1 10 26 https://repository.horizon.ac.id/files/original/112c5c5a614ad7bb366a02b80e6e8e2f.pdf 6914e45efb8dce9a691637e1fb5fc20d 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. 23, NO.3 2025 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 A 6G THz MIMO antenna with high gain and wide bandwidth for high-speed wireless communication Subject The topic of the resource 6G communication Graphene High-gain Industrial and innovation Resistor inductor capacitor Terahertz antenna Wide-bandwidth Description An account of the resource This study presents a comprehensive industrial and innovation design and thorough analysis of a terahertz (THz) multiple-input multiple-output (MIMO) antenna, addressing the increasing demand for high-performance multi-antenna systems in THz communication applications. The primary objective of this research is to develop a compact and efficient MIMO antenna that operates over a wide frequency range and provides high isolation, specifically within the 1–10 THz spectrum. The proposed antenna achieves an impressive total bandwidth of approximately 9 THz, featuring seven distinct resonance frequencies at 1.39 THz, 3.26 THz, 4.72 THz, 5.96 THz, 7.07 THz, 8.194 THz, and 9.426 THz. The design employs a polyimide substrate and a graphene patch. Key performance metrics include a maximum gain of 15 dB, efficiency of 99.8%, and isolation values that range from 28 dB to 63 dB. An resistor inductor capacitor (RLC) equivalent circuit using advanced design system (ADS) software. Additionally, the antenna displays remarkable diversity metrics, with an envelope correlation coefficient (ECC) of 0.000778 and a diversity gain of 9.99961 dB. With compact dimensions of (65×180) μm2 and outstanding performance characteristics, this design is confirmed to be suitable for THz applications, fulfilling the research goal of facilitating efficient and reliable communication in sophisticated multi-antenna systems. Creator An entity primarily responsible for making the resource Redwan Al Mahmud Bin Asad Ananta1, Md. Sharif Ahammed1, Md. Ashraful Haque1, Md. Kawsar Ahmed1, Narinderjit Singh Sawaran Singh2, Jamal Hossain Nirob1, Kamal Hossain Nahin1, Liton Chandra Paul3 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 11, 2025 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 6G communication Graphene High-gain Industrial and innovation Resistor inductor capacitor Terahertz antenna Wide-bandwidth https://repository.horizon.ac.id/files/original/1e26547143b1517caad16e616a77bcf1.pdf c37f5b3b80f5d0978ed8d122ef2965df 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. 23, NO.3 2025 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 A compact triband patch antenna design at terahertz frequencies Subject The topic of the resource Ansys high frequency structure simulator Microstrip antenna Multiband antenna Slot Terahertz antenna Description An account of the resource The rapid evolution of terahertz (THz) technology has fueled an increasing demand for efficient, compact, and adaptable antennas that can function for a number of frequency bands in the THz spectral regime. This research outlines the analyses and design process of a multiband antenna for THz applications. Initially, an antenna with a single frequency band is created without any slot, with its lower resonant mode functions at a singular frequency of 171 GHz. To achieve multiband functionality, various rectangular slots can be added into the microstrip antenna’s radiating element. The suggested structure is constructed on a polyimide substrate, while its radiating elements are crafted from copper, with a compact size of 1.4×1.1×0.14 mm. It can achieve a reflection coefficient of −30.38 dB, −33.37 dB, and −19.33 dB at 123 GHz, 168 GHz, and 182 GHz, respectively. Furthermore, the antenna yields favorable gains at the respective frequencies, measuring 3.97 dB, 4.34 dB, and 5.66 dB for 0.123, 0.168, and 0.182 THz respectively. Additionally, the antenna demonstrates high efficiencies of 81.5%, 85%, and 91.2%, respectively. Hence, the suggested THz antenna will be useful for surveillance radar (123 GHz), medical imaging (168 GHz), and radio astronomy (182 GHz) applications. Creator An entity primarily responsible for making the resource Youssef Amraoui1, Imane Halkhams2, Rachid El Alami1, Mohammed Ouazzani Jamil2, Hassan Qjidaa2 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 11, 2025 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 Ansys high frequency structure simulator Microstrip antenna Multiband antenna Slot Terahertz antenna https://repository.horizon.ac.id/files/original/72e6e4a49cddf55c1cc4cfa62f516930.pdf 8367b10818ad8ebded58cf9ba5a50677 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. 