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7th International Conference on informatics and Computational Sciences, ICICoS 2024

作者： Wulandari, Dewi Arianti Soeprobowati, Tri Retnaningsih Nugraheni, Dinar Mutiara Kusumo Diponegoro University Doctoral Program of Information System Semarang Indonesia Diponegoro University Department of Biology Faculty of Science of Mathematics Semarang Indonesia Diponegoro University Department of Informatics Faculty of Science and Mathematics Semarang Indonesia

ISBN: (纸本)9798350375886

Using digital literacy in the educational environment can make students more responsible and wiser in using and accessing technology. To find out how prepared the educational environment is in facing this digital era a study that aimed to determine whether there is a correlation between age and gender factors with the number of devices used in the online learning process was conducted at one of the private universities in West Jakarta. The results of this study can contribute to the model developments in both online and offline learning processes to increase student motivation. The data collection method used is qualitative with primary data from survey results, using non-probability sampling, and descriptive analysis. The sample of this study was students at that university with a total of 310 students. Based on the test results using the two-way ANOVA test on the SPSS tool, the result is 0.055. This value is above the significance value requirement of 0.05. The age and gender do not significantly affect device usage, with a Sig value of >0.05, and the simultaneous relationship between age and gender does not affect device usage. It can be concluded that digital literacy has been applied in that university which can be proven from the test results that 225 students out of 310 or 72.5% use more than one device in online learning activities. © 2024 IEEE.

关键词： Contrastive Learning

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Optimizing Advantage Actor-Critic with Policy Gradient and Deep Q-learning to Maximize Profit in Forex Trading Prediction 1

Optimizing Advantage Actor-Critic with Policy Gradient and D...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Baradja, Abdillah Gernowo, Rahmat Wibowo, Adi Universitas Diponegoro Doktoral Program of Information System Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350306156

Financial markets, such as forex markets, are inherently complex, with high volatility, noise, trends, and market shocks. Therefore, designing effective trading strategies is crucial for maximizing investment returns. The implementation of deep reinforcement learning (DRL) in financial trading has become an attractive research topic in recent years. This study proposes Advantage Actor-Critic-based algorithms (A2C) algorithms to learn forex trading strategies using deep reinforcement schemes. The ensemble trading strategy was obtained by combining the best features of the two algorithms' Policy Gradient as Actor and Q-learning as Critic, resulting in a robust strategy that adapts to different market situations. Our experiment on the EURUSD currency on the 4 hour candlestick timeframe using five years of data history was divided into three parts for training, validating, and testing. The A2C agent learns to buy, sell, and hold a trade with the goals of maximizing profits and reducing losses. Our proposed method provides an effective and robust solution to various financial trading problems using deep reinforcement learning. Our proposed methods provide effective and robust solutions to various financial trading problems using deep reinforcement learning. The evaluation results improved from each training, where the Cumulative Return evaluation increased from 29.65 to 112.74, the Maximum Drawdown reduced from 36.18% to 4.48%, the Sharp Ratio increased from 0.94 to 2.47%, and the AHPR increased from 0.19% to 3.56%. This indicates that longer training data result in higher cumulative rewards and an increase in total profit. © 2023 IEEE.

关键词： Reinforcement learning

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Data Mining Approach for River Flood Hazard Time-Series: Using a Combination of Triple Exponential Smoothing and Neural Networks, in Demak

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第19期49卷 183-196页

作者： Hakim, Dimara Kusuma Gernowo, Rahmat Nirwansyah, Anang Widhi Haryanto, Totok Department of Informatics Universitas Muhammadiyah Purwokerto 53182 Indonesia Department of Physic Faculty of Mathematics and Natural Sciences Diponegoro University Indonesia Department of Geography Education Universitas Muhammadiyah Purwokerto 53182 Indonesia Bachelor of Management Program Faculty of Economics and Business Universitas Muhammadiyah Purwokerto 53182 Indonesia

Conventional flood hazard maps depict a static perspective on flood risk and are helpful in flood forecasting. Then, time-series flood forecasting research was developed and widely conducted. Still, it has limitations because the univariate method used does not consider the influence of other variables, unlike regression. Also, the determination of flood hazard weights, which is usually conducted through empirical studies using AHP, has a good level of accuracy but has the drawback of expert subjectivity. A new approach is proposed for flood hazard forecasting, combining the Triple ES method with NN and determining weights in a data-driven manner using NN. A combination of time series and regression methods (significantly non-linear), namely Triple ES with NN, results in good accuracy with an MSE value of 3.03, MAE 1.20, RMSE 1.74, R2 0.54, and a MAPE of 32.93%. The evaluation results for flood hazard weight determination with an MSE of 0.0111 and a MAPE of 7.81% show promising results, and the weights can be used in Hazard Flood GIS. Visualization in the form of a GIS Hazard Map can be done after all related raster data have been combined. The computational outcomes, particularly the MSE and MAPE values, demonstrate the effectiveness of the proposed approach, providing a clear understanding of the model's performance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Linear regression

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Bibliometric Analysis: Research Trends in Leaf Image Segmentation and Classification

Bibliometric Analysis: Research Trends in Leaf Image Segment...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Agustini, Eka Puji Gernowo, Rahmad Kurnianingsih Wibowo, Adi Warsito, Budi Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia Faculty of Science and Mathematics Diponegoro Universitty Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350350524

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: The trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights 'Extraction' as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject. © 2024 IEEE.

