1 10 14 https://repository.horizon.ac.id/files/original/eda727feadfb6d84b90f2b3f35c3adea.pdf 7f3fc2d68d39a446a5b5674836371027 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 3 (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 A Middleware Applications Design for Health Information Sharing Subject The topic of the resource healthcare;data sharing;information systems;PHR application;distributed systems Description An account of the resource The interoperability between electronic health records (EHR) and electronic medical records (EMR) from various healthcare facilities for comprehensive patient care is important. However, integrating such systems, including the need for interoperability standards, data privacy, and security, is a highly challenging task, especially since patient rights in data access must be considered. The primary problem addressed is the challenge of integrating electronic health records (EHR) and electronic medical records (EMR) across various healthcare facilities to ensure comprehensive patient care while maintaining data privacy, security, and adherence to patient rights. This work presents an innovative application to consolidate patient health records from various medical facilities. It facilitates seamless data access, improving the efficiency of healthcare delivery. The GGD approach was used in developing the prototype to ensure the delivered product was able to fulfilthe user requirements. Four phases aredivided into six stages used in this method: research, modelling, requirements definition, framework definition, refinement, and support. The evaluation involved two phases, back-end and front-end testing, utilizing white box and black box testing. Whitebox testingdelivers the average frame rendering rate of up to 56 fps, and blackbox testing has shown 100% successful results in the given task. In conclusion, the Med-OID prototype was successfully developed. It integrates and securely transmits medical records across various healthcare services well, demonstrating significant potential to enhance personalized medicine and healthcare coordination. The evaluations underscored the prototype’s robustness and its capability to improve interoperability and data sharing inhealthcare systems. Creator An entity primarily responsible for making the resource Ketut Agus Seputra1,A.A. Gede Yudhi Paramartha2, Gede Aditra Pradnyana3, Kadek Yota Ernanda Aryanto4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5707/932 Publisher An entity responsible for making the resource available Departmentof Informatics, Facultyof Engineering and Vocation, Universitas Pendidikan Ganesha, Singaraja, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 01-06-2024 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/06447dff592adeea235b629e5e8eaeb0.pdf 9c15847bceb03a2743c7cb2a0dc4edf4 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 3 (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 Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3 Subject The topic of the resource intelligent systems; seat belt violation detection; yolo; convolutional neural networks, LSTM Description An account of the resource The application of an electronic violation detection system has begun to be implemented in many countries by utilizing CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in theform of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat beltsand the use of cell phones while driving which is influenced by the number of vehicles, vehicle speed and lightingwhich can increase the difficulty in the detection process. This research developed a traffic violation detection system usingYOLO3. The YOLOis used as the basic architecture of CNN which is then combined withLSTM. The dataset was obtained from RoboFlow Universe with a total of 199 front-viewcar images consistingof 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while LSTM plays a role in the prediction process. Furthermore, the performance evaluation of the CNN+LSTM algorithm will be measured using the value of accuracy to measure the performance of the training process and testing process. In measuring the performance of the training process,it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, YOLO3, and YOLO3+LSTM. The test results show that YOLO3+LSTM has higher accuracy compared to the others at 89%. Next, in the testing process, the CNN+LSTMmodel will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models havehigher accuracy at 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85% Creator An entity primarily responsible for making the resource Erika Devi Udayanti1,Etika Kartikadarma2, Fahri Firdausillah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5784/936 Publisher An entity responsible for making the resource available Facultyof Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 04-06-2024 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/3ffc3cebc5e9f7b8a2f927f2fb208601.pdf a8f8f67db874af5dfd28c9cbcaefafc8 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 3 (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 Remote Sensing Scene Classification using ConvNeXt-Tiny Model with Attention Mechanismand Label Smoothing Subject The topic of the resource remote sensing scene classification;deep learning; ConvNeXt-Tiny, ECANet; label smoothing regularization Description An account of the resource Remote Sensing Scene Classification (RSSC) is the discrete categorization of remote sensing images into various classes of scene categories based on their image