1 10 39 https://repository.horizon.ac.id/files/original/43f58fa2e791e7480fb943d27baeeb38.pdf 155138c049343a8e25d6e7c021a12aab 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 detailed review of blockchain-based applications fo protection against pandemic like COVID-19 Subject The topic of the resource Blockchain COVID-19 Electronic health record Healthcare Privacy Security Description An account of the resource The recent corona virus disease (COVID-19) pandemic has brought the issues of technological deficiencies and challenges of security and privacy, validating and maintaining anonymity, user control over records while fully utilizing the available records etc., that can be encountered in an emergency or pandemic condition. Blockchain technology has evolved as a promising solution in conditions that necessitate immutability, record integrity, and proper records authentication. Blockchain can effectively resolve the technical barriers and effectively utilize the available resources and infrastructure in pandemic situations like the current COVID-19. This paper provides an extensive review of various possible use cases of blockchain and available solutions for protection against the COVID-19 like situation. It gives an insight into the benefits and shortcomings of available solutions. It further provides the issues and challenges of adopting blockchain in a situation like COVID-19 and suggest future directions that can offer a platform for further improved and better solutions. Creator An entity primarily responsible for making the resource Mousa Mohammed Khubrani, Shadab Alam 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 11, 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 Blockchain COVID-19 Electronic health record Healthcare Jurnal Nasional Informatika Privacy security 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/d3bf7427925ab2d0dfb9268e54d5373f.pdf c97cdd216233ccbb282e4cb2022a0a73 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 numerical-analytical iterative method for solving an electrical oscillator equation Subject The topic of the resource Computational method Numerical solution Numerical-analytical method Oscillation problem Successive approximation Van der Pol equation Description An account of the resource Self-excited oscillation problem occurring from a triode electrical circuit has been modelled by van der Pol. Until now, the exact solution to the van der Pol equation is not available. This paper focuses on finding a new method for solving the van der Pol equation simply and accurately. There exists several approximate iterative methods available in the literature for solving the van der Pol equation, such as, the successive approximation method. The successive approximation method is simple, but inaccurate for large time values. In this paper, we propose a new variant of numerical-analytical method, which is simple but accurate, for solving the van der Pol equation. Our new variant of numerical-analytical method solves the van der Pol equation from its equivalent system of first order ordinary differential equations. Our strategy leads to a simple implementation of the numerical- analytical method in the multistage way. Furthermore, computational experiments show that our proposed method is accurate for large sizes of time domain in solving the van der Pol equation. Creator An entity primarily responsible for making the resource Sudi Mungkasi, Damar Widjaja 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 Computational method Jurnal Nasional Informatika Numerical solution Numerical-analytical method Oscillation problem Successive approximation Van der Pol equation 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 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/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/e76a3b418826b9efea40ad112544f8a2.pdf 8088bce77813a7155b967f16cfd0a06e 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 Analysis of hybrid non-linear autoregressive neural network and local smoothing technique for bandwidth slice forecast Subject The topic of the resource Autoregressive neural network Bandwidth slice Forecast Local smoothing Description An account of the resource The demand for high steady state network traffic utilization is growing exponentially. Therefore, traffic forecasting has become essential for powering greedy application and services such as the internet of things (IoT) and Big data for 5G networks for better resource planning, allocation, and optimization. The accuracy of forecasting modeling has become crucial for fundamental network operations such as routing management, congestion management, and to guarantee quality of service overall. In this paper, a hybrid network forecast model was analyzed; the model combines a non-linear auto regressive neural network (NARNN) and various smoothing techniques, namely, local regression (LOESS), moving average, locally weighted scatterplot smoothing (LOWESS), the Sgolay filter, Robyn loess (RLOESS), and robust locally weighted scatterplot smoothing (RLOWESS). The effects of applying smoothing techniques with varied smoothing windows were shown and the performance of the hybrid NARNN and smoothing techniques discussed. The results show that the hybrid model can effectively be used to enhance forecasting performance in terms of forecasting accuracy, with the assistance of the smoothing techniques, which minimized data losses. In this work, root mean square error (RMSE) is used as performance measures and the results were verified via statistical significance tests. Creator An entity primarily responsible for making the resource Mohamed Khalafalla Hassan, Sharifah H. S. Ariffin, Sharifah Kamilah Syed-Yusof, N. Effiyana Ghazali, Mohammed EA Kanona 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 21, 