1 10 28 https://repository.horizon.ac.id/files/original/2a0c36c807df92a3ace4ace9b9b497a3.pdf 54877d49de2f23442359b73d6703ec35 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 3 (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 Multi-Objective Particle Swarm OptimizationApproach for Optimizing K-Means Clustering Centroids Subject The topic of the resource centroid; k-means; multiobjective particle swarm optimization; the sum of square within; the sum of square between Description An account of the resource The K-Means algorithm is a popular unsupervised learning method used for data clustering. However, its performance heavily depends on centroid initialization and the distribution shape of the data, making it less effective for datasets with complex or non-linear cluster structures. This study evaluates the performance of the standard K-Means algorithm and proposes a Multiobjective Particle Swarm Optimization K-Means (MOPSO+K-Means) approach to improve clustering accuracy. The evaluation was conducted on five benchmark datasets: Atom, Chainlink, EngyTime, Target, and TwoDiamonds. Experimental results show that K-Means is effective only on datasets with clearly separated clusters, such as EngyTime and TwoDiamonds, achieving accuracies of 95.6% and 100%, respectively. In contrast, MOPSO+K-Means achieved a substantial accuracy improvement on the complex Target dataset, increasing from 0.26% to 59.2%. The TwoDiamonds dataset achieved the most desirable trade-off: it had the lowest SSW (1323.32), relatively high SSB (2863.34), and lowest standard deviation values, indicating compact clusters, good separation, and high consistency across runs. These findings highlight the potential of swarm-based optimization to achieve consistent and accurate clustering results on datasets with varying structural complexity Creator An entity primarily responsible for making the resource Aina Latifa Riyana Putri1*, Joko Riyono2, Christina Eni Pujiastuti3, Supriyadi4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6533/1086 Publisher An entity responsible for making the resource available Data Science, Telkom University, Purwokerto, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 21, 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/ce2d04e1e1473a399d2feb001bc5ffe9.pdf cfe84a045b49efc0a65db90cc5f4e4e6 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 3 (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 Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques Subject The topic of the resource dynamic educational marketing;fuzzy C-Means; metaheuristic optimization; RFM; student recruitment Description An account of the resource An effective educational marketing strategy requires accurate school segmentation to enhance new student recruitment. Traditional segmentation methods such as K-means are often used, but they have limitations in capturing the flexibility of school characteristics. Fuzzy C-Means (FCM) offers a more adaptive approach by allowing each school to simultaneously have a degree of membership in several clusters. However, the performance of FCM highly depends on determining parameters such as the number of clusters (k) and the level of fuzziness (m), which are not always optimal when determined manually. This study develops a new framework for dynamic educational marketing segmentation in student recruitment by optimizing FCM using three metaheuristic techniques: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Performance was evaluated using theFuzzy Silhouette Index (FSI). The experimental results showed that DE yielded the best results with the highest FSI value (0.8023), producing eight main clusters based on the Recency, Frequency, and Monetary(RFM) model. Based on the clustering results, apersonalized and adaptive marketing strategy was designed to enhance the effectiveness of student recruitment. The proposed framework enhances segmentation accuracy and supports the implementation of dynamic data-driven marketing in the context of higher education. This study also opens new directions for educational data mining research and machine-learning-based marketing strategies. Creator An entity primarily responsible for making the resource Rizal Bakri1*, Bobur Sobirov2, Niken Probondani Astuti3, Ansari Saleh Ahmar4, Pawan Kumar Singh Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6515/1090 Publisher An entity responsible for making the resource available Departmentof Digital Business, Faculty of Economics and Business, Makassar State University, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 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/18bbbea9619077aea7cb62c343156bb6.pdf 2c192a304f6d2730e8dac7d52279bf54 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 3 (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 Automated Indonesian Plate Recognition: YOLOv8 Detection and TensorFlow-CNN Character Classification Subject The topic of the resource YOLO; TensorFlow; optical character recognition (OCR); indonesian license plate detection; deep learning Description An account of the resource The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable ofidentifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected, OCR techniques are performed to segment and read the letters. Both EasyOCR and TesseractOCR performed moderately well on static images achieving accuracy rates of 70% and 68% respectively, but both suffered significantly lower performance in video scenarios. Of the 100 video frames, EasyOCR was able to correctly identify characters in 61 frames and TesseractOCR in 58 frames, while the TensorFlow-based model outperformed the other two with 75 correct recognitions. Furthermore, easy OCR and static images as input while the TensorFlow-based models completed them with 100% accuracy. This observation can be explained by its design, which utilizes a CNN with ReLU activation and Softmax outputs, trained on 10,261 annotated characters and was enhanced with five different data augmentation techniques. The model shows strong performance in its ability to handle dynamic conditions such as motion blur, changing light conditions, and rotation of the plate angle. The results underscore the drawbacks of one-size-fits-all OCR applications in real-world usecases and stress the need for bespoke model training, as well as hierarchical