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5th IEEE International Conference on Technology, informatics, Management, engineering and Environment, TIME-E 2024

作者： Albert Panggabean, Good Fried Romadhon, Rahmat Lubis, Ramot Joelianto, Endra Institut Teknologi Bandung Doctoral Program in Engineering Physics Bandung Indonesia Institut Teknologi Del Faculty of Informatics and Electrical Engineering Toba Indonesia Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health. © 2024 IEEE.

关键词： DDPG indoor vertical farming precision agriculture reinforcement learning smart farming

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Individual Health Profiling Using Multimodal Sensors on Smart Health Care 17

Individual Health Profiling Using Multimodal Sensors on Smar...

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

作者： Ikawati, Vidya Saepudin, Didin Susanto, Bambang Munandar, Ahmad Nurba, Hardy Purnama Prihatmanto, Ary Setijadi Doctoral Program of School of Electrical and Informatics Engineering Institute of Technology Bandung Bandung Indonesia Universitas Sangga Buana Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309164

Health Service, also referred to as Health Care System, plays a pivotal role in maintaining and enhancing health by preventing illnesses and injuries. In this context, electronic health records are essential for both inpatients and outpatients, serving as a crucial tool for healthcare providers and policymakers. However, the potential of these records extends beyond individual care to support broader health policy and research initiatives. Personal Smart Health Care, an individual-based digital health technology, bridges the gap between personal health management and medical records, enabling comprehensive healthcare improvements. Smart healthcare, driven by technologies like IoT, mobile applications, cloud computing, big data, 5G, artificial intelligence, and biotechnology, offers a holistic approach to healthcare, emphasizing diagnosis, treatment, disease prevention, risk monitoring, and virtual assistance. An intelligent framework for smart healthcare includes four layers: The instrumentation layer, information system layer, business intelligence layer, and gamification layer, each contributing to enhanced patient outcomes, streamlined processes, and improved patient care. The gamification layer incentivizes healthy behaviors and fosters patient engagement, especially in healthcare scenarios. This comprehensive framework underscores the transformative potential of smart healthcare, where technology-driven solutions can revolutionize the healthcare landscape, improving patient care, health management, and the overall well-being of individuals and populations. © 2023 IEEE.

关键词： Big data

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Performance Evaluation of Coverage Prediction in 4G Networks Using Machine Learning Classification Algorithm 10

Performance Evaluation of Coverage Prediction in 4G Networks...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Yuliana, Hajiar Iskandar Hendrawan Ridwan, Azwar Mudzakkir Charisma, Atik Somantri, Nivika Tiffany Universitas Jenderal Achmad Yani Faculty of Engineering Department of Electrical Engineering Indonesia Institut Teknologi Bandung Doctoral Program of School of Elecrtical Engineering and Informatics Indonesia Bandung Indonesia UIN Sunan Gunung Djati Bandung Department of Electrical Engineering Indonesia

ISBN: (纸本)9798350377057

Network coverage prediction is an important aspect of 4G network planning and optimization. In this study, we conducted a comprehensive analysis of the performance of various machine learning algorithms in predicting 4G network coverage. The data collected includes information on location, signal intensity, topography, and other important variables. Data pre-processing is performed to clean, normalize, and select relevant features. Next, we compared the performance of several machine learning algorithms, including Logistic Regression, KNN, Naïve Bayes, Decision Tree, SVM, XGBoost, AdaBoost, and Bayesian Network Classifier. The model training process is performed using subsets of training, validation, and testing data, with evaluation performed using various evaluation metrics such as accuracy, precision, recall, F1-score, RMSE, MAE, and R-squared. The experimental results show that Random Forest stands out with excellent performance, while Naïve Bayes tends to produce lower performance. The conclusion of this study provides valuable insights into the potential applications of machine learning algorithms in predicting 4G network coverage with accuracy and reliability. © 2024 IEEE.

关键词： Adversarial machine learning

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Training Swinging Door Trending Compression Algorithm for IoT devices 10

Training Swinging Door Trending Compression Algorithm for Io...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Correa, Juan David Arias Pinto, Alex Sandro Roschildt Montez, Carlos Post-graduate Program of Automation Systems Engineering Florianópolis Brazil Dept. of Informatics and Statistic Dept. of Automation and Systems Engineering Florianópolis Brazil Dept. of Automation and Systems Engineering Florianópolis Brazil

ISBN: (纸本)9798350373011

The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device's resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data Compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the Compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the Compression and Error metrics to evaluate the compression performance in Lossy Algorithms. © 2024 IEEE.

