1 10 31 https://repository.horizon.ac.id/files/original/43f6116c538335f5059043f7f03de69a.pdf 8b8b62dd88f42fc6121bfc1a5932bbf2 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023) Subject The topic of the resource breast cancer; deep learning; image segmentation; bibliometric; WOS Description An account of the resource The objective of this study is to perform a comprehensive bibliometric analysis of the existing literature on breast cancer segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection (WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well. This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this study Creator An entity primarily responsible for making the resource Agus Perdana Windarto, Anjar Wanto, Solikhun, Ronal Watrianthos Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 Bibliometric breast cancer deep learning Image segmentation WOS https://repository.horizon.ac.id/files/original/de97a6b40131fc2c3ffce65ed3b1ac8b.pdf cd126f6fc35177558f318a4960df5cb6 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Application of Deep Convolutional Generative Adversarial Networks to Generate Pose Invariant Facial Image Synthesis Data Subject The topic of the resource face recognition; pose invariant; generative adversarial networks; deep convolutional generative adversarial networks; hyperparameter tuning Description An account of the resource Even though Artificial Intelligence is advancing, artificial intelligence can still find it difficult to solve problems that are easy for humans to do but difficult for computers to describe, such as facial recognition. There are problems related to the existing facial recognition model, namely the facial recognition model. The model is still unable to recognize facial shapes that are not in a perfect state due to several factors. Among several factors, the most influencing factor is the position of the face. Therefore, in this study, Deep Convolutional Generative Adversarial Networks (DCGAN) will be applied to generate fake image data with varying face positions. This research will be carried out starting from collecting data, processing data, designing, and training models, hyperparameter tuning, and lastly analyzing test results. Based on the results of hyperparameter tuning that carried out sequentially, the best hyperparameter combination produced is 200 epoch, 0.002 Generator learning rate, 0.5 Generator momentum/beta1, Adam as Generator optimizer, 0.0002 Discriminator learning rate, 0.5 Discriminator momentum/beta1, and Adam as Discriminator optimizer. The hyperparameter combination gives a result with FID score of 74.05. Based on testing with human observer, generated fake images has relatively good results, but there are still few bad fake image results Creator An entity primarily responsible for making the resource Jagad Nabil Tuah Imanda, Fitra Abdurrachman Bachtiar, Achmad Ridok Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 deep convolutional generative adversarial networks Face Recognition generative adversarial networks hyperparameter tuning pose invariant https://repository.horizon.ac.id/files/original/8b8acd93f07f9ab6ea4ad397730e0798.pdf 26a3491f319b376955099746ee1a3633 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Secondary School Destination for Inclusive Students using Decision Tree Algorithm Subject The topic of the resource inclusive student; education; decision support system; ID3 algorithm; classification Description An account of the resource Inclusive student education has become one of the most important agendas for UNESCO and the Indonesian government. Developing inclusive children's education is critical to adjust their abilities while attending school. However, most parents and educators who assist students in selecting their future secondary school after finishing primary school are frequently not aware of their genuine ability. The problem is mainly because the decision is not based on objective assessments like IQ, average and mental scores. In this study, we aims to create a school-type decision support system using data mining as a factor analytic approach in extracting rules for the knowledge model. The system uses some variables as the basic principles for building school-type classification rules using the ID3 decision tree method. This system can also assist educators in making decisions based on existing graduate data. Evaluation showed that the proposed system produced an accuracy of 90% by allocating 75% of data for training and 25% for testing. The accuracy value from evaluation phase stated that the ID3 Decision tree algorithm have a good peformance. This system also can dynamically create new decision trees based on newly added datasets. Further research is expected to have more variable and more dynamic system that can have more accurate result for the inclusive student classification of secondary school. Creator An entity primarily responsible for making the resource Rizal Prabaswara, Julianto Lemantara, Jusak Jusak Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 classification Decision Support System Education ID3 algorithm inclusive student https://repository.horizon.ac.id/files/original/565227e0fc06d8b5d21c180e1627c65e.pdf 172d890d39b4f2e87dd2fc8c4ed6b72f 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Combination of K-NN and PCA Algorithms on Image Classification of Fish Species Subject The topic of the resource fish species; image classification; k-nearest neighbor; principal component analysis Description An account of the resource To do fish farming, you need to know the types of fish to be cultivated. This is because the type of fish will affect how it is handled and managed. So, this study aims to develop an image processing system for classifying fish species, especially fish that are cultivated, with a combination of the K-Nearest Neighbor (K-NN) algorithm and Principal Component Analysis (PCA). The feature extraction used is feature extraction based on its color and shape. The K-NN algorithm can group certain objects by considering the shortest distance from the object. According to the best criteria, the PCA method is employed in the meanwhile to decrease and keep the