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International Conference of computer science and Information Technology

作者： Rahmadani Siregar Hartono Roslina Muhammad Zarlis Wanayumini Computer Science Postgraduate Study Program Universitas Potensi Utama Medan Indonesia Computer Science Postgraduate Study Program Politeknik Negeri Medan Medan Indonesia Computer Science Postgraduate Study Program University of Bina Nusantara Jakarta Indonesia

Disease detection in rice plants at the Padang Lawas District Agriculture Service is carried out every year by collecting data from the Agriculture Service. Therefore, it is necessary to carry out classification to determine the best percentage of training data and test data in determining rice plant diseases and a comparison between testing data and training data for accurate accurate predictions. So data mining algorithms are needed that must be used to classify these diseases. Classification is carried out to group data into categories or groups so that objects from the same data are more similar to data objects from different groups. In this study, disease classification was processed using the C4.5 algorithm using disease data collection on rice plants from 2017 to 2020 which had a total of 248 data. The results of testing data and training data will be compared to see accuracy performance.

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Analyzing User Comments on Fintech E-wallet Applications in Indonesia Using the LDA Algorithm 7

Analyzing User Comments on Fintech E-wallet Applications in ...

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7th International Conference on Information Technology, InCIT 2023

作者： Widiantoro, Albertus Dwiyoga Mustafid, Mustafid Sanjaya, Ridwan Diponegoro University Doctoral Program of Information System School of Postgraduate Studies Semarang Indonesia Soegijapranata Catholic University Information System Computer Science Fakulcty Semarang Indonesia

ISBN: (纸本)9798350358698

The main objective of this article is to compare user comments from 4 Fintech applications in Indonesia using the Latent Dirichlet Allocation (LDA) algorithm. This research is word processing research carried out using text mining. The statistical population is comments from Fintech users Ovo, Dana, LinkAja, and Doku, with data taken from May 2022-May 2023. LDA and the Python programming language are applied to analyze the data and implement text mining algorithms from topic modeling. Each application group has different topic coherence, each application group is analyzed and 4 large categories are found, namely system convenience, system problems, customer service (CS) problems, and service ease. The results of text mining and processing using Coherence and Latent Dirichlet allocation can be used to identify hidden topics and be used as input for system improvements © 2023 IEEE.

关键词： Population statistics

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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Collaborative Model for Developing Computing Skills in Basic Education 17

Collaborative Model for Developing Computing Skills in Basic...

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17th International Conference on computer Supported Education, CSEDU 2025

作者： Scheffel, Erica J.S. Schneider, Daniel Motta, Claudia L.R. Postgraduate Program in Computer Science Universidade Federal do Rio de Janeiro Brazil Institute Tércio Pacitti of Computational Applications and Research Universidade Federal do Rio de Janeiro Brazil

ISBN: (纸本)9789897587467

Following the recent inclusion of computational skills in Brazil's basic education curriculum, this study explores A+Comp, a gamified, collaborative virtual learning environment designed to enhance computational education. Inspired by online social networks and digital games, A+Comp integrates elements like virtual currency and interactive challenges. The platform aims to boost user participation and mitigate engagement disparities using the Experiential Learning Cycle and Positive Feedback Model. By combining cognitive, conative, and executive function theories with system design, the research assesses the impact of gamification and collaboration on computational competency acquisition, contributing to the discussion on innovative, inclusive learning technologies. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Social networking (online)

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GLCM Feature Extraction and PCA for Tuberculosis Detection with Neural Network

GLCM Feature Extraction and PCA for Tuberculosis Detection w...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Alfa Farah, M. Nafis Hayyin Suristiyanti, Wiwiek Ibad, Sholihul Pramunendar, Ricardus Anggi Fajar Shidik, Guruh Dian Nuswantoro University Faculty of Computer Science Postgraduate Program in Informatics Engineering Indonesia Dian Nuswantoro University Faculty of Computer Science College Teacher in Informatics Engineering Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Automatic recognition system for medical images is quite a challenging job in the medical image processing field. X-rays, CT, and MRI all provide medical pictures and other modalities which are utilized for diagnostic purposes. As in medical sector, detecting tuberculosis (TB) is a very important stage before further treatment is carried out. Human interpretation of a vast array of X-ray pictures can result in detection mistakes, so an automatic recognition system is needed that can detect TB disease. In this study, we use a dataset with two classes and extract GLCM-based texture features from each class, and apply them to a two-layer feed-forward neural network, which gives a classification rate of 99%. © 2022 IEEE.

