3 10 25 https://repository.horizon.ac.id/files/original/4ef77f5b6bee5466c7e515f6e22d2c3c.pdf 0d713543e8c89d36b98908bf2ee7d1f6 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 21, NO. 5 2023 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource TELKOMNIKA Telecommunication Computing Electronics and Control Amazon products reviews classification based on machine learning, deep learning methods and BERT Subject The topic of the resource Deep learning Feature extraction Machine learning Sentiment analysis Transformer technique Description An account of the resource In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1. Creator An entity primarily responsible for making the resource Saman Iftikhar, Bandar Alluhaybi, Mohammed Suliman, Ammar Saeed, Kiran Fatima Source A related resource from which the described resource is derived http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 16, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text deep learning Feature extraction Machine learning Jurnal Nasional Informatika Sentiment analysis Transformer technique https://repository.horizon.ac.id/files/original/3994989f1694ff27bf34feebea66a67f.pdf caec2a3e11fb4aad19d3586903efbdb1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 21, NO. 5 2023 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource TELKOMNIKA Telecommunication Computing Electronics and Control Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection Subject The topic of the resource Automatic channel selection Drug addiction Electroencephalography MLP with SFLA Shuffled frog leaping algorithm Description An account of the resource Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity. Creator An entity primarily responsible for making the resource Grace Mary Kanaga Edward, Angela Esther Rajakumar, Kumudha Raimond, Anitha Jeevanayagam Source A related resource from which the described resource is derived http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Apr 30, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Automatic channel selection Drug addiction electroencephalography Jurnal Nasional Informatika MLP with SFLA Shuffled frog leaping algorithm https://repository.horizon.ac.id/files/original/7553b799a296d090cf2a848111102688.pdf 1432fa15ede2505c695e21e929e644ba Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 21, NO. 5 2023 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource TELKOMNIKA Telecommunication Computing Electronics and Control Brief note on match and miss-match uncertainties Subject The topic of the resource Disturbance Linear systems Nonlinear systems Uncertainty Description An account of the resource The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization. Creator An entity primarily responsible for making the resource Muhammad Nizam Kamarudin, Sahazati Md Rozali, Mohd H. Hairi Muhammad I. Zakaria Source A related resource from which the described resource is derived http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jun 05, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Disturbance Jurnal Nasional Informatika Linear systems Nonlinear systems Uncertainty https://repository.horizon.ac.id/files/original/545b5ca9d0363ae8c74342850d79207b.pdf 9e598447117dd9b41368143414de0a23 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 21, NO. 5 2023 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource TELKOMNIKA Telecommunication Computing Electronics and Control Adaptive segmentation algorithm based on level set model in medical imaging Subject The topic of the resource Active contour Anisotropic diffusion Euler’s equation Medical image segmentation Variational level set Description An account of the resource For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works. Creator An entity primarily responsible for making the resource Boualem Mansouri, Abdelkader Khobzaoui, Mehdi Damou, Mohammed Chetioui, Abdelhakim Boudkhil Source A related resource from which the described resource is derived http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 16, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Active contour Anisotropic diffusion Euler’s equation Jurnal Nasional Informatika Medical image segmentation Variational level set https://repository.horizon.ac.id/files/original/2b474ef52a33862d082808a9e0274857.pdf 9d30da9082129dac644eb6462b5ffce5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 21, NO. 5 2023 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource TELKOMNIKA Telecommunication Computing Electronics and Control Improving the detection of intrusion in vehicular ad-hoc networks with modified identity-based cryptosystem Subject The topic of the resource Identity-based cryptosystem Intrusion On-board unit Road side unit VANET Description An account of the resource Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs. Creator An entity primarily responsible for making the resource Zachaeus K. Adeyemo, Emmanuel B. Ajulo, Damilare O. Akande, Hammed O. Lasisi, Samson I. Ojo Source A related resource from which the described resource is derived http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Apr 30, 2023 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text Identity-based cryptosystem Intrusion On-board unit Jurnal Nasional Informatika Road side unit VANET