contouring, in the context of automatic license plate recognition (ALPR). This study provides additional insights into ALPR systems by delivering a robust, scalable, and real-time tool for plate and character recognition, which is essential for intelligent transportation systems Creator An entity primarily responsible for making the resource Windu Gata1*, Dwiza Riana2, Muhammad Haris3, Maria Irmina Prasetiyowati4, Dika Putri Metalica5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6310/1066 Publisher An entity responsible for making the resource available Computer Science, Faculty of Information Technology, Universitas Nusa Mandiri, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 15, 2025 Contributor An entity responsible for making contributions to the resource The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable ofidentifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected, OCR techniques are performed to segment and read the letters. Both EasyOCR and TesseractOCR performed moderately well on static images achieving accuracy rates of 70% and 68% respectively, but both suffered significantly lower performance in video scenarios. Of the 100 video frames, EasyOCR was able to correctly identify characters in 61 frames and TesseractOCR in 58 frames, while the TensorFlow-based model outperformed the other two with 75 correct recognitions. Furthermore, easy OCR and static images as input while the TensorFlow-based models completed them with 100% accuracy. This observation can be explained by its design, which utilizes a CNN with ReLU activation and Softmax outputs, trained on 10,261 annotated characters and was enhanced with five different data augmentation techniques. The model shows strong performance in its ability to handle dynamic conditions such as motion blur, changing light conditions, and rotation of the plate angle. The results underscore the drawbacks of one-size-fits-all OCR applications in real-world usecases and stress the need for bespoke model training, as well as hierarchical contouring, in the context of automatic license plate recognition (ALPR). This study provides additional insights into ALPR systems by delivering a robust, scalable, and real-time tool for plate and character recognition, which is essential for intelligent transportation systems. 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/165b83d1410f0c2180ed5d0892ff96ce.pdf 7e4bd774aee8bb85a7e213ddbbb97143 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 3 (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 Automatic Classification of MultilanguageScientific Papersto the Sustainable Development Goals Using Transfer Learning Subject The topic of the resource multilingual model; multilabel text classification; scientific papers; SDGs research Description An account of the resource The classification of scientific papers according to their relevance to Sustainable Development Goals (SDGs) is a critical task in identifying the research development status of goals. However, with the growing volume of scientific literature published worldwide in multiple languages, manual categorization of these papers has become increasingly complex and time-consuming. Furthermore, the need for a comprehensive multilingual dataset to train effective models complicates the task, as obtaining such datasets for various languages is resource intensive. This study proposes a solution to this problem by leveraging transfer learning techniques to automatically classify scientific papers into SDG labels. By fine-tuning pretrained multilingual models mBERT on SDG publication datasets in a multilabel approach, we demonstrate that transfer learning can significantly improve classification performance, even with limited labelled data, compared to SVM. Our approach enables the effective processing of scientific papers in different languages and facilitates the seamless mapping of research to the relevance of SDGs, the four pillars of SDGs, and the 17 goals of SDGs. The proposed method addresses the scalability issue in SDG classification and lays the groundwork for more efficient systems that can handle the multilingual nature of modern scientific publications. Creator An entity primarily responsible for making the resource Lya Hulliyyatus Suadaa1*, Anugerah Karta Monika2, Berliana Sugiarti Putri3, Yeni Rimawat Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6560/1093 Publisher An entity responsible for making the resource available Politeknik Statistika STIS, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 23, 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/ba4df9933c24663c15ad56c1f7cfb1bc.pdf b6a98f974df9b493772ae1a86db07365 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 3 (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 Metaheuristic Algorithms Against Optimization Techniques for Transportation Problem in Supply Chain Management Subject The topic of the resource optimization;supply chain management;MODI, simulated annealing;particle swarm optimization Description An account of the resource The optimization of transportation problems plays a significant role in supply chain management (SCM), where minimizing costs and improving efficiency are mandatory. The transition from manual methods to advanced computational approaches, such as metaheuristic algorithms, enhances decision-making and consolidates operations within SCM. Malaysia's transportation system has been confronting crucial challenges, characterized by congested roadways, limited rail connectivityand inefficient port operations, which interfere with the fluidity of goods and supply chain efficiency. This highlights the critical need for optimization techniques to enhance competitiveness and efficiency in the evolving SCM landscape. The research aims to explore the application of metaheuristic algorithms, with the Modified Distribution (MODI) method as the benchmark while employing the NorthWest Corner Method (NWCM) to obtain an initial feasible solution, to evaluate their performance in optimizing transportation problems. Metaheuristic algorithms, specifically Simulated Annealing (SA) and Particle Swarm Optimization (PSO), are implemented to explore alternative near-optimal solutions and assess the performance in terms of cost accuracy and computational efficiency. The results indicate that SA achieves a deviation