majority of the relevant data from the original characteristics. Based on the test results, the accuracy value obtained is 85%. The use of a combination of the K-NN and PCA algorithms in the image classification of fish species in the research that has been done has been shown to be able to increase accuracy by 7.5% compared to only using the K-NN algorithm Creator An entity primarily responsible for making the resource Rini Nuraini, Adi Wibowo, Budi Warsito, Wahyul Amien Syafei, Indra Jaya Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 fish species Image classification K-Nearest Neighbor Principal component analysis https://repository.horizon.ac.id/files/original/3886b48e785789a45ad5d53fb36d3888.pdf c71b7620b5250d8f47da421c9f05945a 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Study of Cloud Forensic Investigation Using ADAM And NIST 800-86 Methods in Private Cloud Computing Subject The topic of the resource framework; ADAM; NIST; cloud; comparison Description An account of the resource As information technology advances, the associated risks also increase, particularly in the field of private cloud computing services. These services are subject to potential risks of internal abuse, either due to system vulnerabilities or other factors. However, the investigation of these incidents in private cloud computing varies greatly due to the different frameworks and unique characteristics of each cloud service. The lack of a standardized approach to analyzing and assessing investigative processes in cloud computing services has been a persistent problem. This lack of consensus impacts the accuracy, efficiency, and process of data acquisition when dealing with digital evidence in each method, causing concern among researchers. To overcome this, a comparative study was carried out with a focus on the ADAM (The Advanced Data Acquisition Model) method and the NIST (National Institute of Standards and Technology) method. The goal is to identify the most effective investigative process for dealing with cyber attack incidents on both the server and client side of cloud computing services. By testing these methods in a network that is built on private cloud computing services, then for the results from this research include the weaknesses and strengths of the ADAM and NIST methods are found when applied to cloud computing case studies and these have not been identified in previous research, then produce recommendations for investigators when conducting investigations on case studies on cloud computing, and in this study managed to find a bug in the ownCloud application version 10.9.1. Then this study also aims to provide researchers with valuable references to carry out analysis and assessment in the investigative process, where standardization is still an unresolved issue. Creator An entity primarily responsible for making the resource Reza Febriana, Ahmad Luthfi Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 ADAM cloud Comparison Framework NIST https://repository.horizon.ac.id/files/original/bc0538d1050c8f38d12146963d81f081.pdf e116ccb3f40af4c2d3fc9bf105fffa6e 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Image Enhancement Methods for Diabetic Retinopathy Screening Subject The topic of the resource image enhancement; diaebetic retinopathy; image processing; fundus image Description An account of the resource The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus pictures to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created, and it is unknown whether one is best suited for use with retinal fundus images. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. Contrast Limited Adaptive Histogram Equalization (CLAHE) method, Gray Level Slicing method, Median Filtering method, and Low Light method are image improvement methods used to enhance retinal fundus images. The parameters Natural Image Quality Evaluator (NIQE), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and entropy will be used to assess each image enhancement technique's performance. An ophthalmologist from Hospital University Sains Malaysia (HUSM) provided the image data. The findings indicate that while each technique has its own benefits, the CLAHE technique, with a standard deviation MSE of 0.0004, is the best. Creator An entity primarily responsible for making the resource Dafwen Toresa, Fana Wiza, Keumala Anggraini, Taslim ,Edriyansyah, Lisnawita Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 diaebetic retinopathy fundus image image enhancement image processing https://repository.horizon.ac.id/files/original/e64193891ff70e983a1805bc71cc7e8c.pdf c427414d4a004e53a57fd485b3afc4d3 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Credit Risk Detection in Peer-to-Peer Lending Using CatBoost Subject The topic of the resource P2P lending; credit risk detection; catboost; AUC; ROC Description An account of the resource P2P (Peer-to-peer) lending has gained popularity among private borrowers, small businesses, and MSMEs due to its ability to provide direct access to loans without the strict requirements imposed by traditional banks and financial institutions. However, P2P lending faces a significant challenge in terms of credit risk, resulting in a high rate of loan repayment failures. To address this issue, the study aimed to develop a credit risk detection system using a loan dataset obtained from the Bondora company by implementing one of the gradient boosting algorithms which are called the CatBoost (Categorical Boosting) method. The performance of the CatBoost algorithm was evaluated using ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve). Three scenarios were considered, and the results revealed that scenario 2, with a data splitting ratio of 90:10, achieved the best outcome with an AUC value of 0.80329. This outperformed scenario 1, with a data splitting ratio of 80:20 and an AUC value of approximately 0.789583, as well as scenario 3, with a data splitting ratio of 70:30 and an AUC value of around 0.781066 Creator An entity primarily responsible for making the resource Fadhlurrahman Akbar Nasution, Siti Saadah, Prasti Eko Yunanto Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 AUC CatBoost credit risk