1 10 26 https://repository.horizon.ac.id/files/original/cc95ccbc3deb4e32caa81c3e15d950e8.pdf 1767b98f8de8b2392a7debd3974f23fb 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 9 No 4 (2025) 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 New Triple-Weighted K-Nearest Neighbor Algorithm for Tomato Maturity Classification Subject The topic of the resource DW-KNN; HSV; KNN; TW-KNN; W-KNN Description An account of the resource As climatic products, tomatoes are highly sensitive to harvesting and processing. The sorting of tomatoes can be significantly improved by utilizing Hue Saturation Value (HSV) color features that are classified using neighboring algorithms, such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (W-KNN), and DW-KNN. However, the DW-KNN algorithm does not consider the relative relationship between the farthest, nearest, and surrounding neighbors, which may impact the classification accuracy, particularly in datasets with uneven distributions. This study proposes a Triple Weighted K-Nearest Neighbor (TW-KNN) algorithm for tomato image classification. This algorithm effectively handles the problem of sensitivity and outliers in the data distribution and considers the relationship between neighboring distances. The classification data consisted of 400 tomato images with five maturity levels divided into training and testing sets using k-fold cross-validation. Tests were conducted using several variations of parameter k, namely 4, 6, 9, and 15, to evaluate the classification performance. The results show that the proposed TW-KNN algorithm consistently outperforms other methods by producing better classification results. This is demonstrated by an accuracy rate of 95.52% across different values of k. The superior performance of the TW-KNN highlights its ability to provide robust and stable classification results compared to conventional KNN variants. This finding indicates that the TW-KNN is more effective in consistently classifying tomato fruits, making it a promising approach for automated fruit sorting applications Creator An entity primarily responsible for making the resource Lidya Ningsih1*, Arif Mudi Priyatno2, Addini Yusmar3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6441/1128 Publisher An entity responsible for making the resource available Department of Digital Business, Faculty of Economics and Business, Universitas Pahlawan Tuanku Tambusai, Kampar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 29, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/712acad4c4b7565aac03c15a634bd5c3.pdf 91e9477f130fc5656913efc479a8351d 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 9 No 4 (2025) 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 Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning Subject The topic of the resource CNN architecture; convolutional neural network (CNN); disease detection; hyperparameter tuning; medical image classification; X-ray Description An account of the resource This study aimed to compare the performance of various Convolutional Neural Network (CNN) architectures, including LeNet, ResNet, AlexNet, GoogleNet, VGGNet, and the proposed model, in medical image classification for disease detection. The proposed model was developed by adding additional layers and fine-tuning the hyperparameters in the ResNet architecture to enhance its ability to extract complex features. The training and testing processes were conducted using an augmented X-ray image dataset to increase the data diversity. The results indicate that the proposed model achieved the highest testing accuracy of 76.33%, surpassing other models in terms of accuracy, precision, recall, and F1-score. Although there are some limitations in specificity and the Matthews Correlation Coefficient (MCC), the proposed model still demonstrates better generalization ability, with an AUC-ROC score approaching an optimal value. These findings suggest that the proposed model has advantages in medical image classification and holds potential for further development to enhance disease classification accuracy Creator An entity primarily responsible for making the resource Sundari Retno Andani1*, Poningsih2, Abdul Karim3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6357/1127 Publisher An entity responsible for making the resource available Informatics Study Program, Master's Program, STIKOM Tunas Bangsa, Pematangsiantar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 25, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/35535d38b7b77c39d49cfa55d87522da.pdf ffcc82d4677d839359dc62a3dea7db7c 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 9 No 4 (2025) 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 Language Processing for Detecting Fake News on Twitter Using a Long Short-Term Memory Architecture Subject The topic of the resource Fake news detection;LSTM;NLP;political hoaxes; social media analysis Description An account of the resource The rapid spread of misinformation on social media platforms, particularly X (formerly Twitter), poses a significant challenge to public trust and democratic integrity. Fake news is often crafted to deceive readers and manipulate public opinion, especially in political contexts such as the 2024 Regional Head Elections (Pilkada 2024). Although various measures have been proposed to mitigate this issue, achieving an effective balance between controlling misinformation and preserving free speech remains a challenge. This study aims to address this problem by developing a fake news detection model based on Natural Language Processing (NLP) and Long Short-Term Memory (LSTM). The dataset used in this study was collected from public tweets related to Pilkada, with Kompas.com serving as the validation source to verify