3 10 33 https://repository.horizon.ac.id/files/original/89f12ffcce44df8c9275c3d759e0cb04.pdf cdaf471db42c7d3883280f0727092031 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 Comparative Evaluation of YOLOv9 and DETR Models in Traffic Object Detection for Intelligent Surveillance Systems Subject The topic of the resource intelligent systems; DETR; object detection; YOLOv9; traffic surveillance Description An account of the resource Object detection plays a crucial role in traffic surveillance, particularly in urban environments characterized by high vehicle density, diverse weather conditions, and limited computational resources. Although YOLOv9 and DETR have demonstrated strong performance in general object detection tasks, there is a lack of comparative research evaluating their effectiveness under specific challenges of traffic surveillance. These challenges include the need for real-time processing, accurate detection of small or partially occluded objects, and adaptability to complex traffic scenarios. This study addresses this gap by conducting a comparative evaluation of YOLOv9 and DETR using a custom traffic image dataset, with training iterations varied from 10 to 50 epochs to observe performance development. Evaluation metrics included mean average precision, precision, recall, F1-score, inference time, and object count per image. The results indicated that DETR achieved the highest accuracy across all metrics at the final training stage and detected up to 22 objects per image. However, the average inference time exceeded seven seconds per image, limiting the real-time applicability. Conversely, YOLOv9 achieved competitive accuracy with a significantly faster inference time of approximately 0.43 seconds per image. These findings provide practical insights into the trade-off between accuracy and processing efficiency, and offer guidance for model selection in operational traffic surveillance systems Creator An entity primarily responsible for making the resource Mohamad Jamil1, Nani Nagu, Muhammad Said Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6556/1169 Publisher An entity responsible for making the resource available Department of Informatics Engineering, Universitas Khairun, Ternate, Maluku Utara, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource Department of Informatics Engineering, Universitas Khairun, Ternate, Maluku Utara, 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/becc675171323428bb62d75b69ed3cd1.pdf c4000e56e5041d65b9f7091a7ed9ac75 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 Early Detection of Grasserie Disease in Silkworms Using Computer Vision and Machine Learning Subject The topic of the resource grasserie; histogram oriented gradient; machine learning; sericulture; silkworm Description An account of the resource One of the major challenges associated with the sericulture industry is silkworm diseases, as they are very difficult to detect in the early stages. Timely identification of infected silkworms is essential to curb the spread of disease and reduce economic damage. This study focuses on diagnosing Grasserie disease, a highly contagious condition that can devastate silkworm populations, leading to substantial financial losses for farmers. To address the shortcomings of expert manual inspections, this study employed camera-captured images of silkworms for automated disease detection. A newly compiled dataset, consisting of 668 healthy silkworms and 574 infected with Grasserie disease are used for this study. The dataset is analyzed with machine learning techniques for image analysis, features are extracted from the pre-processed images using combining Histogram of Oriented Gradients (HOG) and the higher dimensional features are reduced with Kernel Principal Component Analysis (KPCA), and classification using supervised models. The results highlight the effectiveness of this approach in differentiating healthy silkworms from diseased ones. The machine learning model HOG integrated with KPCA and Decision Trees (DT) achieved strong performance, with accuracy, recall, and precision scores of 94.28%, 94.56%, and 92.48%, respectively. While these outcomes are encouraging, further research is needed to develop a practical IoT-based tool that enables sericulture farmers to quickly detect infections and take preventive measures, minimizing unexpected losses. This study marks a crucial advancement in silkworm disease detection, offering a pathway toward greater sustainability and economic stability in the sericulture sector Creator An entity primarily responsible for making the resource Sania Thomas1, Binson V A2*, Sini Rahuman3, Sivakumar K S4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6705/1166 Publisher An entity responsible for making the resource available Department of Computer Science and Engineering, Saintgits College of Engineering, Kerala, India Date A point or period of time associated with an event in the lifecycle of the resource October 27, 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/935622f5cc38b7356505891fa6cc9429.pdf 0b0170d9d75b0999c3d64d156946ff61 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 Modeling and Deploying RESTful Services with SOMF-Based SOA: A Case Study in the Credit Guarantee Industry Subject The topic of the resource credit guarantee system; RESTful web services; service-oriented architecture; service-oriented modeling framework; system integration Description An account of the resource The integration of electronic systems across financial institutions poses significant challenges, particularly when legacy architectures rely on siloed, point-to-point connections. This often leads to what is commonly known as "spaghetti integration," where changes in one system can trigger unintended disruptions in others. This study addresses such integration issues within the Kredit Usaha Rakyat (KUR) credit guarantee service of an Indonesian credit guarantee institution by implementing a Service-Oriented Architecture (SOA) approach, guided by the Service-Oriented Modeling Framework (SOMF). This study aims to improve system performance, scalability, and regulatory adaptability through a structured, multi-phase methodology based on SOMF: conceptualization, discovery and analysis, business integration, logical design, and logical architecture. Data for the study were drawn from system documentation, national regulatory requirements (e.g., Coordinating Minister Regulation No. 1/2023), and the evaluation of service interactions via RESTful APIs using lightweight JSON formatting. These findings demonstrate that the adoption of SOA with SOMF enables the development of modular, interoperable, and adaptable services. This approach reduces redundant processes, enhances real-time data flow, and strengthens integration between the guarantee institution and its partner banks. The resulting system aligns with modern digital governance requirements and provides a sustainable foundation for future growth and compliance. Creator An entity primarily responsible for making the resource Amrid Ramdhani1, Nilo Legowo2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6867/1162 Publisher An entity responsible for making the resource available Information Systems Management Department, Binus Graduate Program - Master of Information Systems Management, Bina Nusantara University, Jakarta, 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 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 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/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/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/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/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/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