关键词： Data compression ratio

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Design of an Infusion Capacity Monitoring System for Patients Based on the Internet of Things in Hospitals 9

Design of an Infusion Capacity Monitoring System for Patient...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Windyasari, Vina Septiana Murad, Silvia Ayunda Syam, Syahriani Mahardika, Maulia Budiman, Johan Rahmawati, Diah Islamic Syekh-Yusuf University Information Systems Study Program Tangerang Indonesia Binus University Binus Online Learning Information System Department Jakarta Indonesia Islamic Syekh-Yusuf University Informatics Engineering Study Program Tangerang Indonesia Islamic Syekh-Yusuf University Civil Engineering Study Program Tangerang Indonesia

ISBN: (纸本)9798350383010

The patient's infusion fluid will only be changed if the nurse changes it or based on information from the patient's family. The research method used in terms of fluid volume utilizes the function of the Arduino microcontroller as the brain, the load cell sensor as an infusion fluid weight detector connected to the IoT, fuzzy logic calculations on the infusion volume weight measuring device in line with real-time decision-making when reading gram weight changes with sensors, and the system development method using a prototype model. The test results of three infusion bottles containing mineral water with a liquid volume of 500 ml and a load cell sensor as an infusion weight detector can be accepted by the Arduino Wemos D1 as a load cell sensor processing component. The infusion liquid weight data is uploaded to the MySQL database, and a notification is sent and displayed on the smartphone when the infusion volume reaches 12% of its weight. The results of this study indicate that the design of tools connected to applications on smartphones can work well and facilitate the process of monitoring infusions for patients in real-time © 2024 IEEE.

关键词： Smartphones

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Aircraft Flight Schedule Announcement at Pionir Indonesian Airport Based on SMS Broadcast 1

Aircraft Flight Schedule Announcement at Pionir Indonesian A...

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

作者： Kusumaningrum, Anggraini Wahyu, Septian Sahal, Ahmad Pujiastuti, Asih Ayuningtyas, Astika Pratiwi, Elizabeth Anna Adisutjipto Institute of Aerospace Technology Informatics Study Program Yogyakarta Indonesia Adisutjipto College of Technology Informatic Engineering Department Yogyakarta Indonesia Respati University D3 Information Technology Study Program Faculty of Science and Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Engineering Study Program Yogyakarta Indonesia

ISBN: (纸本)9798331507930

The design and construction of delivering announcements using a web-based SMS gateway aims to help overcome problems that often occur at pioneer airports;this is because when passengers want to travel, many passengers still arrive late to check-in, even though the passenger ticket has a notification for checking. -in 1 hour before Departure. If a flight's Departure is delayed or accelerated, passengers will only receive this information when they arrive at the airport, whereas if at the time the departure notification is delayed or accelerated, the passenger is still outside the airport or still on their way to the airport, as a result, they will definitely not receive the information. Testing voice announcements on parking, arrival, departure, delay, check-in, and waiting room services, as well as testing SMS broadcasts (30 user numbers and one airline), shows that the announcement application provides information through voice data recording and broadcasts SMS to passengers and families. Testing was carried out at Indonesian Pioner airport for 30 passengers for a week. The percentage of Ease of obtaining test voice information was 81.98%, and the percentage of Ease of obtaining broadcast SMS information was 83.1%. Implementing the announcement is feasible at pioneer airports with a percentage of 82.54%. © 2024 IEEE.

关键词： Text messaging

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Development Of Scholarship Sustainability Analysis Application Every Semester On STMIK Pelita Nusantara Students 4

Development Of Scholarship Sustainability Analysis Applicati...

