1 10 39 https://repository.horizon.ac.id/files/original/8119f237fd0a70b9e28a681bb3ba3175.pdf 6f373ecee150d54fbe352dbd92d8d286 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. 4 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 Online traffic classification for malicious flows using efficient machine learning techniques Subject The topic of the resource Machine learning Malicious traffic flows Online classification Snort alerts Statistical features Description An account of the resource The rapid network technology growth causing various network problems, attacks are becoming more sophisticated than defenses. In this paper, we proposed traffic classification by using machine learning technique, and statistical flow features such as five tuples for the training dataset. A rule- based system, Snort is used to identify the severe harmfulness data packets and reduce the training set dimensionality to a manageable size. Comparison of performance between training dataset that consists of all priorities malicious flows with only has priority 1 malicious flows are done. Different machine learning (ML) algorithms performance in terms of accuracy and efficiency are analyzed. Results show that Naïve Bayes achieved accuracy up to 99.82% for all priorities while 99.92% for extracted priority 1 of malicious flows training dataset in 0.06 seconds and be chosen to classify traffic in real-time process. It is demonstrated that by taking just five tuples information as features and using Snort alert information to extract only important flows and reduce size of dataset is actually comprehensive enough to supply a classifier Creator An entity primarily responsible for making the resource Ying Yenn Chan, Ismahani Bt Ismail, Ban Mohammed Khammas 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 Mar 20, 2021 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 machine learning Malicious traffic flows Online classification Snort alerts Statistical features https://repository.horizon.ac.id/files/original/69324be475386aecdef21aca23ff59ef.pdf 1d3d86848a843368967dc70cdf5d3e14 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. 4 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 High performance binary LDPC-coded OFDM systems over indoor PLC channels Subject The topic of the resource Bit error rate Impulse noise LDPC codes OFDM PLC channel Description An account of the resource Power line communication (PLC) technology is actually among the most renowned technologies for home environments due to their low-cost installation opportunities. In this study, the bit error rate (BER) performances of binary low-density parity check (LDPC) coded orthogonal frequency- division multiplexing (OFDM) systems have been considered over indoor PLC channels. Performances comparison of diverse soft and hard decision LDPC decoder schemes such as Min-Sum (MS), weighted bit flipping (WBF), gradient descent bit-flip (GDBF), noisy gradient descent bit-flip (NGDBF) and its few variants including the single-bit NGDBF (S-NGDBF), multi-bit NGDBF (M-NGDBF) and smoothed-multi-bit NGDBF (SM-NGDBF) decoders were examined in the modeled network. To evaluate the BER performance analyses three different PLC channel scenarios were generated by using new and more realistic PLC channel model proposal were also employed. All of the simulations performed in Canete’s PLC channel model showed that remarkable performance improvement can be achieved by using short-length LDPC codes. Especially, the improvements are striking when the MS or SM-NGDBF decoding algorithms are employed on the receiver side. Creator An entity primarily responsible for making the resource Nejwa El Maammar, Seddik Bri, Jaouad Foshi, Mohammed Amine Ihedrane , El Fadl Adiba 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 Apr 10, 2021 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 Bit error rate Impulse noise Jurnal Nasional Informatika LDPC codes OFDM PLC channel https://repository.horizon.ac.id/files/original/23f96b56b770abc5f7d3368c167e695b.pdf 58f59ea8236e410749bd6938ebd99b0c 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. 4 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 Effect of random sampling on spectrum sensing for cognitive radio networks Subject The topic of the resource Cognitive radio Energy detector Maximum eigenvalue Random sampling Spectrum sensing Description An account of the resource Cognitive radio is a mechanism allowing dynamic access to spectrum channels. Since its beginnings, researchers have been working on using this inventive technology to control and manage the spectrum resources. Consequently, this research field has been progressing rapidly and important advances have been made. Spectrum sensing is a key function of cognitive radios that helps prevent the harmful interference with licensed users, as well as identifies the available spectrum to improve its utilization. Several spectrum sensing techniques are found in scientific literature. In this paper, we investigate the effect of the random sampling in spectrum sensing. We propose a spectrum sensing approach based on the energy detection and on the maximum eigenvalue detection (MED) combined with random sampling. The performance of the proposed approach is evaluated in terms of the receiver operating characteristics curves and in terms of the detection probability for different values of signal to noise ratio. The obtained results are compared to the uniform sampling case to show the added value of random sampling. Creator An entity primarily responsible for making the resource Asmaa Maali, Hayat Semlali, Sara Laafar, Najib Boumaaz, Abdallah Soulmani 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 Jan 20, 2021 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 Cognitive radio Energy detector Jurnal Nasional Informatika Maximum eigenvalue Random sampling Spectrum sensing https://repository.horizon.ac.id/files/original/b53243eb9573f56f670caec01edd5d7c.pdf 27cb43fedd71214e0426ca390d2f5d06 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. 