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11th International Conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

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

ISBN: (纸本)9798350389289

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. © 2024 IEEE.

关键词： Contrastive Learning

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ML-Agents in Kart Racing Game using Microgame 17

ML-Agents in Kart Racing Game using Microgame

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

作者： Ana, Afina Anfa Pratama, Vitradisa Sukoco, Agus Prihatmanto, Ary Setijadi Nurmalasari, Rin Rin Juhana, Agus Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Mayasari Bakti Departement of Informatics Tasikmalaya Indonesia UIN Sunan Gunung Djati Department of Electrical Engineering Bandung Indonesia Universitas Pendidikan Indonesia Multimedia Education Study Program Bandung Indonesia

ISBN: (纸本)9798350309164

This paper presents an innovative approach to integrating ML-Agents into a microgame-based kart racing environment, showcasing the potential for enhancing gameplay experiences through AI-driven opponents. Kart Unity Game is a racing game that uses small motorized vehicles but still uses the concept of racing in the real world. One of the racing game development platforms is Unity. Unity has developed a platform, Unity Karting Microgame, which users can modify. Unity Karting Microgame applies self-driving to ML-Agents trained and designed to drive their karts with suitable sensors and actuators. The method used to develop the game is to modify the racetrack and modify ML-Agents to make the game more exciting and fun. As a result of this study, successful implementation of ML-Agents in a microgame-based kart racing environment. The study highlights the potential of using Unity ML-Agents to create AI-driven opponents that enrich gameplay experiences. Games can run well and seem varied. ML-Agents can also control their karts on their lanes. Thus, kart racing games can be implemented. © 2023 IEEE.

关键词： Software design

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Developing a Waste Management Strategy for Reducing Jakarta's Greenhouse Gas Emissions

SSRN

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SSRN 2024年

作者： Kristanto, Gabriel Andari Dahlan, Astryd Viandila Sekarsari, Nindi Environmental Engineering Study Program Civil Engineering Departement Engineering Faculty Universitas Indonesia Depok16424 Indonesia

Increasing amount of waste poses substantial environmental issues because poor waste management emits higher greenhouse gas (GHG, contributing to global warming and climate change. As Indonesia's most populous city, Jakarta faces challenges related to waste management. The city generates significant waste daily, with limited resources and funding to manage it *** study aims to analyze GHG emissions from Jakarta's waste sector and design management scenarios to optimize GHG emission reductions. The research examines local factors to determine the most effective scenario for Jakarta from three proposed waste management designs. The findings may serve as a basis for policymaking that promotes low-carbon and climate-resilient regional development. The study demonstrates that integrated waste management can significantly reduce GHG emissions compared to current practices in Jakarta. However, challenges related to policy and legal frameworks, stakeholder engagement, waste facility technologies, and financial resources must be addressed to achieve these reductions. © 2024, The Authors. All rights reserved.

关键词： Greenhouse gas emissions

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GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large Systems of Nonlinear Equations 4th

GPU Acceleration of the Enhanced Jaya Optimization Algorit...

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4th International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Silva, Bruno Guerreiro Lopes, Luiz Doctoral Program in Informatics Engineering University of Madeira Madeira Island Funchal Portugal Regional Secretariat for Education Science and Technology Regional Government of Madeira Funchal Portugal Faculty of Exact Sciences and Engineering University of Madeira Madeira Island Funchal9020-105 Portugal

ISBN: (纸本)9783031812408

This paper presents a GPU-based parallel implementation of the Enhanced Jaya (EJAYA) algorithm for solving large-scale systems of nonlinear equations. EJAYA is a gradient-free metaheuristic optimization scheme based on the population-based parameter-less Jaya algorithm that has recently been shown to be capable of solving nonlinear equation systems, a class of challenging problems that are difficult to solve by traditional approaches, particularly as systems become larger. The proposed parallel algorithm was implemented using the Julia programming language on a high-performance GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB GDDR6X VRAM and was tested using a set of difficult scalable nonlinear equation system problems. The obtained results, with average speedups up to 122.25x, evidenced the capability of the proposed GPU-based parallel algorithm to meet the computational demands of the important class of problems under consideration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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Effect of physical and biological pretreatment on sugarcane bagasse waste-based biogas production

