2 10 26 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/443362d001fc96081b3092ac8886f4b2.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 Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis Subject The topic of the resource catboost;liver disease classification; machine learning;predictive modeling Description An account of the resource Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due tovague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimizethe early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of theoutlier, encoding of labels and scalingusing ILPD (Indian Liver Patient Dataset). The classification models comparedwere RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showedthe highest performance with a test accuracy of 93%. The performance was again better than any already published methodsshowing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliableand accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in anyreal-world clinical scenario. These findings add to the growingbody of evidencesupporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present inwidely applied datasets. Ongoingfuture efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use Creator An entity primarily responsible for making the resource Donni Prabowo1*, Bety Wulan Sari2,Yoga Pristyanto3, Afrig Aminuddin4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6449/1105 Publisher An entity responsible for making the resource available Department of Information System, Faculty of Computer Science, Universitas AMIKOM Yogyakarta, Yogyakarta, 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/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 https://repository.horizon.ac.id/files/original/cf077b9164055bd7473191e3f5d9e8c7.pdf 86e0b293c4f9c2d47de71b0992d1bba2 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 Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning Subject The topic of the resource chi-square feature selection;optimization of classification methods; SMOTE oversampling; tourism sentiment Description An account of the resource Tourism is a vital economic sector for Lombok Island, which is renowned for its natural beauty and cultural richness as a topdestination. The rapid growth of tourism in Lombok requires a deep understanding of tourists' perceptions and sentiments to ensure an optimal service quality. The sentiment analysis of online reviews is valuable for identifying service strengths andweaknesses and addressing tourists' needs more effectively. This not only enhances tourist satisfaction, but also aids in the design of more effective marketing strategies. However, text data analysis from online reviews presents unique challenges such as noise, class imbalance, and numerous features that may affect classification results. Therefore,this study aims to classify tourist sentiment toward Lombok tourism using machine learning methods combined with feature selection and oversampling techniques. This study focuses on optimizing sentiment analysis of tourism-related tweets using a combination of SMOTE oversampling and Chi-Square feature selection on improving classification performance without hyperparameter tuning. The study applies machine learning methods, such as SVM and Naïve Bayes, with feature selection and oversampling using Chi-Square and SMOTE. The dataset used was sentiment data regarding Lombok tourism obtained from Twitter in 2023, consisting of 940 instances divided into three classes: Negative, Neutral, and Positive. The research findings show that the use of SMOTE and Chi-Square can improve the accuracy of the SVM and Naive Bayes methods. Without optimization, the SVM method achieved an accuracy of 73.93% and a Naive Bayes of 67.02%. After optimization with SMOTE and Chi-Square, the accuracy increased for SVM by 90% and Naive Bayes by 84% to classify tourist sentiment towards Lombok tourism. The implications indicate that combining data balancing using SMOTE with feature selection via Chi-Square effectively improves the performance of sentiment classification models for tourist opinions on Lombok's tourism Creator An entity primarily responsible for making the resource Hairani Hairani1*, Anthony Anggrawan2, Muhammad Ridho Akbar3, Khasnur Hidjah4, Muhammad Innuddin5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6623/1101 Publisher An entity responsible for making the resource available Departmentof Computer Science, Facultyof Engineering, Universitas Bumigora, Mataram, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource July 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/e6b6b549d93f4972125ccb3836fa3284.pdf 34d1f12cf75edfbb6991d518cfe7de1a 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 Menu Planning for Children with Autism Using Improved Multi-Goal ProgrammingModel Subject The topic of the resource autism; goal programming; menu planning; optimization Description An account of the resource Menu planning for every individual is essential to obtain a balanced and healthy food intake for growth and development. Children with Autism Spectrum Disorders (ASD) face more feeding difficulties than their peers due to neurodevelopmental disorders such as social skills problems and repetitive behaviors. They also tended to refuse or be selective for certain food intakes. Proper menu planning for children with