1 10 10 https://repository.horizon.ac.id/files/original/ff1986f17fc2380b13f24d89b0b6eb30.pdf 729cf01c38f8e3f04cbe82471f2f39ea 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. 18 NO. 2 (2025) 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 A Hybrid Vision Transformer Model for Efficient Waste Classification Subject The topic of the resource Deep Learning, Fine-Tuning, Hybrid Approach, ResNet50, Vision Transformers, Waste Classification Description An account of the resource The rapid and accurate sorting of municipal waste is essential for efficient recycling and sustainable resource recovery. Most existing AI solutions focus only on four common materials (plastic, paper, metal, and glass), overlooking many other routinely encountered waste types and losing accuracy when applied to the mixed waste compositions seen in operational environments. We introduce HR-ViT, a hybrid network that combines ResNet50 residual blocks, which capture fine-grained local cues, with Vision Transformer global self-attention. Trained on a balanced six-class benchmark of about 775 images per class (plastic, paper, organic, metal, glass, batteries), HR-ViT attains 98.27 % accuracy and a macro-averaged F1-score of 0.98, outperforming a pure ViT, VT-MLH-CNN, and Garbage FusionNet by up to five percentage points in both metrics. Gains arise from selective fine-tuning of the last ten ResNet layers, lightweight ViT hyper-parameter optimisation, and targeted data augmentation that mitigates cluttered backgrounds, uneven lighting, and object deformation. These results show that hybrid attention-residual architectures provide reliable predictions under complex imaging conditions. Future work will extend the method to multi-object scenes and domain-adaptive deployment in smart�city recycling systems. Creator An entity primarily responsible for making the resource Amir Mahmud Husein, Baren Baruna Harahap, Tio Fulalo Simatupang, Karunia Syukur Baeha,Bintang Keitaro Sinambela Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1489 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 Fine-Tuning Hybrid Approach ResNet50 Vision Transformers Waste Classification https://repository.horizon.ac.id/files/original/257890e38e1dbd679015f3f0a3348132.pdf cb1af42af8ef21085901d80d2d04f6b4 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. 18 NO. 2 (2025) 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 Comparative Evaluation of Database Systems for High-Volume Seismic Prediction Data Management in Real-Time Applications Subject The topic of the resource Earthquake early warning system, MongoDB, InfluxDB, database efficiency, IOPS, data throughput Description An account of the resource The Earthquake Early Warning System (EEWS) plays a pivotal role in mitigating structural damage and minimizing casualties by issuing alerts prior to the arrival of destructive seismic waves (S-waves), through the detection of the earlier and faster P-waves. The operational effectiveness of EEWS depends not only on the accuracy of its predictive algorithms but also on the efficiency of the underlying data storage and management infrastructure. This study presents a comparative evaluation of three data storage approaches—MongoDB, MongoDB with sharding, and InfluxDB—as well as the MiniSEED (mseed) binary format, with a focus on their performance in managing real-time seismic prediction data. Benchmarking was conducted based on two key metrics: Input/Output Operations Per Second (IOPS) and data throughput. The results indicate that both MongoDB and InfluxDB offer strong performance in high�ingestion scenarios, with MongoDB demonstrating higher IOPS, while InfluxDB exhibits better scalability and consistency as data volume increases. Conversely, the mseed format achieves exceptionally high throughput due to its flat-file structure but lacks the responsiveness and query capabilities required for real-time analytics. These findings suggest that MongoDB and InfluxDB are well-suited for integration into scalable EEWS infrastructures, offering a balance between performance and flexibility. Future work will extend this evaluation to larger-scale datasets and alternative architectures such as data lake systems to improve disaster response readiness. Creator An entity primarily responsible for making the resource Ari Wibisono, Rafif Naufal Rahmadika Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1530 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 data throughput database efficiency Earthquake early warning system InfluxDB IOPS MongoDB https://repository.horizon.ac.id/files/original/b06dc9254e2eb5a21cdbfa2645c22998.pdf 92b9f43e300b478cb818d3b0e53fb3a1 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. 18 NO. 2 (2025) 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 Application of Machine Learning Methods for Classification of Gamma and Hadron Signals in High Energy Particle Detection Subject The topic of the resource Gamma signal detection, particle physics classification, machine learning algorithms,monte carlo simulation, geometric parameter analysis Description An account of the resource A major challenge in particle physics is the binary classification of high-energy gamma signals against a complex hadron background. Accurate dentification of these gamma signals is critical for particle detection, especially as the volume and complexity of data increases as technology advances. The research developed a machine learning-based classification model to efficiently and accurately distinguish gamma signals from hadrons. Logistic Regression, Decision Trees, Random Forests, and Artificial Neural Networks are used for classification. Principal Component Analysis (PCA) and correlation analysis identified dominant