1 10 20 https://repository.horizon.ac.id/files/original/f54381d9ad98cfdec91cef7898bb6203.pdf 2f5604f2377d66e396354fd6e139481c 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 1 (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 PCA and t-SNE Implementation for KNN Hypertension Classification Subject The topic of the resource Hypertension;KNN;Dimensionality Reduction;PCA;t-SNE Description An account of the resource Hypertension is a condition that, if allowed to increase, can significantly injure internal organs due to high blood pressure. The objective of this study is to use the K-Nearest Neighbor (KNN) algorithm along with PCA and t-SNE to accurately identify four categories of Hypertension,Normal, Hypertension, Stage 1 Hypertension, and Stage 2Hypertension. After establishing the scope, a dataset consistingof 7,794 samples was sourced from Labuang Baji Regional General Hospital, Makassar, and contained age, weight, and systolic and diastolic blood pressure parameters. The class distribution is Normal (36.3%), Hypertension (43.12%), Stage 1 Hypertension (8.29%), and Stage 2 Hypertension (12.31%). Experimental results show that the KNN base model achieved 99% accuracy, KNN with PCA reached 100%, and KNN with t-SNE attained 99%. Cross-validation was used to evaluate model generalization, yielding accuracies of 91%, 94%, and 91%, respectively. These findings suggest that KNN, particularly when integrated with t-SNE, is highly effective in visualizing and classifying non-linear data structures. Furthermore, this study demonstrates that incorporating dimensionalityreduction techniques enhances the interpretability of classified hypertension data, which is crucial for informed decision-making by mental healthcommittees Creator An entity primarily responsible for making the resource Andi Aulia Cahyana Resky1*, Jessica Crisfin Lapendy2, Andi Akram Nur Risal3*, Dewi Fatmarani Surianto4, Abdul Wahid Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6208/1025 Publisher An entity responsible for making the resource available Informatics and Computer Engineering, Engineering, Makassar State University, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-02-2025 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/2daec2f4f74c7a394eb826ae4756435f.pdf 936d17ad477ab3446bbafbff4bd7b7ae 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 1 (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 Ant Colony Optimization for Jakarta Historical Tours: A Comparative Analysis of GPS and Map Image Approaches Subject The topic of the resource Ant Colony Optimization; Historical Sites; Intelligent System;Traveling Salesman Problem;Tour Planning Description An account of the resource The Traveling Salesman Problem (TSP) is a problem that represents a difficult combinatorial optimization problem starting from practical problems. The ant colony optimization (ACO) algorithm is implemented in several topics, particularly in solving combinatorial optimization problems. ACO is inspired by the behavior of ants in searching for the shortest path between a food source and their nest. In this research, ACO isused to find the best path or traveling salesman problem for museums and historical sites in Jakartacapital city of Indonesia. This research employs an approach based on the location's coordinates or latitude and longitude, while another method depends on coordinate data obtained from a supplied map image.After implementing both models, it can be concluded that the ACO model is not very good at solving TSP using actual coordinates. Meanwhile, the algorithm can quickly find near-optimalpathswhen using coordinates from a map image. The algorithm generates the optimal path in 11 seconds, reducing the initial distance from 17.938 to 4.430, using 4.731 ants and 75 trips with a distance power of 1.Statistical random variation was also performed, which proved that the algorithm is flexible and reliable when tested under various conditions Creator An entity primarily responsible for making the resource Gabriel Fortino Bodhi1*, Charleen2,Devi Fitrianah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5968/1024 Publisher An entity responsible for making the resource available Computer Science, Binus Graduate Program, Bina Nusantara University, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 20-02-2025 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/facbeacbe626cac246c0ba68024e9ac5.pdf 7652e675417f4e619fdd4dc408b6184a 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 1 (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 Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement Subject The topic of the resource Hybrid Gradient Descent Grey Wolf Optimizer; HyperparameterOptimization; DiabetesPrediction; Machine Learning;Support Vector Machine(SVM Description An account of the resource Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM is a robust machine learning algorithm for classification and regression. Still, itsperformance depends significantly on selecting appropriate hyperparameters such as regularization (C), kernel coefficient (γ), and polynomial kernel degree (d). The HGD-GWO method synergizes Gradient Descent for local optimization and Gray Wolf Optimizer for global solution exploration. Using the Pima Indians Diabetes dataset, the process includes normalization, hyperparameter optimization, data division, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The optimized SVM achieved an accuracy of 81.17%, with precision, recall, and F1-score values of 75.00%, 57.45%, and 65.06%, respectively, at a data ratio of80%:20%. These findings highlight the potential of