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

作者： Salamun Rohman Nudin Rahmat Gernowo Maman Somantri Adi Wibowo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98 % ), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67 % ), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36 % ) and lowest (59 % ). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG.

关键词： Deep learning Accuracy Reviews Psychology Predictive models Brain modeling Multitasking Reliability Information technology Standards

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Diabetes Classification Problem with CatBoost Method and Optuna Gradient Boosting Optimization

Diabetes Classification Problem with CatBoost Method and Opt...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Safar Dwi Kurniawan Purwono Purwono Alfian Ma’Arif Iswanto Suwarno Department of Computer Engineering Politeknik Harapan Bersama Tegal Indonesia Department of Informatics Universitas Harapan Bangsa Purwokerto Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Department of Engineer Professional Program Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

Diabetes mellitus is a dangerous global epidemic that can cause kidney failure, heart attack, blindness, and death. The main cause of this disease is too high blood sugar levels due to the irrelevant work of pancreatic beta cells. Rapid computer technology advances and implementations based on artificial intelligence have penetrated various fields, especially the health sector. Data mining and machine learning are technologies that continue to grow, are reliable, and can be used as research support tools in the medical field. This technology is considered capable of helping to automate diabetes prediction. Based on related research, our contribution to this research is to utilize the CatBoost method with Optuna optimization. In the data preprocessing stage, all 768 data sets have gone through removing outliers, normalizing features to reducing dimensions. The dataset is divided into $85 \%$ composition for training data and $15 \%$ for test data. At the feature selection stage with Correlation Pearson’s, we found that Glucose correlated with diabetes outcome or disease because it had a value range of 0.47. The results’ accuracy is considered less than optimal because it only produces an accuracy value below $80 \%$, even after optimization with Optuna. It can be seen that there was only an increase of $7 \%$ after optimizing with Optuna.

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm 1

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Indriyono, Bonifacius Vicky Pamungkas, Natalinda Abbas, Fatima Hashim Mintorini, Ery Almohamadi, Mohammed Mahmood, Sarmad Nozad Alani, Sameer Alkhayyat, Ahmed H. Rachmawanto, Eko Hari University of Dian Nuswantoro Information System Kediri Indonesia Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq University of Dian Nuswantoro Visual Communication Design Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq University of Mashreq Research Center Medical Laboratories Techniques Baghdad Iraq College of Technical Engineering University of Mashreq the Islamic University Najaf Iraq University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments;they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD. © 2023 IEEE.

关键词： Energy efficiency

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Energy Planning and Utilization in the North Sulawesi Electricity System

Energy Planning and Utilization in the North Sulawesi Electr...

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International Conference on Electrical engineering and informatics, ICEEI

作者： Arif Majiid Nusa Pratama Bagas Maulana Sutardi Irfan Zidny Irvan Lewi Hasanuddin Syarif Hidayat Pradita Octaviandiningrum Hadi Graduate Program PLN-ITB PLN Corporate University Jakarta Indonesia PLN UIP3B Sulawesi Makassar Indonesia Bandung Institute of Technology School of Electrical and Informatics Engineering Bandung Indonesia

Energy sector is one of the contributors to climate change besides forestry, environment, and transportation sectors. Then, the results of the COP 26 in Glasgow meeting states that Indonesia have a target to achieve Net Zero Emission (NZE) by 2060 if there is international support for funding and technology with some note that this project does not interfere with availability and affordability. In achieving the zero-emission target, the government is implementing five main principles, which include increasing the use of new renewable energy (NRE), reducing fossil energy, explore the use of electric vehicles in the transportation sector, increasing the use of electricity in households and industries, and utilizing Carbon Capture and Storage (CCS). In this paper several proposals can be made to further reduce production costs and reduce greenhouse gas emissions in the North Sulawesi system compared to Electricity Supply Business Plan by PLN which is called RUPTL. Two additional scenarios can be used for the proposal, which are addition of geothermal, solar and pump hydro to replace coal power plant and construction of North Sulawesi interconnection with Central Sulawesi. From the simulation results of the cost and carbon emission analysis between three scenarios, the scenario from RUPTL is the cheapest in terms of cost at 3.295 billion USD and the interconnection scenario is the smallest in terms of emissions, with CO 2 emissions reaching 0 Mt.