features, while Monte Carlo simulations validated the distribution of gamma and hadron spectra. This study focuses on geometric parameters such as fLength, fWidth, fAlpha, as well as photon distribution and distance effects (fDist) in gamma signal identification using K-Means clustering. The Random Forest algorithm achieved the highest accuracy of 87.96%, with an F1-score of 0.91, which defines its robustness in the classification task. PCA and correlation analysis showed fSize, fLength, and fWidth as the most influential factorsin classification. Monte Carlo simulations successfully replicated the spectral distribution pattern with high experimental validation. The research presents a novel integration of geometric analysis, clustering techniques, and simulation validation in the classification of high-energy particles. Machine learning methods, in particular Random Forest, effectively distinguish the gamma signal from the hadron background. The combination of PCA and Monte Carlo simulations improves the understanding of data distribution patterns and key classification factors. This research contributes to the development of a more reliable astrophysical signal classification system with potential applications in large-scale astronomical data management. Creator An entity primarily responsible for making the resource Firdaus Andi Wibowo, Tomi Yulianto, Nicholaus Ola Malun, Rizqy Rionaldy, Verdi Yasin, Ruben Cornelius Siagian Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1489 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 Gamma signal detection geometric parameter analysis machine learning algorithms Monte carlo simulation particle physics classification https://repository.horizon.ac.id/files/original/f92de31e491b6421ee88d39a25695503.pdf 98bbcfc2ceace99bc3a41ecf1e80f24e 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. 18 NO. 2 (2025) 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 Indonesian License Plate Detection and Recognition System using Gaussian YOLOv7 Subject The topic of the resource detection, license plate, recognition, rectification, YOLO Description An account of the resource In recent years, Automatic License Plate Recognition (ALPR) systems have garnered attention in computer vision research. However, practical applications face challenges such as inconsistent lighting diverse license plate designs, and environmental variations, which increase the complexity of the task and lead to more false detections. To address these issues, we proposed Gaussian YOLOv7 for license plate detection and character recognition within ALPR systems, along with the Spatial Transformer Network (STN) for rectifying license plate orientation, aiming to enhance performance and adaptability to real-world scenarios. Additionally, we introduced a novel dataset for Indonesian ALPR systems to ensure robust detection and a balanced class distribution. Evaluation results indicate that Gaussian YOLOv7 improves precision and reduces false positives by 37.5% in the detection stage, albeit with poorer performance in other metrics. Conversely, the implementation of STN results in decreased character recognition accuracy, underscoring its limited effectiveness. Despite these challenges, Gaussian YOLOv7 excels in license plate rectification, achieving a recall of 83.8% and reducing false positives by 50.13% compared to YOLOv7. Moreover, post-processing techniques introduced by our approach further enhance precision by 5.3% and recall by 1%. Overall, our approach offers promising advancements in Indonesian ALPR systems, addressing fundamental challenges and enhancing performance. Creator An entity primarily responsible for making the resource Juan Thomas Wijaya and Aniati Murni Arymurthy Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1320 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 25029274 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 Detection license plate Recognition rectification YOLO https://repository.horizon.ac.id/files/original/871d16f22bfe61b2dcef58a2b1521b08.pdf 8876ba09fa0f573825f312be7ba41e47 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. 18 NO. 2 (2025) 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 Analysis of Coding Stress Impact on Students Programming Skills with Random Forest and C4.5 Algorithms Subject The topic of the resource C4.5 Algorithm, Programming Skills, Random Forest, Stress Management, Student Stress Description An account of the resource Students' stress often impedes their advancement in programming, which demands logical reasoning, an understanding of algorithms, and a firm grasp of basic concepts. This research intends to pinpoint the elements that affect students' programming abilities, explore their connection to stress levels, and assess the effectiveness of the Random Forest and C4.5 algorithms in classifying data. Information was gathered through an online questionnaire involving 744 students in 2024 at various leading universities in Islamabad, Pakistan. The dataset used in this study was sourced from Kaggle, which provides insights into factors affecting students' programming performance and stress levels. The analysis utilized a Confusion Matrix and evaluation metrics like accuracy, precision, recall, and F1-Score. The analysis results indicate that the C4.5 algorithm has a higher accuracy of 68.04% compared to Random Forest, which achieved 65.54%. Additionally, C4.5 outperforms Random Forest in terms of precision, scoring 71.7% versus 65.2%. However, in terms of recall, Random Forest performs better with a score of 66.3%, while C4.5 only reaches 59.6%. This study confirms that interest in programming, debugging skills, mathematical and analytical abilities, and perceptions of programming