2 10 20 https://repository.horizon.ac.id/files/original/43b85fc85d23fb0d35f30b1c6ec5e6d1.pdf a12dbbf66a97372083c0052cbcc35e1b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Network Security: Evaluating SDN-Enabled Firewall Solutions and Clustering Analysis Using K-Means through Data-Driven Insights Subject The topic of the resource attack patterns; data-driven analysis;K-Means clustering;network security;SDN-enabled firewalls Description An account of the resource In the face of escalating and increasingly complex cyber threats, enhancing network security has become a critical challenge. This study addresses this issue by investigating the optimization of SDN-enabled firewall solutions using a data-driven approach. The research employs K-Means clustering to analyze attack patterns, aiming to identify and understand distinct patterns for improved firewall effectiveness. Through the clustering process, attack data was classified into three clusters:Cluster 0, indicating concentrated attack sources likely tied to high-activity regions or networks; Cluster 1, representing a disperseddistribution of attacks, pointing to diverse origins; and Cluster 2, linked to specific geographic regions or unique attack behaviors. The clustering efficacy was evaluated using the Silhouette Score (0.606) and the Davies-Bouldin Index (0.614), indicating meaningful and reliable clustering outcomes. These findings provide actionable insights into network threatpatterns, enabling the refinement and enhancement of SDN-enabled firewalls. The study contributes to the field by demonstrating the potential of clustering techniques in uncovering patterns overlooked by traditional methods and paving the way for further research into alternative clustering algorithms and broader applications in networksecurity Creator An entity primarily responsible for making the resource Ahmad Turmudi Zy1*,Isarianto2, Anggi Muhammad Rifai3, Agung Nugroho4, Abdul Ghofir5 Publisher An entity responsible for making the resource available Department of Informatics Engineering, Faculty of Engineering, Pelita Bangsa University, Bekasi, Indonesia 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/8fce7cc75b098f04bc50ab873883d875.pdf 5343b9a3d198e2e895cc931a979f8189 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource CNNPerformanceImprovement forClassifying Stunted Facial ImagesUsingEarly Stopping Approach Subject The topic of the resource CNN;early stopping; faces; Haar Cascade;stunting; stunted Description An account of the resource Stunting, a condition characterisedby short stature, is a growth disorder caused by chronic malnutrition, which often begins in the womb. Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. Research shows that these physical differences can also be observed in facial features. Because faces provide important information and are commonly studied in digital image processing, in this study, we will compare the facial image classification performance of stunted children versus normal children using various Convolutional Neural Network (CNN) architectures. The evaluated architectures include MobileNetV2, InceptionV3, VGG19, ResNet18, EfficientNetB0, and AlexNet. To improve the learning process, augmentation techniques with Haar cascade and Gaussian filters were applied so that the data set increased from 1,000 to 6,000 images. After adding the dataset, training is carried out with an early stop approach to minimiseoverfitting. The main aim of this research is to identify the CNN model that is most effective in differentiating facial images of stunted children from normal children. The results show that the EfficientNetB0 architecture outperforms other models, achieving 100% accuracy. Earlystopping has been shown to improve training efficiency and help prevent overfitting Creator An entity primarily responsible for making the resource Yunidar Yunidar1,2,Y Yusni1,3,NNasaruddin1,2Fitri Arnia*1 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6068/1010 Publisher An entity responsible for making the resource available Program Doktor Ilmu Teknik, Universitas Syiah Kuala, Banda Aceh, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 25-01-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource EN GLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/532e12dad64703a3cbddb8ef1839dca1.pdf 1e80697a3b6909ab61f3d9bc95dd48a7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of Main Transportation Modes using Passive Mobile Positioning Data(Passive MPD) Subject The topic of the resource prediction; Active MPD; Passive MPD; main transportationmode Description An account of the resource Indicators of the main mode of transportation used by domestic touristsduring tourism trips cannot yet be estimated using Passive MPD which is recorded based on the location of the BTS that captures the cellular activity ofdomestictourists. Previous research on identifying transportation modes from Passive MPD has its ownshortcomings because it only relies on speed and travel time features. Meanwhile, there is Active MPD which is recorded using active geo-positioning and real-time, where the research involves many features and has a data structure similar to Passive MPD. Therefore, this research aims to conduct a study of the implementation of the method used to identify modes of transportation in Active MPDs to Passive MPDs as an approachto predicting the main modes of transportation. As a result, the transportation mode identification method in