. The results showed that the highest Green Objectives (GO) weight in environmentally friendly garden management was located on protected forest land used as plantation land by 38.82%, while waste minimization was found in Crude palm oil water content of 48%. Meanwhile, in minimizing and handling greenhouse gas emissions, the highest weight is found in the amount of fuel used in Fresh Fruit Bunch (FFB) shipments of 54.07%. For plantation management and palm oil management mills, the highest weight is in the percentage of water use per ton of FFB in PPKS 60.7%, while in waste management, the highest percentage lies in the standardized wastewater Biological Oxygen Demand (BOD) of 38.8%. In maximizing the use of new and renewable energy, the highest weight lies in renewable energy per tonne of FFB and the use of solid waste (empty shells and shells) as a substitute for fossil fuels of 45.5% each. © 2022

关键词： Palm oil

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Portable Monitoring Systems for Rivers Waste Based on Internet of Things 7

Portable Monitoring Systems for Rivers Waste Based on Intern...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Henderi Nasrullah, Mumammad Hudzaifah Nudiyah, Laura Belani Sunarya, Po Abas Sofiana, Sofa Setiyadi, Didik University of Raharja Informatics Tangerang Indonesia University of Raharja Management of Retail Study Program Tangerang Indonesia Pamulang University Faculty of Engineering Informatics Engineering Departement Tangerang Indonesia Universitas Indonesia Mandiri Infotmatics Faculty of Technolgy and Design Jakarta Indonesia

ISBN: (纸本)9798350345711

River pollution has become a severe problem in Indonesia. Industrial waste causes water pollution because the waste is not filtered before being discharged into the river. Illegal waste dumping cannot be prosecuted due to insufficient data and unknown disposal times. Therefore, a tool is needed to measure river water waste to measure polluted water waste data. Studies on river water monitoring devices have shown that there are no portable monitoring tools based on the internet of things for river water waste that follows regulatory standards from the Minister of Health. This study aims to produce a portable monitoring tool based on the internet of things for water waste. The system was developed using TDS sensors, turbidity, pH, MQ-135 sensors, and the system is equipped with a GPS module to indicate the location's latitude. The monitoring system is developed through the stages of system design, testing water samples, setting water quality standards and required hardware and components, setting river water quality parameters, and conducting system testing. The test results show that the system can send the data obtained to dashboard things via an internet connection, the TDS sensor error rate is 2.17%, the pH sensor error rate is 1.87%, the turbidity sensor error rate is 1.66%, the MQ135 sensor value is at the quality limit. The internet of things-based portable river waste monitoring system produced through this research helps related parties to identify and handle polluted rivers. Water according to the standards of the Ministry of Health, and the GPS module shows location measurements and information according to the location's latitude on google maps. © 2022 IEEE.

关键词： Internet of things

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. 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 Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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