2 10 27 https://repository.horizon.ac.id/files/original/c4169bc862b43f2ad629fa24c1921b9c.pdf f07ff051410b8676e2edd0cdd3cfe469 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. 22, NO.4 2024 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 End-fire multibeam radial line slot array antennas Subject The topic of the resource End-fire High beamsquint Multibeam Radial line slot array Wi-Fi Description An account of the resource This research proposed and verified a novel method in realizing end-fire radial line slot array (RLSA) antennas. This method involved the use of high beamsquint values in the design of slot pairs, which aimed to shift the antenna’s beam toward the end-fire direction. Furthermore, identical slot pairs were also placed in the antenna’s background to further squint the beam in the end-fire direction. By using this method, forty multibeam end-fire RLSA antennas were modeled and simulated to determine the most efficient model to be fabricated. The accuracy of the simulations was confirmed through measurements taken from the fabricated prototype, which demonstrate good agreement with the simulation results and confirm the validity of the proposed method. The result showed that it is possible to design four end-fire beam antennas with a gain of 8 dBi, directions of 0°, 90°, 180°, and 270° in the azimuth direction, and a beamwidth of about 20°. The antenna also showed low reflection and bandwidth of about 500 MHz, which is suitable for Wi-Fi applications. Creator An entity primarily responsible for making the resource Teddy Purnamirza1, Junisbekov M. Shardarbekovich2, Riza A. Jaya1, Imran Mohd Ibrahim3, Kabanbayev A. Batyrbekovich2, Depriwana Rahmi4 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 13, 2024 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 https://repository.horizon.ac.id/files/original/4b439b64ed1f3ac507b33c34fddd7639.pdf ba05720930c8e88911f6ef07b463cd9e 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. 22, NO.4 2024 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 Effectiveness of Bluetooth communication of digital stethoscope using quality of service method Subject The topic of the resource HC-05 Heart sound Quality of service Stethoscope Wireless Description An account of the resource This study aims to design and evaluate a wireless electronic stethoscope that can transmit heart sound using Bluetooth HC-05 and Bluetooth 5.0 transmitter. This novel design contributes to the remote diagnosis and monitoring of heart conditions, especially for patients with infectious diseases. The heart sound signals are captured using a mic condenser mic, amplified, filtered, and converted to digital data by a microcontroller. The data are then transmitted by Bluetooth HC-05 to a module and by Bluetooth 5.0 transmitter to a headset. The quality-of-service parameters such as throughput, delay, and packet loss ratio (PLR) of the data transmission at different distances are measured. The results show that the wireless electronic stethoscope can transmit heart sound data with a small PLR of 0.10% and a throughput of 1002.5 bps. The study concludes that the wireless electronic stethoscope is an effective and useful device for examining heart conditions remotely, without compromising the functionality of the device. Creator An entity primarily responsible for making the resource Endang Dian Setyoningsih, Sumber, Triwiyanto, Farid Amrinsani Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 29, 2024 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 HC-05 Heart sound Quality of service Stethoscope Wireless https://repository.horizon.ac.id/files/original/bd46d5a560ef94fd98aeaa1793a29702.pdf 6c78008950e5ef41d0d70dbefc9f24a6 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. 22, NO.4 2024 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 IoT-based flood disaster early detection system using hybrid fuzzy logic and neural networks Subject The topic of the resource Flood detection Fuzzy model Neural networks Social media Wireless sensor network Description An account of the resource A flood stands as one of the most common natural occurrences, often resulting in substantial financial losses to property and possessions, as well as affecting human lives adversely. Implementing measures to prevent such floods becomes crucial, offering inhabitants ample time to evacuate vulnerable areas before flood events occur. In addressing the flood issue, numerous scholars have put forth various solutions, such as the development of fuzzy system models and the establishment of suitable infrastructure. However, when applying a fuzzy system, it often results in a loss of interpretability of the fuzzy rules. To address this issue effectively, we propose to reframe the optimization problem by incorporating stage costs alongside the terminal cost. Results show the proposed model called hybrid fuzzy logic and neural networks (NNs) can mitigate the loss of interpretability. Results also show that the proposed method was employed in a flood early detection system aligned with integrating into Twitter social media. The proposed concepts are validated through case studies, showcasing their effectiveness in tasks such as XOR-classification problems. Creator An entity primarily responsible for making the resource Muhammad Adib Kamali1, Mochamad Nizar Palefi Ma’ady2 Source A related resource from which the described resource is derived Journal homepage: 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 29, 2024 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 Flood detection Fuzzy model Neural networks Social media Wireless sensor network https://repository.horizon.ac.id/files/original/d06d873c6c8cc277917905904e4b1d4c.pdf ad72306f6306630750e1ca22a5c84a22 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. 