1 10 7 https://repository.horizon.ac.id/files/original/51a7944562487717f912986d42967966.png 0374c59a31eb525bda02b357ac0a0e28 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. 15 NO. 1 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. 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 Cover Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 https://repository.horizon.ac.id/files/original/727f2e9f6509a7ff0f8b95309cee9773.pdf f0878336238d668e38d21e5f06da75df 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. 15 NO. 1 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Adaptive Multi-level Backward Tracking for Sequential Feature Selection Subject The topic of the resource classification accuracy; data mining; dimensionality reduction; sequential feature selection; supervised learning; wrapper approach. Description An account of the resource Abstract. In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets. Creator An entity primarily responsible for making the resource Knitchepon Chotchantarakun & Ohm Sornil Source A related resource from which the described resource is derived DOI: 10.5614/itbj.ict.res.appl.2021.15.1.1 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 07 Mei 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 classification accuracy data mining dimensionality reduction Jurnal Nasional Informatika sequential feature selection supervised learning wrapper approach https://repository.horizon.ac.id/files/original/ba89e9c7b6bd71728f38c4f12885cf87.pdf 6116388a674cc3bd82b5616a00e83662 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. 15 NO. 1 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks Subject The topic of the resource cell selection; femtocell; handover learning; LTE-A; Q-learning. Description An account of the resource Abstract. Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased. Creator An entity primarily responsible for making the resource Ammar Bathich, Saiful Izwan Suliman, Hj. Mohd Asri Hj. Mansor, Sinan Ghassan Abid Ali & Raed Abdulla Source A related resource from which the described resource is derived DOI: 10.5614/itbj.ict.res.appl.2021.15.1.4 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 07 Mei 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 cell selection femtocell handover learning Jurnal Nasional Informatika LTE-A Q-learning https://repository.horizon.ac.id/files/original/67e5f3902a5b1643963aeb3058d31085.pdf f4aef3e499cda6147493101ef9331bf6 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. 15 NO. 1 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Extraction of the Major Features of Brain Signals using Intelligent Networks Subject The topic of the resource brain-computer interface; EEG signal; P300 component; recurrent neural network; twin support vector machine. Description An account of the resource Abstract. The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy. Creator An entity primarily responsible for making the resource Shirin Salarian & Amir Shahab Shahabi Source A related resource from which the described resource is derived DOI: 10.5614/itbj.ict.res.appl.2021.15.1.5 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 07 Mei 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 brain-computer interface EEG signal Jurnal Nasional Informatika P300 component recurrent neural network twin support vector machine https://repository.horizon.ac.id/files/original/0c83a6eedee377c416aa1949b69a163d.pdf 8741d5c829b594d249b45c9259f2b41d 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. 15 NO. 1 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features Subject The topic of the resource Balinese dance; feature; movement; pattern; sequence. Description An account of the resource Abstract. The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros. Creator An entity primarily responsible for making the resource Made Windu Antara Kesiman, I Made Dendi Maysanjaya, I Made Ardwi Pradnyana, I Made Gede Sunarya & Putu Hendra Suputra Source A related resource from which the described resource is derived DOI: 10.5614/itbj.ict.res.appl.2021.15.1.6 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 07 Mei 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Balinese dance feature Jurnal Nasional Informatika movement pattern sequence https://repository.horizon.ac.id/files/original/a7c6134143959abedd57cc779c83f41c.pdf 0fd8b66e35e16269b056b1957f719c6b 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. 15 NO. 1 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Tsunami Impact Prediction System Based on TsunAWI Inundation Data Subject The topic of the resource early warning system; inundation data analysis; tsunami impact prediction system; tsunami modeling; tsunami warning information. Description An account of the resource Abstract. It is very important for tsunami early warning systems to provide inundation predictions within a short period of time. Inundation is one of the factors that directly cause destruction and damage from tsunamis. This research proposes a tsunami impact prediction system based on inundation data analysis. The inundation data used in this analysis were obtained from the tsunami modeling called TsunAWI. The inundation data analysis refers to the coastal forecast zones for each city/regency that are currently used in the Indonesia Tsunami Early Warning System (InaTEWS). The data analysis process comprises data collection, data transformation, data analysis (through GIS analysis, predictive analysis, and simple statistical analysis), and data integration, ultimately producing a precalculated inundation database for inundation prediction and tsunami impact prediction. As the outcome, the tsunami impact prediction system provides estimations of the flow depth and inundation distance for each city/regency incorporated into generated tsunami warning bulletins and impact predictions based on the Integrated Tsunami Intensity Scale (ITIS-2012). In addition, the system provides automatic sea level anomaly detection from tide gauge sensors by applying a tsunami detection algorithm. Finally, the contribution of this research is expected to bring enhancements to the tsunami warning products of InaTEWS. Creator An entity primarily responsible for making the resource Yedi Dermadi & Yoanes Bandung Source A related resource from which the described resource is derived DOI: 10.5614/itbj.ict.res.appl.2021.15.1.2 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 07 Mei 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Early Warning System inundation data analysis Jurnal Nasional Informatika tsunami impact prediction system tsunami modeling tsunami warning information https://repository.horizon.ac.id/files/original/b59ac3499d6dc98eeb39696156d2dc04.pdf 366547ee108ff4134908b38ffc3235dc 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. 15 NO. 1 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net Subject The topic of the resource anomaly detection; anomalous sound; auto-encoder; spectrogram; U-Net. Description An account of the resource Abstract. Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC. Creator An entity primarily responsible for making the resource Hoang Van Truong, Nguyen Chi Hieu, Pham Ngoc Giao & Nguyen Xuan Phong Source A related resource from which the described resource is derived DOI: 10.5614/itbj.ict.res.appl.2021.15.1.3 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 07 Mei 2021 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021 anomalous sound anomaly detection auto-encoder Jurnal Nasional Informatika spectrogram U-Net