detection P2P lending ROC https://repository.horizon.ac.id/files/original/372b2fa71d0e6b707ff38999fed9ce14.pdf acb35b88fa4faf63cd94e4891e041a74 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Critical Factors to Improve Teamwork Quality in Indonesian Startups Using aTWQ Framework Subject The topic of the resource teamwork quality; agile; aTWQ; startup; teamwork quality factors Description An account of the resource Due to the ever-changing needs of IT in today's businesses, agile software development has become popular due to its adaptive capabilities. Startups are among those who continuously strive to meet changing needs. Despite the potential benefits of Agile methodology, teamwork quality remains a challenge. Moreover, the global recession has made it increasingly important for startups to have effective teamwork, given the high level of uncertainty that leads to challenges in surviving, especially with cost-cutting efforts and downsizing. Therefore, this study aims to evaluate teamwork quality in Indonesian startups using the Agile Teamwork Quality (aTWQ) approach. TWQ is a comprehensive set of criteria designed to assess teamwork quality in agile environments. The primary objective of this study is to identify the factors that most strongly increase the teamwork quality of Indonesian startups. To accomplish this objective, data will be gathered from Indonesian companies using an online survey based on the aTWQ framework. The challenges were identified most on the cohesion, balance of contribution, and effort dimensions. The findings of this study are consistent with previous research, which hopefully may assist startups in Indonesia to enhance their teamwork quality and achieve greater success in their respective industries. Creator An entity primarily responsible for making the resource Muhammad Ridwan Fathin, Teguh Raharjo Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 Agile aTWQ startup teamwork quality teamwork quality factors https://repository.horizon.ac.id/files/original/e987ad35da59fde8df7643b4bbb3e20d.pdf bddc6b3c31aa31432509422068a9c57f 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Design Factors in Evaluating and Formulating IT Governance Systems in Public Organizations Subject The topic of the resource capacity levels; core model; design factors; IT governance; public organizations Description An account of the resource The application of information technology (IT) by central and regional governments in public services is intended to efficiently and effectively improve performance and public services. However, many studies have shown the ineffective application of IT. In Gorontalo Province, this can be seen in the e-readiness value of Gorontalo Province as a prerequisite for successful IT implementation, which is still at 58.15 points, which means it is at a moderate level of readiness. This shows that implementing IT Governance in the local government of Gorontalo Province is still not optimal in terms of performance or public services. This study aims to identify the design factors that need to be considered when implementing IT Governance to achieve better public service performance. This study uses a quantitative approach based on a survey method. The results showed six models at Level 3: BAI06, BAI07, DSS01, DSS03, DSS04, and DSS05. In addition, four models were at level 4: APO12, APO13, BAI10, and DSS02. Levels 3 and 4 show that the IT governance capability of the Gorontalo provincial government in each model is still not optimal. This study recommends that the Gorontalo provincial government evaluate and formulate an effective IT Governance system by focusing on each model's IT Governance design factors to improve public service performance. Creator An entity primarily responsible for making the resource Muhammad Rifai Katili, Lanto Ningrayati Amali, Sitti Suhada Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 capacity levels Core model design factors IT governance public organizations https://repository.horizon.ac.id/files/original/9c3708a332e34300a6d3025e8572f363.pdf 6ef0adecf0775e5aab1fcc32202330a0 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 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni 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 Designing A WSNs-based Smart Home Monitoring System through Deep Reinforcement Learning Subject The topic of the resource smart home monitoring system; wireless sensor networks; deep reinforcement learning; deep learning; internet of things (IoT) Description An account of the resource Smart home system technology has developed rapidly and provides convenience for human life. Several smart home technologies, especially monitoring systems, have been developed by integrating several aspects, including security systems, fuzzy methods, and energy-saving methods. However, the issue is how to build a smart home system that is accurate, convenient, and low-cost. In this research, the development of a smart home monitoring system that integrates wireless sensor networks (WSNs) and deep reinforcement learning (DRL) is carried out based on three parameters, i.e., temperature, humidity, and CO2 level. The experimental method is carried out by (1) validating the accuracy quality of WSNs; (2) determining the best model implemented in the system; and (3) measuring the quality of the DRL system on the smart home monitoring system. Based on the test results, several indicators were obtained: (1) Testing the WSNs resulted in an accuracy of 98.52%; (2) the accuracy of the modeling results implemented in the system is 97.70%; and (3) DRL system test on the smart home monitoring system through 21 test scenarios resulted in an accuracy of 95.52%. The indicators of testing this smart monitoring system prove that the developed system provides the advantages of accuracy, ease of use, and low cost Creator An entity primarily responsible for making the resource Ahmad Taqwa, Indra Griha Tofik Isa, Indri Ariyanti Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 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 deep learning deep reinforcement learning internet of things (IoT) smart home monitoring system wireless sensor networks