of 12.92% in cost accuracy compared to the optimal MODI method, making it suitable for scenarios where precision is critical, whereas PSO which is 296.92 seconds faster, is ideal for time-sensitive applications. Finally, this study encourages future studies to explore additionalalgorithms, external factors and broader applications for enhanced real-world relevance and scalability to accentuate the potential of metaheuristic algorithms. Creator An entity primarily responsible for making the resource Felicia Lim Xin Ying1, Suliadi Firdaus Bin Sufahani2* Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6513/1064 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, Muar, Malaysia Date A point or period of time associated with an event in the lifecycle of the resource June 12, 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/8e4009fb01dd43a21641f52df15fc78e.pdf d030a7011af66f7c894f49b99a4449d9 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 3 (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 Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 Subject The topic of the resource red fox images; image classification; deep learning models Description An account of the resource Deep learning models demonstrate a high degree of accuracy in image classification. The task of distinguishing between various sources of red fox images—such as authentic photographs, game-captured images, hand-drawn illustrations, and AI-generated images—raises important considerations regarding realism, texture, and style.This study conducts an evaluation of three deep learning architectures: Inception V3, VGG-16, and VGG-19, utilizing images of red foxes. The research employs Silhouette Graphs, Multidimensional Scaling (MDS), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to assess clustering and classification efficiency. Support Vector Machines (SVM) and Logistic Regression are utilized to compute the Area Under the Curve (AUC), Classification Accuracy (CA), and Mean Squared Error (MSE). The MDS plots and t-SNE data clearly demonstrate the capability of the three deep learning models to distinguish between the image categories.For game-captured images, VGG-16 and VGG-19 demonstrate quite outstanding performance with silhouette scores of 0.398 and 0.315, respectively. This study explores the enhancement of classification accuracy in logistic regression and support vector machines (SVM) through the refinement of decision boundaries for overlapping categories. Utilizing Inception V3, an artificial intelligence-generated image silhouette score of 0.244 was achieved, demonstrating proficiency in image classification. The research highlights the challenges posed by diverse datasets and the efficacy of deep learning models in the classification of red fox images. The findings suggest that integrating deep learning with machine learning classifiers, such as logistic regression and SVM, may improve classification accuracy. Creator An entity primarily responsible for making the resource Brian Sabayu1*, Imam Yuadi2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6356/1054 Publisher An entity responsible for making the resource available Master’s Program Human Resource Development-Data Analytics, Graduate School, Universitas Airlangga,Surabaya,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 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/40c47546e0bbeec6c93ce995f42bfbca.pdf 54877d49de2f23442359b73d6703ec35 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 3 (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 Classification of Retinoblastoma Eye Disease on Digital Fundus Images Using Geometric Features and Machine Learning Subject The topic of the resource retinoblastoma; digital fundus images; classification; geometric features; machine learning Description An account of the resource Medical image analysis is essential for detecting retinoblastoma tumors due to the ability of the method to assist doctors in examining the morphology, density, and distribution of blood vessels. The classification of normal and retinoblastoma-affected retinas is a preliminary stepin treating retinoblastoma tumors. Therefore, this research aimed to propose the new development of a method to classify normal and retinoblastoma-affected retinas using geometric feature extraction and machine learning. The workflow consisted of (1) Fundus image data collection for retinoblastomas, (2) image segmentation, (3) feature extraction process, (4) building a classification model using machine learning, (5) splitting testing and training data, (6) classification process using machine learning methods, and (7) evaluation of classification results using a confusion matrix. The results showed that the segmentation method used could detect retinoblastoma areas and extract geometric features. The SVM method achieved an accuracy of 0.96 while the RF andDT had 0.55 and 0.63, respectively. Moreover, the comparison with previous research showed that the method proposed had a 4% improvement in classification performance. This led to the conclusion that the classification using geometric features combined with the SVM on digital fundus images of retinoblastoma eye disease produced the best results Creator An entity primarily responsible for making the resource Arif Setiawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6337/1058 Publisher An entity responsible for making the resource available Department of Information System, Faculty of Engineering, Muria Kudus University, Kudus, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 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/1429755af966552d26d35c637c538123.pdf 5cd10e98fe9ded1c1318e52dbf291e59 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 3 (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 Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification Subject The topic of the resource waste;MobileNetV1; MobileNetV2; preprocessing; waste classification Description An account of the resource Waste management remains a critical challenge for many countries, including Indonesia, which ranks as the world's second-largest contributor of waste. As tens of millions of tons are produced each year and the management system remains ineffective, environmental conditions and public health continue to deteriorate. To address this issue, it is