1 10 41 https://repository.horizon.ac.id/files/original/6488a97b525d23ee6f69539dd72ed740.pdf 3befe053e16627243a3fc2d8c027e0be 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. 19, NO. 3 2021 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 Design of compact microstrip bandpass filter using square DMS slots for Wi-Fi and bluetooth applications Subject The topic of the resource Band pass filter DMS Microstrip technology Rectangular resonator Description An account of the resource This paper presents the design of a compact bandpass filter based on two identical rectangular resonators and is implemented on microstrip technology for Wi-Fi and bluetoothapplications. To reduce the size of the filter, the defected microstrip structure (DMS) technique is proposed. This technique consists of etching slots in the rectangular resonator, which results in a change in the line properties and increase of the effective inductance and capacitance. This feature is used for miniaturization. The designed filter has a compact size (6.82x8.3) mm2 with a low insertion loss of -0.1 dB and a good return loss of -36 dB. The simulation results are realized using the (computer simulation technology) CST Microwave software. Creator An entity primarily responsible for making the resource A. Belmajdoub, M. Jorio, S. Bennani, A. Lakhssassi, M. Amzi Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Nov 25, 2020 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 Band pass filter DMS Jurnal Nasional Informatika Microstrip technology Rectangular resonator https://repository.horizon.ac.id/files/original/132135abc547f0739ae748216c4c37ca.pdf 74fc94202c2967bb20261202ea202309 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. 19, NO. 3 2021 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 fingerprint classification network Subject The topic of the resource Classification Deep learning Fingerprint Description An account of the resource Fingerprint is one of the most well-known biometrics that has been used for personal recognition. However, faked fingerprints have become the major enemy where they threat the security of this biometric. This paper proposes an efficient deep fingerprint classification network (DFCN) model to achieve accurate performances of classifying between real and fake fingerprints. This model has extensively evaluated or examined parameters. Total of 512 images from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved high classification performance of 99.22%, where fingerprint images are successfully classified into their two categories. Moreover, comparisons with state-of-art approaches are provided. Creator An entity primarily responsible for making the resource Abdulsattar M. Ibrahim, Abdulrahman K. Eesee, Raid Rafi Omar Al-Nima Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Nov 25, 2020 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 classification deep learning Fingerprint Jurnal Nasional Informatika https://repository.horizon.ac.id/files/original/ff56b3451e7a5fec7a51fdd4681c197f.pdf 7f42a951cd5600c9e876c3a0837d3577 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. 19, NO. 3 2021 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 Unidirectional-bidirectional recurrent networks for cardiac disorders classification Subject The topic of the resource Bidirectional Gated recurrent unit Long short-term memory Recurrent neural networks Unidirectional Description An account of the resource The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study. The unidirectional and bidirectional for each cardiac disorder (CDs) class is also compared. Comparing both phases is needed to figure out the optimum phase and the best model performance for ECG using the Physionet dataset to classify five classes of CDs with 15 leads ECG signals. The result shows that the bidirectional RNNs method produces better results than the unidirectional method. In contrast to RNNs, the unidirectional LSTM and GRU outperformed the bidirectional phase. The best recurrent network classifier performance is unidirectional GRU with average accuracy, sensitivity, specificity, precision, and F1-score of 98.50%, 95.54%, 98.42%, 89.93%, 92.31%, respectively. Overall, deep learning is a promising improved method for ECG classification. Creator An entity primarily responsible for making the resource Annisa Darmawahyuni, Siti Nurmaini, Muhammad Naufal Rachmatullah, Firdaus Firdaus, Bambang Tutuko Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Nov 25, 2020 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 Bidirectional Gated recurrent unit Jurnal Nasional Informatika Long short-term memory recurrent neural networks Unidirectional https://repository.horizon.ac.id/files/original/323bb46e2b5c1650d564d05391a14723.pdf fe51915ee8523665c8b197ea9c99267f 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. 19, NO. 3 2021 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 comprehensive review of vehicle detection using computer vision Subject The topic of the resource Computer vision GMM Intelligent transport system KDE Vehicle detection Description An account of the resource A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the tech- niques are also highlighted with a conclusion as to the most accurate one for vehicle detection. Creator An entity primarily responsible for making the resource Aymen Fadhil Abbas, Usman Ullah Sheikh, Fahad Taha AL-Dhief, Mohd Norzali Haji Mohd Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Dec 3, 2020 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 Computer Vision GMM Intelligent transport system Jurnal Nasional Informatika KDE Vehicle detection https://repository.horizon.ac.id/files/original/ea8c29181d442fa639ecfb286d0b211a.pdf 4aaead6fd146f29389889b16d8005bf6 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. 