content. RSSC plays an important role in many fields, such as agriculture, land mapping, and identification of disaster-prone areas. Therefore, a reliable and accurate RSSC algorithm is required to ensure the accuracy of land identification. Many existing studies in recent years have used deep learning methods, especially CNN combined with attentionmodules to solve this problem. This study focuses on solving the RSSC problem by proposing a deep learning-based method (CNN) with a ConvNeXt-Tiny model integrated with the Efficient Channel AttentionModule (ECANet)and label smoothing regularization (LSR).The ConvNeXt-Tiny model shows that a persistent superior outperforms the ‘large’ model in convinced metrics.ConvNeXt-Tiny model also has a huge advantage in high-precision positioning and higher classification accuracy and localization precision in a variety of complicated scenarios of remote sensing scene recognition.Experiments in this study also aim to prove that the integration of the attention module and LSR in the basic CNN network can improve accuracy because the attention module can strengthen important features and weaken features that are less useful for classification. The experimental results proved that the integration of ECANetand LSR in the ConvNeXt-Tiny base network obtained a higher accuracyof 0.38% in the UC-Merced dataset, 0.7% in the AID, and 0.4% in the WHU-RS19dataset than the ConvNeXt-Tiny model without ECANetand LSR. The ConvNeXt-Tiny model with ECANetintegration and LSR obtained an Accuracyof 99.00±0.41% in the UC-Merced dataset, 95.08±0.20% in AID, and 99.50±0.31% in the WHU-RS19 dataset Creator An entity primarily responsible for making the resource Rachmawan Atmaji Perdana1*, Aniati Murni Arimurthy2, Risnandar Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5731/940 Publisher An entity responsible for making the resource available Computer Science, Faculty of Computer Science, University of Indonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 21-06-2024 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/8bb4e3ee9123db3a0557545e5a4d012f.pdf 7ea8dc7dc9bfe93424150aa02fa3a427 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 3 (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 Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases Subject The topic of the resource machine learning;classification;improving;performance;heart disease Description An account of the resource Heart disease is a majorproblem that must be overcome for human life. In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data have collaborated with information technology such as machine learning in detecting, predicting,and classifying diseases.This research aimsto improve the performance of machine learning classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree (C4.5) with the particle swarm optimization(PSO) feature in cases of heart disease. In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improvethe K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935 Creator An entity primarily responsible for making the resource Pareza Alam Jusia1, Abdul Rahim2, Herti Yani3, Jasmir Jasmir4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5710/933 Publisher An entity responsible for making the resource available <span>Informatic Engineering Universitas Dinamika Bangsa, Jambi, Indonesia</span> Date A point or period of time associated with an event in the lifecycle of the resource 01-06-2024 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/018b439ed0f4725df630007b8e2ae999.pdf d99dbdbd66a9a6abdde008e201eb90cf 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 3 (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 Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network Subject The topic of the resource convolutional neural network;hyperparameter;metaheuristics;ACO;GA. HS Description An account of the resource Deep learning is an artificial intelligence technique that has been used for various tasks. The performance of deep learning is determined by its hyperparameter, architecture as well as training (connection weight and bias). Finding the right combination of those aspects isvery challenging. Convolution Neural Networks (CNN) is a deep learning method that is commonly used for image classification. It has many hyperparameterstherefore tuning its hyperparameter is difficult. In this research, a metaheuristics approach is proposed to optimisethe hyperparameter of convolution neural networks. Threemetaheuristics methodsare used in this research, ant colony optimization (ACO,) genetic algorithm (GA)and Harmony Search (HS). Themetaheuristics methods are usedtofind the best combination of8 hyperparameterswith 8 optionseach which creates1.6. 