关键词： Image segmentation

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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RFI measurements at Bosscha observatory to prepare a new VGOS station

RFI measurements at Bosscha observatory to prepare a new VGO...

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2023 International Symposium From the Universe Back to Earth: Developing Astronomy to Meet Today's Natural Challenges

作者： Ramadhan, S. Hidayat, T. Suryana, J. Romadhoni, A. Kurniasari, M. Bosscha Observatory Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Astronomy Study Program Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Laboratory of Radio and Microwave Telecommunication School of Electronics and Informatics Engineering Institut Teknologi Bandung Indonesia

A new VGOS (VLBI Global Observing System) radio telescope will be constructed at Bosscha Observatory Lembang, West Java, in cooperation between Shanghai Astronomical Observatory (SHAO) and Institut Teknologi Bandung. One of the preparation steps is to conduct RFI (Radio Frequency Interference) measurements to obtain the RFI environment from the vicinity of the observatory. In this work, we report preliminary measurements of the RFIs that were done for several days in August and September 2023. Instruments used includes: A-INFOTM LB-20180 horn antenna, Wright TechnologiesTM low noise amplifier, and AaroniaTM Spectran V5 80200 OEM spectrum analyzer. The frequency range of 2.0 - 18.0 GHz is targeted referring to VGOS frequencies. Measurements in one day are carried out for about 24 hours in eight cardinal directions and two polarizations. The results and analysis are presented in spectral power flux density plots. © Published under licence by IOP Publishing Ltd.

关键词： Observatories

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Long Short Term Memory (LSTM) Improvement Accuracy Using FastText and Glove for Language Translation Batak - Indonesian 2

Long Short Term Memory (LSTM) Improvement Accuracy Using Fas...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Mandasari, Atikah Rahayu Nababan, Erna Budhiarti Mawengkang, Herman Informatics Program Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Faculty Of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Medan Indonesia

ISBN: (纸本)9798350351613

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages. © 2024 IEEE.

关键词： Semantics

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Use of an Expert System to Diagnose and Provide Solutions for Pests and Diseases in Hydroponic Mustard Plants Using Certainty Factor and Forward Chaining Methods 4

Use of an Expert System to Diagnose and Provide Solutions fo...

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4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Baihaqi, Mahesa Bayu Widodo, Aris Puji Jie, Ferry Diponegoro University Master of Information System Study Program Central Java Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Central Java Indonesia Edith Cowan University School of Business and Law Australia

ISBN: (纸本)9798350376111

Mustard greens are a vegetable that is very popular with people, especially hydroponic mustard greens, which appear fresher. However, like other vegetables, mustard greens grown hydroponically are also susceptible to pests and diseases, which can cause the vegetables to lose their freshness and may even result in crop failure. This condition can cause losses for mustard greens farmers in running their businesses. This research proposes an information system that represents the expertise of hydroponic mustard greens farmers in diagnosing pests and diseases in mustard plants as well as solutions to overcome them. This is to make it easier for novice growers and anyone interested in learning about diseases or pests that impact hydroponic mustard greens. The expert system developed is web-based and functions as a user consultation medium in relation to mustard pests and diseases using the forward chaining and certainy factor method to explain the decision tree procedure for mustard plant disease symptoms. With this expert system, farmers do not need to look for people who can explain the problems they face. We found that there were 10 types of pests and diseases and 29 symptoms in the hydroponic mustard plants reviewed in this research. © 2024 IEEE.

关键词： Expert systems

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Initial Exploration and Understanding of Parameters for Successful Enterprise Architecture Implementation 11

Initial Exploration and Understanding of Parameters for Succ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Khairina, Dyna Marisa Purwanto, Purwanto Nugraheni, Dinar Mutiara Kusumo School of Postgraduate Studies Universitas Diponegoro Program in Information Systems Semarang Indonesia Universitas Diponegoro Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389654

This study provides an initial exploration and further understanding of the factors that are parameters in evaluating the success of Enterprise Architecture (EA) implementation. An evaluation process is needed to ensure that the EA project implementation provides effectiveness, efficiency, and feasibility of the EA information system as well as assessing previous project experience to avoid future EA project risks. The research aims to present a Systematic Literature Review based on the stages of identification, screening, eligibility, and inclusion so that 14 main articles are selected for review with strong actual relevance. The study results provide insights and references that can be used to further explore key factors in evaluating the success of EA implementation in the future. It was found that there were ten factors that most influenced the evaluation of the success of EA implementation and it was concluded that two potential factors were the most influential and required further exploration in evaluating the success of EA implementation which could be applied in various sectors/dimensions. This study contributes as an initial reference for overcoming problems in investigating and exploring the factors that influence the success of EA implementation as an effort to assess the success of achieving future organizational goals. © 2024 IEEE.

关键词： Enterprise Architecture Parameters Success Factors Systematic Literature Review

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