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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Jonny Mangindaan, Dave Faculty of Engineering Bina Nusantara University Professional Engineer Study Program Industrial Engineering Department Jakarta Indonesia Bina Nusantara University Professional Engineer Study Program Faculty of Engineering Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Organizational resilience is about combination of operational and strategic resilience to sustain company's performance. In the time of turbulence like COVID-19 pandemic era, this resilience is extremely needed for any organizations not only to bounce back from its disruption but also ensures its sustained performance. However, knowledge related to this ability is still lacking and developing to cover especially factors needed to develop resilience for organization. Thus, this paper is proposing an organizational resilience model that covers what factors known so far. For this research, 100 organizations were invited for the research using questionnaires as its instrument. Among these invited organizations, only 47 organizations have given their responses at rate of 47%. Partial Least Square Structural Equation Modeling (PLS-SEM) method using SmartPLS 3.0 was utilized for data processing. This confirms that organization should promote its resilience through operational dan strategic resilience to sustain its performance especially in times of turbulence especially during COVID-19 pandemic era. © 2022 IEEE.

关键词： COVID-19

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Batik Industrial Waste Monitoring and Control System Based on the Internet of Things 5

Batik Industrial Waste Monitoring and Control System Based o...

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5th International Conference on electrical, Electronics, Informatics, and Vocational Education, ICEELINVO 2022

作者： Ali, M. Firdaus, M.F. Electrical Engineering Education Department Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Electrical Study Program Vocational School Universitas Negeri Yogyakarta Indonesia

The aims of this research are to (1) Design and build a Batik Industrial Waste Monitoring and Control System Based on the Internet of Things (BIWMCS -IoT);(2) Test the performance of the BIWMCS-IoT system. The design and implementation of the BIWMCS-IoT system consist of five stages, namely: (1) Needs analysis, (2) Design, consisting of electronic planning, hardware, and software;(3) Development, including hardware and software development;(4) Testing, and (5) implementation. The BIWMCS-IoT system consists of a sensor unit to measure the parameters of water turbidity, temperature, Power of Hydrogen (pH), and the flow velocity of the wastewater then obtained by the microcontroller and the results are sent to the Wi-Fi module to adjust the solenoid valve opening and closing. Based on the testing of the system, has been shown that the BIWMCS-IoT system can work well according to the standards in the Minister of Indonesian Environment Regulation Number 05 of 2014. The reading accuracy of the batik waste monitoring system reaches 96.6% with the delivery time to the smartphone less than 30 seconds. This shows that this system can work well to monitor and control the liquid waste produced by the batik industry. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Java Bali Grid Mean Time To Failure Prediction using Machine Learning 1

Java Bali Grid Mean Time To Failure Prediction using Machine...

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1st International Conference on Technology, engineering, and Computing Applications, ICTECA 2023

作者： Sesotyo, Priyo Adi Pandjaitan, M.M. Lanny W. Lukas Universitas Katolik Indonesia Atma Jaya Professional Engineer Study Program Faculty of Engineering Jakarta12930 Indonesia Universitas Semarang Electrical Engineering Dept. Semarang50196 Indonesia Universitas Katolik Indonesia Atma Jaya Electrical Engineering Master Program Jakarta12930 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350360554

The role of Mean Time To Failure (MTTF) is critical to maintain cost-efficient in smart grids, as the predictive maintenance means for ensuring their reliability. Java Bali grid is the most important power network in Indonesia, because it holds 75% energy sales and the foundation of the Indonesian government, business and industrial sectors. One method for prediction in the machine learning is the Linear Regression, as part of the Supervised Learning technique. The regression model extracts the grid overload variable from MV distribution system, where connected load (MVA) divided by the number of MV customers. The model constructs the Linear Regression function, where the slope and intercept values found from the correlation between predicted response and actual response. After confronting the function f(x) and the coefficient of determination (R2), the test results for the MTTF prediction values for Java-Bali's region shows the higher accuracy when the R2 shows highest value. © 2023 IEEE.