关键词： Diagnosis

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MQTT-Chain: An MQTT approach using blockchain and smart contracts to achieve a new Quality of Service level

MQTT-Chain: An MQTT approach using blockchain and smart cont...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Bruno Machado Agostinho Stefano Chessa Raffaele Perego Mario Antônio Ribeiro Dantas Alex Sandro Roschildt Pinto Computer Science Postgraduate Program Federal University of Santa Catarina Florianópolis Brazil Computer Science Department University of Pisa Pisa Italy ISTI National Research Council of Italy Pisa Italy Computer Science Postgraduate Program Federal University of Juiz de Fora Brazil

ISBN: (数字)9798331527198

ISBN: (纸本)9798331527204

The proliferation of Internet of Things (IoT) applications has surged in recent years, necessitating efficient communication protocols. The Message Queuing Telemetry Transport (MQTT) protocol, designed specifically for IoT devices, has gained prominence due to its lightweight publisher/subscriber model. However, inherent concerns arise within the conventional MQTT architecture, particularly pertaining to broker-side vulnerabilities. In addressing these concerns and enhancing data security, we advocate the utilization of blockchains and smart contracts for storing and transmitting broker messages. We designed and compared two different approaches, bringing a detailed latency analysis for each step and validating its functional viability, establishing a robust environment for IoT applications.

关键词： Smart contracts Quality of service Orbits Blockchains Internet of Things Proposals Telemetry Reliability Optimization Testing

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Ardiansyah, Deden Mustafid Mantoro, Teddy Universitas Diponegoro Doctoral Program of Information Systems School of Postgraduate Central Java Semarang Indonesia Universitas Pakuan Vocational School Computer Engineering Department West Java Bogor Indonesia Sampoerna University Media-Tech-Lab Computer Science Department DKI Jakarta Indonesia

ISBN: (纸本)9798350364101

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm. © 2024 IEEE.

关键词： Energy management systems

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Predictive Value Chain Analysis to Improve Generalization Accuracy In Quality Assurance

Predictive Value Chain Analysis to Improve Generalization Ac...

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Harmayani Poltak Sihombing Mahyudin Marischa Elveny Postgraduate Program in Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Postgraduate Doctorate in Computer Science Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Adaptive predictive analysis model utilizing Spiking Neural Networks (SNNs) to enhance quality assurance processes in Smart Campus environments. Traditional predictive models often struggle with the dynamic and complex nature of educational data, leading to inaccurate predictions and inefficient decision-making. SNNs, inspired by biological neurons, are particularly suited for processing temporal data and adapting to evolving patterns. The proposed model integrates spike-timing-dependent plasticity (STDP) and dynamic weight adjustment to continuously learn and refine predictions in response to real-time data, ensuring improved accuracy and robustness. The study uses historical data from the Internal Quality Assurance System (SPMI) over a three-year period, focusing on key performance indicators such as student enrollment trends, faculty productivity, and resource utilization. Experimental results demonstrate that the SNN-based model outperforms traditional neural networks, achieving a 20% reduction in Mean Squared Error (MSE) and a higher R-squared value. These findings indicate that the model not only enhances predictive accuracy but also offers significant improvements in operational efficiency and resource management. By providing timely and precise insights, this model supports proactive decision-making in educational institutions, enabling administrators to optimize resource allocation, monitor academic quality, and anticipate future challenges. This research contributes to the growing field of artificial intelligence in education, offering a scalable, adaptive approach to managing complex data and improving institutional performance in real-time.

关键词： Adaptation models Quality assurance Accuracy Biological system modeling Decision making Spiking neural networks Predictive models Real-time systems Resource management Predictive analytics

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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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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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