23, NO.3 2025 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 A multiband sub-6 THz patch antenna with high gain for IoT and 6G communication Subject The topic of the resource 6G communication Industrial and innovation Microstrip patch antenna Multiple-input multiple-output antenna Resistor, inductor, and capacitor Sub-6 Description An account of the resource This comprehensive study introduces a meticulously designed and characterized terahertz (THz) multiple-input multiple-output (MIMO) antenna engineered to operate within the 0.4 THz to 1.6 THz frequency range. The antenna’s construction includes a copper patch and ground plane integrated into a polyimide substrate, ensuring exceptional durability and robust performance. Significantly, the antenna reveals four distinct resonance frequencies at 0.46 THz, 0.9 THz, 1.31 THz, and 1.44 THz each accompanied by bandwidths of 0.005 THz, 0.17 THz, and 0.34 THz, respectively. Moreover, the antenna delivers notable gains of 8.52 dB, 11.54 dB, and 13.25 dB at these frequencies, coupled with substantial efficiencies of 88.32%, 92.02%, and 89.89%, respectively. Additionally, the antenna showcases exceptional isolation of 26 dB, a low envelope correlation coefficient (ECC) of 0.003, and a diversity gain (DG) of 9.98. These remarkable attributes underscore the antenna’s aptness for high-performance THz applications, offering substantial advantages in terms of gain, efficiency, and isolation for next-generation wireless communication systems. Creator An entity primarily responsible for making the resource Redwan Al Mahmud Bin Asad Ananta1, Md. Sharif Ahammed1, Md. Ashraful Haque1, Narinderjit Singh Sawaran Singh2, Kamal Hossain Nahin1, Jamal Hossain Nirob1, Md. Kawsar Ahmed1, Liton Chandra Paul3 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 11, 2025 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/aaa677f68ca298b3a1f183f05c4fc152.pdf 61bb198acec04d535fc17bc244e228ef 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. 23, NO.3 2025 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 Addressing overfitting in comparative study for deep learning-based classification Subject The topic of the resource Deep learning Feature extraction Few-shot learning InceptionV3 Overfitting Stanford dog Xception Description An account of the resource Despite significant advancements in deep learning methodologies for animal species classification, there remains a notable research gap in effectively addressing biases inherent in training datasets, combating overfitting during model training, and enhancing overall performance to ensure reliable and accurate classification results in real-world applications. Therefore, this study explores the complex challenges of dog species classification, with a specific focus on addressing biases, combatting overfitting, and enhancing overall performance using deep learning methodologies. Initially, the Stanford Dog dataset serves as the foundation for training, complemented by additional data from annotated datasets. The primary aim is to mitigate biases and reduce overfitting, which is essential for improving the performance of deep learning-based classification in terms of dataset size and computational time. Feature extraction and few-shot learning techniques are compared to assess and improve the model performance. The experimentation involves the utilization of optimal classifiers, specifically InceptionV3 and Xception. In order to tackle overfitting, a range of strategies are deployed, including data augmentation, early stopping, and the integration of dropout and freezing layers which particularly achieved a better performance with Xception on the augmented dataset. Creator An entity primarily responsible for making the resource Jing-Yee Ong, Lee-Yeng Ong, Meng-Chew Leow 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 11, 2025 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 Deep learning Feature extraction Few-shot learning InceptionV3 Overfitting Stanford dog Xception https://repository.horizon.ac.id/files/original/239a8a9656f4b807c88559f520c40a35.pdf 103031664fe50c6f85a691232000b7a1 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. 23, NO.3 2025 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 Adulterated beef detection with redundant gas sensor using optimized convolutional neural network Subject The topic of the resource Adulterated beef Convolutional neural network Machine learning Pork adulteration Redundant gas sensor Description An account of the resource Various types of research have been developed to detect beef adulteration, but the accuracy and reliability of these results still require improvement. This study proposes designing a highly precise redundant electronic nose system using an optimized convolutional neural network (CNN) method to detect adulterated beef mixed with pork. As baselines, other classifiers are also utilized, namely the decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). Several data preprocessing methods are employed to increase prediction accuracy, namely feature selection, principal component analysis (PCA), and time series smoothing. The weight of each data sample was 100 g with 15 classes of pork and beef mixing ratios of 0%, 0.1%, 0.5%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% pork. With the single-layer sensor configuration, the average CNN classification success rates were 97.15%, 96.29%, and 99.64% for layers 1, 2, and 3, respectively. In addition, from the combination of the three layers, a prediction results of 99.72% was obtained. Thus, a redundant gas sensor array configuration can improve the classification results. In addition, the relatively high accuracy of the optimized CNN provides a convincing alternative for identifying possible beef adulteration. Creator An entity primarily responsible for making the resource Ardani Cesario Zuhri1, Agus Widodo1, Mario Ardhany1, Danny Mokhammad Gandana1, Galang Ilman Islami1, Galuh Prihantoro2 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 11, 2025 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 Adulterated beef Convolutional neural network Machine learning Pork adulteration Redundant gas sensor https://repository.horizon.ac.id/files/original/e38effc02ec9d68bff049850208f17d5.pdf 0122a7918cdd87f7f5d227492a6e2f6d 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. 