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/d3ba11c82a5ed500f8f334a5ec6e6903.pdf 71e5654ff3062bf6cbb79fd2618c6e89 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 Adaptive Stress Prediction with GSR, SMOTE Balancing, and Random Forest Models Subject The topic of the resource GSR sensor; perceived stress scale; random forest;SMOTE balancing; stress detection Description An account of the resource Stress is a pervasive condition that affects mental health, productivity, and quality of life across populations. Traditional methods for stress assessment, such as the Perceived Stress Scale (PSS), rely on retrospective self-reporting and are limited by subjectivity and delayed feedback. To address this gap, this study developed an integrated real-time stress monitoring system combining Galvanic Skin Response (GSR) sensors, Internet of Things (IoT) technology, and machine learning algorithms.Primary GSR data were collected from 30 participants under varied conditions, supplemented by secondary data from the WESAD dataset. A Random Forest classifier was employed to categorize stress into four levels: normal, mild, moderate, and severe. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, leading to improved model robustness. The system achieved a cross-validated classification accuracy of 69%, with substantial improvements in the detection of moderate and severe stress cases compared to traditional threshold-based methods. A strong agreement (Cohen’s Kappa κ = 0.82) was observed between system predictions and PSS-based stress assessments.Feature importance analysis identified mean GSR value and Skin Conductance Response (SCR) amplitude as the most influential indicators of stress. The system was evaluated for usability, receiving high user ratings in terms of accessibility, simplicity, and interactivity. A simple Python-based command-line interface (CLI) was also developed for real-time stress prediction based on input features.This research demonstrates the feasibility and effectiveness of combining physiological sensing, predictive analytics, and user-friendly interfaces to enable scalable and adaptive stress monitoring. Future developments will focus onintegrating additional physiological modalities and deep learning techniques to enhance predictive performance and personalization in clinical and everyday contexts Creator An entity primarily responsible for making the resource Rino Ferdian Surakusumah1*, Rechi Yudha Apza Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6588/1112 Publisher An entity responsible for making the resource available Department of Medical Electronics Engineering Technology, Faculty of Health Technology, Al Insyirah Institut of Health and Technology, Pekanbaru, 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/53390545989d262a68b7cc14a2f39e4c.pdf 32a4517472107a7a7d016cf15e498a8c 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 Advancing Vehicle Logo Detection with DETRto Handle Small Logos and Low-Quality Images Subject The topic of the resource detection transformers; logo; object detection; vehicle Description An account of the resource Image-based vehicle logo detection is an important component in the implementation of vehicle information recognition technology, which supports the development of intelligent transportation systems. Vehicle logos, as elements that represent the identities of vehicle brands and models, play a significant role in completing vehicle identity data. The information obtained from this logo can be utilized to solve various traffic problems, such as vehicle document counterfeiting and theft, and for better traffic planning and management purposes. However, the main challenge in developing an accurate logo detection system lies in the wide variety of shapes, sizes, and positions of logos in different types of vehicles. In addition, the generally small size of logos, especially on certain vehicles, often makes it difficult for computer-based detection systems to recognize logos consistently, thus affecting the overall performance of the detection model. In this research, the Detection Transformers (DETR) method is used to build a vehicle logo detection system that focuses on small-scale logo. The testing process was conducted using the VL-10 dataset, which was specifically designed forvehicle logo detection evaluation. The results show that the DETR model can detect vehicle logos very well, even for small-scale logos. The model achieved an AP50 value of 0.952, which indicates a high level of accuracy and reliability in detecting the vehicle logo in the dataset used. Creator An entity primarily responsible for making the resource Rifky Fahrizal Ubaidillah1, Mahmud Dwi Sulistiyo2*, Gamma Kosala3, Ema Rachmawati4, Deny Haryadi5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6236/1111 Publisher An entity responsible for making the resource available School of Computing, Telkom University, Bandung, 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/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/7700cf7f6ce15eddbc8d3f572d56f89c.pdf af1942563bda77302ae544944b7bcd6c 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 Benchmarking YOLOv8 Variants with Transfer Learning for Real-Time Detection and Classification of Road Cracks and Potholes Subject The topic of the resource classification;deep learning; road damage detection;transfer learning;YOLOv8 Description An account of the resource Road damage, including potholes and cracks, is a significant issue frequently encountered in road infrastructure in many regions. Such conditions accelerate road degradation, increase the risk of traffic accidents, and