19, NO. 3 2021 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 Dialogue management using reinforcement learning Subject The topic of the resource Dialogue management Human-robot interaction Knowledge growing Reinforcement learning Description An account of the resource Dialogue has been widely used for verbal communication between human and robot interaction, such as assistant robot in hospital. However, this robot was usually limited by predetermined dialogue, so it will be difficult to understand new words for new desired goal. In this paper, we discussed conversation in Indonesian on entertainment, motivation, emergency, and helping with knowledge growing method. We provided mp3 audio for music, fairy tale, comedy request, and motivation. The execution time for this request was 3.74 ms on average. In emergency situation, patient able to ask robot to call the nurse. Robot will record complaint of pain and inform nurse. From 7 emergency reports, all complaints were successfully saved on database. In helping conversation, robot will walk to pick up belongings of patient. Once the robot did not understand with patient’s conversation, robot will ask until it understands. From asking conversation, knowledge expands from 2 to 10, with learning execution from 1405 ms to 3490 ms. SARSA was faster towards steady state because of higher cumulative rewards. Q-learning and SARSA were achieved desired object within 200 episodes. It concludes that reinforcement learning (RL) method to overcome robot knowledge limitation in achieving new dialogue goal for patient assistant were achieved. Creator An entity primarily responsible for making the resource Binashir Rofi’ah, Hanif Fakhrurroja, Carmadi Machbub Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Oct 14, 2020 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 Dialogue management Human-robot interaction Jurnal Nasional Informatika Knowledge growing Reinforcement learning https://repository.horizon.ac.id/files/original/b779f08c13c98646d0f9f3c5bfe65c97.pdf cc5349e1824612b8ac669c40659388a2 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. 19, NO. 3 2021 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 Parallel classification and optimization of telco trouble ticket dataset Subject The topic of the resource Classification Hadoop Optimization Spark Trouble ticket Description An account of the resource In the big data age, extracting applicable information using traditional machine learning methodology is very challenging. This problem emerges from the restricted design of existing traditional machine learning algorithms, which do not entirely support large datasets and distributed processing. The large volume of data nowadays demands an efficient method of building machine- learning classifiers to classify big data. New research is proposed to solve problems by converting traditional machine learning classification into a parallel capable. Apache Spark is recommended as the primary data processing framework for the research activities. The dataset used in this research is related to the telco trouble ticket, identified as one of the large volume datasets. The study aims to solve the data classification problem in a single machine using traditional classifiers such as W-J48. The proposed solution is to enable a conventional classifier to execute the classification method using big data platforms such as Hadoop. This study’s significant contribution is the output matrix evaluation, such as accuracy and computational time taken from both ways resulting from hyper-parameter tuning and improvement of W-J48 classification accuracy for the telco trouble ticket dataset. Additional optimization and estimation techniques have been incorporated into the study, such as grid search and cross-validation method, which significantly improves classification accuracy by 22.62% and reduces the classification time by 21.1% in parallel execution inside the big data environment. Creator An entity primarily responsible for making the resource Fauzy Che Yayah, Khairil Imran Ghauth, Choo-Yee Ting Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Sep 20, 2020 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 classification Hadoop Jurnal Nasional Informatika Optimization Spark Trouble ticket https://repository.horizon.ac.id/files/original/63e7f87d59254ed803b4d0a3dcb7b6ae.pdf 38c90b2aae34024164a99ad49ca8e718 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. 19, NO. 3 2021 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 Author identification in bibliographic data using deep neural networks Subject The topic of the resource Author name disambiguation Bibliographic data Deep neural networks Homonym Synonym Description An account of the resource Author name disambiguation (AND) is a challenging task for scholars who mine bibliographic information for scientific knowledge. A constructive approach for resolving name ambiguity is to use computer algorithms to identify author names. Some algorithm-based disambiguation methods have been developed by computer and data scientists. Among them, supervised machine learning has been stated to produce decent to very accurate disambiguation results. This paper presents a combination of principal component analysis (PCA) as a feature reduction and deep neural networks (DNNs), as a supervised algorithm for classifying AND problems. The raw data is grouped into four classes, i.e., synonyms, homonyms, homonyms- synonyms, and non-homonyms-synonyms classification. We have taken into account several hyperparameters tuning, such as learning rate, batch size, number of the neuron and hidden units, and analyzed their impact on the accuracy of results. To the best of our knowledge, there are no previous studies with such a scheme. The proposed DNNs are validated with other ML techniques such as Naïve Bayes, random forest (RF), and support vector machine (SVM) to produce a good classifier. By exploring the result in all data, our proposed DNNs classifier has an outperformed other ML technique, with accuracy, precision, recall, and F1-score, which is 99.98%, 97.98%, 97.86%, and 99.99%, respectively. In the future, this approach can be easily extended to any dataset and any bibliographic records provider. Creator An entity primarily responsible for making the resource Firdaus, Siti Nurmaini, Reza Firsandaya Malik, Annisa Darmawahyuni, Muhammad Naufal Rachmatullah, Andre Herviant Juliano, Tio Artha Nugraha, Varindo Ockta Keneddi Putra Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Aug 31, 2020 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 Author name disambiguation Bibliographic data Deep neural networks Homonym Jurnal Nasional Informatika Synonym https://repository.horizon.ac.id/files/original/20147de9b6c9f66f8d1a9798f393dae1.pdf 7c4c11402b10deeb1f08483104c69e6e 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. 