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

作者： Anderes Gui Idris Gautama So Dea Novantia Elfindah Princes Nesti Fronika Sianipar Hasnah Haron Information System Program School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department Binus Business School Bina Nusantara University Jakarta Indonesia Information Systems Management Department Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Faculty of Economics and Muamalat Universiti Sains Islam Malaysia Nilai Malaysia

This study embarked on a rigorous examination of the factors driving user satisfaction and usage behavior in the context of telehealth applications. Utilizing a well- structured Google Form survey, distributed through an array of social media platforms, including WhatsApp, Discord, Instagram, and Line, the research gathered 251 responses. The purposive sampling technique was employed to ensure that the data captured the experiences of individuals who had actively engaged with telehealth services. One hypothesis, relating to the influence of Online Reviews, was found to be unsupported. This surprising result suggests that, contrary to expectations, the opinions and reviews shared online do not significantly affect users’ satisfaction or their behavioral decisions in the context of telehealth applications. This finding highlights the need for a more nuanced understanding of the factors influencing user preferences and choices in this rapidly evolving sector. Conversely, other hypotheses examining factors such as Social Influence, Facilitating Conditions, Perceived Reliability, Price Value, and Purchase Intention were substantiated. The positive influence of these factors on customer usage behavior underscores their significance in shaping user experiences and satisfaction within the telehealth ecosystem. Furthermore, the study posits that facilitating conditions should be closely aligned with technological advancements, as this alignment is conducive to the development of enhanced application features. This insight suggests that telehealth providers should continuously innovate and integrate advanced technologies to ensure that the facilitating conditions meet the ever-evolving needs and expectations of users.

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Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network

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Journal of Neuro-Oncology 2024年第1期166卷 167-174页

作者： Chou, Chi-Jen Yang, Huai-Che Chang, Po-Yao Chen, Ching-Jen Wu, Hsiu-Mei Lin, Chun-Fu Lai, I-Chun Peng, Syu-Jyun Division of Neurosurgery Department of Surgery Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Neurosurgery Neurological Institute Taipei Veterans General Hospital Taipei Taiwan Department of Electrical Engineering National Central University Taoyuan Taiwan Department of Neurological Surgery University of Virginia Health System Charlottesville 22903 VA United States Department of Radiology Taipei Veterans General Hospital Taipei Taiwan Department of Heavy Particles & Radiation Oncology Taipei Veterans General Hospital Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Purpose: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema. Methods: The model was trained using Gamma Knife surgical data (90 MRI sets from 46 patients), including the initial treatment plan and follow-up images (T1-weighted contrast-enhanced (T1cWI) and T2-weighted images (T2WI)) manually annotated by neurosurgeons to indicate the target tumor and edema regions. A mask region-based convolutional neural network was used to extract brain parenchyma, after which the DeepMedic 3D convolutional neural network was in the segmentation of tumors and edemas. Results: Five-fold cross-validation demonstrated the efficacy of the brain parenchyma extraction model, achieving a Dice similarity coefficient of 96.4%. The segmentation models used for metastatic tumors and brain edema achieved Dice similarity coefficients of 71.6% and 85.1%, respectively. This study also presents an intuitive graphical user interface to facilitate the use of these models in clinical analysis. Conclusion: This paper introduces a deep learning model for the automated segmentation and quantification of brain metastatic tumors and perilesional edema trained using only T1cWI and T2WI. This technique could facilitate further research on metastatic tumors and perilesional edema as well as other intracranial lesions. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep learning neural network Identification Metastatic brain tumors Perilesional edema Quantification

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Social media-based online entrepreneurship approach on millennials: A measurement of job pursuit intention on multi-level marketing

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Procedia Computer Science 2022年 197卷 110-117页

作者： Reny Nadlifatin Satria Fadil Persada Mutiara Clarinda Gogor Arif Handiwibowo Rr Ratih Laksitowati Yogi Tri Prasetyo Anak Agung Ngurah Perwira Redi Department of Information System Institut Teknologi Sepuluh Nopember Campus ITS Sukolilo Surabaya 60111 Indonesia Department of Business Management Institut Teknologi Sepuluh Nopember Campus ITS Sukolilo Surabaya 60111 Indonesia Department of Technology Management Institut Teknologi Sepuluh Nopember Campus ITS Sukolilo Surabaya 60264 Indonesia School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

The present research explores the behavioral perspective of the millennial generation on job pursuit intention on Multi-Level Marketing companies through social media. Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) are used to assess the job pursuit intention. The data were analyzed by Structural Equation Model (SEM). Analysis reveals the strong influence of attitude on job pursuit intention. The result in this research can be used as the theoretical basis that can be very beneficial for MLM companies, investors, and academicians in marketing. Further theoretical and practical suggestions are discussed.