HGD-GWO in enhancing predictive models, particularly for early diabetes detection. Creator An entity primarily responsible for making the resource Sri Rossa Aisyah Puteri Baharie1*, Sugiyarto Surono2, Aris Thobirin3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6203/1021 Publisher An entity responsible for making the resource available Matematika, Sains dan Teknologi Terapan, Universitas Ahmad Dahlan, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 16-02-2025 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/ab4446eea6bdc48db8319aa4441d9859.pdf 83059670fb5abce64c361c2165eb413c 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 1 (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 Hate Speech Detection in Indonesian Language Using Graph Neural NetworksandTF-IDF Subject The topic of the resource Context-Aware Sentiment Analysis;Graph Neural Network (GNN);Hate SpeechDetection;SocialMedia;XTF-IDF Description An account of the resource Most of thehate speech and abusive content on social media, particularly in the Indonesian language, presents significant challenges for content moderation systems. Previous research has applied machine learning models such as Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to address this issue. However, these approaches are limited in their abilityto capture the relational and contextual nuances inherent in the data, resulting in suboptimal performance.This study introduces anapproach by combining Graph Neural Networks (GNN) with Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction to improve hate speech detection on Twitter (platformX). The dataset consists of 13,169 Indonesian tweets, manually labeled for Hate Speech and Abusive categories. Preprocessing steps include text cleaning, stemming, stop-wordremoval, and normalization. The GNN model achieved superior results, with accuracy scores of 92.90% for Abusive and 89.78% for Hate Speech, significantly outperforming the RNN model, which achieved accuracyof 86.09% and 86.15%, respectively. Thisstudy highlights the advantage of graph-based approaches in capturing complex relationships within text data. Future research can explore expanding datasets to include regional dialects and integrating advanced feature extraction techniques like Word2Vec or BERT. This study establishes a robust framework for improving hate speech detection, offering a valuable contribution to safer digital environments. Creator An entity primarily responsible for making the resource Syaikha Amirah Zikrina1*, Fitriyani2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6179/1020 Publisher An entity responsible for making the resource available Department of Data Science, Facultyof Informatics, Telkom University, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 16-02-2025 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/603b035a2d10614a33fcfdaf02257400.pdf 4324774ac0448ba4c3f073e6bfa167eb 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 1 (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 Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images Subject The topic of the resource Deep Learning; Bone Fracture; X-Ray Images; Convolutional Neural Network; VGG16 Description An account of the resource The purpose of this research is to determine whether or not a deep learning model called VGG16 can automatically identify bone fractures in X-ray pictures. The dataset, sourced from Kaggle, includes 10,522 images of human hand and foot bones, which underwent preprocessing steps such as normalization and resizing to 224x224 pixels to enhance data quality. The study utilizes the VGG16 architecture, pre-trained on ImageNet, as a base model, with transfer learning applied to adapt the model for fracture detection by fine-tuning its weights. This architecture consists of five blocks of convolutional and max-pooling layers to effectively extract and enhance information from the images for precise classification. The training and testing phases utilized an 80:20 split of the data, employing binary cross-entropy as the loss function and the Adam optimizer for efficient weight updates. The model achieved high performance, with an accuracy of 99.25%, precision of 98.62%, recall of 98.88%, and an F1-score of 99.16% over 25epochs with a batch size of 128.Experimental results indicate that smaller batch sizes generally enhance accuracy and reduce loss values, with batch sizes of 128 and 16 yielding optimal performance. The study's findings underscore the potential of VGG16 in improving diagnostic accuracyand reliability in medical imaging, providing a robust tool for fracture detection. Future research should continue exploring hyperparameter optimization to further enhance model performance while balancing computational efficiency Creator An entity primarily responsible for making the resource Resky Adhyaksa1, Bedy Purnama2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6101/1018 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 14-02-2024 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/39b6ccf643b87e19e86beabe51fd8d35.pdf 4324774ac0448ba4c3f073e6bfa167eb 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 1 (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 NLP-Based Intent Classification Model for Academic Curriculum Chatbots in Universities Study Programs Subject The topic of the resource Chatbot, IndoBERT, Intent Classification Description An account of the resource Chatbots are increasingly prevalent in various fields, including academic fields. Universities often rely on lecturers and staff for information access, which can lead to delays, limited availability