content authenticity. Experimental results show that the proposed LSTM model outperformed traditional classification methods, achieving a precision, recall, and F1-score of 0.95, along with an overall accuracy of 94.90%. Confusion matrix analysis further confirmed the reliability of the model by demonstrating low misclassification rates. This study contributes to the advancement of AI-driven hoax detection systems, offering an automated and scalable solution for combating misinformation in political discourse Creator An entity primarily responsible for making the resource Rini Sovia1*, Dwi Andhara Valkyrie2,Ruri Hartika Zain3,Firdaus4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6570/1125 Publisher An entity responsible for making the resource available DepartmentInformation Technology, Facultyof Computer Science, Universitas Putra Indonesia YPTK, Padang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 25, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/c08842ac6efa49d3df4ac8404d87cbae.pdf fc839c2dae1bbcb2a605085de9170c64 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 9 No 4 (2025) 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 Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions Subject The topic of the resource ensemble learning; kernel function; sentiment analysis; smote; support vector machine Description An account of the resource This study targets improved sentiment classification by combining the strengths of multiple SVM kernels within an ensemble framework. We introduce SVM Porlis, which fuses Linear, RBF, Polynomial, and Sigmoid kernels using both hard and soft voting to boost performance on skewed data. The task is binary sentiment recognition (positive vs. negative). A corpus of 2,248 tweets concerning the debate over the naturalization of Indonesia’s national football players was gathered via the official X/Twitter API, with a marked dominance of negative tweets. The preprocessing pipeline encompassed cleaning, labeling, tokenization, stopword removal, stemming, and TF-IDF feature extraction. To counter the imbalance, SMOTE was applied to synthesize additional minority-class samples. Each kernel was first trained and assessed independently, then aggregated into the SVM Porlis ensemble. Evaluation used accuracy, precision, recall, F1-score, and confusion-matrix analysis. The soft-voting SVM Porlis model achieved the best results—98% for accuracy, precision, recall, and F1—outperforming single-kernel baselines and otherensembles such as SVM + Chi-Square and SVM + PSO. These outcomes indicate that integrating diverse kernels effectively captures both linear and nonlinear patterns, yielding a robust and adaptive approach for sentiment analysis on real-world, imbalanced datasets Creator An entity primarily responsible for making the resource M. Khairul Anam1*,Tri Putri Lestari2, Lusiana Efrizoni3, Nadya Satya Handayani4, Imam Andhika Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6708/1123 Publisher An entity responsible for making the resource available Departmentof Informatics, Facultyof Science and Technology, Universitas Samudra, Langsa, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 22, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/62ad96092b8dbc29d535b63cf126e248.pdf 8ca38fcfe315d7991c826da7f1addbe8 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 9 No 4 (2025) 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 Optimizing Tourism Recommendations with a Hybrid Model: Bridging User Preferences and Behavioral Patterns Subject The topic of the resource apriori; content-based filtering; cosine similarity; hybrid model; tourism Description An account of the resource Recommender systems play a crucial role in personalized decision-making, particularly in the tourism industry, where users seek destinations that align with their preferences. However, traditional recommendation methods often struggle to provide accurate recommendations. This researchintroduces a hybrid recommendation framework that combines Content-Based Filtering (CBF) with Apriori-based association rule mining to improve the accuracy and relevance of recommendations. First, CBF was implemented using TF-IDF, Word2Vec, and BERT embeddings to compute the similarity between user preferences and tourism destinations. The Top-N recommended destinations from each method were then used as antecedents in Apriori to identify associative patterns and co-occurrence relationships among tourism destinations. By leveraging both semantic preference matching and association rule mining, the proposed system refines the recommendation process, ensuring not only personalized suggestions but also uncovering implicit travel patterns. The experimental results demonstrate that the hybrid model improves recommendation relevance and accuracy compared to standalone CBF methods. The accuracy of the CBF model was 53.96%, whereas that of the hybrid model was 94.31%. The integration of CBF and Apriori offers a morecomprehensive and data-driven recommendation framework, which is valuable for personalized tourism applications Creator An entity primarily responsible for making the resource Rifqi Hammad1*, Muhammad Azwar2, M. Aswin Syarif Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6510/1122 Publisher An entity responsible for making the resource available Department of Software Engineering, University of Bumigora, Mataram, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 22, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/65afd27bff7f639adcf08b5cfe3fd37f.pdf 686d953fe348320cb79dd761dffee5ba 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 9 No 4 (2025) 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 Harnessing BERT for Semantic Understanding in Tourism Recommendation