4 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 Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer Subject The topic of the resource Fuzzy C-means Fuzzy RBFNN Prostate cancer Singular value decomposition Description An account of the resource In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level co- occurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis. Creator An entity primarily responsible for making the resource Agus Maman Abadi, Dhoriva Urwatul Wutsqa, Nurlia Ningsih 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 Apr 9, 2021 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 Fuzzy C-Means Fuzzy RBFNN Jurnal Nasional Informatika prostate cancer Singular value decomposition https://repository.horizon.ac.id/files/original/7142213b3fcd6afcb9b5b4fc66208181.pdf 02c191d3a18fed32b5a378a4f74eb7eb 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. 4 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 Enhancing text classification performance by preprocessing misspelled words in Indonesian language Subject The topic of the resource Indonesian language Levenshtein distance Text classification Typo correction User feedback Description An account of the resource Supervised learning using shallow machine learning methods is still a popular method in processing text, despite the rapidly advancing sector of unsupervised methodologies using deep learning. Supervised text classification for application user feedback sentiments in Indonesian Language is one of the applications which is quite popular in both the research community and industry. However, due to the nature of shallow machine learning approaches, various text preprocessing techniques are required to clean the input data. This research aims to implement and evaluate the role of Levenshtein distance algorithm in detecting and preprocessing misspelled words in Indonesian language, before the text data is then used to train a user feedback sentiment classification model using multinomial Naïve Bayes. This research experimented with various evaluation scenarios, and found that preprocessing misspelled words in Indonesian language using the Levenshtein distance algorithm could be useful and showed a promising 8.2% increase on the accuracy of the model’s ability to classify user feedback text according to their sentiments. Creator An entity primarily responsible for making the resource Reza Setiabudi, Ni Made Satvika Iswari, Andre Rusli Date A point or period of time associated with an event in the lifecycle of the resource Jan 20, 2021 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 Indonesian language Jurnal Nasional Informatika Levenshtein distance text classification Typo correction User feedback https://repository.horizon.ac.id/files/original/91ad7ec3a2544a66639f2459ba725665.pdf 3a9869e701db15a870bc4abbdced2f24 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. 4 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 An optimized power allocation algorithm for cognitive radio NOMA communication Subject The topic of the resource Cognitive radio Interweave sharing NOMA Successive-interference cancellation Superposition-coding Underlay sharing Description An account of the resource The primary objective of cognitive radio network is to effectively utilize the unused spectrum bands. In cognitive radio networks, spectrum sharing between primary and secondary users is accomplished using either underlay or interweave cognitive radio approach. Non orthogonal multiple access (NOMA) is the proven technology in the present wireless developments, which allows the coexistence of multiple users in the same orthogonal block. The new paradigm cognitive radio NOMA (CR-NOMA) is one of the potential solutions to fulfill the demands of future wireless communication. This paper emphasizes on practical implementation of NOMA in cognitive radio networks to enhance the spectral efficiency. The goal is to increase the throughput of the secondary users satisfying the quality of service (QOS) requirements of primary users. To achieve this, we have presented the optimized power allocation strategy for underlay downlink scenario to support the simultaneous transmission of primary and secondary users. Furthermore, we have proposed QOS based power allocation scheme for CR-NOMA interweave model to support the coexistence of multiple secondary networks. Also, the changes adopted in implementing superposition coding (SC) and successive interference cancellation (SIC) for CR-NOMA are highlighted. Finally, simulation results validate the mathematical expressions that are derived for power allocation coefficient and outage probability. Creator An entity primarily responsible for making the resource Madan H. T., P. I. Basarkod 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 Mar 17, 2021 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 Cognitive radio Interweave sharing Jurnal Nasional Informatika NOMA Successive-interference cancellation Superposition-coding Underlay sharing https://repository.horizon.ac.id/files/original/31678465eed970e77fb3480b6d2c19c5.pdf 3d67ce6fe8cb15c2e52dcfe92afadc59 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. 4 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 machine learning approach for the recognition of melanoma skin cancer on macroscopic images Subject The topic of the resource Artificial intelligence Image processing Machine learning Melanoma Skin cancer Description An account of the resource In the last years, computer vision systems for the detection of skin cancer have been proposed, especially using machine learning techniques for the classification of the disease and features based on the ABCD dermatology criterion, which gives infor- mation on the status of the skin lesion based on static properties such as geometry, color, and texture, making it an appropriate criterion for medical diagnosis systems that work through images. This paper proposes a novel skin cancer classification sys- tem that works on images taken from a standard camera and studies the impact on the results of the smoothed bootstrapping, which was used to augment the original dataset. Eight classifiers with different topologies (KNN, ANN, and SVM) were com- pared, with and without data augmentation, showing that the classifier with the highest performance as well as the most balanced one was the ANN with data augmentation, achieving an AUC of 87.1%, which saw an improvement from an AUC of 84.3% of the ANN trained with the original dataset. Creator An entity primarily responsible for making the resource Jairo Hurtado, Francisco Reales 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 Jan 20, 2021 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 image processing Jurnal Nasional Informatika machine learning Melanoma Skin cancer https://repository.horizon.ac.id/files/original/9b3263aa65cb3e2eddf09cc4b04ae45a.pdf 761790416c9cc379df1029c5874a7876 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. 