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Materials Today: Proceedings 2023年 87卷 41-44页

作者： Sumardiono S. Matin H.H.A. Sulistianingtias I. Nugroho T.Y. Budiyono B. Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang 50239 Indonesia Study Program of Environmental Science Faculty of Mathematic and Natural Science Universitas Sebelas Maret Surakarta 57126 Indonesia Vocational School Universitas Diponegoro Semarang 50239 Indonesia

Sugarcane bagasse is a biomass with very potential, especially in agricultural countries. In this research, a study related to pretreatment and the ratio of C/N to biogas production from sugarcane bagasse was conducted. The study was carried out on a laboratory scale with physical pretreatment by grinding with a size of 6 mesh, then biological pretreatment with the microbial consortium 5 % g/V, and variations in the C/N ratio at 25 and 30. Cow rumen fluid was used in this study as a source of anaerobic bacteria and technical urea is used to adjust the C/N ratio. The solid total is set at 7 %. Based on research that has been carried out for 60 days, it was found that physical pretreatment by grinding with a size of 6 mesh can increase biogas production. Biogas production increased both at C/N ratio conditions of 25 and 30. Furthermore, biological pretreatment with the microbial consortium 5 % g/V was proven to increase biogas production at both C/N ratios of 25 and 30. Further studies on the C/N ratio, it was found that the C/N ratio of 25 was better than the C/N ratio of 30. This is evident in several variables, including raw and fine substrate conditions, as well as on variables with or without a microbial consortium, with the amount of biogas productivity at a C/N 25 ratio of 53-90.5 % higher than the C/N 30 ratio. © 2023 The Authors. All rights reserved.

关键词： Biogas Biological pretreatment Physical pretreatment Sugarcane bagasse

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An Improved Modified Jaya Optimization Algorithm: Application to the Solution of Nonlinear Equation Systems 4th

An Improved Modified Jaya Optimization Algorithm: Applicatio...

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4th International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

ISBN: (纸本)9783031812408

Population-based metaheuristic algorithms have been used to solve challenging optimization problems, and numerous modifications to make these algorithms more efficient have been proposed. Jaya is one of these algorithms that has been modified and utilized to solve many hard optimization problems. One such modification to the original algorithm is the Modified Jaya (MJAYA) algorithm, which, like Jaya, uses information about the best and worst candidate solutions to determine the population’s search direction, thereby limiting and focusing the search. Recent research has demonstrated that the MJAYA algorithm is not effective at solving systems of nonlinear equations transformed into equivalent nonlinear optimization problems. Systems of nonlinear equations are probably the most difficult class of numerical mathematics problems to solve, which justifies the use of metaheuristic approaches in their resolution. This paper proposes a new population-based optimization algorithm, the Improved Modified Jaya (IM-Jaya) algorithm, which, despite being a generic metaheuristic optimization algorithm, is here used to solve systems of nonlinear equations. The proposed algorithm strikes a better balance between global exploration and local exploitation, allowing for a broader and more exhaustive exploration of the search space as well as a more efficient local search in its most promising regions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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In-Depth Examination of Student Attrition in Higher Education Through Decision Tree, Random Forest, and XGBoost Algorithms

In-Depth Examination of Student Attrition in Higher Educatio...

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International Conference on Electrical engineering, Computer Science and informatics (EECSI)

作者： Clement Aurelio Johan Setiawan Faculty of Engineering and Informatics Information Systems Study Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Customer Customer churn, a critical metric for businesses reliant on customer loyalty, involves the loss of customers from a company. This study examines churn among college and university students, leading to financial losses, decreased accreditation, and diminished reputations for educational institutions, ultimately reducing their appeal to prospective students. To identify factors contributing to student churn and classify students as likely to churn or not, this research utilizes Decision Tree, Random Forest, and XGBoost algorithms. These classification algorithms, with XGBoost and Random Forest enhancing Decision Trees through boosting and bagging techniques, respectively, are well-suited for the task. The data was sourced from the Academic Information Bureau and processed before model development. The model with the best performance will predict student churn on new, unseen data. Findings indicate varying performance metrics for each model, including accuracy, precision, recall, and F1-score. The XGBoost model excelled, achieving an accuracy of 98%, precision of 99%, recall of 96%, and an F1-score of 97%, significantly improving over the base Decision Tree model across all metrics. Notably, all models showed better performance in predicting the majority class (not churn) compared to the minority class (churn).