Engines Subject The topic of the resource BERT; classification; deep learning; fine-tuning; NLP Description An account of the resource It will be necessary for attraction managers withinhotels to track guests' lifestyles to keep the business running. Such an understanding may be achieved, forexample by analyzing reviews on attractions to capture the attitudes of the visitors towards the services and business within the tourism industry. The approach utilizes web scraping to gather user-generated reviews, using text preprocessing, datapre-processing, and further improvement of the model using labelled sentiment data divided into three sentiment classes: positive, negative, or neutral. The dataset consisting of 908 reviews were divided in 70:15:15 ratio for training, validationand testing. The effectiveness of the model was evaluated using accuracy, precision, recall, and the F1-score. In this study, theBERT deep learning model is used to classify sentiments of Indonesian tourist. Using the SmallBERT variant fine-tuned on515k reviews for 5 epochs, the model achieved 91.40% accuracy, 90.51% precision, recall, and F1 score. The results indicate a dominance of positive sentiments, visualized using tableau. This research provides a robust foundation for developing intelligentsentiment-based recommendation systems in the tourism sector and suggests future exploration using other transformer-based models such as GPT, T5, or BART for comparative analysis Creator An entity primarily responsible for making the resource Renita Astri1*, Lai Po Hung2, Suaini Binti Sura3, Ahmad Kamal Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6575/1120 Publisher An entity responsible for making the resource available Sistem Informasi, Fakultas Farmasi Sains dan Teknologi, Universitas Dharma Andalas, Padang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 19, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/8bc62f3db9a2a6163294170993230141.pdf 93da1f074871430f3299081900264d8a 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 9 No 4 (2025) 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 Thermal Comfort Projection on Northern Coast of Central Java Using Machine Learning Subject The topic of the resource coastal north central java; random forest (RF); temperature humidityindex (THI); thermal comfort; XGBoost Description An account of the resource The Thermal Humidity Index (THI) serves as a critical measure of environmental thermal comfort, particularly vital for living beings in densely populated regions. This study projects and classifies THI in the western northern coastal areas of Central Java using Machine Learning (ML) techniques. Utilizing temperature and humidity data from 2018 to 2024, THI projections were conducted using the XGBoost algorithm, whereas comfort level classifications were performed using the Random Forest algorithm. The results indicate that Semarang City, eastern Kendal, Pemalang, and Tegal frequently experienced slightly uncomfortable conditions (THI 27–30), particularly during the rainy and transitional seasons, whereas other regions maintained comfortable levels (THI < 27). The THI projection model for 2025–2029 achieved an accuracy of 73%, while the classification model attained a remarkably high accuracy of 99.94%. These findings highlight the need for enhanced regional management strategies in areas with reduced thermal comfort Creator An entity primarily responsible for making the resource Afandi1, Aji Supriyanto2* Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6537/1118 Publisher An entity responsible for making the resource available Departmentof Informatioan Technology, Facultyof Information Information and Industry, Universitas Stikubank, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 19, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/de8199cf65fa56cb4ddb6586f10e42be.pdf 37a90b9c3c5efe216c1934e0905f447f 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 9 No 4 (2025) 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 Optimizing DBSCAN Parameters for Depth-Based Earthquake Clustering Using Grid Search Subject The topic of the resource clustering; DBSCAN; earthquake data; grid search; Sulawesi; seismic gap Description An account of the resource This study addresses the challenge of accurately clustering earthquake events based on depth to better understand seismic activity patterns in Sulawesi from 2019 to 2023. Traditional clustering algorithms often fail to capture the complex spatial and depth-based structures of earthquake data. To overcome this, we employed the DBSCAN algorithm, which is well-suited for identifying irregularlyshaped clusters and handling noise in spatial datasets. A key focus of this research is the systematic optimization of DBSCAN’s parameters—epsilon (ε) and minimum samples (min_samples)—using a grid search approach. Epsilon values varied from 0.1 to 0.5, and min_samples ranged from 6 to 60. The optimal parameters, determined using the Calinski-Harabasz (CH) index, were ε = 0.4 and min_samples = 54. Compared with previous heuristic settings, the optimized configuration produced better separated and more interpretable clusters. Using the optimized parameters, nine distinct clusters were identified, capturing meaningful patterns in both depth and magnitude. The results revealed that shallow earthquakes (0–20 km) tend to exhibit greater magnitude variation, withsome clusters averaging magnitudes up to 3.7. This suggests a higher seismic hazard potential associated with brittle crustal activity. The findings contribute to seismic hazard analysis by providing a more robust understanding of three-dimensional earthquake distribution, aiding regional risk assessment and disaster preparedness efforts. These insights can support