ASD is important to maintain their overall well-being and mitigate autism-related dietary issues. The manual menu planning for children with ASD does not consider proper nutritional intake, food variation, or total cost minimization. Currently, the application of mathematical modelling for menu planning in children with ASD is limited. Thus, this study aims to explore the extent to which the optimization approach can solve the menu planning problem with more than one objective. Finally, this research constructed daily menu planning for children with ASD, which indicates the feasibility of utilizing the Improved GP (IGP) model compared to the Goal Programming model (GP) in terms of the value for the deviational variables for the unachieved goals. The unachieved deviational variables by IGP model for Day-2 had decreased by 17.69% and by 34.43 % on Day-3.The total cost of the IGP model is also less than RM 0.50 of the GP model Creator An entity primarily responsible for making the resource Nur Rasyida Mohd Rashid1,2*,Suliadi Firdaus Sufahani2and Nur Hana Hamzaid Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6649/1124 Publisher An entity responsible for making the resource available Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Campus, 84600 Pagoh, Johor, Malaysia 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/822eaed1cde56cd5865b617eef986940.pdf 09d3b76c1a7b21796a0f1046be47d056 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 Improving the Accuracy of Tourism Recommendation System Based on Neural Collaborative Filtering Subject The topic of the resource neural collaborative filtering; rating; recommendation system; review; tourism Description An account of the resource This study proposes a Neural Collaborative Filtering (NCF) model for tourism recommendation systems by integrating user ratings and review data. This model was developed to overcome the limitations of conventional recommendation systems that rely solely on numerical data, by adding contextual information from user reviews to improve the accuracy of preference prediction. The development process includes data preprocessing, conversion of text reviews into numerical representations using embedding techniques, and the application of NCF models with various parameter configurations. Experimental results show that the NCF model that combines rating and review data produces the best performance with Root mean Square Error (RMSE) values of 0.892, Hit Ratio at 10( HR@10) of 0.735, and Normalized Discounted Cumulative Gain at 10 (NDCG@10) of 0.629, outperforming models that only use one type of data. These results demonstrate that combining numerical and textual information can improve the model's understanding of user preferences, resulting in more relevant tourist destination recommendations. These findings contribute to the development of artificial intelligence-based recommendation systems in the tourism sector.(,) Creator An entity primarily responsible for making the resource Renita Astri1*, Lai Po Hung2, Suaini Binti Sura3, Ahmad Kamal4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6516/1119 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 20, 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/9567351903d6d4279db4e4ad75e11249.pdf 221908a12d5d9fea1822a95e449cbfa5 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 Open-Set Recognition for Potato Leaf Disease Identification Using OpenMax Subject The topic of the resource computer vision; Open-Set Recognition; OpenMax; potato leaf diseases; Xception Description An account of the resource Traditional methods for identifying potato leaf diseases rely on manual visual inspection, which is prone to human error and inefficiencies. Although machine learning models have improved automation, conventional closed-set classifiers fail to recognize unknown diseases outside their training scope, limiting real-world applicability. This study addresses this gap by implementing Open-Set Recognition (OSR) using the OpenMax framework to classify known potato leaf diseases while effectively rejecting unknown pathologies. By leveraging the Xception architecture with dual learning schedulers (ReduceLROnPlateau and StepLR), we optimized the OpenMax parameters, including distance metrics (Euclidean, Eucos) and rejection thresholds. After rigorous tuning, the model achieved 86.8% accuracy and 86.4% F1-score under an openness score of 18.3%, with optimal performance using Euclidean distance and a 0.95 threshold. The results demonstrate robust discrimination between known classes (potato late blight, early blight, healthy leaves) and visually similar unknown classes (e.g., tomato diseases, healthy bell peppers). This study enhances AI-driven agricultural diagnostics by bridging the gap between closed-set precision and open-set practicality, offering a scalable solution for real-world disease identification where novel pathogens may emerge Creator An entity primarily responsible for making the resource Ike Verawati1*, Mambaul Hisam2, Yoga Pristyanto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6525/1116 Publisher An entity responsible for making the resource available Informatika, Fakultas Ilmu Komputer, Universitas Amikom Yogyakarta, Sleman, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 18, 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/2fd75f7d73ea77b6d1b79a72e5d3d819.pdf