the Active MPD can be implemented in the Passive MPD. The best accuracy of 83.56% was obtained by the LightGBM model using all features. However, the Multinomial Logistic Regression model, which only uses 10 selected features, is the most effective and efficient model with an accuracy of 76.43% and a much shorter execution time. Creator An entity primarily responsible for making the resource Muhammad Farhan1,Lya Hulliyyatus Suadaa2*, Sugiri3, AlfatihahReno MNSP Munaf4, Setia Pramana5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6128/1008 Publisher An entity responsible for making the resource available Politeknik Statistika STIS, Jakarta,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 24-01-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/ff629988f7af1efea521c36d55890c5c.pdf 4324774ac0448ba4c3f073e6bfa167eb Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultural Cultivation Cost Prediction Using Neural Networks and Feature Importance Analysis Subject The topic of the resource artificial intelligence; cultivation cost; deep learning; mean absolute error; mean squared error; R-squared Description An account of the resource Agriculture is one of the most important sectors integral to human civilization, and technological adaptation is necessary to maintain its quality. This research aims to achieve high productivity in the agricultural sector by using neural networks or Deep Learning methods to predict the cost of agricultural cultivation, as well as identifying significant factors that affect the profitability of potato commodities with Feature Importance analysis. The research process includes the stages of Data Preparation, Data Understanding, Split Data Training, Classification Model Building, Training, and Evaluation. Evaluation techniques such as MAE, MSE, and R² were used to assess the effectiveness of the model. The results showed that the prediction model almost achieved optimal performance, with the Cost of Cultivation C2 factor having the greatest influence in understanding the data and guiding improvements to the significant factors affecting cultivation cost prediction. The main contribution of this research is the application of optimal Deep Learning methods to predict the cost of cultivation as well as identifythe main components that impact the profitability of potato farming in India Creator An entity primarily responsible for making the resource Salmania Putri1, Tora Fahrudin2*, Asti Widayanti Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6003/1006 Publisher An entity responsible for making the resource available Accounting Information System, Faculty of Applied Sciences, Telkom University, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 23-01-2024 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/58e4d64e7a1488ab31e72b23878989f3.pdf af8263e43f6fad2c6f224c6f336686f0 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Evaluating Transformer Models for Social MediaText-Based Personality Profiling Subject The topic of the resource Profiling analysis;Transformer, BERT Variants Description An account of the resource This research aims to evaluate the performance of various Transformer models in social media-based classification tasks, specifically focusingon applications in personality profiling. With the growing interest in leveraging social media as a data source for understanding individual personality traits, selecting an appropriate model becomes crucial for enhancing accuracyand efficiency in large-scale data processing. Accurate personality profiling can provide valuable insights for applications in psychology, marketing, and personalized recommendations. In this context, models such as BERT, RoBERTa, DistilBERT, TinyBERT, MobileBERT, and ALBERT are utilized in this study to understand their performance differences under varying configurations and dataset conditions, assessing their suitability for nuanced personality profiling tasks.The research methodology involves four experimental scenarios with a structured process that includes data acquisition, preprocessing, tokenization, model fine-tuning, and evaluation. In Scenarios 1 and 2, a full dataset of 9,920 data points was used with standard fine-tuning parameters for all models. In contrast,ALBERT in Scenario 2 was optimized using customized batch size, learning rate, and weight decay. Scenarios 3 and 4 used 30% of the total dataset, with additional adjustments for ALBERT to examine its performance under specific conditions. Each scenario is designed to test model robustness against variations in parameters and dataset size.The experimental results underscore the importance of tailoring fine-tuning parameters to optimize model performance, particularly for parameter-efficient models like ALBERT. ALBERT and MobileBERT demonstrated strong performance across conditions, excelling in scenarios requiringaccuracy and efficiency. BERT proved to be a robust and reliable choice, maintaining high performance even with reduced data, while RoBERTa and DistilBERT may require further adjustments to adapt to data-limited conditions. Although efficient, TinyBERT may fall short on tasks demanding high accuracy due to its limited representational capacity. Selecting the right model requires balancing computational efficiency,task-specific requirements,and data complexity Creator An entity primarily responsible for making the resource nggit Dwi Hartanto1*, Ema Utami2, Arief Setyanto3, Kusrini Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6157/1005 