22, NO.4 2024 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 Pansharpening with multi-CAE: impact of patch size and overlapping pixels on spectral and spatial distortion Subject The topic of the resource Adaptive intensity-hue-saturation Convolutional-autoencoder Multispectral Pansharpening Remote sensing Description An account of the resource A novel technique utilizing a convolutional autoencoder (CAE) is introduced with the aim of enhancing the spatial resolution of multispectral (MS) images while concurrently mitigating spectral distortion. First, an original panchromatic (PAN) image is constructed from its spatially degraded version. Then, the relationship between the original PAN image and its degraded version is utilized to reconstruct the high-resolution MS image; in addition, an intensity component of MS image, which is obtained using an adaptive intensity-hue-saturation (AIHS), is reconstructed by utilizing the aforementioned relationship. Two types of remote sensing datasets are adopted, and the effect of the patch size with the overlapping pixel on spectral and spatial distortion is considered. After training CAE, the low-resolution MS image and its intensity component are given to the trained network as input to obtain the MS image and intensity component with better details. Eventually, the fused image is obtained by using a component substitution (CS) framework. Experimental findings corroborate that the proposed method yields superior outcomes compared with several existing approaches, demonstrating advantages in both objective metrics and visual fidelity. Creator An entity primarily responsible for making the resource Ahmad Al Smadi1, Ahed Abugabah2, Mutasem Khlail Alsmadi3, Ala Alsanabani4, Atif Mehmood5, Ahmad Mohammad Al-Smadi6 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 2, 2024 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 intensity-hue-saturation Convolutional-autoencoder Multispectral Pansharpening Remote sensing https://repository.horizon.ac.id/files/original/086931ea311ee297f52f9821b3b10a9b.pdf 57ef4de4b357051cc5b27767c3024bbb 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. 22, NO.4 2024 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 Convolutional neural network enhancement for mobile application of offline handwritten signature verification Subject The topic of the resource Convolutional neural network Genuine Image classification Mobile Signature Description An account of the resource The increase in signature forgery cases can be attributed to the escape of forged signatures from manual signature verification systems. Researchers have developed various machine learning and deep learning methods to verify the authenticity of signatures, one of which uses convolutional neural networks (CNNs). This research aims to develop a mobile application for handwritten signature verification using CNN architecture by adding a batch normalization technique to its layer. The performance of our proposed method achieved a verification accuracy of 86.36%, with a 0.061 false acceptance rate (FAR), 0.303 false rejection rate (FRR), and 0.182 equal error rate (EER), which is compatible to be embedded in smartphones. However, there is still a need for further development of the CNN model and its integration with mobile applications. Creator An entity primarily responsible for making the resource Wifda Muna Fatihia, Arna Fariza, Tita Karlita Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Mar 29, 2024 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 network Genuine Image classification Mobile Signature https://repository.horizon.ac.id/files/original/7aa34a1a62ec2e55fc3d7ad4cbffee03.pdf e8481a4a384740b2f5a43ca5348522ef 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. 22, NO.4 2024 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 Multi objective hyperparameter tuning via random search on deep learning models Subject The topic of the resource Convolutional neural network Deep learning models Hyperparameter tuning Multiobjective optimization Random search Description An account of the resource This research examines the efficacy of random search (RS) in hyperparameter tuning, comparing its performance to baseline methods namely manual search and grid search. Our analysis spans various deep learning (DL) architectures-multilayer perceptron (MLP), convolutional neural network (CNN), and AlexNet implemented on prominent benchmark datasets of Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research-10 (CIFAR-10). In the context of this study, the evaluation will be adopting a multi-objective framework, navigating the delicate trade-offs between conflicting performance metrics, including accuracy, F1-score, and model parameter size. The primary objective of employing a multi-objective evaluation framework is to enhance the understanding regarding the interactions of these performance metrics interact and influence each other. In real-world scenarios, DL models often need to strike a balance between these conflicting objectives. This research adds to the increasing wealth of knowledge in hyperparameter tuning for DL models and serves as a reference point for practitioners seeking to optimize their DL architectures. The results of our analysis are positioned to provide invaluable insights into the intricate balancing act required during the process of hyperparameter fine-tuning. These insights will contribute to the ongoing advancement of best practices in optimizing DL models and facilitating the ongoing optimization of the DL models. Creator An entity primarily responsible for making the resource Abdul Rahman Mohamad Rom, Nursuriati Jamil, Shafaf Ibrahim Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Mar 29, 2024 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 https://repository.horizon.ac.id/files/original/a0c12e717cecf96c2cb7e62604a65b43.pdf 44fa1dfcff1b4ca46b0f9495c31f538d 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. 22, NO.4 2024 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 Optimizing hyperparameters in multiview convolutional neural network for improved breast cancer detection in mammograms Subject The topic of the resource Breast cancer classification Digital dataset for screening mammography dataset Health Hyperparameter Image preprocessing Multiview convolutional neural network Description An account of the resource High accuracy in breast cancer classification contributes to the effectiveness of early breast cancer detection. This study aimed to improve the multiview convolutional neural network (MVCNN) performance for classifying breast cancer based on the combined mediolateral (MLO) and craniocaudal (CC) views. The main contribution of this study is the development of a system, consisting of an effective image pre-processing method to create datasets using background removal techniques, and image enhancement. Also, a simplicity of preprocessing stage in the classifier machine, which