significantly impact students' performance and stress levels. Students with strong logical abilities and adequate support demonstrate better performance and lower stress levels, whereas those with weak technical skills and negative perceptions are more vulnerable to stress, which adversely affects their performance. These findings emphasize the importance of creating a positive learning environment through interactive methods, structured problem-solving, and additional support. Creator An entity primarily responsible for making the resource M. Akiyasul Azkiya, Yahya Nur Ifriza Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1487 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 C4.5 Algorithm Programming Skills Random Forest stress management Student Stress https://repository.horizon.ac.id/files/original/42c58a5de651bc2fae8db76b30d5aea7.pdf d04f0fa4d7c1edfcb76462942fb67cba 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. 18 NO. 2 (2025) 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 YOLOv11 Model as a Smart Solution for Waste Identification and Classification in Automated Waste Management System Subject The topic of the resource YOLOv11, computer vision, machine learning, waste classification, waste management system Description An account of the resource Urbanization and population growth present significant challenges for efficient and sustainable waste management. This research develops an IoT-based intelligent system for waste classification and management utilizing RFID technology, ESP32, a camera, an ultrasonic sensor, and the YOLOv11 object detection model. The system accurately identifies three categories of waste: organic, inorganic, and hazardous. The classification process is automated, incorporating user identification via RFID, servo-controlled bin lid operation, and capacity monitoring through an ultrasonic sensor. Data management is facilitated through a mobile application and a website, which provide user guidance and support for administrators. Test results indicate that the system achieves an average accuracy of 87.5% in the mAP50-95 evaluation, with specific accuracies of 89.0% for inorganic waste, 86.0% for hazardous waste, and 87.0% for organic waste. Despite these results, challenges remain, including object detection errors related to background interference. Future research should focus on enhancing the dataset and implementing data encryption to improve model accuracy and information security. This system demonstrates significant potential for enhancing waste management efficiency and promoting sustainable environmental practices. Creator An entity primarily responsible for making the resource Muhammad Fajar Jati Permana, Julio Caesar Ray Bakar Gani, Naufal Ahmad Fauzan, Anugrah Adiwilaga Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1490 Publisher An entity responsible for making the resource available Faculty of Computer Science Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 Computer Vision machine learning Waste classification waste management system. YOLOv11 https://repository.horizon.ac.id/files/original/0d9a50ee6e33d3109149ca393720acfa.pdf 98c8d3eeb27d123ce9cbf1a30461ea3e 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. 18 NO. 2 (2025) 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 Classification of Economic Activities in Indonesia Using IndoBERT Language Model Subject The topic of the resource Multiclass Classification, IndoBERT, DistilBERT, CatBoost, Activity Economy Description An account of the resource Classification of economic activities plays a vital role in understanding, analyzing, and managing complex economic processes in a society or country. It facilitates economic analysis, data collection, policy formulation, and informed decision-making. In Indonesia, economic activities are classified according to the Indonesian Standard Industrial Classification (KBLI). This classification process requires in-depth knowledge about KBLI, and this process is still performed manually, which is therefore time-consuming. To address this challenge, this paper proposes to use a transformer-based language model that was pretrained using a large Indonesian corpus, i.e., IndoBERT, to better understand the contextual meanings of text in order to improve the accuracy of automatic economic activity classification. Our results show that the finetuned IndoBERTLARGE model achieves superior results, with an F1 score of 96.82% and a balanced accuracy of 96.10%, outperforming other recent methods used for similar task, i.e., CatBoost and DistilBERT models. Creator An entity primarily responsible for making the resource Muhammad Rizki Syazal, Evi Yulianti Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v1 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 Activity Economy CatBoost DistilBERT IndoBERT Multiclass Classification https://repository.horizon.ac.id/files/original/e4add7f3cec59c248fcdc3dce97ff639.pdf ccb34e81287e10ee69b2be3f2aaaa82c 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. 18 NO. 2 (2025) 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 Attention-based Residual Long Short-Term Memory for Earthquake Return Period Prediction in the Sulawesi Region Subject The topic of the resource earthquake prediction, LSTM, return period, time series analysis, Sulawesi Description An account of the resource Indonesia, particularly the Sulawesi region, experiences significant seismic activity due to its position at the convergence of three major tectonic plates. This study seeks to construct a model for predicting earthquake return periods in the Sulawesi area by employing the Residual Long Short-Term Memory (Residual LSTM) architecture integrated with an attention mechanism. The dataset utilized originates from the United States Geological Survey (USGS), focusing on the Sulawesi Island region within the coordinates of latitude -6.184° to 2.021° and longitude 118.433° to 125.552°, spanning the years 1975 to 2024. The research methodology is structured into three primary phases: (1) data collection and preprocessing, including data cleaning, missing value handling, and normalization, (2) exploratory data analysis to understand seismic data characteristics, and (3) development of the Residual LSTM model with an attention mechanism. The evaluation results show excellent model performance with Train Loss 0.0090, Test Loss 0.0091, Training MAE 0.0698, Testing MAE 0.0717, Training RMSE 0.0947, Testing RMSE 0.0951, and stable Huber Loss of 0.0045 for both training and testing data. The implementation of residual connections successfully addressed the vanishing gradient problem, while the attention mechanism enhanced prediction interpretability. The small discrepancy between the training and testing metrics confirms the model's robust generalization ability, indicating its strong potential for applications in predicting earthquake return periods. Creator An entity primarily responsible for making the resource Muhdad Alfaris Bachmid, Daniel Febrian Sengkey, Fabian Johanes Manoppo Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1506 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 prediction LSTM return period Sulawesi time series analysis https://repository.horizon.ac.id/files/original/c1c383be599c709c6d79e6f01deabfbd.pdf 91102366a10178e08e410db0694b55b1 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. 18 NO. 2 (2025) 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 Context-Aware Detection of Deceptive Design Patterns in E-Commerce Websites Using Word Embedding Based Deep Learning Paradigms Subject The topic of the resource deceptive design detection, word embeddings, CNN, BiLSTM Description An account of the resource Deceptive designs (DDs) are a hidden technological tactic that manipulates the user's consumer behavior in a way that benefits website vendors without them knowing. Proper identification of deceptive designs is essential to prevent users from being misled by hidden tactics. To fulfill this requirement, this study assesses Word2Vec word embedding based deep learning models for text based deceptive design detection. Models trained consist of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a hybrid model (CNN + BiLSTM) that combines the two aforementioned models. These four key score indices of accuracy, precision, sensitivity, and F1-score are computed to compare the performance of each proclaimed model. When compared to the existing DD detection techniques, all three of these approaches attain state-of-the-art performance. The results of this evaluation illustrate that the hybrid model achieves the highest accuracy of 95% in capturing the nuanced text context of deceptive designs. Furthermore, even when other metrics are considered, the hybrid model performs more effectively. To guarantee the independence and security of user activities, intelligent deep learning paradigms are integrated to identify hidden deceptive activities automatically. This allows for the accurate detection and classification of deceptive designs in intricate e-commerce environments. Creator An entity primarily responsible for making the resource Rukshika Premathilaka Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1530 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 BiLSTM CNN deceptive design detection Word embeddings https://repository.horizon.ac.id/files/original/fbffa35c75ae07925b1ac4be5485bd13.pdf 2b777fbb70c021094205d483fc7cc457 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. 18 NO. 2 (2025) 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 Comparing ASM and Learning-Based Methods for Satellite Image Dehazing Subject The topic of the resource satellite, scattering, dehazing, prior, deep learning Description An account of the resource Recent advancements in optical satellite technologies have significantly improved image resolution, providing more detailed information about Earth’s surface. However, atmospheric interference, such as haze, is still a major factor in image capture. The interference results in visibility degradation of the acquired images, hindering computer vision tasks. Numerous studies have proposed various methods to recover haze-affected regions in satellite images, highlighting the need for more effective solutions. Motivated by this, this paper compares different atmospheric dehazing methods, including Atmospheric Scattering Model (ASM)-based and deep learning-based. The results show that SRD is the best ASM-based method, with a PSNR value of 19.09 dB and an SSIM of 0.908. Among deep learning models, DW-GAN achieves the best restoration results with a PSNR value of 26.22 dB and an SSIM of 0.959. SRD offers faster inference times, but still suffers from residual haze and noticeable color degradation compared to DW-GAN. In contrast, DW-GAN provides a more complete haze removal at the cost of higher computational demands than ASM-based methods. Creator An entity primarily responsible for making the resource Steven Christ Pinantyo Arwidarasto, Laksmita Rahadianti Source A related resource from which the described resource is derived DOI: http://dx.doi.org/10.21609/jiki.v18i2.1521 Publisher An entity responsible for making the resource available Faculty of Computer Science UI Date A point or period of time associated with an event in the lifecycle of the resource 2025-06-26 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2502-9274 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 dehazing prior satellite scattering