Publisher An entity responsible for making the resource available Faculty of Computer Science, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 21-01-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/0244cfddceab8e7eee16fcc1ef809405.pdf ec1cd68a45c279c081d2512b457cbc6d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Identifying Rice Plant Damage Due to Pest Attacks Using Convolutional Neural Networks Subject The topic of the resource convolutional neural networks; rice; stem borer pest Description An account of the resource Rice (Oryza Sativa) is an important crop for meeting global food needs; however, one of the main challenges in its cultivation is the attack of stem borer pests, which can cause significant damage. This study aims to identify the damage caused by these pest attacks using Convolutional Neural Networks (CNN) methods. We developed and trained several CNN architectures, including the proposed architecture, MobileNet, and EfficientNetB0, to detect pest attacks on rice. The dataset used consists of 700 images per class taken directly from the field, where the images depict rice plants that have been peeled or opened toinspect for the presence of pests, specifically stem borer pests. To enhance the quality and diversity of the dataset, we applied a rigorous selection process, ensuring that only high-quality images were used. Additionally, augmentation techniques such as rotation were employed toexpand the dataset to 2000 images per class. Labeling was carried out carefully to ensure that each image accurately reflected the condition of the pest attack. The results of the study indicate that the proposed CNN model can identify damage with high accuracy, thereby contributing to efforts to increase rice production through early detection of pest attacks using computer vision technology. Creator An entity primarily responsible for making the resource Andi Tenriola1*, Putri Alysia Azis2, Andi Baso Kaswar3, Fhatiah Adiba4, Dyah Darma Andayani5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6125/1004 Publisher An entity responsible for making the resource available Department of Informatics and Computer Science, Faculty of Engineering, Universitas Negeri Makassar, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 19-01-2024 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/d5fc9a5c5bbe6392d8991d1abd4a5fb9.pdf a04bc760f0c6d68f865a67985a800a4b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of Transfer Learning Architecture Performance for Indonesian Auction Object Classification Subject The topic of the resource image classification;indonesian auction;PNBP; transfer learning Description An account of the resource The Indonesian auction, one of the sources of Indonesia's income for Non-Tax State Revenue (PNBP), faces challenges in accurately classifying auction objects, limiting revenue optimisation. This research aims to compare the performance of several transfer learning architectures on the Indonesian Auction Object Dataset, which includes categories such as Buildings, Cars, Motorbikes, and Salvage Materials. Seven pre-trained transfer learning models—MobileNetV2, NASNetMobile, EfficientNetV2B0, DenseNet121, Xception, InceptionV3, and ResNet50V2—were evaluated against a baseline model, focusing on validation accuracy, model size, and computational efficiency. MobileNetV2, NASNetMobile, DenseNet121, Xception, InceptionV3, and ResNet50V2 all achieved 100% validationaccuracy, outperforming the baseline model's 96.5% accuracy. MobileNetV2 stands out for its efficiency, reaching 100% accuracy in just eight epochs with a compact model size of 11.1 MB. In contrast, EfficientNetV2B0 performed poorly on this dataset, achieving only 25% validation accuracy. These findings confirm that transfer learning architectures can significantly improve auction object classification accuracy while reducing the model size and training time, highlighting the benefit of transfer learning for optimising Indonesian auction systems Creator An entity primarily responsible for making the resource Hanif Noer Rofiq1* Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6082/1003 Publisher An entity responsible for making the resource available Directorate Transformation and Information Systems, Directorate General of State Assets Management, Ministry of Finance, Date A point or period of time associated with an event in the lifecycle of the resource 19-01-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/3d611cde19adadc86e6b65f9eed39865.pdf 33c7361d27aa982182eb71dc673d46a1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Impact of XBRL Technology Implementation on Information Asymmetry in Indonesia’s Capital Market Subject The topic of the resource financial reporting quality; information asymmetry; wilcoxon signed rank test; XBRL Description An account of the resource This research aims to examine whether the publication of financial statements in XBRL format could reduce the level of information asymmetry, measured by the bid-ask spread, in Indonesia’s capital market over eight years of its implementation. Furthermore,this research examines differences in the level of information asymmetry in two observation periods, which are the early stage and the advanced stage of the XBRL implementation. The population in this study are listed companies on the IDX80 index which were sampled using the purposive method. The analytical instrument used is a panel data regression test using