19, NO. 3 2021 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 Particle swarm optimization for solving thesis defense timetabling problem Subject The topic of the resource Optimization Particle swarm optimization Scheduling Timetabling Description An account of the resource The thesis defense timetabling problem is a fascinating and original NP-hard optimization problem. The problem involves assigning the participants to defense sessions, composing the relevant committees, satisfying the constraints, and optimizing the objectives. This study defines the problem formulation that applies to Universitas Multimedia Nusantara (UMN) and use the particle swarm optimization (PSO) algorithm to solve it. As a demonstration of concept and viability, the proposed method is implemented in a web-based platform using Python and Flask. The implementation is tested and evaluated using real-world instances. The results show that the fastest timetable generation is 0.18 seconds, and the slowest is 21.88 minutes for 25 students and 18 department members, without any violation of the hard constraints. The overall score of the EUCS evaluation for the application is 4.3 out of 6. Creator An entity primarily responsible for making the resource Gilbert Christopher, Arya Wicaksana Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Nov 25, 2020 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 Jurnal Nasional Informatika Optimization Particle Swarm Optimization Scheduling Timetabling https://repository.horizon.ac.id/files/original/ae58de5584f71c74b556d0f37a627e61.pdf 32e1511700283c8220b11a9a170d7748 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. 19, NO. 3 2021 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 comparative analysis of automatic deep neural networks for image retrieval Subject The topic of the resource Content-based image retrieval Convolutional neural networks Image classification Image retrieval deep learning Description An account of the resource Feature descriptor and similarity measures are the two core components in content-based image retrieval and crucial issues due to “semantic gap” between human conceptual meaning and a machine low-level feature. Recently, deep learning techniques have shown a great interest in image recognition especially in extracting features information about the images. In this paper, we investigated, compared, and evaluated different deep convolutional neural networks and their applications for image classification and automatic image retrieval. The approaches are: simple convolutional neural network, AlexNet, GoogleNet, ResNet-50, Vgg-16, and Vgg-19. We compared the performance of the different approaches to prior works in this domain by using known accuracy metrics and analyzed the differences between the approaches. The performances of these approaches are investigated using public image datasets corel 1K, corel 10K, and Caltech 256. Hence, we deduced that GoogleNet approach yields the best overall results. In addition, we investigated and compared different similarity measures. Based on exhausted mentioned investigations, we developed a novel algorithm for image retrieval. Creator An entity primarily responsible for making the resource Hanan A. Al-Jubouri, Sawsan M. Mahmmod Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Sep 20, 2020 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 Content-based image retrieval convolutional neural networks Image classification Image retrieval deep learning Jurnal Nasional Informatika https://repository.horizon.ac.id/files/original/cc6d16850aa839235c2d56a5d0831439.pdf ed7a1af67ed46badc2d3f527ec6cf848 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. 19, NO. 3 2021 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 Overlapped hierarchical clusters routing protocol for improving quality of service Subject The topic of the resource Average delay Energy consumption Quality of service Routing protocol Wireless sensor network Description An account of the resource The rapid development in communications and sensors technologies make wireless sensor networks (WSNs) as essential key in several advanced applications such as internet of things (IoT). The increasing demands on using WSNs required high quality of services (QoS) because most WSNs applications have critical requirements. This work aims to offer a routing protocol to improve the QoS in WSNs, taking in consideration its ability to prolong the lifetime of the network, optimize the utilization of the limited bandwidth available, and decrease the latency that accompanies the packets transmitted to the gateway. The proposed protocol is called overlapped hierarchical cluster routing protocol (OHCRP). OHCRP is compared with the traditional routing protocols such as SPEED, and Two-hop velocity-based routing protocol (THVR). The results show that OHCRP reduces latency effectively and achieve high energy conservation, which lead to increase the network lifetime and insure network availability. Creator An entity primarily responsible for making the resource Hayder Fakher Jassim, Mohammed A. Tawfeeq, Sawsan M. Mahmoud Source A related resource from which the described resource is derived http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Oct 23, 2020 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 Average delay Energy consumption Jurnal Nasional Informatika Quality of service Routing protocol Wireless sensor network