3 10 26 https://repository.horizon.ac.id/files/original/d1f8a710f23d295428af206e109e6310.pdf d8382b96fca7277f51ff0bcbe0ec2097 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 4 (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 Agile Defect Prediction with Optimized Machine Learningand Feature Selection Subject The topic of the resource agile software practices; bug prediction; defect classification; feature selection; metaheuristic optimization Description An account of the resource n Agile software development, efficient defect prediction is crucial because of the rapid and iterative nature of the delivery. Conventional methods that rely on source code or commit logs often fail to capture the critical contextual signals necessary for early bug detection. This study proposes a hybrid machine learning framework that leverages enriched contextual features from Jira issue tickets and combines them with optimized feature selection techniques. Various classification models, including Random Forest, XGBoost, CatBoost, SVM, and Transformer, are employed to predict defects. To further enhance model performance, metaheuristic-based feature selection methods such as the Bat Algorithm (BA) and Particle Swarm Optimization (PSO) are applied to reduce dimensionality and improve predictive relevance. Experimental results show that Random Forest with BA optimization achieves the highest performance, with an F1-score of 0.83 and an AUC-ROC of 0.86, outperforming other models. While the Transformer modeldoes not surpass tree-based algorithms in all metrics, it shows high recall and competitive F1-scores, making it suitable for high-sensitivity applications. These findings highlight the importance of integrating optimized machine learning models and feature selection techniques to improve model robustness, reduce computational complexity, and meet the needs of Agile development. This approach supports software teams in prioritizing quality assurance tasks, reducing long-term maintenance costs, and optimizing defect management processes Creator An entity primarily responsible for making the resource Faiq DhimasWicaksono1*, Daniel Siahaan2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6713/1113 Publisher An entity responsible for making the resource available Master Program of Technology Management, Interdisciplinary School of Management and Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 18, 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/d3ba11c82a5ed500f8f334a5ec6e6903.pdf 71e5654ff3062bf6cbb79fd2618c6e89 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 4 (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 Adaptive Stress Prediction with GSR, SMOTE Balancing, and Random Forest Models Subject The topic of the resource GSR sensor; perceived stress scale; random forest;SMOTE balancing; stress detection Description An account of the resource Stress is a pervasive condition that affects mental health, productivity, and quality of life across populations. Traditional methods for stress assessment, such as the Perceived Stress Scale (PSS), rely on retrospective self-reporting and are limited by subjectivity and delayed feedback. To address this gap, this study developed an integrated real-time stress monitoring system combining Galvanic Skin Response (GSR) sensors, Internet of Things (IoT) technology, and machine learning algorithms.Primary GSR data were collected from 30 participants under varied conditions, supplemented by secondary data from the WESAD dataset. A Random Forest classifier was employed to categorize stress into four levels: normal, mild, moderate, and severe. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, leading to improved model robustness. The system achieved a cross-validated classification accuracy of 69%, with substantial improvements in the detection of moderate and severe stress cases compared to traditional threshold-based methods. A strong agreement (Cohen’s Kappa κ = 0.82) was observed between system predictions and PSS-based stress assessments.Feature importance analysis identified mean GSR value and Skin Conductance Response (SCR) amplitude as the most influential indicators of stress. The system was evaluated for usability, receiving high user ratings in terms of accessibility, simplicity, and interactivity. A simple Python-based command-line interface (CLI) was also developed for real-time stress prediction based on input features.This research demonstrates the feasibility and effectiveness of combining physiological sensing, predictive analytics, and user-friendly interfaces to enable scalable and adaptive stress monitoring. Future developments will focus onintegrating additional physiological modalities and deep learning techniques to enhance predictive performance and personalization in clinical and everyday contexts Creator An entity primarily responsible for making the resource Rino Ferdian Surakusumah1*, Rechi Yudha Apza Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6588/1112 Publisher An entity responsible for making the resource available Department of Medical Electronics Engineering