1 10 33 https://repository.horizon.ac.id/files/original/8db5f07f470ad1db6f20d0044e97e24f.pdf 22a4c1292d8807e903e5d0e868826211 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 Optimization of Accuracy Improvement through Modified ShuffleNet Architecture in Rice Classification Subject The topic of the resource CNN model; optimization; MobileNet; rice classification; RestNet; ShuffleNe Description An account of the resource Accurate rice classification is essential to determine the quality and market value of rice. Traditional methods of rice classification are often time-consuming and error-prone, so a more efficient and accurate solution is needed. This study aims to optimize rice classification using Convolutional Neural Networks (CNN) combined with the ShuffleNet architecture, which offers high computational efficiency without sacrificing accuracy. The dataset used comes from Kaggle, containing 8750 rice grain images divided into five classes: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The uniqueness of this study is the application of ShuffleNet Proposed in rice classification, which provides improved performance compared to basic CNN models such as MobileNet, ShuffleNet, and RestNet. The results showed that the MobileNet model achieved 80% accuracy, RestNet 94%, and ShuffleNet achieved 100% accuracy with precision, recall, and F1 values also 100%. However, the ShuffleNet model experienced overfitting when tested with new data, resulting in an accuracy of only 20%. To overcome this, further optimization was carried out on the model. The results of statistical tests (paired t-test and Wilcoxon test) show significant differences between ShuffleNet Proposed and other models, which proves that the improvements applied to this model provide significant improvements. The implications of this study can improve the efficiency and accuracy of rice classification, which has the potential to improve the quality and market value of rice in the agricultural industry Creator An entity primarily responsible for making the resource Abdullah Ahmad1, Dedy Hartama2, Agus Perdana Windarto3, Anjar Wanto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6411/1159 Publisher An entity responsible for making the resource available Student of Magister Informatika, STIKOM Tunas Bangsa, Pematangsiantar City, North Sumatra, 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 BAGSU 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/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/9f047394976d39e3b3f5fc702506f00e.pdf 82cb736ded51dd0e8ad0a4ec67a26428 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 Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning Subject The topic of the resource Automated Diagnosis; Data Augmentation; EfficientNet-B5; Eye Disease Detection; Fine-tuning Description An account of the resource ye diseases significant global health challenges, underscoring the need for efficient and accurate diagnostic. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the ODIR dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. This result approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making. Creator An entity primarily responsible for making the resource Anggi Muhammad Rifai1, Muhammad Fatchan2, Ahmad Turmudi Zy3, Donny Maulana4, Sufajar Butsianto5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6519/1148 Publisher An entity responsible for making the resource available Department of informatics engineering, Faculty of engineering, Pelita Bangsa University, Bekasi, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 24, 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/3d74f4b0b0ee798524bd2c99d6843f36.pdf 2c70c049549298a9ef145dc7b393d81e 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 Enhancing Recruitment Efficiency in the Indonesian Pharmaceutical Industry Using Backpropagation Neural Networ Subject The topic of the resource backpropagation; deep learning; employee selection model; multi-layer perception; recruitment Description An account of the resource PT K-24 Indonesia is one of the foremost companies in Indonesia, with a primary focus on distributing pharmaceutical products and healthcare services. Over the last two years, PT K-24 has received more than 110,000 job applicants, offering various position vacancies. The recruitment process began with registrations, online tests, and interviews. The need for manpower has increased annually in recent years. More attention is required when selecting prospective employees who meet the selection criteria. However, PT K-24 found that the recruitment process was less efficient because the applicants did not meet the company’s criteria. To overcome this problem, it is necessary to create an employee selection model for candidate selection. This study developed a recommendation using the multilayer perceptron method, namely, backpropagation. The results of this study add to the confidence of prior research that the backpropagation algorithm is effective for prediction and reliable forsimilar prediction tasks. This study found that using 112,397 relevant data, 23 input parameters, 1 hidden layer containing 10 neurons, ReLu activation function, and Adam solver produced better prediction, with an MSE of 0.029. Our study contributes to the methodological approach by implementing real-world problems and measuring specific criteria relevant to the selection process. Creator An entity primarily responsible for making the resource Apfia Normalita1, Jong Jek Siang2*, Halim Budi Santoso Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6303/1133 Publisher An entity responsible for making the resource available Department of Information Systems, Duta Wacana Christian University, Yogyakarta, Indonesia Contributor An entity responsible for making contributions to the resource October3, 2025 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/106e4ef0d40fa154891c86dfdcf99598.pdf daf431ddd69811b17d22fbe617914d20 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 Hyperparameter Tuning with Optuna to optimize the YOLOv11n Model for Weed Detection Subject The topic of the resource image augmentation; Optuna; residential weed detection; YOLO nano; YOLOv11n Description An account of the resource Accurate weed detection is essential for maintaining the cleanliness and aesthetic appeal of residential yards. This study aimed to optimize YOLOv11n, a lightweight object detection model, to achieve high precision in weed identification under real-world conditions. The novelty of this study lies in the application of Optuna, an automatic hyperparameter optimization framework, to enhance model performance while maintaining computational efficiency for resource-limited devices such as drones and IoT systems. The research involved data augmentation techniques including crop (0–20% zoom), hue (±20°), saturation (±30%), brightness (±20%), exposure (±15%), and mosaic augmentation. These augmented images were used to train four YOLO nano variants (v5n, v8n, v11n, v12n), which were evaluated using standard metrics: Precision, Recall, F1-Score, and mean Average Precision (mAP). Among the models tested, YOLOv11n with Custom Optuna configuration delivered the highest performance, achieving a 94.6% F1-score and 97.8% mAP@0.5. These results demonstrate that the optimized YOLOv11n model can support accurate and efficient real-time weed detection in household environments, particularly on edge devices with limited hardware capabilities. This makes it a viable solution for practical implementation in precision agriculture and smart gardening. Creator An entity primarily responsible for making the resource Candhy Fadhila Arsyad1, Pulung Nurtantio Andono2, Moch Arief Soeleman Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6682/1153 Publisher An entity responsible for making the resource available Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 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/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/f0c10e0bd270ea432569f5439a608ba8.pdf daf431ddd69811b17d22fbe617914d20 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 Sentiment Classification on Indodax Using Term Frequency, FastText, and Neural Attention Models Subject The topic of the resource fasttext embedding; hybrid feature representation; lexicon-based labeling; sentiment analysis; BiLSTM attention Description An account of the resource The rapid growth of mobile-based investment platforms such as Indodax has triggered a surge in user-generated reviews that reflect public perception and sentiment. This study aimed to develop and evaluate sentiment classification models that can accurately classify Indonesian user reviews on the Indodax app into negative, neutral, and positive sentiments. A dataset of 11,000 reviews was collected via web scraping from the Google Play Store. Reviews were preprocessed, labeled using a lexicon-based unsupervised method, and balanced using oversampling. Two models were built: a Bidirectional LSTM (BiLSTM) with attention mechanism using FastText embeddings, and a Feedforward Neural Network (FFNN) using a hybrid feature vector combining TF-IDF and FastText. The evaluation was performed using accuracy, classification report, confusion matrix, and PCA visualization. The FFNN model outperformed the BiLSTM-Attention model with an accuracy of 97.07% compared to 96.00%. Both models demonstrated strong performance in classifying three sentiment classes, though the FFNN showed better separation in PCA space and higher macro-average metrics. This study demonstrates the effectiveness of combining statistical and semantic feature representations for sentiment classification in Indonesian text. The proposed approach is particularly valuable for low-resource languages and informal user-generated content Creator An entity primarily responsible for making the resource Dedy Hartama1, Ginanti Riski2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6871/1155 Publisher An entity responsible for making the resource available Department of Information Systems, 2Department of Informatics Engineering, STIKOM Tunas Bangsa, Pematangsiantar, 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/668b38b110bf700c267726247870c007.pdf dbf91f97f5a1e0b132ff0ac0575a000c 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 Application of Reinforcement Learning to Solve Rubrik’s Cube with Markov Decision Process Subject The topic of the resource Reinforcement Learning, Rubik’s Cube; Markov Decision Process Description An account of the resource The Rubik's Cube is a tricky puzzle that can be arranged in countless ways, making it hard for both people and computers to figure out. While standard solving methods use fixed strategies, this research looks into using reinforcement learning (RL) to create a flexible and effective way to solve it. The goal of this research is to develop an RL-based solver that uses the Markov Decision Process (MDP) system, focusing on speed, efficient moves, and the number of steps needed to solve the cube. The suggested model uses Q-learning and Monte Carlo Tree Search (MCTS) to figure out the best moves at each stage of the game, training through lots of Rubik's Cube simulations. What makes this research unique is the combination of MCTS with Q-learning, which improves decision-making by needing fewer moves than standard methods. The tests show that this model reaches almost perfect solutions with fewer moves, doing better than simple rule-based methods. Also, a web app was created to give live solving techniques based on the cube arrangements that users provide. This research helps grow the use of RL in puzzles like the Rubik's Cube and gives a useful tool for fans who want to get better at solving the cube Creator An entity primarily responsible for making the resource Defni1, Andi Fathul Mukminin2, Ainil Mardhiah3, Titin Ritmi4, Junaldi5, Yuhefizar6, Fibriyanti7 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6552/1135 Publisher An entity responsible for making the resource available Information Technology, Padang State Polytechnic, West Sumatera, Indonesi Date A point or period of time associated with an event in the lifecycle of the resource October 8, 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