agencies such as BMKG and BPBD in hazard mapping, sensor deployment, and contingency planning for high-risk zones. Creator An entity primarily responsible for making the resource Rushendra1*, Ody Octora Wijaya2, Mohamad Yusuf3, Andri Setiyaji4, Djoko Prabowo5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6521/1117 Publisher An entity responsible for making the resource available Department of Informatics, Universitas Mercu Buana, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 19, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/43fee82eedfb9fcc97aa2fc07ef577b7.pdf 5d23a602f7c94db10fbe70b0975ac206 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 9 No 4 (2025) 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 the Impact of Backpropagation Hyperparameter Optimization on Heart Disease PredictionModels Subject The topic of the resource backpropagation neural network (BPNN); early diagnosis; heart disease prediction; hyperparameter optimization; machine learning Description An account of the resource Heart disease represents a significant global health concern, underscoring the importance of early and accurate predictive models to minimize complications and improve patient prognosis. The Backpropagation Neural Network (BPNN) has been widely utilized for heart disease prediction; however, its effectiveness is highly dependent on the selection of appropriate hyperparameters, including the number of neurons, activation function, optimizer, and batch size. In this study, the influence of hyperparameter optimization on BPNN performance was investigated. A baseline BPNN model was evaluated alongside an optimized counterpart in which key hyperparameters had been systematically fine-tuned to improve both predictive accuracy and model stability. Both models were trained and validated using an identical dataset, and their performances were assessed based on Accuracy, Precision, Recall, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The optimized model demonstrated marginally higher accuracy (99.11% compared to 99.09%) and slightly lower error rates (MSE and MAE of 0.0089 versus 0.0091). Moreover, it achieved superior precision, indicating enhanced reliability incorrectly identifying heart disease cases. While the performance improvement was relatively small, the optimized model exhibited greater consistency and balance. These results emphasize the critical role of hyperparameter tuning in enhancing the predictive capability of neural network models in medical applications. The study contributes to the advancement of more accurate and dependable AI-based tools for early heart disease diagnosis. Future research may benefit from employing advanced optimization strategies such as Bayesian Optimization or Genetic Algorithms and leveraging larger, more diverse datasets to improve generalizability. Creator An entity primarily responsible for making the resource Nita Syahputri1*, Putrama Alkhairi2, Enok Tuti Alawiah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6473/1110 Publisher An entity responsible for making the resource available Faculty of Engineering and Computer Science, Department of Information Systems, Universitas Potensi Utama,Medan,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 17, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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/8364a522492804457d88a8c2821bb19b.pdf 83736f776156dca348cd625a6301e1e6 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 9 No 4 (2025) 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 Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic Subject The topic of the resource explainable AI; frechet inception distance (FID); generative adversarial network (GAN); network security; threat visualization Description An account of the resource Despite the growing sophistication of cyberattacks, current network traffic analysis tools often lack intuitive visual support, limiting human analysts’ ability to interpret complex threat behaviors. To address this gap, this study proposes a novel deep learning-based visualization framework using a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize threat-specific infographics from structured numerical features in the CICIDS2017 dataset. Unlike conventional methods, such as PCA or static dashboards, which often result in abstract or non-adaptive visuals, our approach generates class-distinct grayscale images that preserve the behavioral patterns of various attacks, includingdenial-of-service, brute force, and port scanning. The preprocessing pipeline reshapes the selected flow-based features into 28×28 matrices to train the generative model. Evaluation using the Frechet Inception Distance (FID) yielded a score of 28.4, whereas a CNN classifier trained on the generated images achieved 91.2% accuracy, confirming visual fidelity and semantic integrity. Additionally, a panel of human experts rated the interpretability of the generated images at 4.3 out of 5.0. These findings demonstrate that generative visualization can enhance human-centered threat analysis by bridging raw data with interpretable imagery, thereby offering a scalable and explainable approach for integrating AI into real-time security workflows Creator An entity primarily responsible for making the resource Mars Caroline Wibowo1*, Iwan Setyawan2, Adi Setiawan3, Irwan Sembiring Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6717/1107 Publisher An entity responsible for making the resource available Departmentof Visual Communication Design, Faculty of Academic Studies, Universitas Sains dan Teknologi Komputer, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 15, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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