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4th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2022

作者： Br Tarigan, Nera Mayana Sinaga, Bosker Panggabean, Erwin Sagala, Jijon Raphita Informatics Engineering Study Program Stmik Pelita Nusantara Medan Indonesia Software Engineering Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (纸本)9798350399073

Decision making in an organization is needed a system. Decision support system is a system that helps company management and education in decision making that provides information, modeling and analyzing data with the application of a method. The management, especially higher education, must maximize sophisticated technology. For example, to analyze scholarship data. Problems are found in manual data analysis so that human errors often occur in addition to using more time than the target, losing old data, resulting in inaccurate analysis of scholarship sustainability every semester. The research was conducted to overcome the problems that have been found by analyzing the existing data with the simple additive weighting method, building a system by applying the method and testing the system. The result of this research is to produce an application for the development of a decision support system in managing student data to determine the sustainability of the scholarship every semester. Applications that are built can help the management in managing scholarships. © 2022 IEEE.

关键词： Decision support systems

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Effective Alzheimer's Stage Classification Using Transfer Learning Based on MRI Images 7

Effective Alzheimer's Stage Classification Using Transfer Le...

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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Hamdani, Hamdani Septiarini, Anindita Pawitra, Theresia Amelia Sumampouw, Aldion Tejawati, Andi Kurniawan, Tri Basuki Mulawarman University Dept. of Informatics Faculty of Engineering Samarinda Indonesia Mulawarman University Dept. of Industrial Engineering Faculty of Engineering Samarinda Indonesia Universitas Bina Darma Magister Information of Technology Postgraduate Program Palembang Indonesia

ISBN: (纸本)9798331505103

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most prevalent cause of dementia, significantly impacting cognitive functions and daily activities. Accurate and timely classification of AD stages is of paramount importance for the effective treatment and management of the disease. The aim of this research is to employ transfer learning in a convolutional neural network (CNN) to classify the stages of AD using magnetic resonance imaging (MRI) brain images, building on the aforementioned foundational principles. The CNN model employed in this study is MobileNetV3-Small, which has been trained on a comprehensive dataset of labeled MRI scans with the objective of accurately distinguishing the various stages of AD. The dataset, referred to as the 'Augmented Alzheimer MRI Dataset,' comprises 3 3, 9 8 4 augmented and 6, 4 0 0 original MRI images, which have been categorized into four stages: Very Mild Demented, Moderate Demented, Mild Demented, and NonDemented. The methodology comprises three principal stages: data preparation, model training, and model evaluation. A systematic assessment of various hyperparameters, including split ratio, learning rate, batch size, and dropout rate, was conducted to optimize model performance. The results demonstrate that fine-tuning the model significantly enhances classification accuracy, achieving a maximum accuracy of 9 6 % compared to 7 2 % without fine-tuning. A smaller learning rate was found to improve performance metrics by preventing the model from converging to local minima. These findings highlight the potential of transfer learning and hyperparameter optimization in developing robust models for the early detection and classification of AD, contributing to better patient outcomes and advancing the field of medical image analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Reducing Dependency on Imported Electricity: Utilizing Renewable Energy and Minimizing Fossil Fuel Power Plant Operations in West Kalimantan

Reducing Dependency on Imported Electricity: Utilizing Renew...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Akbar, Ilham Zakaria, Ariesa Budi Muljadi, Eduard Suwarno Hariyanto, Nanang Corporate University Graduate Program PLN-ITB Pln Auburn United States Auburn University Auburn United States School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350376067

This paper discusses West Kalimantan's dependency on imported electricity and fossil fuels and advocates for integrating renewable energy sources, especially solar power. With abundant solar resources and its equatorial location, West Kalimantan presents a significant solar PV generation opportunity. However, its dependence on imported electricity from the ASEAN Grid initiative poses risks to energy security. By promoting solar power as an alternative energy source and reducing reliance on fossil fuels, the paper aims to improve energy independence and sustainability. Integrating solar PV requires assessing frequency stability to ensure system resilience. Moreover, the interconnection between the two grids enhances system resilience and facilitates renewable energy integration. In summary, integrating solar PV power plants into the West Kalimantan system aims to decrease dependence on electricity imports from Sarawak while preserving the system's frequency stability. © 2024 IEEE.

关键词： Frequency stability

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Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

TechRxiv

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TechRxiv 2024年

作者： Muharram, Arief Purnama Sajid, Fahmi Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XGBoost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F-1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies. © 2024, CC BY.

关键词： Self-supervised learning

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