Technology, Faculty of Health Technology, Al Insyirah Institut of Health and Technology, Pekanbaru, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 17, 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/53390545989d262a68b7cc14a2f39e4c.pdf 32a4517472107a7a7d016cf15e498a8c 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 4 (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 Advancing Vehicle Logo Detection with DETRto Handle Small Logos and Low-Quality Images Subject The topic of the resource detection transformers; logo; object detection; vehicle Description An account of the resource Image-based vehicle logo detection is an important component in the implementation of vehicle information recognition technology, which supports the development of intelligent transportation systems. Vehicle logos, as elements that represent the identities of vehicle brands and models, play a significant role in completing vehicle identity data. The information obtained from this logo can be utilized to solve various traffic problems, such as vehicle document counterfeiting and theft, and for better traffic planning and management purposes. However, the main challenge in developing an accurate logo detection system lies in the wide variety of shapes, sizes, and positions of logos in different types of vehicles. In addition, the generally small size of logos, especially on certain vehicles, often makes it difficult for computer-based detection systems to recognize logos consistently, thus affecting the overall performance of the detection model. In this research, the Detection Transformers (DETR) method is used to build a vehicle logo detection system that focuses on small-scale logo. The testing process was conducted using the VL-10 dataset, which was specifically designed forvehicle logo detection evaluation. The results show that the DETR model can detect vehicle logos very well, even for small-scale logos. The model achieved an AP50 value of 0.952, which indicates a high level of accuracy and reliability in detecting the vehicle logo in the dataset used. Creator An entity primarily responsible for making the resource Rifky Fahrizal Ubaidillah1, Mahmud Dwi Sulistiyo2*, Gamma Kosala3, Ema Rachmawati4, Deny Haryadi5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6236/1111 Publisher An entity responsible for making the resource available School of Computing, Telkom University, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 17. 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/c2e378a65c2feae559cb6abc819489a8.pdf df1b3c6126548b31b8b63ee19e81c315 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 4 (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 Comparing Optimization Algorithms in ANN Models for House Price Prediction in Pekanbaru Subject The topic of the resource AdaDelta; stochastic gradient descent (SGD); adaptive moment estimation (Adam); adaptive sharpness-aware minimization (ASAM);artificial neural network (ANN); house price prediction; optimization; nadam Description An account of the resource This study evaluates the performance of five optimization algorithms in Artificial Neural Network (ANN) models for predictinghouse prices in Pekanbaru. The optimizers tested include Adam, AdaDelta, Stochastic Gradient Descent (SGD), Nadam, and Adaptive Sharpness-Aware Minimization (ASAM). A total of 3,149 house sales records were collected from rumah123.com between January and December 2024. After cleaning 148 incomplete entries, 3,001 valid records remained. The dataset included seven features: price, location, number of bedrooms, number of bathrooms, land area, building area, and garage capacity, with the location encoded using one-hot encoding. The research involved a literature review, problem formulation, data acquisition, preprocessing, model development, and evaluation. Model performance was assessed using the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). The results show that SGD consistently achieved the best performance, particularly at a 90:10 train-test split, with the lowest MAPE (1.74%) and MSE (0.3279). Adam and Nadam also performed well, while ASAM had the highest error (MAPE 6.14%). These findings indicate that SGD was the most effective optimizer for this dataset. Future research should explore larger datasets and advanced hyperparameter tuning to improve the generalizability of this model Creator An entity primarily responsible for making the resource Doni Winarso1*, Edo Arribe2, Syahril3, Aryanto4, Muhardi5, Sharulniza Musa Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6619/1109 Publisher An entity responsible for making the resource available Departmentof Information Systems, Facultyof Computer Science, Universitas Muhammadiyah Riau, Riau, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 17, 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/7700cf7f6ce15eddbc8d3f572d56f89c.pdf af1942563bda77302ae544944b7bcd6c 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 4 (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 Benchmarking YOLOv8 Variants with Transfer Learning for Real-Time