significantly increase the maintenance andrepair costs. Although several deep learning models have been proposed for road damage detection, few studies have systematically compared the performance of lightweight YOLOv8 variants using a consistent dataset. To address this gap, this study proposes a road defect detection and classification model based on the YOLOv8 series, which is enhanced using transfer learning to improve performance and efficiency. The dataset, obtained from Roboflow, comprises 3,846 images categorized into training, validation,and testing sets. Three YOLOv8 variants—YOLOv8n, YOLOv8s, and YOLOv8m—were benchmarked for performance. A performance evaluation was performed using the metrics of precision, recall, and mean Average Precision (mAP). Results show that YOLOv8m achieved thehighest precision (99.00%), recall (98.40%), and mAP (99.50%). In the pothole category, precision reached 98.70% and recall 99.30%; in the crack category, precision was 99.30% and recall 97.60%. The findings demonstrate that YOLOv8, particularly the YOLOv8m variant, is highly effective for real-time road damage detection and classification, offering a viable solution for intelligent transportation systems and automated infrastructure monitoring. This research has the potential to revolutionize infrastructure monitoring by enabling scalable, real-time, and cost-effective assessments of road conditions. It minimizes reliance on manual inspections, reduces human errors, and contributes to the development of intelligent transportation systems and predictive maintenance strategie Creator An entity primarily responsible for making the resource Dede Kurniadi1*, A. Abdul Latif2,Asri Mulyani3, Hilmi Aulawi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6710/1108 Publisher An entity responsible for making the resource available Department of Computer Science, Institut Teknologi Garut, Garut, 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 https://repository.horizon.ac.id/files/original/dea3af72cfb743cc1e7a642c20ec2cfc.pdf 06ac7b61fbfa02929dacd1ce61cfc785 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 BERT Model Fine-tuned for Scientific Document Classification and Recommendation Subject The topic of the resource BERT; cosine similarity; document classification; fine-tuning; recommendation system Description An account of the resource The increasing number of academic documents requires efficient and accurate classification and recommendation systems to assist in retrieving relevant information. This system is built using the "bert-base-uncased” model from Hugging Face, which has been fine-tuned to improve the classification accuracy and relevance of document recommendations. The dataset used consists of 2.000 academic documents in the field of computer science, with features including titles, abstracts, and keywords, which were combined into a single input for the model. Document similarity is measured using cosine similarity, resulting in recommendations based on semantic proximity. Unlike traditional approaches, which rely primarily on word frequency or surface-level matching, the proposed method leverages BERT’s contextual embeddings to capture deeper semantic meanings and relationships between documents. This allows for more accurate classification and more context-aware recommendations. Evaluation results show that the best model configuration (learning rate 3e-5, batch size 32, optimizer AdamW) achieved 89.5% training accuracy and an F1-score of 0.8947, while testing yielded 91% accuracy and 90% F1-score. The recommendation system consistently produced Precision@k values above 92% for k between 5 and 30, with Recall@k reaching 1.0 as k increased. These results indicate that the system not only performs reliably in classifying complex academic texts but also effectively recommends contextually relevant documents. This integrated approach shows strong potential for enhancing academic document retrieval and supports the development of semantically aware information management systems Creator An entity primarily responsible for making the resource Muhammad Deagama Surya Antariksa1*, Aris Sugiharto2, Bayu Surarso Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6789/1106 Publisher An entity responsible for making the resource available Master of Information Systems, Postgraduate School, Universitas Diponegoro, Semarang, IndonesiA Date A point or period of time associated with an event in the lifecycle of the resource August 13, 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/00d880ceba21f33035e9e707d2306f98.pdf 0c30b901a3b06fa316a384d5b8839409 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 Comparative Performance of ResNet Architectures for Toraja Carving Image Classification with Data Augmentation Subject The topic of the resource resnet; classification; toraja carving; data augmentation; cnn Description An account of the resource The complexity of the motifs and large number of different patterns make the classification of Toraja carvings challenging. The objective of this study is to develop a Convolutional Neural Network automatic classification model using a comparative analysisof the performance of three ResNet architectures. Data augmentation techniques were used to enrich the diversity of the training samples and improve the robustness of the model. The experimental results showed that ResNet101V2 had the highest validation accuracy, which was greater than 97%, followed by ResNet50V2 with more than 96%, and finally, ResNet152V2 with more than 94.74%. These test results indicate that the ResNet101V2 architecture has a better classification performance for