outside working hours, and the risk of missedquestions. This study aims to develop a chatbot model capable of addressingquestionsabout the curriculum through intent classification, reducing reliance on manual responses, and providing a solution that ensures quick,accurate information retrieval. The research focuses on optimizingthe IndoBERT model for intent classification andaddresses challengesthatarose due to imbalanceddata, which could have impacted model performance. Data was collected through an open poll on common curriculum-related questions asked by students.To address data imbalance, wetried oversampling techniques, such as SMOTE, B-SMOTE, ADASYN, and Data Augmentation. Data augmentation was chosen and successfully addressed the imbalance problem while maintaining data semantics effectively. Weachieved the best model with hyperparameters batch size of 8,learning rate of 0.00001, 15 epochs, and 64 neurons in the hidden layer,resultingin 98.7% accuracy on the test data. Evaluation metrics further demonstrate the model's robustness across multiple intents. This research demonstrates the advantages of the IndoBERT model inintent classification foracademic chatbots, achieving excellent performance Creator An entity primarily responsible for making the resource Najma Rafifah Putri Syallya1*, Anindya Apriliyanti Pravitasari2, Afrida Helen Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6276/1017 Publisher An entity responsible for making the resource available Department of Statistics, Universitas Padjadjaran, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 14-02-2024 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/a08d7c62dc192a47e284684b94b550c6.pdf 9077e27698f134f0eba71ccd9ccb3a8c 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 1 (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 Question Answering through Transfer Learning on Closed-Domain Educational Websites Subject The topic of the resource NLP;Question Answering; Transfer Learning, Closed Domain;XLM-RoBERTa Description An account of the resource Navigating complex educational websites poses challenges for users looking for specific information. This research discusses the problem of efficient information search on closed-domain educational platforms, focusing on the Universitas Indonesia website. Leveraging Natural Language Processing (NLP), we explore the effectiveness of transfer learning models in Closed Domain Question Answering (QA). The performance of three BERT-based models, including IndoBERT, RoBERTa, and XLM-RoBERTa, are compared in transfer and non-transfer learning scenarios. Our result reveals that transfer learning significantly improves QA model performance. The models using a transfer learning scenario showed up to a 4.91\% improvement in the F-1 score against those using a non-transfer learning scenario. XLM-RoBERTa base outperforms all other models, achieving the F-1 score of61.72\%. This study provides valuable insights into Indonesian-language NLP tasks, emphasizing the efficacy of transfer learning in improving closed-domain QA on educational websites. This research advances our understanding of effective information retrieval strategies, with implications for improving user experience and efficiency in accessing information from educational websites. Creator An entity primarily responsible for making the resource Matiin Laugiwa Prawira Putra1*, Evi Yulianti Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6163/1016 Publisher An entity responsible for making the resource available Computer Science, Facultyof Computer Science, University of Indonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 08-02-2025 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/341b476a52f44a96b05697a9aaa55f47.pdf f3d9ce2844148c95c5fb657cabf59294 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 1 (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 Premier League Match Outcome Prediction Using Support Vector Machine with Ensemble Techniques: A Comparative Study on Bagging and Boosting Subject The topic of the resource Support Vector Machine (SVM);Ensemble Techniques;Bagging;Boosting;Model Accuracy;Sports Analytics Description An account of the resource Predicting football match outcomes is a significant challenge in sports analytics, requiring accurate and resilient models. This study evaluates the effectiveness of ensemble techniques, specifically Bagging and Boosting, in enhancing the performance of Support Vector Machine (SVM) models for predicting match outcomes in the English Premier League. The dataset comprises detailed match statistics from 1,520 matches across multiple seasons, including features such as team performance, player statistics, and match outcomes. Four models were examined: baseline SVM, SVM with Bagging, SVM with Boosting, and a combined SVM + Bagging + Boosting approach. Evaluation metrics include accuracy, recall, precision, F1 score, and ROC-AUC, providing a comprehensive assessment of each model's performance. Experimental results indicatethat ensemble methods substantially improve model accuracy and stability, with the SVM + Bagging + Boosting combination achieving perfect accuracy, recall, precision, and F1 scores, alongside anROC-AUC value of 0.88. However, this model's slightly reduced ROC-AUC compared to others and its high computational cost highlight potential risks of overfitting and the need for significant resources. These findings underscore the practical potential of combining Bagging and Boosting with SVM for robust and accurate predictions. Limitations include the dataset's focus on a single league and the high resource requirements for ensemble methods. Future research could expand this approach to other sports and leagues, improve computational efficiency, and explore real-time predictive applications. Creator An entity primarily responsible for making the resource Agus Perdana Windarto1*, Putrama Alkhairi2, Johan Muslim3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6173/1015 Publisher An entity responsible for making the resource available Master's Program, Informatics Study Program, STIKOM Tunas Bangsa, Pematangsiantar, North Sumatra, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 06-02-2025 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/c025505cdd6df3c0d896cf53e8020bd6.pdf 92e1ecd718a88704b73e3be840c9b3bd 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 1 (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 mpact of Adaptive Synthetic on Naïve Bayes Accuracy in Imbalanced Anemia Detection Datasets Subject The topic of the resource ADASYN;Class Imbalance; Oversampling; Machine Learning; Naïve Bayes; Description An account of the resource This research aims to analyze the impact of the Adaptive Synthetic (ADASYN) oversampling technique on the performance of the Naïve Bayes classification algorithm on datasets with class imbalance. Class imbalance is a common problem in machine learning thatcan cause bias in prediction results, especially in minority classes. ADASYNis one of the oversampling methods that focuses on adaptively synthesizing new data for minority classes. In this study, the performance of the Naïve Bayes algorithm was tested onAnemia Diagnosisdatasets before and after the application of ADASYN. This dataset contains 104 instances, 5 attributes, and 2 classes, and has an imbalance ratio of 3. The evaluation was carried out by comparing accuracy, confusion matrix, precision, recall, and F1-score to obtain a more comprehensive picture of the effectiveness of ADASYNin improving Naïve Bayes.The results of the study show that the performance of the oversampling method depends on the imbalance ratio so it is important to ensure that the oversampling method does not cause overfitting and this can be overcome by using ADASYN which only selects Selected Neighbors.The results showed that ADASYNsignificantly increased accuracy from 0.57 to 0.78, precision from 0.17 to 0.74, recall from 0.20 to 0.88, and F1-Score from 0.18 to 0.80.In this study, we also compared the application of ADASYN and SMOTE on the Naïve Bayes algorithm. The results show that ADASYN outperforms SMOTE across all key metrics—accuracy, precision, recall, and F1-Score—while the accuracy improvements were statistically significant (p-value = 0.00903) Creator An entity primarily responsible for making the resource Muhammad Khahfi Zuhanda1*, Lisya Permata2, Hartono3, Erianto Ongko4, Desniarti Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6031/1013 Publisher An entity responsible for making the resource available Department of Informatics, Faculty of Engineering, Universitas Medan Area, Medan,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 27-01-2025 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/f14a92dc690f6d5a0587590f442a2baf.pdf 460d011eeb4ddb9abe1eca660995bad7 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 1 (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 Real-Time Location Monitoring and Routine Reminders Based on Internet of Things Integrated with Mobile for Dementia Disorder Subject The topic of the resource dementia;Global Positioning System(GPS);Internet of Things (IoT); real-time monitoring; reminder application Description An account of the resource The increasing number of dementia sufferers worldwide demands a new approach to monitoring daily activities and locations to reduce the risk of getting lost. This study develops a real-time location monitoring and routine reminder system based on the Internet of Things (IoT), integrated with a mobile application. The system is designed to assist individuals with dementia, particularly elderly and younger adults with cognitive impairments, in performing daily routines independently, while providing a sense of security for families and caregivers through real-time location tracking features. This technology utilizes GPS for accurate location monitoring, daily activity reminders, and automatic notifications for caregivers in case of deviations from usual routes.The system development includes prototype creationthat consistsof a mobile application and IoT tools such as the ESP32 WROOM microcontroller, Ublox Neo6M V2 GPS module, and SIM800L V2 GSM module. Functionality testing and impact evaluation were conducted to assess its effectiveness in improving the quality of life for dementia sufferers and facilitating monitoring for caregivers. With features such as daily reminders, emergency contacts, and real-time data integration, this system is intended not only for dementia patients but also for families and caregivers seeking tools to ensure the safety and comfort of the sufferers. It is expected that this research will enhance the independence of dementia patients in performing daily activities and provide innovative solutions through IoT technology to improve well-being across different age groups Creator An entity primarily responsible for making the resource Dwi Rangga Okta Zuhdiyanto1*, Yuli Asriningtias2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6105/1012 Publisher An entity responsible for making the resource available Department of Informatics, Faculty of Science and Technology, Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 26-01-2025 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