Detection and Classification of Road Cracks and Potholes Subject The topic of the resource classification;deep learning; road damage detection;transfer learning;YOLOv8 Description An account of the resource Road damage, including potholes and cracks, is a significant issue frequently encountered in road infrastructure in many regions. Such conditions accelerate road degradation, increase the risk of traffic accidents, and significantly increase the maintenance andrepair costs. Although several deep learning models have been proposed for road damage detection, few studies have systematically compared the performance of lightweight YOLOv8 variants using a consistent dataset. To address this gap, this study proposes a road defect detection and classification model based on the YOLOv8 series, which is enhanced using transfer learning to improve performance and efficiency. The dataset, obtained from Roboflow, comprises 3,846 images categorized into training, validation,and testing sets. Three YOLOv8 variants—YOLOv8n, YOLOv8s, and YOLOv8m—were benchmarked for performance. A performance evaluation was performed using the metrics of precision, recall, and mean Average Precision (mAP). Results show that YOLOv8m achieved thehighest precision (99.00%), recall (98.40%), and mAP (99.50%). In the pothole category, precision reached 98.70% and recall 99.30%; in the crack category, precision was 99.30% and recall 97.60%. The findings demonstrate that YOLOv8, particularly the YOLOv8m variant, is highly effective for real-time road damage detection and classification, offering a viable solution for intelligent transportation systems and automated infrastructure monitoring. This research has the potential to revolutionize infrastructure monitoring by enabling scalable, real-time, and cost-effective assessments of road conditions. It minimizes reliance on manual inspections, reduces human errors, and contributes to the development of intelligent transportation systems and predictive maintenance strategie Creator An entity primarily responsible for making the resource Dede Kurniadi1*, A. Abdul Latif2,Asri Mulyani3, Hilmi Aulawi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6710/1108 Publisher An entity responsible for making the resource available Department of Computer Science, Institut Teknologi Garut, Garut, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource August 15, 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/70924ae0e94b4247adfc83646c937cef.pdf 5d23a602f7c94db10fbe70b0975ac206 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 4 (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 Emotion Recognition Using the MobileNetV2 Architecture Subject The topic of the resource facial recognition; deep learning, MobileNetV2, CNN, tensorflow Description An account of the resource Facial recognition technology is now advancing quickly and is being used extensively in a number of industries, including banking, business, security systems, and human-computer interface.However, existing facial recognition models face significant challenges in real-time emotion classification, particularly in terms of computational efficiency and adaptability to varying environmental conditions such as lighting and occlusion. Addressing these challenges, this research proposes a lightweight, yet effective deep learning model based on MobileNetV2 to predict human facial emotions using a camera in real time. The model is trained on the FER-2013 dataset, which consists of seven emotion classes: anger, disgust, fear, joy, sadness, surprise, and neutral. The methodology includes deep learning-based feature extraction, convolutional neural networks (CNN), and optimization techniques to enhance real-time performance on resource-constrained devices. Experimental results demonstrate that the proposed model achieves a high accuracy of 94.23%, ensuring robust real-time emotion classification with a significantly reduced computational cost. Additionally, the model is validated using real-world camera data, confirming its effectiveness beyond static datasets and its applicability in practical real-time scenarios. The findings of this study contribute to advancing efficient emotion recognition systems, enabling their deployment in interactive AI applications, mental health monitoring, and smart environments. Real-world camera data is also used to evaluate the model, demonstrating its usefulness in real-time applications and its efficacy beyond static datasets. The results of this work advance effective emotion identification systems, making it possible to use them in smart settings, interactive AI applications, and mental health monitoring Creator An entity primarily responsible for making the resource Triyani Hendrawati1*,Anindya Apriliyanti Pravitasari2, Nazamuddin3, Riza Fazhriansyah Hermawan4, Satrio Adilia Subekti5, Muhammad Yasyfi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6158/1102 Publisher An entity responsible for making the resource available Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource July 17, 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