does not require a feature extraction process. Furthermore, the performance of the classifier was improved by combining preprocessing dataset techniques and evaluating the best hyperparameter in MVCNN architecture. The digital dataset for screening mammography (DDSM) dataset was used for evaluation in this study. The best result from this proposed method achieved accuracy, precision, sensitivity, and specificity of 98.63%, 97.29%, 100%, and 97.29%. The evaluation results demonstrated the capability to improve classification performance. The method proposed in this work can be applied to the detection of breast cancer. Creator An entity primarily responsible for making the resource Sisilia Anggraini, Tri Arief Sardjono, Nada Fitrieyatul Hikmah Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 2, 2024 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 Breast cancer classification Digital dataset for screening mammography dataset Health Hyperparameter Image preprocessing Multiview convolutional neural network https://repository.horizon.ac.id/files/original/638a3f6850acd19ac3e5eefd58688467.pdf 65d2664ca075908f647969958e8c71b7 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. 22, NO.4 2024 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 Applying artificial neural network on heart rate variability and electroencephalogram signals to determine stress Subject The topic of the resource Artificial neural network Electrocardiogram Electroencephalography stratified K-fold Health Stress detection Description An account of the resource Emotions are mental states, categorizes them into positive and negative feelings, and uses stress as an example of a negative emotion. Research demonstrated that acute and chronic stress can change physiological variables, such as heart rate variability (HRV) and electroencephalography (EEG). This research aims to early prevention and management of stress that is comfortable to use, reliable and accurate for stress detection. The Einthoven triangle rule was used to gather electrocardiogram (ECG) signals, while EEG signals were obtained from Fp1 and F3 connected to mikromedia 7 with the STM32F746ZG chipset. Various parameters were examined, including ECG signals in the time domain, frequency domain, non-linear analysis, and EEG signals in the frequency domain. Healthy subjects aged 18-23 undergoing different stress-inducing stages, with stress levels validated through the STAI-Y1 questionnaire. To process the HRV and EEG features, Pearson’s correlation function (PCF) was employed to select appropriated features into classification method. The proposed classification method in this research is the artificial neural network (ANN) with stratified K-fold, which yielded a stress level output accuracy of 95%. Additionally, the STAI-Y1 questionnaire results evaluation indicated a similarity score of 90.91%. This research has potential applications for individuals experiencing stress, providing a valuable tool for stress detection. Creator An entity primarily responsible for making the resource Steven Matthew Gondowijoyo, Rachmad Setiawan, Nada Fitrieyatul Hikmah Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 20, 2024 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 neural network Electrocardiogram Electroencephalography stratified K-fold Health Stress detection https://repository.horizon.ac.id/files/original/e36777fa2c6ee2f8f5cc86367ef0c7ac.pdf 5f95d039d078bb1e0462cb57fb785c37 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. 22, NO.4 2024 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 Automated classification of diseased cauliflower: a feature-driven machine learning approach Subject The topic of the resource Analysis of variance Bagging classifier Cauliflower Image processing Machine learning Description An account of the resource Cauliflower is a popular winter crop in Bangladesh. However, cauliflower plants are vulnerable to several diseases that can reduce the cauliflowers’ productivity and degrade their quality. The manual monitoring of these diseases takes a lot of effort and time. Therefore, automatic classification of the diseased cauliflower through computer vision techniques is essential. This study has retrieved ten different statistical and gray-level co-occurrence matrix (GLCM)-based features from the cauliflower image dataset by implementing a variety of image processing techniques. Afterwards, the SelectKBest method with the analysis of variance f-value (ANOVA F-value) has been used to identify the most important attributes for classification of the diseased cauliflower. Based on the ANOVA F-value, the top N (5≤N ≤9) most dominant attributes is used to train and test five machine learning (ML) models for classification of diseased cauliflower. Finally, different performance metrics have been used for evaluating the effectiveness of the employed ML models. The bagging classifier achieved the highest accuracy of 82.35%. Moreover, this model has outperformed other ML classifiers in terms of other performance metrics also. Creator An entity primarily responsible for making the resource Mala Rani Barman1, Al Amin Biswas2, Marjia Sultana3, Aditya Rajbongshi4, Md. Sabab Zulfiker2, Tasnim Tabassum5 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Dec 27, 2023 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Analysis of variance Bagging classifier Cauliflower Image processing Machine learning https://repository.horizon.ac.id/files/original/6b6d5a0b5f98d94e4f258a77f7c326fb.pdf 18cb222170a7d166da8f02746fd60858 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. 22, NO.4 2024 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 The impact of software metrics in NASA metric data program dataset modules for software defect prediction Subject The topic of the resource K-nearest neighbor NASA metric data program Software defect Software defect prediction Software metrics Description An account of the resource This paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics. Creator An entity primarily responsible for making the resource Adinda Ayu Puspita Ramadhani, Radityo Adi Nugroho, Mohammad Reza Faisal, Friska Abadi, Rudy Herteno Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 2, 2024 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 K-nearest neighbor NASA metric data program Software defect Software defect prediction Software metrics