a random effect model and the non-parametric Wilcoxon test of statistical differences. Consistent with similar studies, the results show that the publication of financial reports in XBRL format could reduce the level of information asymmetry by providing accurate, integrated, and universally accessible reporting. The difference test further reveals that the level of information asymmetry is lower in the advanced stages compared to the early stages. This suggeststhat XBRL implementation becomes more effective over time due to the positive developments in institutional readiness and stakeholder facilitation Creator An entity primarily responsible for making the resource Aldika Wahyudi1*, Dyah Anggraini2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5908/1023 Publisher An entity responsible for making the resource available Sistem Informasi Akuntansi, Program Pascasarjana, Universitas Gunadarma, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 27-02-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/2f51cd6874d9c35f8be62000669d5b82.pdf 441c31a87dd415ffa8d041a2f67059a5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of Transfer Learning Model Performance for Breast Cancer Type Classification in Mammogram Images Subject The topic of the resource Breast Cancer Classification;Deep Learning;Mammography Image; Transfer Learning Description An account of the resource Globally, breast cancer is the type of cancer that most women suffer from. Early detection of breast cancer is very important because there is a big chance of cure. Mammography screening makes it possible to detect breast cancer early. The study of computer-assisted breast cancer diagnosis is gaining increasing attention. Breast cancercomes in two forms: benign cancer and malignant cancer. advances in deep learning (DL) technology and its use to overcome obstacles in medical imaging, and classification using a number of transfer learning models to identify the type of breast cancer (malignant, benign, or normal).This work conducted a thorough comparison analysis of eight prevalent pre-trained CNN algorithms (VGG16, ResNet50, AlexNet, MobileNetV2, ShuffleNet, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2) for breast cancer classification.In this study, we permonData is divided into training, testing, and validation. Using the publicly accessible mini-DDSM dataset, we assess the proposed architecture. were used to measurethe classification accuracy(Acc).For genBased on test results, the best accuracy was obtained using EfficientNetb2 with an accuracy value of 94% for training data and 98% for test data on mammogram images Creator An entity primarily responsible for making the resource Cahya Bagus Sanjaya1, Muhammad Imron Rosadi2*, Moch. Lutfi3, Lukman Hakim Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6177/1019 Publisher An entity responsible for making the resource available Departmentof Informatics Engineering, Facultyof Enginering, Universitas Yudharta Pasuruan, Pasuruan, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 15-01-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/30e0701131486e22e041236b906f0a87.pdf a774fe1085ed5abf5f2ad7e7b3622b60 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 1 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Internet-of-Things-based FishpondSecurity System Using NodeMCU ESP32-CAM Microcontroller Subject The topic of the resource Arduino Uno R3;fishpondsecuritysystem;Internet of Things (IoT);NodeMCU ESP32-CAM;real-time notification Description An account of the resource Fish theft in ponds is a common problem, especially in freshwater fish farms. To solve this problem, a security system that can detect human movement and provide real-time notifications is needed. This research aims to design and implement an Internet of Things (IoT)-based fishpond security system using NodeMCU ESP32-CAM Microcontroller equipped with HB100 Radar Sensor to detect human entitymovement with NodeMCU ESP32-CAM to take pictures of the approaching human entity, as well as Arduino Uno R3 to control system inputs and outputs. The system also sends real-time notifications and can be managed independently by a social media application. The results show that thesystemcan detect human movement well, provide real-time notifications, and be handled easily. The test results show that the HB100 Radar Sensor can detect entitieswith a maximum distance of 9 meterswith overall accuracy of 90%,the Buzzer performs well according to the human entitydetected by the sensor, the Arduino Uno R3successfully sends a trigger signal to NodeMCU ESP32-CAMtoactivatethe OV2640 camera to capture the detectedhumanentitieswith a maximum distance of up to60 meterswith an optimal distance between 1 to 9 meters.Integrated system test results show that all components of the fishpond security system perform 100% well and are prospective to protect a fishpond with an area of 7x7 meter squares.This system providesa solution to reduce fish theft in ponds anddeliversreal-time information to the fishpondowner. Creator An entity primarily responsible for making the resource Arwin Datumaya Wahyudi Sumari1, Ilyas Annurroni2, Astika Ayuningtyas Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6033/1009 Publisher An entity responsible for making the resource available Department of Electrical Engineering, State Polytechnic of Malang, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 24-02-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT