1 10 33 https://repository.horizon.ac.id/files/original/21e42433a211ee46ec348000a9675eb0.pdf 1f7b4842f4f6c4b3afb8bf897934c73e 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 5 (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 Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models Subject The topic of the resource breast cancer; histopathological image classification; deep learning; convolutional neural network; support vector machine Description An account of the resource Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs. Creator An entity primarily responsible for making the resource sil Unaldi1, Leman Tomak Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6420/1130 Publisher An entity responsible for making the resource available Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ondokuz Mayis University, Samsun, Türkiye Date A point or period of time associated with an event in the lifecycle of the resource September29, 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/9da6740c76ded7220b2837976ea8a603.pdf 1f7b4842f4f6c4b3afb8bf897934c73e 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 5 (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 Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models Subject The topic of the resource breast cancer; histopathological image classification; deep learning; convolutional neural network; support vector machine Description An account of the resource Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs Creator An entity primarily responsible for making the resource Isil Unaldi1, Leman Tomak2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6420/1130 Publisher An entity responsible for making the resource available Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ondokuz Mayis University, Samsun, Türkiye Date A point or period of time associated with an event in the lifecycle of the resource September29, 2025 Contributor An entity responsible for making contributions to the resource Isil Unaldi1, Leman Tomak Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource INDONESIA Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/961a91d4d3ccef3e2068842b247f20c3.pdf d2f677b2f84493cbc285ad9bef85a238 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 5 (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 Stacking Ensemble Learning Model for Intrusion Detection in Electrical Substation Subject The topic of the resource electrical substations; intrusion detection system; machine learning; stacking ensemble learning Description An account of the resource Electrical substations are crucial infrastructure in power transmission and distribution but are increasingly vulnerable to cyber threats. However, existing intrusion detection systems (IDS) face several limitations, such as high false positive rates,weak in anticipating new attack patterns, and imbalances in detecting different types of intrusions. This study proposes a Stacking Ensemble Learning model to enhance intrusion detection accuracy in electrical substations. The proposed model integrates Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost (XGB) as base models with XGB acting as the meta-model. A real-world electrical substation IEC 60870-5-104 network traffic dataset comprising 319,949 instances with multiple attacks, such as DoS, Port Scan, NTP DdoS, IEC 104 Starvation, Fuzzy Attack, Flood Attack, and MITM, was used in this study. The results demonstrate that the stacking model achieves the best performance, with accuracy (0.99990), precision (0.99990), recall (0.99990), and F1 score (0.99990), surpassing the base model, Bagging, and Boosting. T-test results further confirmed statistical significance, with p-values of 0.00428 (LR), 0.04237 (SVM), 0.00000 (XGB), 0.00057 (KNN), 0.00549 (Boosting), and 0.00000 (Bagging) reinforcing the superiority of the proposed methodapproach. These findings highlight the effectiveness of Stacking Ensemble Learning in enhancing the detection performanceof IDS for electrical substations and outperforming traditional models and other ensemble learning methods Creator An entity primarily responsible for making the resource Mohammad Mahruf Alam1, Feddy Setio Pribadi2, Rizky Ajie Aprilianto3, Arvina Rizqi Nurul’aini4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6502/1139 Publisher An entity responsible for making the resource available Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Semarang, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 11, 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/238b6a2bcd7c22c13c4e77f932b7ada1.pdf 9ae3c716129b1affc03efe1fc8989060 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 5 (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 the Accuracy of a Hybrid Neural Model with RBF-Polynomial Kernel for Rainfall Prediction: A Comparative Analysis of Trainlm and Trainrp Functions Subject The topic of the resource backpropagation; neural network; RBF-polynomial; relevance vector machine; rainfall Description An account of the resource Accurate rainfall prediction is crucial for effective water management and disaster mitigation. This study introduces a novelhybrid neural model that employs a fourth-degree polynomial kernel and provides the first empirical comparison of the trainlm and trainrp functions to enhance forecasting accuracy. This study explored the application of a neural network algorithm with RBF-Polynomial (degree 4) kernel for training and testing data in rainfall forecasting. This study focused on monthly rainfall data collected from Mataram City, Indonesia. We developed a hybrid BP-RVM algorithm as the main algorithm that offers a predictive approach to compare the trainlm and trainrp functions. We conducted 20 trials with combinations of learning, momentum, and gamma-RBF at internal values of 0.01-0.9. The training results from trainrp with more than 118 iterations yielded the best performance with learning rate 0.8 and momentum 0.2; MSE value of 2,236.25 and RMSE of 47.29. These results indicate a relatively low error rate for the proposed method. In contrast, the trainlm method, which only requires 18 iterations with a learning rate of 0.6 and momentum of 0.4, produces an MSE of 2,689.25 and RMSE of 51.86, showing its efficiency in reducing the computation time but with a slightly higher error rate than trainrp. Overall, the trainrp method was more accurate in capturing actual rainfall patterns with lower error rates, whereas the trainlm method exhibited good stability but greater sensitivity to parameter variations. This comparative analysis highlights the potential of trainrp to achieve more precise rainfall predictions within the study area. Creator An entity primarily responsible for making the resource Syaharuddin1, Abdillah2, Alfiana Sahraini3, Lilis Suriani4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6388/1140 Publisher An entity responsible for making the resource available Department of Mathematics Education, Universitas Muhammadiyah Mataram, Mataram, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 11, 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/8e18baaaf92e28a08d24f4b673ef0014.pdf 9ae3c716129b1affc03efe1fc8989060 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 5 (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 Automated Ripeness Detection of Oil Palm Fruit Using a Hybrid GLCM-HSV-KNN Model Subject The topic of the resource HSV: GLCM; KNN; image processing; oil palm fruit ripeness Description An account of the resource Accurately determining the ripeness of oil palm fruit is crucial for ensuring the quality of palm oil. However, traditional manual methods are often time-consuming and less accurate. This study aimed to develop an automated system for detecting the ripeness of oil palm fruit by combining the Hue Saturation Value (HSV) model, Gray Level Co-occurrence Matrix (GLCM), and K-Nearest Neighbor (KNN) algorithms. This system utilizes K-Nearest Neighbors to classify the relationship between color features extracted using the HSV model and texture features derived from GLCM analysis to categorize fruit ripeness. The color features represent the fruit's chromatic characteristics associated with ripeness, while the texture features provide information regarding surface patterns related to ripeness. The color features represent the fruit's color characteristics associated with ripeness, whereas the texture features provide information about the surface patterns related to ripeness. The results indicate that the system can classify oil palm fruit into four distinct categories: Over-Ripe, Ripe, Half-Ripe, and Raw. The dataset was divided with an 80:20 ratio, where 80% was allocated for training data and the remaining 20% for test data. An accuracy rate of 85% was achieved. The results of this study demonstrate that the developed system effectively classifies oil palm fruit images based on ripeness levels. This system supports a sustainable automated palm oil production model through accurate ripeness detection, thereby reducing reliance on manual methods and enhancing consistency and productivity in palm oil processing. These findings indicate that the proposed hybrid method is feasible for integration into an automated classification system to support decision-making in oil palm harvesting. Creator An entity primarily responsible for making the resource Mirfan1, Billy Eden William Asrul2, Mila Jumarlis3, Juliani4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6683/1143 Publisher An entity responsible for making the resource available Department of Computer Science, Faculty of Computer Science, Universitas Handayani Makassar, Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 12, 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/8fb109fa1efa6030fbdae1834356c91f.pdf 9ae3c716129b1affc03efe1fc8989060 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 5 (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 A Data-Driven Comparison of Linear Mixed Model and Mixed Effects Regression Tree Approaches for Dairy Productivity Analysis Subject The topic of the resource hierarchical data; indonesian dairy cow milk productivity survey 2024; linear mixed model (LMM); mixed effects regression tree (MERT); SDGs: goal 2 and 3 Description An account of the resource Dairy productivity studies often involve hierarchical and longitudinal data structures that violate the assumptions of linearregression. This study compares two modeling approaches, Linear Mixed Model (LMM) and Mixed Effects Regression Tree (MERT), in predicting dairy productivity based on the 2024 National Dairy Productivity Survey data. Predictive performance was evaluated using MSEP, RMSEP, MAD, and MAPE, with MERT consistently outperforming LMM in accuracy and robustness. Permutational Multivariate Analysis of Variance (PERMANOVA) test results reinforced this finding, yielding a pseudo-F value of 224.7 and a p-value of 0.001, indicating statistically significant differences in model performance. Key predictors of MERT model included farm altitude, the previous week’s milk production, and the amounts of concentrate feed given, which are part of significant predictor variables in LMM. This finding underscores MERT’s superiority in modeling complex agricultural datasets while providing interpretable insights through data-driven segmentation. The study advocates policy focus in sustainable milk production as well as the availability of high quality of feed and altitude-based dairy farms location to improve milk productivity. Should these focuses implemented by the industry, combined with the MBG Program, Indonesia would be progressing better towards achievement of SDGs Goal 2 and 3. Creator An entity primarily responsible for making the resource Achmad Fauzan1,2, Fatkhurokhman Fauzi3,4, Rhendy K P Widiyanto5,6Khairil Anwar Notodiputro7, Bagus Sartono8 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6751/1144 Publisher An entity responsible for making the resource available Study Program of Statistics and Data Science, School of Data Science, Mathematics and InformaticsIPB University, Bogor, Indonesia Contributor An entity responsible for making contributions to the resource October13, 2025 Format The file format, physical medium, or dimensions of the resource FAJAR BAGUS W Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/25b3cb85e16101f0f7afdcf4642fdbc8.pdf 9ae3c716129b1affc03efe1fc8989060 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 5 (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 Explainable Ensemble Learning for Maternal Health Risk in Low-Resource Settings Subject The topic of the resource ensemble learning; explainable AI; maternal health; risk prediction; SHAP Analysis Description An account of the resource Maternal health remains a global challenge, particularly in low-resource settings where accurate and timely risk prediction is essential to reducing maternal mortality. This study proposes an explainable machine learning framework for predicting maternal health risks by integrating ensemble learning methods with SHAP (Shapley Additive exPlanations) for interpretability. This study utilized the publicly available Maternal Health Risk Data Set (MHRDS), comprising physiological features such as systolic and diastolic blood pressure, blood sugar level, body temperature, and age. A total of 18 machine learning models including Random Forest, XGBoost, LightGBM, Neural Networks, and TabNet were evaluated to compare individual classifiers and ensemble approaches comprehensively. The selection of this diverse set of models is grounded in the need to benchmark different algorithmic paradigms, as variations in inductive bias, learning capacity, and robustness to clinical data noise can influence predictive performance and generalizability. This comprehensive comparison enables the identification of optimal model types for integration into ensemble frameworks. Evaluation was performed across three different test scenarios (test sizes of 10%, 20%, and 30%) to assess model consistency under varying data partitions. Stacking, Voting, and Histogram-based Gradient Boosting showed consistently high performance, with Stacking achieving the highest accuracy of 87.2%, followed by Histogram Gradient Boosting (86.9%) and Voting (86.7%) at test size 0.2. SHAP analysis identified blood sugar, systolic blood pressure, and maternal age as the top predictors across all test scenarios. The best-performing models were deployed into a web-based clinical decision support system designed for healthcare practitioners in Indonesia. The proposed approach balances predictive accuracy and model transparency, offering a practical solution for improving maternal care in data-limited environments Creator An entity primarily responsible for making the resource Lilik Widyawati1, Neny Sulistianingsih2* Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6765/1150 Publisher An entity responsible for making the resource available Computer Science, Faculty of Engineering, Universitas Bumigora, Mataram, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource Computer Science, Faculty of Engineering, Universitas Bumigora, Mataram, 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/26a7faa79c9a1c31fb50b37eb9e4a52f.pdf 9ae3c716129b1affc03efe1fc8989060 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 5 (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 IoT-Based Smart Infusion Monitoring and Control System Using ESP32 Subject The topic of the resource automatic control; ESP32; infusion fluids; internet of things; monitoring system Description An account of the resource Infusion is a common medical procedure used to treat conditions such as gastric acid and typhoid, where precise fluid administration is critical. This study presents the development of an IoT-based smart infusion monitoring and control system using an ESP32 microcontroller, designed to automatically monitor infusion volume and regulate drip rate in real-time. The system integrates a load cell sensor to measure infusion fluid weight, a photodiode sensor to detect drip rate, and a servo motor to adjust the flow rate adaptively. It features web-based monitoring, buzzer alerts, and an LCD display for local feedback. The system was tested in a clinical simulation with an infusion requirement of 1500 mL per 24 hours and various drip factors (15,20, and 60 drops/mL). The infusion volume status is automatically categorized into three levels: FULL (>350 mL), HALF (150–350 mL), and WARNING (<150 mL). Based on 10 test scenarios, the system accurately classified volume levels and triggered warnings when volume dropped below 150 mL. For example, in Test-08 to Test-10, volumes of 139.67 mL, 87.34 mL, and 40.53 mL were correctly detected as “WARNING” with buzzer alerts activated. The load cell sensor achieved excellent accuracy, with an error margin between 0.02% and 0.06%, while the system maintained drip-rate stability within a ±5% tolerance range. It also dynamically adjusted the servo angle to correct under- or over-drip conditions. These results confirm that the system delivers accurate, automated, and responsive infusion control, making it suitable for healthcare settings with limited staff to improve safety and efficiency. Creator An entity primarily responsible for making the resource Frengki Simatupang1, Istas Pratomo Manalu2, Ana Muliyana3, Paian Manalu4, Erna Meliana Manurung5, Batara Hasintongan Nadapdap Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6632/1151 Publisher An entity responsible for making the resource available Department of Computer Technology, Faculty of Vocational, Institut Teknologi Del, Toba, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 25, 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/e52e5409eeab7c6d24e2ad2eb18fa746.pdf 9ae3c716129b1affc03efe1fc8989060 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 5 (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 Student-Generated User Story Quality: A Study on Practitioner and ChatGPT Evaluation Subject The topic of the resource user stories; INVEST framework; practitioners; ChatGPT; quality evaluation Description An account of the resource valuating the quality of student-generated user stories is important in software engineering education, but only a limited number of industry practitioners can assist. The integration of generative AI can facilitate this process. To do so, the INVEST quality evaluation framework is widely recognized for assessing user story quality; however, prior research has not explored its use in conjunction with generative AI. This study investigated ChatGPT's ability to evaluate user stories using the INVEST framework. This study compares two ChatGPT-based evaluation approaches with those of experienced practitioners, focusing on student-generated user stories. Discrepancies between ChatGPT and practitioner evaluations were measured using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Statistical significance was tested using the Mann-Whitney U Test. The results indicate that ChatGPT’s 1st approach yielded lower discrepancies than practitioner evaluations. Moreover, significance testing showed no statistically significant differences between the ChatGPT and practitioner results for the two INVEST criteria- Independent and Estimable. These findings suggest that the 1st approach can assist in the evaluation process, although practitioners must ensure comprehensive and accurate evaluations. ChatGPT can provide preliminary evaluations in educational contexts, enabling students to receive formative feedback and allowing educators to streamline evaluation processes. Although practitioner validation is still required, their role may shift toward verifying AI-generated results, thus reducing the overall workload and accelerating quality evaluation Creator An entity primarily responsible for making the resource Muhammad Ihsan Zul1*, Suhaila Mohd. Yasin2, Dadang Syarif Sihabudin Sahid Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6950/1154 Publisher An entity responsible for making the resource available Department of Software Engineering, Faculty of Computer Science and Information Technolog Date A point or period of time associated with an event in the lifecycle of the resource October 25, 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/66a372582d3ae4206b3e7c4d40545ab8.pdf 9ae3c716129b1affc03efe1fc8989060 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 5 (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 Empowering Low-Resource Languages: Javanese Machine Translation Subject The topic of the resource javanese translation; NMT; linguistic heritage; low-resource language Description An account of the resource his study addresses the critical need to preserve and revitalize the Javanese language, which despite its widespread popularity, faces challenges as a low-resource language in Indonesia. The decline in Javanese proficiency among younger generations poses a significant threat to the language's cultural significance and heritage. To address this issue, this study introduces an innovative approach to machine translation, focusing on the development of a robust Indonesian-Javanese translation system. Utilizing advanced neural machine translation (NMT) techniques, including Long Short-Term Memory (LSTM) networks, the proposed system aims to bridge the linguistic gap between Indonesian and Javanese. Special attention was given to the unique linguistic characteristics and challenges of Javanese, with the goal of achieving exceptional translation accuracy and fluency. Through extensive experimentation and evaluation, this study aims to demonstrate the effectiveness of the translation system in facilitating cross-cultural communication and language preservation efforts within the Javanese-speaking community. By emphasizing the significance of Javanese as a widely spoken yet under-resourced language, this study underscores the importance of innovative technological solutions in safeguarding linguistic diversity and cultural heritage. Through its contributions, the research seeks to address the pressing need for language preservation and revitalization, particularly in the context of low-resource languages like Javanese Creator An entity primarily responsible for making the resource Danang Arbian Sulistyo1, Aji Prasetya Wibawa2, Wayan Firdaus Mahmudy3, Fadhli Almu’iini Ahda4, Andrew Nafalsk Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6887/1163 Publisher An entity responsible for making the resource available nformation Technology, Technology and Design Faculty, Institut Teknologi dan Bisnis Asia, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 26, 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