23, NO.3 2025 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 Clustering of swamp land types against soil resistivity and grounding resistance Subject The topic of the resource Comparison resistivity Grounding resistance Potential of hydrogen Soil type resistivity Swamp land clustering Description An account of the resource In theory, the resistivity value of the soil is one of the factors that must be taken into account when planning a grounding installation. The resistivity value of swamp soil is 30 Ωm, as per the general requirements for electrical installation of 2011 (PUIL 2011). This value is identical to the resistivity of the soil type in The Institute of Electrical and Electronics Engineers (IEEE standard 80 in 2000), where the wet soil type has a resistivity value of 100 Ωm. It is difficult for electrical engineers to design construction on swamp land because the standard's representation of the features of swamp land does not accurately reflect the types of swamps or wetlands that exist in reality. The focus of this investigation is the resistivity value of swamp soil types. The results of this investigation will make a scientific contribution to the clustering of land at each soil resistivity value in freshwater, brackish water, saltwater, and acidic water swamp land. These soils have pH values that range from 3.5 to above 6. The research on swamp land clustering has revealed that each swamp has a distinctive resistivity value for the different types of swamp soil. Creator An entity primarily responsible for making the resource Dian Eka Putra1, Muhammad Irfan Jambak2, Zainuddin Nawawi 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 11, 2025 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 Comparison resistivity Grounding resistance Potential of hydrogen Soil type resistivity Swamp land clustering https://repository.horizon.ac.id/files/original/236d6241ab7f6f1ad2d33b147c5cbb68.pdf a16b0d669268f9e2a06515145a93deba 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. 23, NO.3 2025 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-based real-time drowsy driver detection for accident prevention Subject The topic of the resource Convolutional neural network Deep learning Driver Drowsiness Lightweight convolutional neural network Description An account of the resource Drowsy driving significantly threatens road safety, contributing to many accidents globally. This paper presents a convolutional neural network (CNN)-based real-time drowsy driver detection system aimed at preventing such accidents, particularly for deployment in Android applications. We propose a lightweight CNN architecture that effectively identifies drowsiness and microsleep episodes by categorizing driver facial expressions into four distinct categories: close-eye expressions, open-eye expressions, yawns, and no yawns. Our model, which employs facial landmark detection and various pre-processing techniques to enhance accuracy, achieves an impressive 96.6% accuracy. This performance surpasses several popular CNN architectures, including VGG16, VGG19, MobileNetV2, ResNet50, and DenseNet121. Notably, our proposed model is highly efficient, with only 0.4 million parameters and a memory requirement of 1.51 MB, making it ideal for real-time applications. The comparative analysis highlights the superior balance between accuracy and resource efficiency of our model, demonstrating its potential for practical deployment in reducing accidents caused by driver fatigue. Creator An entity primarily responsible for making the resource Nippon Datta1, Tanjim Mahmud2, Manoara Begum3, Mohammad Tarek Aziz1, Dilshad Islam4, Md. Faisal Bin Abdul Aziz5, Khudaybergen Kochkarov6, Temur Eshchanov7, Valisher Sapayev Odilbek Uglu8, Sobir Parmanov9, Mohammad Shahadat Hossain10,11, Karl Andersson11 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 11, 2025 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 Deep learning Driver Drowsiness Lightweight convolutional neural network https://repository.horizon.ac.id/files/original/517c43644be5fb8dac9efce0b0b72c3d.pdf 4b2e65821fc877ed6c3cda027fcf15f0 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. 23, NO.3 2025 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 Deep learning approaches for accurate wood species recognition Subject The topic of the resource Deep convolutional neural network Deep learning Recognition system Wood images Wood species Description An account of the resource Wood species identification is a crucial task in various industries, including forestry, woodworking, and conservation. Traditional methods rely on manual expertise, which can be time-consuming and error prone. Hence, an automatic wood species recognition system is developed in this study using deep learning (DL) models. In this study, three deep convolutional neural network (CNN) architectures, SqueezeNet, GoogLeNet, and ResNet-50 was tailored for wood species classification. The accuracy of the DL models was evaluated in recognizing fifty different wood species. Additionally, the wood species images were altered using JPEG Compression, Gaussian Blur, Salt and Pepper, and Speckle noises to assess the models' performance