complex motifs, with agood balance between precision and recall. However, the confusion matrix and per-class performance metrics indicated that motifs with high similarity, such as Paqdon-Bolu and Paqtedong, remained challenging. This study demonstrated that deeper CNN architectures and data augmentation techniques are effective in improving the classification accuracy of complex carving patterns. Further research should explore hybrid or advanced augmentation methods to improve the overall robustness and accuracy of the model Creator An entity primarily responsible for making the resource Herman1*, Muhammad Akbar2, Haidawati Nasir3, Herdianti4, Huzain Azis5, Lilis Nur Hayati6 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/issue/view/65 Publisher An entity responsible for making the resource available Information System, FacultyComputer Science, Universitas Muslim Indonesia, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 9, 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/c2e378a65c2feae559cb6abc819489a8.pdf df1b3c6126548b31b8b63ee19e81c315 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 Comparing Optimization Algorithms in ANN Models for House Price Prediction in Pekanbaru Subject The topic of the resource AdaDelta; stochastic gradient descent (SGD); adaptive moment estimation (Adam); adaptive sharpness-aware minimization (ASAM);artificial neural network (ANN); house price prediction; optimization; nadam Description An account of the resource This study evaluates the performance of five optimization algorithms in Artificial Neural Network (ANN) models for predictinghouse prices in Pekanbaru. The optimizers tested include Adam, AdaDelta, Stochastic Gradient Descent (SGD), Nadam, and Adaptive Sharpness-Aware Minimization (ASAM). A total of 3,149 house sales records were collected from rumah123.com between January and December 2024. After cleaning 148 incomplete entries, 3,001 valid records remained. The dataset included seven features: price, location, number of bedrooms, number of bathrooms, land area, building area, and garage capacity, with the location encoded using one-hot encoding. The research involved a literature review, problem formulation, data acquisition, preprocessing, model development, and evaluation. Model performance was assessed using the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). The results show that SGD consistently achieved the best performance, particularly at a 90:10 train-test split, with the lowest MAPE (1.74%) and MSE (0.3279). Adam and Nadam also performed well, while ASAM had the highest error (MAPE 6.14%). These findings indicate that SGD was the most effective optimizer for this dataset. Future research should explore larger datasets and advanced hyperparameter tuning to improve the generalizability of this model Creator An entity primarily responsible for making the resource Doni Winarso1*, Edo Arribe2, Syahril3, Aryanto4, Muhardi5, Sharulniza Musa Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6619/1109 Publisher An entity responsible for making the resource available Departmentof Information Systems, Facultyof Computer Science, Universitas Muhammadiyah Riau, Riau, 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 https://repository.horizon.ac.id/files/original/fdaf9ca8337f0a4e1457c5dbe5d17eef.pdf 2a39d0eb397b9976cb4d5fadd7b1b594 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 DiG-MFV: Dual-integrated Graph for Multilingual Fact Verification Subject The topic of the resource fact verification; graph fusion; LaBSE; multilingual model; mBERT; political claim; XLM-R Description An account of the resource The proliferation of misinformation in political domains, especially across multilingual platforms, presents a major challenge to maintaining public information integrity. Existing models often fail to effectively verify claims when the evidence spans multiple languages and lacks a structured format. To address this issue, this study proposes a novel architecture called Dual-integrated Graph for Multilingual Fact Verification (DiG-MFV), which combines semantic representations from multilingual language models (i.e., mBERT, XLM-R, and LaBSE) with two graph-based components: an evidence graph and a semantic fusion graph. These components are processed through a dual-path architecture that integrates the outputs from a text encoder and a graph encoder, enablingdeeper semantic alignment and cross-evidence reasoning. The PolitiFact dataset was used as the source of claims and evidence. The model was evaluated by using a data split of 70% for training, 20% for validation, and10% for testing. The training process employed the AdamW optimizer, cross-entropy loss, and regularization techniques, including dropout and early stopping based on the F1-score. The evaluation results show that DiG-MFV with LaBSE achieved an accuracy of 85.80% and an F1-score of 85.70%, outperforming the mBERT and XLM-R variants, and proved to be more effective than the DGMFP baseline model (76.1% accuracy). The model also demonstrated stable convergence during training, indicating its robustness in cross-lingual political fact verification tasks. These findings encourage further exploration in graph-based multilingual fact verification systems. Creator An entity primarily responsible for making the resource Nova Agustina1*, Kusrini2, Ema Utami3, Tonny Hidayat4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6695/1104 Publisher An entity responsible for making the resource available Department of Informatics Doctorate, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource July 27, 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