1 10 14 https://repository.horizon.ac.id/files/original/9c2c7c6dff7a7965abef794b8e154c6c.pdf a883af143ed57c5eff6939341fd7015c 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 Making AI Work for Government: Critical Success Factors Analysis Using R-SWARA Subject The topic of the resource artificial intelligence;government; critical success factors Description An account of the resource Artificial Intelligence (AI) stands at the forefront of modern technologyand isextensively leveraged by the public sector to enhance management and service delivery. Despite its potential, many organizations face challenges in AI implementation, including organizational readiness, strategic vision, and leadership support, with the organization dimension being the most critical, followed by technology, process, and environment. Addressing these challenges requires a focus on organizational readiness, strategic vision, and leadership support, alongside improvements in technology, processes, and environmental factors. This study aims to quantify the Critical Success Factors (CSFs) for successful AI implementation within the Directorate General of Taxes (DGT), offering actionable insights for decision-making and resource allocation. The research utilizes the Rough Stepwise Weighted Assessment Ratio Analysis (R-SWARA) method to analyze and prioritize the CSFs for AI implementation, focusing on dimensions such as technology, organization, process, and environment. The findings highlight the importance of organizational readiness, strategic vision, and leadership support in driving successful AI integration within DGT. Organization is identified as the most critical factor, followed by technology, process, and environment. This research provides valuable insights for DGT and other public sector organizations, aiding in strategic resource allocation and AI strategy refinement to enhance operational efficiency through AI adoption. Creator An entity primarily responsible for making the resource Bramanti Brillianto1*, YovaRuldeviyani2, Darmawan Sidiq Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5813/945 Publisher An entity responsible for making the resource available Master of Information Technology, Facultyof Computer Science, Universitas Indonesia, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-06-2024 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/83f2b9ec433ceb7ec867e58b9dba6ab8.pdf 8f7a6d17abd2256f0924c89fda2f4c34 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 Forecasting theStockPrice of PT Astra InternationalUsing theLSTM Method Subject The topic of the resource forecasting; stock price; recurrent neural network; long short-term memor Description An account of the resource Stocks are one of the long-term investment options and represent ownership in a company that can be acquired through buying and selling. Investment carries both profit potential and risks that must be faced by investors when they provide their capital to companies. Accurate stock price forecasts are very important because they provide an estimate of risk. This research aims to forecast the stock price of PT Astra International Tbk (ASII.JK) using a Long Short-TermMemory (LSTM) method. Datasetclosing stockprices weretaken from January 2,2015,to December 30,2020,with atotal observationof1506.This data setis dividedinto 80% for training and 20% for training.Theforecasting resultsshow that the best performance has MSE, MSE, MAE, and MAPE are 151.910, 23076.561, 118.128, and 2.3%, respectively. The model has a batch size of 4 and epochs of 50. This research recommends thatother partiesconsider this method when theyneed tomanage their investment risk in stocks Creator An entity primarily responsible for making the resource Edwin Setiawan Nugraha2, Zalfani Alika2, Dadang Amir Hamzah3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5699/944 Publisher An entity responsible for making the resource available Departmentof Actuarial Science, Facultyof Business, President University, Bekasi, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-06-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS 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/26299073e10d1d0b08b52af1b42a4690.pdf 4f7d0386f4c9a4fc1f5303813cabd568 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 GLCM-BasedFeature Extraction for Alpha Matting on Natural Images Subject The topic of the resource mage matting;threshold determination;feature extraction;region of interest and gray-level of co-occurrence matrix Description An account of the resource The main objective of this research is to determine the optimal threshold value in the unknown region in the alpha-matting operation of natural images. Alpha-mating serves to draw matte from the image used in segmentation. The alpha value is very influential on the quality of segmentation which is determined by the level of threshold value accuracy. The determination ofthe threshold begins by breaking the grayscale image into several sub-imagesusing Region of Interest (RoI). Each sub-imagewas extracted using the Gray Level Co-occurrence Matrix (GLCM) considered by the parameters of contrast, energy, and entropy at angles of 0°, 45°, 90°, and 135 °. Each feature results in extractions, which are then averaged and normalized in each sub-image. The value is determined as the local threshold value used in the alpha matting operation. Experiments were carried out on 12 natural images from the image-mating dataset to evaluate the performance of the proposed algorithm. The increase in accuracy shows up to 63% by the measurements of experiments, compared to the calculation of adaptive threshold by using the fuzzy CMs Algorithm Creator An entity primarily responsible for making the resource Ruri Suko Basuki1*, Jehad A.H Hammad Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5644/943 Publisher An entity responsible for making the resource available 1Faculty of Computer Science, Department of Informatic Engineering, Dian Nuswantoro University, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 27-06-2024 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/98d2052d94a252d70ca110c60cc1a097.pdf 57455d9e791c2c75803f54ebc7c6e843 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 Integration of YOLOv5Algorithm and OpenCV in InnovativeSmart Parking Management Approach Subject The topic of the resource smart parking;YOLOv5;OpenCV;vehicle detection;parking space identification Description An account of the resource The problem of automatic parking lot identification and vehicle detection in open areas is becoming increasingly important due to the growth in the number of vehicles in Indonesia, particularlyin big cities, resulting in difficulties in finding parking spaces during peak hours. In thiscondition,themotorists often have to compete for parking spaces. This research aims to develop a smart parking system that integrates YOLOv5 and OpenCV algorithms. This approach combines both algorithms thoroughly to identify parking spaces and detect vehicles in real-time indiverse parking scenarios. It is conducted in an open area with reference to parking conditions at the BRIN Bandung office.This study collected data fromthree different parking lot conditions, namelyempty, partially occupied, and full. In each condition, the system successfully detected the parking lots and vehicles accurately. The novel contribution of this research is the development of a smart parking system that uses an integrated approach, providing an effective solution forthe challenges of parking lot availability and vehicle detection. By utilizing the advantages of both algorithms, we successfully created a system that can identify the parking spaces and detect the vehicles accurately and efficiently under various parking circumstances. Thus, this research makes a significant contribution to the development of smart and adaptive parking management technology. Creator An entity primarily responsible for making the resource Akmal Hidayah1,6*, Sitti Zuhriyah2, Billy Eden William Asrul3,Yuyun4,5, Esa Prakasa Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5728/942 Publisher An entity responsible for making the resource available Dept. of Computer System, Faculty of Computer Science, Handayani University, Makassar, 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 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 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/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/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/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/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