1 10 20 https://repository.horizon.ac.id/files/original/341b476a52f44a96b05697a9aaa55f47.pdf f3d9ce2844148c95c5fb657cabf59294 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 Premier League Match Outcome Prediction Using Support Vector Machine with Ensemble Techniques: A Comparative Study on Bagging and Boosting Subject The topic of the resource Support Vector Machine (SVM);Ensemble Techniques;Bagging;Boosting;Model Accuracy;Sports Analytics Description An account of the resource Predicting football match outcomes is a significant challenge in sports analytics, requiring accurate and resilient models. This study evaluates the effectiveness of ensemble techniques, specifically Bagging and Boosting, in enhancing the performance of Support Vector Machine (SVM) models for predicting match outcomes in the English Premier League. The dataset comprises detailed match statistics from 1,520 matches across multiple seasons, including features such as team performance, player statistics, and match outcomes. Four models were examined: baseline SVM, SVM with Bagging, SVM with Boosting, and a combined SVM + Bagging + Boosting approach. Evaluation metrics include accuracy, recall, precision, F1 score, and ROC-AUC, providing a comprehensive assessment of each model's performance. Experimental results indicatethat ensemble methods substantially improve model accuracy and stability, with the SVM + Bagging + Boosting combination achieving perfect accuracy, recall, precision, and F1 scores, alongside anROC-AUC value of 0.88. However, this model's slightly reduced ROC-AUC compared to others and its high computational cost highlight potential risks of overfitting and the need for significant resources. These findings underscore the practical potential of combining Bagging and Boosting with SVM for robust and accurate predictions. Limitations include the dataset's focus on a single league and the high resource requirements for ensemble methods. Future research could expand this approach to other sports and leagues, improve computational efficiency, and explore real-time predictive applications. Creator An entity primarily responsible for making the resource Agus Perdana Windarto1*, Putrama Alkhairi2, Johan Muslim3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6173/1015 Publisher An entity responsible for making the resource available Master's Program, Informatics Study Program, STIKOM Tunas Bangsa, Pematangsiantar, North Sumatra, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 06-02-2025 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/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/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/f54381d9ad98cfdec91cef7898bb6203.pdf 2f5604f2377d66e396354fd6e139481c 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 PCA and t-SNE Implementation for KNN Hypertension Classification Subject The topic of the resource Hypertension;KNN;Dimensionality Reduction;PCA;t-SNE Description An account of the resource Hypertension is a condition that, if allowed to increase, can significantly injure internal organs due to high blood pressure. The objective of this study is to use the K-Nearest Neighbor (KNN) algorithm along with PCA and t-SNE to accurately identify four categories of Hypertension,Normal, Hypertension, Stage 1 Hypertension, and Stage 2Hypertension. After establishing the scope, a dataset consistingof 7,794 samples was sourced from Labuang Baji Regional General Hospital, Makassar, and contained age, weight, and systolic and diastolic blood pressure parameters. The class distribution is Normal (36.3%), Hypertension (43.12%), Stage 1 Hypertension (8.29%), and Stage 2 Hypertension (12.31%). Experimental results show that the KNN base model achieved 99% accuracy, KNN with PCA reached 100%, and KNN with t-SNE attained 99%. Cross-validation was used to evaluate model generalization, yielding accuracies of 91%, 94%, and 91%, respectively. These findings suggest that KNN, particularly when integrated with t-SNE, is highly effective in visualizing and classifying non-linear data structures. Furthermore, this study demonstrates that incorporating dimensionalityreduction techniques enhances the interpretability of classified hypertension data, which is crucial for informed decision-making by mental healthcommittees Creator An entity primarily responsible for making the resource Andi Aulia Cahyana Resky1*, Jessica Crisfin Lapendy2, Andi Akram Nur Risal3*, Dewi Fatmarani Surianto4, Abdul Wahid Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6208/1025 Publisher An entity responsible for making the resource available Informatics and Computer Engineering, Engineering, Makassar State University, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-02-2025 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/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/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 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/f14a92dc690f6d5a0587590f442a2baf.pdf 460d011eeb4ddb9abe1eca660995bad7 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 Real-Time Location Monitoring and Routine Reminders Based on Internet of Things Integrated with Mobile for Dementia Disorder Subject The topic of the resource dementia;Global Positioning System(GPS);Internet of Things (IoT); real-time monitoring; reminder application Description An account of the resource The increasing number of dementia sufferers worldwide demands a new approach to monitoring daily activities and locations to reduce the risk of getting lost. This study develops a real-time location monitoring and routine reminder system based on the Internet of Things (IoT), integrated with a mobile application. The system is designed to assist individuals with dementia, particularly elderly and younger adults with cognitive impairments, in performing daily routines independently, while providing a sense of security for families and caregivers through real-time location tracking features. This technology utilizes GPS for accurate location monitoring, daily activity reminders, and automatic notifications for caregivers in case of deviations from usual routes.The system development includes prototype creationthat consistsof a mobile application and IoT tools such as the ESP32 WROOM microcontroller, Ublox Neo6M V2 GPS module, and SIM800L V2 GSM module. Functionality testing and impact evaluation were conducted to assess its effectiveness in improving the quality of life for dementia sufferers and facilitating monitoring for caregivers. With features such as daily reminders, emergency contacts, and real-time data integration, this system is intended not only for dementia patients but also for families and caregivers seeking tools to ensure the safety and comfort of the sufferers. It is expected that this research will enhance the independence of dementia patients in performing daily activities and provide innovative solutions through IoT technology to improve well-being across different age groups Creator An entity primarily responsible for making the resource Dwi Rangga Okta Zuhdiyanto1*, Yuli Asriningtias2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6105/1012 Publisher An entity responsible for making the resource available Department of Informatics, Faculty of Science and Technology, Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 26-01-2025 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/2daec2f4f74c7a394eb826ae4756435f.pdf 936d17ad477ab3446bbafbff4bd7b7ae 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 Ant Colony Optimization for Jakarta Historical Tours: A Comparative Analysis of GPS and Map Image Approaches Subject The topic of the resource Ant Colony Optimization; Historical Sites; Intelligent System;Traveling Salesman Problem;Tour Planning Description An account of the resource The Traveling Salesman Problem (TSP) is a problem that represents a difficult combinatorial optimization problem starting from practical problems. The ant colony optimization (ACO) algorithm is implemented in several topics, particularly in solving combinatorial optimization problems. ACO is inspired by the behavior of ants in searching for the shortest path between a food source and their nest. In this research, ACO isused to find the best path or traveling salesman problem for museums and historical sites in Jakartacapital city of Indonesia. This research employs an approach based on the location's coordinates or latitude and longitude, while another method depends on coordinate data obtained from a supplied map image.After implementing both models, it can be concluded that the ACO model is not very good at solving TSP using actual coordinates. Meanwhile, the algorithm can quickly find near-optimalpathswhen using coordinates from a map image. The algorithm generates the optimal path in 11 seconds, reducing the initial distance from 17.938 to 4.430, using 4.731 ants and 75 trips with a distance power of 1.Statistical random variation was also performed, which proved that the algorithm is flexible and reliable when tested under various conditions Creator An entity primarily responsible for making the resource Gabriel Fortino Bodhi1*, Charleen2,Devi Fitrianah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5968/1024 Publisher An entity responsible for making the resource available Computer Science, Binus Graduate Program, Bina Nusantara University, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 20-02-2025 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/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