关键词： Millennial MLM social media TPB TAM

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Reaction Kinetics of Levulinic Acid Synthesis from Glucose Using Bronsted Acid Catalyst

Research Square

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Research Square 2021年

作者： Toif, Meutia Ermina Hidayat, Muslikhin Rochmadi, Rochmadi Budiman, Arief LPP Agro Nusantara Jalan LPP No. 1 Yogyakarta Indonesia Department of Chemical Engineering Faculty of Engineering Gadjah Mada University Jalan Grafika 2 Yogyakarta Indonesia Master Program in System Engineering Gadjah Mada University Jalan Teknika Selatan Yogyakarta Indonesia

Glucose is the primary derivative of lignocellulosic biomass, which is abundantly available. Glucose has excellent potential to be converted into valuable compounds such as ethanol, sorbitol, gluconic acid, and levulinic acid (LA). Levulinic acid is a very promising green platform chemical. It is composed of two functional groups, ketone and carboxylate groups which can act as highly reactive electrophiles for nucleophilic attack so it has extensive applications, including fuel additives, raw materials for the pharmaceutical industry, and cosmetics. The reaction kinetics of LA synthesis from glucose using hydrochloric acid catalyst (bronsted acid) were studied in a wide range of operating conditions, i.e., temperature of 140-180 oC, catalyst concentration of 0.5-1.5 M, and initial glucose concentration of 0.1-0.5 M. The highest LA yield is 48.34 %wt at 0.1 M initial glucose concentration, 1 M HCl, and temperature of 180 oC. The experimental results show that the bronsted acid catalyst's reaction pathway consists of glucose decomposition to levoglucosan (LG), conversion of LG to 5-hydroxymethylfurfural (HMF), and rehydration of HMF to LA. The experimental data yields a good fitting by assuming a first-order reaction model. © 2021, CC BY.

关键词： Kinetics

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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Computer system, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Calibration of 100 A/50mA instrument current transformer energy meter using bisection method 2

Calibration of 100 A/50mA instrument current transformer ene...

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2nd IEEE International Conference on Innovative Research and Development, ICIRD 2019

作者： Bimantoro, Renardi Ardiya Rahardjo, Amien Husnayain, Faiz Aryani, Dwi Riana Hudayala, Chairul Universitas Indonesia Department of Electrical Engineering Faculty of Engineering Depok16424 Indonesia Energy System Engineering Master Study Program Faculty of Engineering Universitas Indonesia Depok16424 Indonesia

ISBN: (纸本)9781728128252

Instrument current transformers (CTs) are widely used in various types of electrical energy measurement. Eddy current losses, losses of hysteresis, a saturation of CT, and inevitable flux leaks could lead to errors including errors in the ratio and phase angle. Improving the accuracy of CT measurement could be achieved in many ways. In this study, a bisection calibration method to overcome the error by implementing correction factor integrated with the Arduino codes is investigated. The reliability of this system has been tested in two different situations, which are a constant and variable load experiments. The result shows that the readout of the developed instrument using 100 A/50mA CT could achieve the deviation value of less than 2%, confirming the excellence accuracy of energy meter. © 2019 IEEE.

关键词： Electric instrument transformers

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Prediction of power transformers lifetime using thermal modeling analysis 2

Prediction of power transformers lifetime using thermal mode...

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2nd IEEE International Conference on Innovative Research and Development, ICIRD 2019

作者： Afifah, Sarah Nainggolan, Jannus Maurits Wibisono, Gunawan Hudaya, Chairul Universitas Indonesia Departement of Electrical Engineering Faculty of Engineering Depok16424 Indonesia Energy System Engineering Master Study Program Faculty of Engineering Universitas Indonesia Depok16424 Indonesia

