2 10 15 https://repository.horizon.ac.id/files/original/76f1db88ce68af9a5042eb4b57533b4e.pdf 9951e8be719aa4224633e19e6e8125e7 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 8 No 2 (2024) 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 Digital Image Object Detectionwith GLCM Multi-Degrees and Ensemble Learning Subject The topic of the resource Object detection; object rotation; Gray-level Co-occurrence Matrix Multi-Degrees; Ensemble Voting Description An account of the resource Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours(K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours(KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degreesand the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degreesis 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracyfor detecting objects experiencing rotation. Creator An entity primarily responsible for making the resource Florentina TatrinKurniati1, Hindriyanto Dwi Purnomo2, Irwan Sembiring3, Ade Iriani4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5597/931 Publisher An entity responsible for making the resource available Facultyof Information Technology, Universitas Kristen Satya Wacana Date A point or period of time associated with an event in the lifecycle of the resource Facultyof Information Technology, Universitas Kristen Satya Wacana 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/f0fc32892060b627a083aec9e6ff2dbe.pdf 815a7cb7938121d5e7a08653f536d18a 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 8 No 2 (2024) 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 Integration Waterfall and ScrumMethodology in The Development of SIMARGA Web Application Subject The topic of the resource waterfall; scrum; web application; methodologies integration; software development Description An account of the resource This research explores the integration of Waterfall and Scrum methodologies in the development of the SIMARGA web application. The integration aims to maximize the strengths of each methodology, with Waterfall contributing to planning, analysis, and initial system structure, while Scrum supports the creation of Product Backlog and implementation of Scrum events. The combination results in a structured and methodical product management process, simplifying task lists and enhancing efficiency. The achievement of good efficiency and effectiveness in task execution is facilitated by leveraging both methodologies, reducing wastage. Progress in product development and team productivity is measured through daily meetings, evaluations, and sprint reviews. Emphasis is placed on fostering strong relationships among the Scrum team, customers, and stakeholders, promoting effective communication and collaboration. However, challenges are identified in team commitment to daily meetings, potentially influenced by their involvement in additional business activities. Future efforts should focuson technological resource enhancement and software maintenance to achieve the initially outlined product goals. The effectiveness of this product development initiative can be measured using metrics such as views and user data, particularly in Kabupaten Pegunungan Bintang Creator An entity primarily responsible for making the resource Nikson Theys Muay1, Eko Sediyono2, Johan Tambotoh Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5652/920 Publisher An entity responsible for making the resource available Magister Sistem Informasi, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga, Jawa Tengah Date A point or period of time associated with an event in the lifecycle of the resource 20-04-2024 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/ca340f66ece562e7b5d3a622db4b6a13.pdf 7b21d2a0de206ba9b03b35996f27b22e 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 8 No 2 (2024) 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 Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-Criteria Spatial Analysis Approach: Integration of AHP and WPM Methods Subject The topic of the resource spatial analysis;MCDM;WPM method;AHP method; cohen's kappa Description An account of the resource Along with the increase in population and the acceleration of economic growth, the urgent need for additional property that can serve as a place for various community activities also increases. It is typicalfor big cities that are the epicenter of urbanization to experience a sharp spike in land demand. One area that has excellent accessibility is Sidarjo Regency, which is comparable to SurabayaCity in this regard. This research aims to use Web-GIS to conduct spatial data analysis to identify the most suitable land functions for use in residential areas. Analytic Hierarchy Process (AHP) and Weighted Product Model (WPM) are used in this research for spatial data modeling based on multi-criteria decision-making (MCDM). Parameters were based on distance to the city center, distance to the market, distance to the hospital, distance to public transport, slope, soil type, and rainfall intensity. The results of spatial data modeling categorize the suitability of new settlement land into the categories of uninhabitable and habitable land. The K value of 0.27 is the outcome of a comparison test between the two MCDM approaches using Cohen's Kappa coefficient. The test indicates that the K value falls into the category of having a fair strength of agreement between the two methods that came into play Creator An entity primarily responsible for making the resource Anik Vega Vitianingsih1, Choirul Ullum2, Anastasia Lidya Maukar3, Verdi Yasin4, Seftin Fitri Ana Wati5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/4520/918 Publisher An entity responsible for making the resource available Informatics Department, Universitas Dr. Soetomo, Surabaya, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 20-04-2024 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 https://repository.horizon.ac.id/files/original/91141e3ef6b6030260a30066d631fd72.pdf d68d7f496a6245d27a0ca1d00b9b26d4 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 8 No 2 (2024) 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 Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation Subject The topic of the resource computer vision; blindpeople; YOLOv8; OpenCV Description An account of the resource The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation abilities. This research has the main aim of introducing a superiorobject detection method, especially in supporting the sustainability and effectiveness of navigation for the blind. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method, and distance measurement technology from OpenCV. The main challenge to be addressed involves improving the accuracy and performance of object detection, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking around environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects, which may involveobstacles or potential threats, thereby preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution in presenting technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact in facilitating their daily lives Creator An entity primarily responsible for making the resource Erwin Syahrudin1, Ema Utami2, Anggit Dwi Hartanto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5529/917 Publisher An entity responsible for making the resource available Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-03-2024 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/77e4a2efb9d7b374f24cb3cf41699793.pdf 5450b828bed18af63bb1f131da8b578a 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 8 No 2 (2024) 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 Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data Subject The topic of the resource feature selection;high-dimensional data;recursive feature elimination;genetic algorithm Description An account of the resource The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the wdbc dataset with 30 features and 569 data, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy, and the use of weighted macros on precision, recall and f1 score, using GA selection features has slightly higherresults than RFE so it is concluded that GA selection features are better at solving problems in high-dimensional data Creator An entity primarily responsible for making the resource Yoga Pristyanto1, Dipa Wirantanu Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5375/916 Publisher An entity responsible for making the resource available Departmentof Information System, Facultyof Computer Science, UniversitasAmikom, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-03-2025 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