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 Autoregressive neural network Bandwidth slice Forecast Jurnal Nasional Informatika Local smoothing https://repository.horizon.ac.id/files/original/d9c63ce41ae4cb05649b0720f378d9e1.pdf 0b2bb8c9a4377b38f64367b9e3e6915f 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 Application of big data for distribution and consumption of power Subject The topic of the resource Analytics Big data Electricity Energy Power consumption Description An account of the resource The exponentially growing and tremendous collection of data stored in the power sector, combined with the need for data analysis, has produced an urgent need for powerful tools to extract hidden data as to effectively distribute the power for proper consumption for the household. This research work was embarked on to show the business value of big data analytics in Energy and utilities with a focus on how analytics can help solve problems of inefficiency and wastages in electricity generation, production and distribution and how raw energy datasets can be converted into insights that can be used by energy policy makers to make major business decisions. To explicitly show how raw data can be turned into insights, the study deploys the use of the Hadoop on Hortonworks’ open-source apache-Hive licensed data warehousing framework run on a windows operating system to turn raw datasets (in excel formats converted to .csv format) gotten from the prepaid meters of 196,000 consumers (households and businesses) in 11 business units of Ikeja Electricity Distribution Company (IKEDC, Nigeria) to analyze the distribution and consumption of power. Creator An entity primarily responsible for making the resource Olagunju Mukaila, Adeniyi Abidemi Emmanuel, Ogundokun Roseline Oluwaseun, Ojo Olufemi Samuel, Kolawole Paul Oluwatoba 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 May 5, 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 Analytics Big data Electricity Energy Jurnal Nasional Informatika power consumption https://repository.horizon.ac.id/files/original/51a742bdbed13bb22aaee5c36637f378.pdf 893d0961fc96822b29a14441ddc9a768 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 Characterization and structural analysis of RF magnetron sputtered strontium stannate thin films Subject The topic of the resource Perovskite RF magnetron sputtering SrSnO3 Strontium Stannate Thin Film Description An account of the resource This paper presents physical and morphology properties of strontium stannate (SrSnO3) perovskite-type as a candidate of an n-type material thin film for organic-inorganic hybrid diode heterojunction for optoelectronics application. Typical wet-process of SrSnO3 deposition produce thick film and having 10-8 S/cm order in conductivity. The SrSnO3 thin films were deposited on ITO glass substrates by RF magnetron sputtering using a purity 99.9% SrSnO3 target with 5.0 mTorr of gas pressure and 100 W of RF power at room temperature. The gas composition of pure argon (75%) and reactive oxygen gas (25%) was used for 60 min. XRD diffraction patterns revealed that the thin films are orthorhombic crystal structure with lattice parameter a=5.7040 Ǻ, b=8.06 Ǻ and c=5.7080 Ǻ with a strong orientation in the (002) direction. SEM images showed that films exhibited uniform surface morphology with a roughness average of Ra=2.258 nm and thickness of 311 nm. The EDX spectrum confirmed the presence of O, Sr, and Sn elements in the films with 75.22%, 8.29%, 16.49% in atomic number, respectively. The films were having a conductivity of 8.33102 S/cm with low resistivity of 12.410-3 -cm. Creator An entity primarily responsible for making the resource Yusmar Palapa Wijaya, Khairul Anuar Mohamad, Abu Bakar Abdul Rahman, Afishah Alias, Mohammad Syahmi Nordin 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 Jurnal Nasional Informatika Perovskite RF magnetron sputtering SrSnO3 Strontium Stannate Thin Film https://repository.horizon.ac.id/files/original/c3048e8e16c9021b9b438ef82ac7e476.pdf 64c97780d328cd632d5e168a4efdf9bb 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 Classification using semantic feature and machine learning: Land-use case application Subject The topic of the resource Convolutional neural networks Feature extraction Land-use classification Machine learning Description An account of the resource Land cover classification has interested recent works especially for deforestation, urban are monitoring and agricultural land use. Traditional classification approaches have limited accuracy especially for non- heterogeneous land cover. Thus, using machine may improve the classification accuracy. The presented paper deals with the land-use scene recognition on very high-resolution remote sensing imagery. We proposed a new framework based on semantic features, handcrafted features and machine learning classifiers decisions. The method starts by semantic feature extraction using a convolutional neural network. Handcraft features are also extracted based on color and multi-resolution characteristics. Then, the classification stage is processed by three learning machine algorithms. The final classification result performed by majority vote algorithm. The idea behind is to take advantages from semantic features and handcrafted features. The second scope is to use the decision fusion to enhance the classification result. Experimentation results show that the proposed method provides good accuracy and trustable tool for land use image identification. Creator An entity primarily responsible for making the resource Hela Elmannai, Abeer Dhafer Al-Garni 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 Feb 26, 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 convolutional neural networks Feature extraction Jurnal Nasional Informatika Land-use classification machine learning