107 ofsolution space.The solution space is too big to explore using manual tuning. The Metaheuristics method willbring benefitsin termsof finding solutions in the search space more effectively and efficiently.The performance of the metaheuristics methodsisevaluated using MNIST datasets. The experiment resultsshow that theaccuracy of ACO, GA and HS are 99,7%, 97.7% and 89,9% respectively. The computationaltime for the ACO, GA and HS algorithmsare 27.9 s, 22.3 s and 56.4 srespectively. It showsthat ACO performsthe best among the three algorithmsin termsof accuracy however its computational time is slightly longer than GA.The experiment results revealthat themetaheuristic approach is promising for the hyperparameter tuning of CNN.Future research can be directed to solve larger problems or enhancethemetaheuristics operator to improve its performance Creator An entity primarily responsible for making the resource Hindriyanto Dwi Purnomo1, Tad Gonsalves2, Evangs Mailoa3, Fian Yulio Santoso4, Muhammad Rizky Pribadi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5730/935 Publisher An entity responsible for making the resource available Departmentof Information Technology, Facultyof Information Technology, Satya Wacana Christian University, Salatiga, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 01-06-2024 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/710ed0b85dd62375a023e66fb0ab899d.pdf 9aaa55e9e182dc815a4676ce5662139b 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 3 (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 Comparative Analysis ofHybrid ModelPerformanceUsing Stacking and Blending Techniques for Student Drop-OutPrediction in MOOC Subject The topic of the resource machine learning, classification, stacking, blending, MOOC Description An account of the resource Despite being in high demand as a lifelong learner and academic material supplement, the implementation of Massive Open Online Courses(MOOC) has problems, one of which is the dropout rate (DO) of students which reaches 93%. As one of the solutions to this problem, Machine Learning can be utilized as a risk management and early warning system for students who have the potential to drop out. The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. As a form of contribution, this study will compare the performance of models built from stacking and blending techniques. The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-modeluses XGBoost. These algorithms are used to build models with stacking and blending techniques. The experimental results using stacking are 82.53% accuracy, 84.48% precision, 94.12% recall, and 89.04% F1-Score. Meanwhile, blending obtained 83.39% accuracy, 85.31% precision, 94.21% recall, and 89.54% F1-Score. These results are supported by model testing using k-fold cross-validationand confusion matrix techniques which show the same results. That is, blending is 0.86% higher than stacking so it can be concluded that blending has better performance than stacking in the MOOC student dropout prediction case study Creator An entity primarily responsible for making the resource Muhammad Ricky Perdana Putra1, Ema Utami Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5760/934 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 01-06-2024 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/619693f999bae2c219704eefc5270f41.pdf 5638c960b70abf10b942d33ea010394f 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 3 (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 Comparisonof Matrix Decomposition in Null Space-Based LDA Method Subject The topic of the resource linear discriminant analysis; small sample size; null space;singular value decomposition (SVD);Cholesky decomposition; QRdecomposition Description An account of the resource Problems with small sample sizesand high dimensionality are common in pattern recognition.Almost all machine learning algorithms degrade in high-dimensional data, so thatsingularities in the scatter matrices, the main problem of the Linear Discriminant Analysis (LDA) technique, might result.A null space-based LDA (NLDA) has been conceived to address the singularity issue. NLDA aims to maximize the distance between classesin the null space of the within-class scatter matrix.In the earliest research, the NLDA method was performed by computing eigenvalue decomposition and singular value decomposition (SVD). This research led to several new implementations of the NLDA method using other matrix decompositions.The new implementations include NLDA using Cholesky decomposition and NLDA using QR decomposition. This paper compares the performance of three NLDA methods using different matrix decompositions, namely SVD, Cholesky decomposition, and QR decomposition.Two sets of data were used in the experiments that used three different NLDA algorithms.To determine the performance of the NLDA methods, the classification accuracy of the three methods was measuredusing the Confusion Matrix. The results show that the NLDA method using SVD has the best performance when compared to the other two methods, achieving 77.8% accuracy for the Colon dataset and 98.8% accuracy for the TKI-resistance dataset Creator An entity primarily responsible for making the resource Carissa Devina Usman1,Farikhin2, Titi Udjiani Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5637/937 Publisher An entity responsible for making the resource available Departmentof Mathematics, Facultyof Science and Mathematics, Diponegoro University, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 05-06-2024 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/919f1e275f953425eaab5a1799cb3c28.pdf 10baec153f8e8b7c4fa2e6424342391a 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 3 (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 mplementing Continuous Integration and Deployment Strategy: Cloversy.id RESTful API Development Subject The topic of the resource automation;DevOps;continuous integration;continuous delivery Description An account of the resource The software development cycle involves testing and deployment stages that can be laborious and time-consuming, especially in collaborative projects that involve several developers. Implementing Continuous Integration (CI) and Continuous Delivery (CD) offers a solution to streamline this process. This study