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Kombucha tea derived from papaya (Carica papaya L.) as a potential functional food: physicochemical parameters, biological activities, and sensory evaluation

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International Journal of Food Science and Technology 2024年第10期59卷 7786-7794页

作者： Do, Anh Duy Nguyen, Le Bao Xuyen Pham, Dang Quoc Le, Thang Truong Leya, Mwense Phan Van, Thach Department of Biotechnology NTT Hi-tech Institute Nguyen Tat Thanh University Ho Chi Minh City700000 Viet Nam Department of Life Science College of Natural Sciences Hanyang University Seoul04763 Korea Republic of Faculty of Environmental and Food Engineering Institute of Applied Technology and Sustainable Development Nguyen Tat Thanh University Ho Chi Minh City700000 Viet Nam Smart Medicine and Health Informatics Program National Taiwan University Taipei City10617 Taiwan Biosafety Research Institute and Laboratory of Pathology College of Veterinary Medicine Jeonbuk National University Iksan54896 Korea Republic of

Fermented foods, such as kombucha, have gained popularity due to their rich content of beneficial bioactive compounds. Papaya leaves, known for their abundance of bioactive substances such as alkaloids, flavonoids, and phenolic compounds, have garnered attention for their potential to improve immune function by neutralising free radicals. This study investigates the suitability of papaya leaves and petioles as substrates for kombucha fermentation. Our findings indicate that papaya leaves and petioles were suitable substrates for kombucha fermentation, indicated by the SCOBY mass accumulation during fermentation. The gradual decrease in brix and pH values over a 14 days fermentation period corresponds to sugar metabolism and the accumulation of acetic acid. Additionally, fermentation increases total polyphenol (PPK: 250.07 ± 3.00 mg/L, PLK: 330.87 ± 15.56 mg/L) and total flavonoid content (PPK: 26.67 ± 6.31 mg/L, PLK: 34.44 ± 5.55 mg/L). Moreover, papaya-derived kombucha demonstrates enhanced antibacterial properties and inhibits ⍺-amylase linked to diabetes. Sensory evaluations confirm that papaya kombucha achieves a well-balanced and appealing sensory profile, catering to diverse palates. In summary, papaya leaves and petiole exhibit promise as substrates for kombucha production, offering elevated nutritional value and potential health advantages. © 2024 Institute of Food, Science and Technology (IFSTTF).

关键词： Substrates

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Finding the Important Elements Linked to Integration Problems in the Use of Enterprise Resource Planning Systems

Finding the Important Elements Linked to Integration Problem...

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International Conference on Information Management and Technology (ICIMTech)

作者： Wahyu Sardjono Desi Maya Kristin Dewi Sagita Pranata Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London UK

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Organizations face an urgent need to become faster, more efficient, and more successful in their commercial operations, given the current state of technological evolution and the arrival of Industry 4.0. Many businesses have adopted the idea of enterprise resource planning (ERP) systems to achieve this goal. The ERP system is expected to increase worker productivity, improve business performance to maximize profitability and profits, and provide accurate and up-to-date data and information to management and stakeholders. This research aims to find new variables related to integration problems in the use of ERP systems. Using the SPSS statistical program, the data collected will be examined using quantitative exploratory factor analysis (EFA). Data processing then includes collecting demographic information from respondents, reliability testing, conducting the Kaiser-Meyer-Olkin (KMO) test, testing using an anti-image matrix, and deriving new factors using a rotation component matrix. This research seeks to identify new elements underlying ongoing integration problems in ERP system implementation. The research results show several reasons for underlying integration problems and offer in-depth information to management that can be used to analyze and evaluate the ERP system in their company appropriately in the future so that the company's performance becomes superior to the competition.