imperative to develop more accurate and efficient solutions to enhance waste classification and management. This study investigates the influence of various image preprocessing techniques on the performance of MobileNetV1 and MobileNetV2 models in the classification of waste images. Preprocessing is crucial for enhancing data quality, particularly when dealing with real-world images that are affected by inconsistent lighting, texture, and clarity. Five preprocessing scenarios were evaluated: Baseline, CLAHE with Bilateral Filtering, CLAHE with Sharpening, Grayscale with CLAHE, and Gaussian Blur with Bilateral Filtering. Among these, the combination of CLAHE and Bilateral Filtering applied to MobileNetV1 achieved the best results, with 85% training accuracy, 96% validation accuracy, a training loss of 0.3178, and the lowest validation loss of 0.1630. Overall, MobileNetV1 benefited more significantly from preprocessing variations than MobileNetV2, particularly in terms of accuracy improvement and reduction in prediction error. These findings underscore the importance of effective preprocessing in enhancing model performance for waste image classification Creator An entity primarily responsible for making the resource Aulia Afifah1, Endah Ratna Arumi2*, Maimunah3, Setiya Nugroho Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6211/1055 Publisher An entity responsible for making the resource available Informatics Engineering, Engineering, Universitas Muhammadiyah Magelang, Magelang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 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/dfd0b0c090318af06e95d7ae99c2d9dd.pdf 54877d49de2f23442359b73d6703ec35 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 3 (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 Development of IoT-based Automatic Water Drainage System on Fishing Boatto Improve Operational Efficiency Subject The topic of the resource automatic; inverter; IOT; system; sensor Description An account of the resource The profession of fishermen requires a reliable system to remove stagnant water from fishing boats, as manual drainage is time-consuming and inefficient. This study proposes an IoT-based automatic water drainage systemwithout using an inverter or ultrasonic sensor, offering a cost-effective alternative. The system utilizes a water level sensor and a DC water pump, controlled via a smartphone application. The research model used is the Research and Development (R&D) model, through several stages, namely potential and problems, initial data needs, prototype creation, prototype validation, prototype revision, validation, implementation. Problems occur at the prototype stage, problems that must be revised include aspects of wiring, Power Suitability, Water Level Sensor Test, and the configuration of the relay used. The IOT-based automatic water drainage system can function based on the results of white-box testing including Hardware Implementation, Software Implementation, Implementation of Application Usage, and Automatic Drainage System Testing. This is indicated by the results of the Liquid Water Level Sensor Functionality test, DC Water Pump Functionality Test, Solar Panel and Battery Functionality Test, and IOT Functionality Test. IOT-based automatic water discharge systems on fishing boats are more efficient and cost-effective in the long run, although diesel engines offer more reliability under adverse weather conditions or in places with limited access to sunlight Creator An entity primarily responsible for making the resource Zulfachmi1*, Zulkipli2, Vita Rahayu3, Aggry Saputra4, Muthiah As Saidah5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6222/1084 Publisher An entity responsible for making the resource available Program Studi S1 Teknik Informatika,STT Indonesia Tanjung Pinang,Tanjungpinang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 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/baea07f27d41bd371d8e1f5022ec5390.pdf eb9020a4d5b5534a77d9e0fda98978ac 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 3 (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 Developmentof aDocument-Based Gait System With Interactive Visualisation forClinical Analysis Subject The topic of the resource biomechanics; dashboard; database; gait analysis; mongoDB Description An account of the resource Gait analysis is a crucial aspect of biomechanics and medical rehabilitation, used to detect movement disorders, assess therapy effectiveness, and understand human walking patterns. In Indonesia, gait research remains limited, with most data sourced from abroad, which may not reflect the characteristicsof the local population. This study uses data from Vicon camera recordings that track marker movements on the subject's body and convert them into kinematic data in spatial coordinates, stored in Excel files. To support clinical applications, an efficientsystem is needed to manage gait data and present analysis results interactively. Therefore, a MongoDB-based gait data management system was developed due to its flexibility in handling unstructured data and scalability. The system was designed to preprocess gait data and display the results through an interactive Streamlit dashboard. The analysis involved calculating gait angle parameters, visualized in a gait cycle anglegraph and analyzed statistically using mean and standard error to improve interpretation accuracy. Testing shows that the system can store data in an average of 1.52 seconds, retrieve it in 3.598 seconds, and render visualizations in 0.192 seconds, with high accuracy and only a 0.1-degree error between the input and output. This system effectively addresses the challenge of managing local gait data and supports comprehensive biomechanical analysis, enabling clinicians to make informed decisions regarding rehabilitation needs based on deviations from normal gait angle ranges. Creator An entity primarily responsible for making the resource Rizal Rahman Rizkika1, Helisyah Nur Fadhilah2, Tanzilal Mustaqim3, Rifdatun Ni’mah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6451/1074 Publisher An entity responsible for making the resource available Departmentof Data Science, Surabaya Directorate, Telkom University, Surabaya, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 16, 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