关键词： Measurement Accuracy Computational modeling Predictive models Approximation algorithms Prediction algorithms Data models Classification algorithms Decision trees Random forests

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Review of Nano-Composite PCM AlN/EG and Heat Pipe as a Hybrid Battery Thermal Management System 4

Review of Nano-Composite PCM AlN/EG and Heat Pipe as a Hybri...

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4th International Symposium on Advances and Innovation in Mechanical engineering, ISAIME 2023

作者： Kadek Ervan Hadi Wiryanta, I. Nindhia, Tjokorda Gde Tirta Septiadi, Wayan Nata Made Joni, I. Study Program of Mechanical Engineering Faculty of Engineering Udayana University Bali Bukit Jimbaran80361 Indonesia Mechanical Engineering Department Politeknik Negeri Bali Bali Badung Indonesia Department of Physics Padjadjaran University Jl. Raya Bandung Sumedang KM 21 Jatinangor Jawa Barat45363 Indonesia

The battery thermal management system (BTMS) is essential for maintaining the battery's temperature and hence its performance. BTMS is classified into three types: active cooling, passive cooling, and hybrid approaches. Because of its simple design, lack of additional power, and even temperature distribution, PCM is extensively used as a BTMS as a passive cooling technique. PCM also has drawbacks like leak sensitivity and limited heat conductivity. This stimulates PCM performance enhancement attempts. Adding reinforcing material to PCM is a standard way to increase its performance. Aluminum nitride (AlN) and expanded graphite (EG) are commonly employed as fillers. The addition of EG and nano-AlN materials can improve the performance of PCM composites compared to pure PCM. Chemical interactions between these materials do not take place either. It is undeniably advantageous that these two materials may be combined into pure paraffin-based PCM for energy storage applications. The combined or hybrid system of BTMS is the optimum alternative for increasing heat transmission from the battery modules into the surrounding environment. The battery temperature will at good maintained well with the combination of 2 (two) and 3 (three) passive cooling methods or even combined with active cooling methods such as air-forced convection. In BTMS, the heat pipe is widely employed as a heat transfer improvement. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Heat pipes

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Performance of Pico Scale Turgo Turbine in Difference the Nozzle Diameter

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International Journal of Fluid Machinery and Systems 2022年第1期15卷 130-136页

作者： Adanta, Dendy Syofii, Imam Sari, Dewi Puspita Wiyono, Apri Department of Mechanical Engineering Faculty of Engineering Universitas Sriwijaya Indralaya South Sumatera 30662 Indonesia Study Program of Mechanical Engineering Education Universitas Sriwijaya Indralaya South Sumatera 30662 Indonesia Department of Automotive Engineering Education Universitas Pendidikan Indonesia Bandung West Java 40154 Indonesia

Pico scale Turgo turbine (PSTT) is proper as a power plant to be applied for remote areas because of its simple construction, cheap investment cost, and good performance above 35%. However, the design of PSTT is still based on estimation because the wheel diameter and nozzle diameter (D/d) ratio are not yet in reasonable agreement. Furthermore, the literature on D/d affects PSTT performance has not been comprehensive. Accordingly, this study investigates the relation of D/d on PSTT performance and its effect on the U/Vjet. Based on experimental results, the relation of D/d and U/Vjet to PSTT performance is parabolic (expressed by polynomial quadratic);the maximum configuration of D/d of 22, and U/Vjet between 0.42 to 0.47. © 2022, Turbomachinery Society of Japan. All rights reserved.

关键词： Energy conversion

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