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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Rahmawati, Lailia Rustad, Supriadi Marjuni, Aris Soeleman, Moch Arief Nurtantio Andono, Pulung Pujiono Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Universitas Darul Ulum Faculty of Informatics Engineering Jombang Indonesia

ISBN: (纸本)9798350339215

This paper discusses a comparison of several methods used for object detection in bad weather conditions, such as the presence of fog that interferes with the object's view. The methods compared in this paper include Faster R-CNN, Yolov3, and SSD (Single Shot Multi-Box Detector). This work contributes to evaluating the best method to overcome the object detection problem in bad weather, which is measured by mean average precision (mAP). The evaluation is executed using video objects that are converted into frames. Based on experimental results, the Faster R-CNN method is superior in terms of the mAP value where the Faster R-CNN is 87%, using SSD is 48%, and Yolov3 is 31%. Faster R-CNN provides better performance than the other two methods because the basic idea of FRCNN is the use of a Region Proposal Network (RPN) in a feature map which aims to propose many objects that can be identified in a particular image. © 2023 IEEE.

关键词： Object detection

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students 6

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Yusa, Mochammad Coastera, Funny Farady Rahmadani, Deni Sari, Julia Purnama Kusrini, Kusrini Hartanto, Anggit Dwi University of Bengkulu Faculty of Engineering Department of Informatics Bengkulu Indonesia University of Bengkulu Faculty of Engineering Department of Information System Bengkulu Indonesia Universitas Amikom Yogyakarta Magister of Informatics Engineering Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Information System Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals;2) Filtering and selecting criteria for student academic activities;3) Transforming and normalizing the data;4) Applying automatic weighting using CRITIC;5) Calculating criteria rankings using the PROMETHEE II technique;and 6) Validating the ranking outcomes. © 2023 IEEE.

关键词： Decision support systems

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Factors Influencing Knowledge Sharing in Prison: A Review and AHP Analysis 6

Factors Influencing Knowledge Sharing in Prison: A Review an...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan University of Indonesia Faculty of Computer Science Depok Indonesia University of Pardubice Faculty of Electrical Engineering and Informatics Pardubice Czech Republic

ISBN: (纸本)9798350345162

Knowledge sharing has been recognized as the most critical factor in successful knowledge management. Knowledge sharing in prison is a complicated topic that frequently faces resource restrictions, stigma and isolation, communication hurdles, and other security concerns. Moreover, resources for knowledge sharing in a secure environment are still few. This research investigates the critical factors for knowledge sharing in prisons. This research uses the Kitchenham approach with five reputable publishers from 2018 to 2023. There are 1284 articles in the initial phase and 25 in the final phase. Critical factors were ranked using Analytical Hierarchy Process (AHP) with the Triple-helix approach to the representatives of academics, policymakers, and the community in the Indonesian context. The critical knowledge-sharing factors are leadership support, organizational culture, organizational structure, incentives, and individual beliefs. However, acknowledging the critical factors can aid in improving the knowledge-sharing system in prison and changing the criminal justice system. © 2023 IEEE.

关键词： analytic hierarchy process critical factors knowledge sharing prison

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A distributed B+Tree indexing method for processing range queries over streaming data

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Cluster Computing 2024年第2期27卷 1251-1274页

作者： Safaee, Shahab Mirabi, Meghdad Rahmani, Amir Masoud Safaei, Ali Asghar Department of Computer Engineering Faculty of Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Medical Informatics Faculty of Medical Sciences Tarbiat Modares University Tehran Iran

A data stream exhibits as a massive unbounded sequence of data elements continuously generated at a high rate. Stream databases raise new challenges for query processing due to both the streaming nature of data which constantly changes over time and the wider range of queries submitted by the user when compared with the traditional databases. In this paper, we propose a system architecture which includes components for both distributed indexing of streaming data and distributed processing of range queries on streaming data. Instead of creating a large and centralized B+Tree index structure, we create a set of small B+Tree indexes in such a way that a B+Tree index can be created for every partition of streaming data. We also design a distributed range search algorithm which can be used by each individual machine inside a Spark cluster to independently process range queries on each partition of streaming data. By exploiting the proposed system architecture, the process of indexing of streaming data and the process of querying over streaming data can be performed in a distributed and parallel manner. By performing several experiments, we demonstrate that our proposed indexing method is scalable and efficient for processing range queries on streaming data compared to the existing centralized B+Tree indexing methods and therefore, it can be used for applications involving data streams with a large volume of data elements and a large number of range queries. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Query processing

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Facial Palsy Detection Through Changes in Facial Muscle Functionality Using CNN Algorithm 6

Facial Palsy Detection Through Changes in Facial Muscle Func...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Sari, Chindy Aulia Bachtiar, Fitra Abdurrachman Muflikhah, Lailil Widayati, Aris University of Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia University of Brawijaya Faculty of Medicine Malang Indonesia