in identifying the wood species from the distorted images. Results show that the ResNET-50 based wood recognition system is the most accurate model to recognise the wood species. The implications of this research extend to forestry management, quality control in woodworking industries, and the preservation of endangered wood species in conservation efforts. Creator An entity primarily responsible for making the resource Heshalini Rajagopal1, Nicky Christian2, Devika Sethu1, Mohd. Azwan Ramlan3, Hanis Farhah Jamahori4, Mardhiah Awalludin3, Norul Ashikin Norzain3, Renuka Devi Rajagopal5, Narayanan Ganesh 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 11, 2025 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 Deep convolutional neural network Deep learning Recognition system Wood images Wood species https://repository.horizon.ac.id/files/original/c94058e7a28c7604c0c1a5e3d2350d7f.pdf 70f6d938a12de69c0ac138d6d6aa0d04 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. 23, NO.3 2025 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 Design and simulation of rectangular patch antenna arrays with high bandwidth for 2.4 GHz ISM band applications Subject The topic of the resource Ansys high frequency structure simulator Impedance matching Microstrip patch antenna array Quarter-wave transformer Wide bandwidth Description An account of the resource Ongoing advancements in microstrip patch antenna (MPA) development research is driven by its compact size, cost-effectiveness and ease of fabrication. This paper presents a flexible design of patch antenna array (PAA) to address bandwidth (BW) limitations within the 2.4 GHz industrial, scientific, and medical (ISM) band, where narrow BW is a common challenge. To explore the effectiveness of different array configurations, we designed and evaluated 1×2, 2×2, and 2×4 element rectangular PAA, employing a quarter-wave transformer (QWT) method and parallel scheme for connecting patches. By utilizing Ansys high frequency structure simulator (HFSS) as the modeling environment, we conducted extensive simulations to refine the antenna parameters and achieve the most optimal MPA prototype. Our investigation demonstrates sufficiently good results, including BWs of 290 MHz and 210 MHz for 2×2 and 2×4 PAAs respectively, which account for 8.75% and 12% of the total value. The parameter return loss (RL) (S_11) reached -51dB for single-element patch antenna (SPA) and -37.5 dB for 8-element PAA, that shows an ideal impedance matching. In addition, the designed 2×4 PAA exhibited impressive performance metrics, accounting for 9.17 dB in gain, 13 dBi in directivity, and voltage standing wave ratio (VSWR) maintained below 0.5, ensuring excellent signal transmission and reception. Creator An entity primarily responsible for making the resource Kymbat Kopbay, Madiyar Nurgaliyev, Ahmet Saymbetov, Nurzhigit Kuttybay, Askhat Bolatbek, Sayat Orynbassar, Batyrbek Zholamanov 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 11, 2025 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 Ansys high frequency structure simulator Impedance matching Microstrip patch antenna array Quarter-wave transformer Wide bandwidth https://repository.horizon.ac.id/files/original/95d39fc0cd11050906158a8fe9430003.pdf 01d9c391a2c0f4c9de789e6ace1121c5 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. 23, NO.3 2025 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 Earthquake magnitude prediction based on radon cloud data near Grindulu fault, Indonesia using the statistical method Subject The topic of the resource Earthquake magnitude Mitigation Prediction Radon Statistic Description An account of the resource Earthquake prediction is one of the most challenging and vital tasks that demands new methodologies for improving the accuracy of predictions. The research aims to present how radon gas concentration fluctuations are associated with the prediction of earthquakes in the Eurasian-Indo-Australian Plates. The paper discusses a statistical method of forecasting earthquake magnitudes greater than M4.5 from real-time radon gas monitoring close to the Grindulu Fault, Pacitan, East Java, Indonesia. This developed model has had the least errors in the form of mean absolute error (MAE), 0.30; mean absolute percentage error (MAPE), 0.06; root mean square error (RMSE), 0.55; mean squared error (MSE), 0.30; symmetric mean absolute percentage error (SMAPE), 0.06; complex normalized mean absolute percentage error (cnMAPE), 0.97; error absolute average (EAA), 0.30; and error relative average (ERA), -0.11, showing great accuracy and uniformity in prediction. These observations support the model’s efficiency that may be adopted in earthquake early warning systems for better disaster preparedness. Predictive errors are reduced, and there is support for improved disaster management strategy, public safety education, and effective emergency response personnel training. This study can be used as a foothold for further advances in earthquake prediction methodologies and refinement of early warning systems. Creator An entity primarily responsible for making the resource Sunarno1, Thomas Oka Pratama1, Faridah1, Nugroho Ananto2, Hermin Kartika Sari3, Rony Wijaya1,4, Memory Motivanisman Waruwu1,4 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 23, 2025 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 Earthquake magnitude Mitigation Prediction Radon Statistic