关键词： Training Standards organizations Companies Enterprise resource planning Business process re-engineering Stakeholders Reliability Best practices System implementation Testing

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Exploring Personal Knowledge Ecologies: Dealing with Digital Platform Asymmetries

Exploring Personal Knowledge Ecologies: Dealing with Digital...

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International Conference on Computer Supported Cooperative Work in Design

作者： Marcos Antonio De Almeida Jano Moreira De Souza António Correia Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The rise of artificial intelligence agents and information technology platforms governed by data-driven algorithms has had profound impacts on the way knowledge work is managed and carried out. Concerning the novel uses of digital work platforms, the arising tensions might suggest that the socio-technical design frameworks adopted by major tech firms are becoming less viable and in some cases ineffective. We propose a conceptual architecture to support a socio-technical framework for exploring Personal Knowledge Ecologies in the context of the digital knowledge economy. This framework aims to increase the degree of individual freedom for the interoperation of these knowledge information infrastructures.

关键词： Knowledge engineering Sociotechnical systems Computer architecture Ecology Software Heterogeneous networks Complexity theory

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Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

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Cyber Science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

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A Comparative Study of Battery Energy Storage Systems in Isolated Grids: Novel Approach to Shave Peaks & Optimize Energy Costs

A Comparative Study of Battery Energy Storage Systems in Iso...

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International Conference on Power engineering and Renewable Energy (ICPERE)

作者： Bagas Maulana Sutardi Ariesa Budi Zakaria Ilham Akbar Kevin Marojahan Banjar-Nahor Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

ISBN: (纸本)9781665455497

Isolated power systems are typically found in rural areas that are far separated from the power grid. Rapid decrease in BESS (Battery Energy Storage Systems) cost provides new opportunities and preferences for utilities to store cheap energy at low loads and power out that energy at peak loads. BESS is installed to shave or level demand during peak hours in an isolated system can avoid the use of peaking generations mostly coming from oil fuels. Considering the BESS function for peak shaving, the approach is to simulate the unit commitment of different battery sizes at 10 MW, because the use of oil fuels about 10 MW at peak load, so the simulation will be representing the total energy of the battery needed by the system. Based on the simulation, the life cycle cost is significantly greater than the benefit at all power and energy ratings. The purpose of this paper is to demonstrate the comprehensive technical and financial analysis needs to find the optimum battery type and capacity to provide optimum impact and financially benefit the system with an approach to shaving peaks and energy costs. A study-case method is demonstrated by taking an isolated system as the base and BESS performance can accelerate voltage in the subsystem for every substation when discharging at peak load. The simulation results that 10 MW/10 MWh lithium-ion batteries provide the highest benefit compared to the life cycle cost.

关键词： Renewable energy sources Costs Substations Oils Simulation Voltage Power grids

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Classification of Paddy Leaf Disease Using MobileNet Model

Classification of Paddy Leaf Disease Using MobileNet Model

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

作者： Fauzan Masykur Kusworo Adi Oky Dwi Nurhayati Doctoral Program Of Information System School Of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Informatics Engineering Muhammadiyah University of Ponorogo Ponorogo Indonesia

ISBN: (纸本)9781665453905

Paddy is a rice-producing plant as a staple food raw material for most of the population in Indonesia. The rice planting process cannot be separated from pests and diseases that can cause crop failure and impact on rice production stocks. Currently, the process of monitoring the development of rice plants is done conventionally by direct observation by farmers. Meanwhile, in this paper, a classification model and determination of object detection in rice plants infected with pests and diseases is proposed based on leaf color. Leaf classification applies convolution conditional network algorithm with mobilenet as the architecture. The leaf image dataset was taken from public data from *** by involving 4 classes, namely healthy leaf images, brownspot, hispa and leafblast images. In each class the leaf images are grouped into 2 groups, namely 70% training and 30% validation. On the mobilenet backbone, it produces 97% accuracy and 22% loss with the time required for 195s 19s/step in 1 epoch round.

关键词： Training Image color analysis Sociology Computer architecture Production Object detection Raw materials

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