1 10 25 https://repository.horizon.ac.id/files/original/5babc57701609bd3676b31348358268b.pdf 348debd6d251aeb2e9a31d653147c83a 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 A novel compact dual-band bandstop filter with enhanced rejection bands Subject The topic of the resource Bandstop filter Dual-band Microstrip technology Nonuniform transmission lines Wideband Description An account of the resource In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior. Creator An entity primarily responsible for making the resource Ayyoub El Berbri, Hassna Agoumi, Seddik Bri, Youssef El Amraoui, Adil Saadi 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 Ayyoub El Berbri1 , Hassna Agoumi1 , Seddik Bri2 , Youssef El Amraoui1,3, Adil Saadi1 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 Bandstop filter Dual-band Jurnal Nasional Informatika Microstrip technology Nonuniform transmission lines Wideband https://repository.horizon.ac.id/files/original/f6f1f240b7d0d3b23940d4d91d2996df.pdf fff2ec9098f7a5aae8d466a413dc88d7 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 A progressive learning for structural tolerance online sequential extreme learning machine Subject The topic of the resource Extreme learning machine Progressive learning Sequential learning Stable sequential learning Description An account of the resource This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately. Creator An entity primarily responsible for making the resource Sarutte Atsawaraungsuk, Wasaya Boonphairote, Kritsanapong Somsuk, Chanwit Suwannapong, Suchart Khummanee 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 Mar 25, 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 Extreme learning machine Jurnal Nasional Informatika Progressive learning Sequential learning Stable sequential learning 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/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/ae103b7a36c366402fe47bae6f5bc87c.pdf a108ec47db065674d74f2a72a6344abf 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 Big data cloud-based recommendation system using NLP techniques with machine and deep learning Subject The topic of the resource Artificial intelligence Big data Keras Natural language processing Recommendation system Description An account of the resource Recommendation systems (RS) are crucial for social networking sites. Without it, finding precise products is harder. However, existing systems lack adequate efficiency, especially with big data. This paper presents a prototype cloud-based recommendation system for processing big data. The proposed work is implemented by utilizing the matrix factorization method with three approaches. In the first approach, singular value decomposition (SVD) is used, which is an old and traditional recommendation technique. The second recommendation approach is fine-tuned using the alternating least squares (ALS) algorithm with Apache Spark. Finally, the deep neural network (DNN) algorithm is utilized with TensorFlow. This study solves the challenge of handling large-scale datasets in the collaborative filtering (CF) technique after tuning the algorithms by adjusting the parameters in the second approach, which uses machine learning, as well as in the third approach, which uses deep learning. Furthermore, the results of these two approaches outperformed conventional techniques and achieved an acceptable computational time. The dataset size is about 1.5 GB and it is collected from the Goodreads website API. Moreover, the Hadoop distributed file system (HDFS) is used as cloud storage instead of the computer’s local disk for handling larger dataset sizes in the future. Creator An entity primarily responsible for making the resource Hoger K. Omar, Mondher Frikha, Alaa Khalil Jumaa 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 May 01, 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 artificial intelligence Big data Keras Jurnal Nasional Informatika Natural language processing Recommendation system https://repository.horizon.ac.id/files/original/d07a602ad942ae1e1a3ff0cf7caf5f42.pdf 6659aa93ac5621046160e321c8feaae5 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 Blockchain-IoT supply chain: systematic literature review Subject The topic of the resource Authentication Blockchain IoT Supply chain Description An account of the resource Since 2016, the implementation of blockchain in the supply chain has attracted wide interest among researchers. The creation and study of blockchain technology have grown at an exponential rate in the last decade. The supply chain would be a massive technological leap with the combination of blockchain technology that brings major changes to different facets of the industry, such as record immutability, improved traceability, quality assurance, improved knowledge flows, decentralized power structure, and decreased chances of fraud. With the growth in blockchain Internet-of-Thing (IoT) supply chain technologies, the security risk of implementing such technologies and methods to counter it has been largely discussed among researchers. This study analyses peer-reviewed literature attempting to use authentication in the blockchain-IoT supply chain to counter security risks in the technologies and provides a comprehensive analysis of the important feature when creating an authentication technique for the blockchain-IoT supply chain. Finally, the study discusses some of the problems and research objectives in the realm of authentication in the blockchain-IoT supply chain. Creator An entity primarily responsible for making the resource Umi Nabilah Badrol Said, Mohd Rizuan Baharon, Mohd Zaki Mas’ud, Ariff Idris, Noor Azurati Ahmad Salleh 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 Authentication Blockchain IoT Jurnal Nasional Informatika Supply chain 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/fc283a09234b6c88474b89d9530e6c11.pdf cccc0178afeae2f9f4901f3031332fb7 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 Conceptual model of internet banking adoption with perceived risk and trust factors Subject The topic of the resource Internet banking Perceived risk Technology acceptance model Technology adoption Trust Description An account of the resource Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption. Creator An entity primarily responsible for making the resource Waleed A. Hammood, Afrig Aminuddin, Omar A. Hammood, Khairul Hafezad Abdullah, Davi Sofyan, Majid Rahardi 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 22, 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 Internet banking Jurnal Nasional Informatika Perceived risk Technology Acceptance Model Technology adoption trust https://repository.horizon.ac.id/files/original/861b8bedcc45cd36890fb05711a5b97e.pdf d571f339009b1f42093ad9dc8cb3dad9 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 Deep learning approach to DDoS attack with imbalanced data at the application layer Subject The topic of the resource ADASYN Application layer DDoS Deep learning LDAP SMOTE Description An account of the resource A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method. Creator An entity primarily responsible for making the resource Rahmad Gunawan, Hadhrami Ab Ghani, Nurulaqilla Khamis, Januar Al Amien, Edi Ismanto 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 Mar 25, 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 ADASYN Application layer DDoS deep learning Jurnal Nasional Informatika LDAP SMOTE