bf4ac6860c92e1b88f140fdbb7aa33e2 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 Sonified Cryptography: Secure Text Encoding with DNA and Non-Speech Audio Subject The topic of the resource cryptography; dual layer encryption; DNAencryption; information security; fast fourier transforms; non speech audification Description An account of the resource The increasing demand for data security in digital communication, particularly in high-risk sectors like defense, has led to the exploration of innovative encryption approaches. This study presents a dual-layer encryption model that enhances information concealment by integrating DNA-based cryptography with audio signal encoding. Initially, plaintext is converted into binary and obfuscated using XOR operations with randomly generated DNA sequences. The resulting DNA nucleotide sequences (A, G, C, T) form the first layer of encryption. In the second layer, these sequences are audified by mapping each nucleotide to a specific frequency, thereby transforming the encrypted data into non-speech audio signals. To evaluate the integrity and uniqueness of the encryption-decryption process, Fast Fourier Transform (FFT)-based cross-correlation is applied, comparing the original and recovered audio signals. The proposed method is implemented in MATLAB and tested on various input strings. Results demonstrate significant improvements in encryption speed and security, with the added benefit of imperceptibility in audio form. The method outperforms existing DNA-based techniques in terms of computational efficiency and resistance to brute-force attacks. This hybrid cryptographic technique offers a promising solution for secure, covert data transmission in sensitive applications Creator An entity primarily responsible for making the resource Chandrasekaran Saravanakumar1*, Neelamegam Subhashini Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6591/1115 Publisher An entity responsible for making the resource available Departmentof Electronics and Communication Engineering, SRM Valliammai Engineering College, Potheri, India Date A point or period of time associated with an event in the lifecycle of the resource August 18, 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/7932b685dad63ff2c3df3e69c47e7408.pdf d0c3149a92ed9f68646e76f39543ee98 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 Handling Imbalance in Javanese Manuscript Character Dataset using Skeleton-based Balancing Generative Adversarial Networks Subject The topic of the resource character classification; data imbalance; generative adversarial networks; javanese manuscript; skeleton-based generation Description An account of the resource Javanese script is an important part of Indonesia’s cultural heritage, representing cultural values from the past. However, recognizing and classifying Javanese characters within manuscripts is challenging due to the limited availability of data anduneven distribution of character classes. The decline in formal use of Javanese script has drastically reduced the pool of manuscript samples, causing certain characters to appear rarely and skewing class frequencies. Existing methods that utilize Generative Adversarial Networks (GANs) attempt to address this problem. However, they often struggle to generate characters that are both consistent and visually accurate in terms of structural details. To address these issues, this study introducesa skeleton-based balancing GAN (SkelBAGAN), which improves the structural details of the previous method for generating characters. The proposed method introduces three main enhancements: (i) a layer for extracting the character skeleton structure, (ii) an optimized pretrained network using an autoencoder for learning the skeleton distribution, and (iii) refinement of the evaluation function, preserving both the distribution and structural fidelity in the adversarial process. The performance of the proposed model is evaluated against previous methods using the Fréchet Inception Distance (FID) to assess distribution quality and the Structural Similarity Index Measure (SSIM) to evaluate structural fidelity. The results indicate that the proposed methods outperform previous methods in balancing the FID and SSIM metrics.The integration of all enhancements in SkelBAGAN achieves the lowest FID, indicating improved generative quality while maintaining competitive SSIM values. The qualitative study indicates that SkelBAGAN outperforms previous methods in character generation. These results highlight how the skeleton-based improvement of the quality of generated characters enhances the recognition performance for underrepresented Javanese characters in imbalanced datasets. Ultimately, this work contributes to the broader effort to preserve the Javanese script as a vital element of Indonesia’s cultural identity Creator An entity primarily responsible for making the resource Muhammad ‘Arif Faizin1, Nanik Suciati2*,Chastine Fatichah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6572/1121 Publisher An entity responsible for making the resource available Departmentof Informatics, Faculty of Intelligent Electrical and Informatics Technology Date A point or period of time associated with an event in the lifecycle of the resource August 18, 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