关键词： Forecasting

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Implementation of Dama Mandala Application in Realizing Smart City Vision in Denpasar City

Implementation of Dama Mandala Application in Realizing Smar...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Dewa Ayu Trisna Adhiswari Wedagama Decky Cipta Indrashwara I Gusti Agung Ananda Putra Environmental Engineering Study Program Universitas Pendidikan Nasional Denpasar Indonesia Civil Engineering Study Program Universitas Pendidikan Nasional Denpasar Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

The incorporation of information and communication technology (ICT) to improve infrastructure and establish a efficient and responsive environment is a key element of Denpasar Citys ambition to evolve into a Smart City. This research delves into the utilization of E Government, via the Dama Mandala app underscoring its significance in this transition. Through surveys and interviews employing a methodology the study demonstrates that the Dama Mandala app has greatly enriched the lives of Denpasar Citys inhabitants by granting them access to government services and information online. These results imply that the app plays a role, in realizing Denpasar Citys aspiration of becoming a culture oriented Smart City advancing towards the vision of Denpasar Maju.

关键词： Surveys Knowledge engineering Technological innovation Irrigation Smart cities Navigation Roads Government Information and communication technology Stakeholders

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Synergistic effect of silicon availability and salinity on metal adsorption in a common estuarine diatom

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Journal of Environmental Sciences 2025年第2期148卷 364-374页

作者： Fengyuan Chen Jie Ma Ke Pan SZU-HKUST Joint PhD Program in Marine Environmental Science Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Marine Microbiome Engineering Institute for Advanced StudyShenzhen UniversityShenzhen 518060China Department of Ocean Science The Hong Kong University of Science and TechnologyHong Kong 999077China

Increasing nitrogen and phosphorus discharge and decreasing sediment input have made silicon(Si)a limiting element for diatoms in *** in nutrient structure and salinity fluctuation can greatly affect metal uptake by estuarine ***,the combined effects of Si and salinity on metal accumulation in these diatoms have not been *** this study,we aimed to investigate how salinity and Si availability combine to influence the adsorption of metals by a widely distributed diatom Phaeodactylum *** data indicate that replete Si and low salinity in seawater can enhance cadmium and copper adsorption onto the diatom *** the single-cell level,surface potential was a dominant factor determining metal adsorption,while surface roughness also contributed to the highermetal loading capacity at lower *** a combination of noninvasive micro-test technology,atomic force microscopy,X-ray photoelectron spectroscopy,and Fourier transform infrared spectroscopy,we demonstrate that the diversity and abundance of the functional groups embedded in diatom cell walls vary with salinity and Si *** results in a change in the cell surface potential and transient metal *** study provides novel mechanisms to explain the highly variable metal adsorption capacity of a model estuarine diatom.

关键词： Diatom Metal adsorption Silicon Salinity Functional group Surface potential

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Drivers of the Sustainability Performance of Induction Stove Conversion program in Indonesia

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International Journal of Sustainable Energy Planning and Management 2025年 43卷 5-30页

作者： Damayanti, Retno Wulan Safira, Silvia Dhea Laksono, Pringgo Widyo Setiadi, Haryono Entifar, Nisa Aqilla Ellenahaya Industrial Engineering Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Center of Technology Development and Industrial Collaboration Institute for Research and Community Service Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Informatics and Science Data Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Smart Green Technology Engineering Department Pukyong National University 45 Yongso-ro Nam-Gu Busan Korea Republic of

In 2022, Indonesia initiated a pilot project to transition from liquid petroleum gas (LPG) stoves to induction stoves, targeting 8.2 million households by 2025. While LPG is considered a clean cooking fuel by the World Health Organization due to its low PM2.5 emissions, this program aims to further reduce CO2 emissions from cooking activities, aligning with Indonesia’s clean energy transition and 2060 Net Zero Emission goals. Throughout the program, doubts and criticisms arose among various sectors of society, particularly concerning the sustainability of this program. This research aims to determine and analyze the driving factors that support the sustainability of the induction stove transition program. Five sustainability aspects, namely, economic, social, environmental, technical, and institutional, were analyzed. A total of 315 Indonesian citizens participated as respondents in this research survey. The Partial Least Squares-Structural Equation Model (PLS–SEM) method was utilized as the analytical approach for the study. The results revealed that nine factors significantly influenced program sustainability, with the strongest impacts from technology costs (path coefficient = 0.859), contextual factors (0.803), electrical infrastructure (0.792), and community perception (0.773). Economic sustainability was driven by technology costs and business opportunities (0.643), while environmental sustainability was strongly influenced by electrical infrastructure. Post-installation support (0.307) and financial management (0.391) significantly impacted technical and institutional sustainability respectively. These empirical findings improve previous research on the sustainability of an energy transition program, which provides insights for researchers and stakeholders in the energy management field, especially in developing countries, for policy determination and decision-making. © 2025, Aalborg University Press. All rights reserved.