ISBN: (纸本)9798350345162

Facial palsy is a common condition in the medical field that results from facial nerve disorders, leading to weakened facial motor functions. Detecting facial palsy typically involves observing the functional ability of facial muscles, a process currently performed manually by healthcare professionals relying on visual cues. However, manual detection is prone to errors, this needs for early and automated facial palsy detection. Overall, our research aims to improve the detection of facial palsy by leveraging deep learning techniques. By automating the detection process, we hope to provide a more accurate and efficient approach for early identification of this condition. In this research, we aim to develop a facial palsy detection system using the Convolutional Neural Network (CNN) algorithm. We utilize three datasets, namely CK+, JAFFE, and YouTube Facial Palsy, which are organized into normal and dysfunctional classes. The dataset is divided into training and testing data in an 80:20 ratio, and preprocessing techniques such as image resizing and grayscale conversion are applied. To find the optimal parameters for our model, we employ k-fold cross validation during the training process. The evaluation results show that the best model achieved optimal performance with the following parameters: 3 hidden layers, a fully connected layer (fc)-1 with a hidden size of 128, fc-2 with a hidden size of 16, and fc-3 with a hidden size of 32. The optimizer used was Adam, with a learning rate of 0.001, a batch size of 64, and trained for 10 epochs. This best model attained an average training accuracy of 0.958, testing accuracy of 1, recall of 1, and precision of 0.99. © 2023 IEEE.

关键词： CNN deep learning face detection facial palsy

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The Improvement of Personal Service in the MSMEs Marketplace Using a Rank-Based Collaborative Filtering Approach

The Improvement of Personal Service in the MSMEs Marketplace...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Pratama, Yan Aditiya Informatics Engineering Study Program Faculty of Computer Science Lampung Bandar Indonesia Management Study Program Faculty of Business Economics Lampung Bandar Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Indonesian MSMEs in 2022 will begin to enter the transformational recovery phase. Various preparations must be made in order to adapt. One that has been done is to increase the number of MSMEs entering the digital ecosystem, such as the MSME marketplace. The MSME Marketplace is used for marketing various MSME products online. The MSME Marketplace must be supported by methods that are able to provide good and fast personal services, considering the types and variety of products offered are increasing in number and varying from time to time. Therefore, this study proposes the Porat_Rank method to produce product recommendations that match user interests quickly so that users are served well. The experimental results show that the Porat_Rank method is superior to the Copelan Score method both in producing recommendations and in processing speed. © 2022 IEEE.

关键词： Collaborative filtering

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A Transfer Learning-Based Framework for Enhanced Classification of Perceived Mental Stress Using EEG Spectrograms

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IEEE Access 2025年 13卷 89266-89286页

作者： Khan, Sheharyar Noorani, Sadam Hussain Frnda, Jaroslav Rauf, Usman Arsalan, Aamir Saeed, Sanay Muhammad Umar University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan HITEC University Department of Computer Science Taxila47050 Pakistan University of Žilina Faculty of Operation and Economics of Transport and Communications Department of Quantitative Methods and Economic Informatics Žilina01026 Slovakia Fatima Jinnah Women University Department of Software Engineering Rawalpindi46000 Pakistan

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals. We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies. © 2013 IEEE.

关键词： Noninvasive medical procedures

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Selection of a Simple 5G Handover Algorithm: Preparation for Named Data Networking 10

Selection of a Simple 5G Handover Algorithm: Preparation for...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Nurhayati, Ade Ahdan, Syaiful Juhana, Tutun Syambas, Nana Rachmana Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Telkom University Faculty of Applied Science Jakarta11710 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science 35132 Indonesia

ISBN: (纸本)9798350377057

Named Data Networking as an alternative network for 5G network traffic is required to be able to provide better performance compared to other networks such as internet protocol networks. In NDN wireless, it is known that there is producer mobility and consumer mobility, so optimal mobility management is needed, including the handover process. In this paper the author proposes a simple RSSI-based 5G handover algorithm with distance as a reference RSSI value. This algorithm is a preparation for the handover algorithm on the NDN wireless network. By using the assumed distance value MS to eNB as the basic value for calculating RSSI, handover determination is determined by comparing the RSSI value to the RSSI threshold value. With the scenario of different numbers of users and cluster sizes, from the handover process involving 8 eNB. It can be calculated how big the handover probability is for each user. From the simulation results, it can be concluded that the more users and the larger the cluster size, the greater the handover probability value. This is linear, with the wider the cluster, the probability of getting an RSSI signal from the original eNB becomes smaller, making it possible to get an RSSI signal from neighboring eNB, so that a handover occurs. © 2024 IEEE.

关键词： 5G mobile communication systems

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider 6

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Atmaja, Ardian Budi Kusuma Hidayat, Egi Muhammad Idris Riyanto Trilaksono, Bambang Siregar, Simon Habibullah, Natsir School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Telkom University Doctoral Program of Electrical Engineering Department Computer Engineering Faculty of Applied Science Indonesia

ISBN: (纸本)9798350358346

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees. © 2023 IEEE.

关键词： Geometry

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Modeling heterogeneous behaviors with different strategies in a terrorist attack

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Virtual Reality & Intelligent Hardware 2023年第4期5卷 351-365页

作者： Le BI Tingting LIU Zhen LIU Jason TEO Yumeng ZHAO Yanjie CHAI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Science&Technology Ningbo UniversityCixi 315300China Faculty of Computing&Informatics Universiti Malaysia SabahKota Kinabalu 88000Malaysia

Existing simulations of terrorist attacks do not consider individual *** overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist *** constructed an emotional model that integrated personality and visual perception for *** emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by *** demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

关键词： Terrorist attack simulation computer animation Big Five personality Intelligent decision making Heterogeneous behaviors

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