4 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 An automatic screening approach for obstructive sleep apnea from photoplethysmograph using machine learning techniques Subject The topic of the resource Multivariate regression Obstructive sleep apnea Photoplethysmogram Random forest Support vector machine Univariate regression Description An account of the resource Obstructive sleep apnea (OSA), a very common sleep disorder remains as an underdiagnosed root cause for several cardiovascular and cerebrovascular diseases. In this paper, we propose an efficient and accurate system that utilizes a single sensor for effective screening of OSA using machine learning algorithms. The automatic screening system involves a photoplethysmogram (PPG) signal, a novel algorithm to detect and remove the corrupted part of the signal, a feature extraction module to extract several features from the PPG waveform and a classifier module which helps in screening for OSA. The elemental idea behind this work is that there is a characteristic relationship between the shape of the PPG waveform and the oxygen desaturation in the apnea patients. The method as described was tested on 285 subjects, inclusive of both normal and apnea patients, and the results were obtained after 10-fold-cross validation of the different machine learning techniques viz., univariate regression, multivariate regression, support vector machine and random forest. The best results in screening OSA were obtained from random forest algorithm with the highest performance (Acc: 98.0%, Sen: 98.6%, Spec: 99.3%) for all the combined features. The proposed work is an effective system for automatic screening of OSA from a single PPG sensor, thereby reducing the need for a very expensive and overnight polysomnography sleep study. Creator An entity primarily responsible for making the resource Smily Jeya Jothi E., Anitha J., Jude Hemanth 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 Mar 30, 2021 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 Multivariate regression Obstructive sleep apnea Photoplethysmogram Random Forest Support Vector Machine Univariate regression https://repository.horizon.ac.id/files/original/fa3f72b0366e7581097d5fb312b5ae36.pdf b77768e7dfef871931fd392e9006b3b7 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. 4 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 Dynamic mobile anchor path planning for underwater wireless sensor networks Subject The topic of the resource Localization algorithm Mobile anchor Mobile-assistant localization Path planning UWSN Description An account of the resource In an underwater wireless sensor network (UWSN), the location of the sensor nodes plays a significant role in the localization process. The location information is obtained by using the known positions of anchor nodes. For underwater environments, instead of using various static anchor nodes, mobile anchor nodes are more efficient and cost-effective to cover the monitoring area. Nevertheless, the utilization of these mobile anchors requires adequate path planning strategy. Mzost of the path planning algorithms do not consider irregular deployment, caused by the effects of water currents. Consequently, this leads towards the inefficient energy consumption by mobile anchors due to unnecessary transmission of beacon messages at unnecessary areas. Therefore, an efficient dynamic mobile path planning (EDMPP) algorithm to tackle the irregular deployment and non-collinear virtual beacon point placement, targeting the underwater environment settings is presented in this paper. In addition, EDMPP controls the redundant beacon message deployment and overlapping, for beacon message distribution in mobile assistant localization. The simulation results show that the performance of the EDMPP is improved by increasing the localization accuracy and decreasing the energy consumption with optimum path length. Creator An entity primarily responsible for making the resource Mohammad Hafiz Mohamed, Kamalrulnizam Abu Bakar, Ismail Fauzi Isnin, M. Zulkifli M. Zaki, Herman, Fatima Tul Zuhra 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 Mar 17, 2021 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 Localization algorithm Mobile anchor Mobile-assistant localization Path planning UWSN https://repository.horizon.ac.id/files/original/e991c73fb86d483e52bf352bcc166c06.pdf b7ee1639fa09aefcdee3b4bb6f289193 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. 4 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 An adaptive IoT architecture using combination of concept-drift and dynamic software product line engineering Subject The topic of the resource Adaptive Concept drift Internet of things Software architecture Software product line Description An account of the resource Internet of things (IoT) architecture needs to adapt autonomously to the environment and operational to maintain their supreme services. One common problem in the IoT architecture is to manage the reliability of data services, such as sensors’ data, that only sending data to the collector via gateway. If there is a disruption of services, then it is not easy to manage the system reliability. To this, an adaptive environment which is based on software reconfiguration creates a great challenge to provide better services. In this work, the software product line engineering (SPLE) reconfigures the edge devices via rules and software architecture. To identify disruption of data services which can be detected based on anomaly and truncated data. Our work makes use of concept drift to provide a recommendation to the system manager. This is important to avoid misconfiguration in the system We demonstrate our method using an open-source internet of things portal system that integrated to a cluster of sensors which is attached to specific gateway before the data are collected into a cloud storage for further processes. In identifying drifting data, the adaptive sliding window (ADWIN) method outperforms the Page-Hinkley (PH) with more selective identification and sensitive reading. Creator An entity primarily responsible for making the resource I Made Murwantara, Pujianto Yugopuspito 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 Jan 31, 2021 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 Adaptive Concept drift Internet of Things Jurnal Nasional Informatika Software architecture Software product line