关键词： Clean energy

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Internet of Things for Real-Time Multi Room Monitoring System

Internet of Things for Real-Time Multi Room Monitoring Syste...

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

作者： Kim Sung Tae Antonius Suhartomo Vincent Study Program of Electrical Engineering President University Bekasi Study Program of Electrical Engineering President University Bekasi Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Electronic devices have become an integral component in our daily activities. In fact, the consumption of electrical energy in households reached 49% in 2018, and is expected to increase to 58% in 2050. Concurrently, the LPG is the second highest consumed energy in the household and is expected to increase to 37% in 2050. However, the devices utilizing these energies could lead to a danger such as housefire if not operated and treated carefully. An Internet of Things (IoT)-based smart home monitoring system would solve this problem, especially by effectively monitoring several parameters in the house simultaneously. In this study, a system is built to monitor and control three rooms in the house independently and simultaneously. The sensors and/or actuators in all rooms are controlled by NodeMCU ESP8266s. All microcontrollers are linked via the Internet to a local server, where all data is analyzed, saved, and transmitted to the user using the MQTT protocol. The monitoring is done by the user via MQTT dashboard in the smartphone. Following testing, the three microcontrollers are fully capable of exchanging data with the user's smartphone. The humidity changed by 1%, while the temperature changed by 0.1 °C. Perfect data interchange is possible between the relays, flame, gas, and movement activity sensor.

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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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Arrhythmia Disease Detection Through ECG Signal Monitoring with Decision Tree Method

Arrhythmia Disease Detection Through ECG Signal Monitoring w...

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

作者： Erwin Sitompul Muhammad Aqshal Said Noor Arjon Turnip Study Program of Electrical Engineering President University Cikarang Indonesia Department of Electrical Engineering Padjadjaran University Sumedang Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Arrhythmia is a heart disorder that is a leading cause of death worldwide. Early detection and proper management are essential to reduce its impact. With Machine Learning, cardiovascular disease detection can be performed more quickly, accurately, and efficiently. This study aims to classify patient data with arrhythmia using Machine Learning algorithms, specifically the Decision Tree method. The recorded electrocardiography (ECG) signals were processed through filtering and feature extraction to produce data that is easy to classify. The results showed that data processing using the Decision Tree method achieved an accuracy rate of 92.96%.

关键词： Heart Accuracy Machine learning algorithms Arrhythmia Mortality Machine learning Electrocardiography Decision trees Robots Monitoring

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Patient Face Recognition for Medical Robots Based on Convolutional Block Attention Module

Patient Face Recognition for Medical Robots Based on Convolu...

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

作者： Arjon Turnip Adnnia Nafis Qulub Aina Erwin Sitompul Department of Electrical Engineering Padjadjaran University Sumedang Indonesia Study Program of Electrical Engineering President University Cikarang Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

The shortage of medical personnel to care for patients has led to a decline in public health services. This issue has been exacerbated by the COVID-19 pandemic, which has claimed numerous lives, particularly among healthcare workers. To address this challenge, this study explores the application of a Deep Learning method, specifically the Convolutional Neural Network (CNN), integrated with the Convolutional Block Attention Module (CBAM) for patient face recognition in Medical Robot Assistive (MRA) systems. The findings demonstrate that combining CNN with CBAM offers a highly efficient solution for optimizing face recognition in MRAs. This approach not only delivers high accuracy but also enhances the visual representation of the CNN model and reduces training time. Moreover, it improves the safety and effectiveness of interactions between the MRA and patients during face recognition. The proposed method has been tested and achieved an impressive accuracy of 95 % at an optimal distance of 30 cm.

关键词： Training Deep learning COVID-19 Visualization Accuracy Medical robotics Face recognition Convolutional neural networks Standards Testing

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