ISBN: (纸本)9781728128252

Transformer is one of the main components in distribution system of electrical power system towards the consumers, thereby any damage to the transformers will hinder the distribution of electricity towards the consumers, and in turn will make the SAIDI and SAIFI levels go up. High temperature in transformers can cause degradation in the insulation of transformers which in turn will cause failure in transformers. When the temperature in winding reaches or goes beyond the limit of 110°C, a degradation in insulation will start happening and the remaining life of transformers will decrease. Damage in transformers will cause disturbance in electrical power system and result in a major economic loss. Before damages occur, transformers need to be changed up until it is deemed to be no longer efficient, this can be done by replacing the transformer that is about to be damaged. To predict when a transformer is about to break, a calculation is made based on thermal modelling according to IEEE Std C57.91-1995 with its most prominent variable being Hot Spot Temperature (HST). HST is obtained by MATLAB programming using Annex G of IEEE Std. C57.91-1995. By obtaining HST, thus the remaining lifetime of transformers can be predicted. This study analysed the effect of loading, hot-spot temperature, and ambient temperature on the remaining lifetime of a transformer. The higher those three factors, the quicker the transformer will break, with loss of life percentage increasing exponentially. The highest loading, hot spot temperature, and ambient temperature given in this research gives percent loss of life of 0.0888332%, 0.0193394%, 0.020753% respectively. © 2019 IEEE.

关键词： Power transformers

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User Centered Design: Design and Development Methodology of Seed Planting Tools 6

User Centered Design: Design and Development Methodology of ...

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Joint Conference of the 6th Annual Conference of Industrial and system engineering 2019, ACISE 2019 and 1st International Conference on Risk Management as an Interdisciplinary Approach 2019, ICRMIA 2019

作者： Purnomo, H. Achmadi, O. Hasan, I. Mardijanto, M. Industrial Engineering Department Islamic University of Indonesia Yogyakarta Indonesia Postgraduate Program of Industrial Engineering Islamic University of Indonesia Yogyakarta Indonesia Postgraduate Program of Resource Engineering Master of Engineering System Gadjah Mada University Yogyakarta Indonesia

Gejlek or Galah is a conventional wood-made seed planting tools aid common used by corn farmers. The limited functions and heavy weight of the existing seed planting caused low productivity and long duration in planting activity. The aim of the research is to design and to develop seed planting tools which able to accommodate user needs. User-Centered Design (UCD) methods used to design product focused on customer satisfaction. 15 respondents involved in this research to measure productivity, practicality, reliability and ergonomic. Validity test represent by F-value produce result test in between 0.354 to 0.625 where Ftest > Ftab means reject H0 or seed planting tools design able to fulfil user needs and different from previous design. User test show how work load against pulse measurement improve 26.44% and Nordic Body Map decrease by average 25% which mean there are significant improvement in terms of work load and ergonomics. It helps increase work productivity. © Published under licence by IOP Publishing Ltd.

关键词： Ergonomics

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Analysis of components in light tar and producer gas from the organic waste gasification process

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AIP Conference Proceedings 2024年第1期3115卷

作者： M. Faisal Ain Lubis Rochim Bakti Cahyono Ade Kurniawan Alinda Fitrotun Nisya Arief Budiman Master Program in System Engineering Universitas Gadjah Mada Jalan Teknika Utara 3 Yogyakarta 55281 Indonesia Department of Chemical Engineering Universitas Gadjah Mada Jalan Grafika 2 Yogyakarta 55284 Indonesia Department of Chemical Engineering Universitas Islam Indonesia Jalan Kaliurang km 145 Yogyakarta 55584 Indonesia

Indonesia, as a developing country with an ever-increasing population, has an increasing amount of municipal solid waste, most of which is organic. Organic waste with high, medium, and low moisture content was acquired from TPST Piyungan, Yogyakarta. Moisture content is a organic waste quality parameter that must be studied for its impact on the gasification process. Gasification produces products in the form of gas (producer gas), solid (char+ash), and liquid (heavy tar+light tar). The light tar is the most difficult to separate. The light tar component has a high moisture content and contains 1-6 ring aromatic carbon 46.91%, heterocyclic 42.61%, phenolic 1.68%, benzene 0.60%, and other 8.20%. Meanwhile, 52.05% of the medium is aromatic carbon rings 1-6, 39.02% is heterocyclic, 0.44% is phenolic, 0.63% is benzene, and 7.86% is other. The content of 1-6 rings at low moisture content is 39.00% aromatic carbon, 46.60% heterocyclic, 1.20% phenolic, and 13.20% others. Producer gas has a high moisture content for chemicals that contribute to calorific value: CO 49.27%, H2 2.49%, and CH4 4.23%. CO is 50.37%, H2 is 2.41%, and CH4 is 4.40% with medium moisture level; at low moisture content, CO is 53.82%, H2 is 2.55%, and CH4 is 4.70%. Light tar and producer gas are crucial products for determining the quality of the gasification process based on the quality of the organic waste. It is critical to determine the magnitude of the influence on the energy conversion equipment for waste power generation.

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