presents a case study of the Cloversy.id RESTful API project, highlighting challenges encountered during development and the implementation of a new system using GitHub Actions as the DevOps tool. The research resulted in the adoptionof a new system, replacing the conventional practices previously employed by the Cloversy.id development team. Employing flowcharts, the study meticulously mapped out the development flow, pinpointing bottlenecks and areas for optimization within the cycle. Notably, the implementation of a CI/CD pipeline resulted in a notable improvement, with a 35% increase in speed for CI and a remarkable 39% enhancement for CD. GitHub Actions played a pivotal role in automating critical tasks, reducing reliance on manual intervention, and minimizing dependency on team leaders. The platform's features, including detailed logs and email notifications, empowered team leaders and developersalike to take informed actions swiftly. Furthermore, the study highlights the noveltyof integrating CI/CD by considering factors such as branching strategy, code review practices, testing methodologies, deployment methods, and infrastructure Creator An entity primarily responsible for making the resource Eric Prima Wijaya1*, Sandy Kosasi2, David3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5527/938 Publisher An entity responsible for making the resource available Teknik Informatika, STMIK Pontianak Date A point or period of time associated with an event in the lifecycle of the resource 05-06-2024 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/bec76078294c9e116be7177ee44a5922.pdf 342d0e7c2b9cb7327a7298f0ea70589c 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 3 (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 Comparative Analysis of Recurrent Neural Network ModelsPerformance in Predicting Bitcoin Prices Subject The topic of the resource deep learning;recurrent neural network;LSTM;GRU;bitcoin Description An account of the resource Recurrent Neural Network is a Deep Learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, Long Short Term Memory (LSTM), and Gated Recurrent Unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential data or time-series data is required so that an accurate prediction can be made. Sequential or time-series data involves data arranged in time sequence, such as weather data, financial data, carbon emission data and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance andfind out what performance measurescan be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation, in the meantime Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predictingBitcoinprices Creator An entity primarily responsible for making the resource Zidane Ikkoy Ramadhan1*, Harya Widiputra Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5810/939 Publisher An entity responsible for making the resource available Departmentof Informatics, Faculty of Information Technology, Perbanas Institute Jakarta, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 20-06-2024 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/31f755b45674d0cea58d84463ebca28b.pdf f5b90fabb6c0de03acb803e8065ed235 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 3 (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 Recommendation for Scrum-Based Software Development Process with Scrum at Scale: A Case Study of Software House XYZ Subject The topic of the resource scrum at scale;scrum;scaling agile method;agile;maturity level Description An account of the resource Software House XYZ employs Scrum as one of its software development processes. However, the company faces several challenges in the implementation of Scrum, leading to delays in their product releases. Two specific problems are the control of a large-scale Scrum team and the management of team commitments. In addressing these issues, the Scrum at Scale framework has been chosen as a solution. Before implementing Scrum at Scale, an assessment of the current Scrum maturity level at Software House XYZ is deemed necessary.The Scrum Maturity Model, adapted to the Scrum Guide 2020, is selected as the method to evaluate how effectively the company is implementing Scrum. A questionnaire comprising 81 practices was distributed to development teams, with 10 valid responses collected. Based on the assessment using the Scrum Maturity Model, the current Scrum implementation maturity at Software House XYZ is rated at level 1, Initial. A total of 61practices are proposed for improvement in the Scrum process.Scrum at Scale can be implemented once the suggested Scrum process improvements have been made. These recommendations are structured following the framework outlined in the Scrum at Scale Guide 2022. Validation of the Scrum at Scale recommendations has been conducted by usthrough interviews with representatives from Software House XYZ. From the validation results, the company expresses interest in trying to implement Scrum at Scale. However, the company agrees to enhance the existing Scrum process within the organization before fully adopting Scrum at Scale Creator An entity primarily responsible for making the resource Ahmad Jalaluddin1*, Eko K. Budiardjo2, Kodrat Mahatma Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5646/941 Publisher An entity responsible for making the resource available Department of Information Technology, Faculty of Computer Science, Universitas Indonesia, Jakarta,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 22-06-2024 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