1 10 12 https://repository.horizon.ac.id/files/original/79fa3a9da7f9201539f1a08447f094d5.pdf c777fc625ab31687122831a33a841a27 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 8 No 5 (2024) 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 Cross-Spectral Cross-Distance Face Recognition via CNN with Image Augmentation Techniques Subject The topic of the resource Face recognition;Cross spectral;Data augmentation technique, Deep learning;CNN architecture Description An account of the resource Facial recognition is a critical biometric identification method in modern security systems, yet it faces significant challenges under varying lighting conditions, particularly when dealing with near-infrared (NIR) images, which exhibit reduced illumination compared to visible light (VIS) images. This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. Three CNN models—VGG16, ResNet50, and EfficientNetB0—were assessed using a dataset comprising 800 facial images from 100 individuals, captured at four different distances (1m, 60m, 100m, and 150m) and across two wavelengths (NIR and VIS). The Multi-task Cascaded Convolutional Networks (MTCNN) algorithm was employed for face detection, followed by image preprocessing steps including resizing to 224x224 pixels, normalization, and homomorphic filtering. Two distinct data augmentation strategies were applied: one utilizing 10 different augmentation techniques and the other with 4 techniques, trained with a batch size of 32 over 100 epochs. Among thetested models, VGG16 demonstrated superior performance, achieving 100% accuracy in both training and validation phases, with a training loss of 0.55 and a validation loss of 0.612. These findings underscore the robustness of VGG16 in effectively adapting to the CSCD setting andmanaging variations in both lighting and distance. Creator An entity primarily responsible for making the resource Nisa Adilla Rahmatika1*, Maulisa Oktiana2, Fitri Arnia Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5929/982 Publisher An entity responsible for making the resource available Master in Electrical Engineering, Universitas Syiah Kuala, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/eeec1c06542bd586f4e8b8133528ebe9.pdf f9911b0bfdd61a93159e5782c1e930ac 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 8 No 5 (2024) 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 Rice Price Prediction with Long Short-Term Memory (LSTM) Neural Network Subject The topic of the resource prediction; variability; long short-term memory; artificial neural networks Description An account of the resource Rice is a crucial commodity, especially in countries that rely on rice as a staple food. Fluctuations in rice prices can impact inflation, purchasing power, and economic stability. Therefore, an effective method for forecasting rice prices is essential for timely decision-making. This study aims to develop a rice price forecasting model by incorporating weather variability. Using Long Short-Term Memory (LSTM) neural networks, the model is expected to provide accurate predictions and guide decision-making in rice trading. LSTM is effective in analyzing time-series data. In this study, LSTM was used to examine the relationship between weather variability, crop yields, and land area with rice prices. Daily data from 2015 to 2023 were collected to build a model capable of predicting future rice prices. The results showed that the LSTM model achieved a Root Mean Squared Error (RMSE) of 0.054, indicating high prediction accuracy. This model allows stakeholders, including farmers, traders, and government officials, to better understand future rice price movements. This, in turn, helps them implement more effective strategies in managing rice supply and stabilizing prices Creator An entity primarily responsible for making the resource Rahmat Hidayat1*, Irawan Wibisonya Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6041/978 Publisher An entity responsible for making the resource available Computer Science, Sains and Technology, Universitas Putra Bangsa, Kebumen, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 23-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/6badb9e141c6d753352370ea4916bbfe.pdf 3e0cfa1dfc47689c9d41d0de4872e10a 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 8 No 5 (2024) 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 Machine Learning Methods for Forecasting Intermittent Tin Ore Production Subject The topic of the resource orecasting;classification, machine learning;mining;CatBoost Description An account of the resource Effective production forecasting is important for resource planning and management in the mining industry.Tin ore production from Cutter Section Dredges (CSD) may fluctuatedue to a variety of factors, in which there are periods when the production is zero.This study compares various combinations of machine learning-based classification and forecastingto predictfuturetin ore production values, which havenot been found in previous studies.The presence of zero values in the forecast in the next day's tin ore production forecast is addressed by combining classification and forecasting techniques.Random Forest and CatBoost classification techniques are used to determine the next day's CSD production operating status. Then, for each time point when the CSD is operational, a forecasting model is created using CatBoost and Bi-LSTM.This study's findings show that a serial combination of the Random Forest classification method and CatBoost forecasting can produce accurate tin ore production forecasts for the selected CSD (RMSE = 0.271, MAE = 0.179, MAE = 0.730, F1-score = 0,80). This study demonstrates how a serial combination of classification and forecasting models can improve the accuracy and efficiency of production forecasting for intermittent time series data Creator An entity primarily responsible for making the resource Nabila Dhia Alifa Rahmah1*, BudhiHandoko2,AnindyaApriliyanti Pravitasari Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5990/974 Publisher An entity responsible for making the resource available Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 15-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/bd298a0c4418047158a51b9c369a4fc7.pdf 6c93c6abc4a8ec2eca507b277cf366a7 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 8 No 5 (2024) 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 Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data Subject The topic of the resource K-Medoids;Manhattan; Performance; Quantum Computing; Stroke Description An account of the resource Stroke is a severe medical condition that occurs when the blood supply to parts of the brain is interrupted or reduced, resulting in brain tissue that lacks oxygen and nutrients. This causes brain cells to start to die in minutes. Early prevention reduces the risk of stroke. In this study, a quantum computing approach is used to improve the performance of the K-Medoids method. A comparative analysis of these methods was carried out with a focus on their performance, especially on the accuracy of the test results. The investigation was carried out using a data set of stroke patient medical records. The data setwas tested using the classical and K-Medoids methods with a quantum computing approach utilizing Manhattan distance calculations. The findings of this research reveal improvements in the K-Medoids algorithm with Manhattan distance calculation influenced by the integration of a quantum computing framework. In particular, the simulation test results show an increase in accuracy from the classical K-Medoids method to the K-Medoids method with a quantum computing approach, from 52% to 64%. These results highlight that the performance of the K-Medoids method with a quantum computing approach is superior to that ofthe classical K-Medoids method Creator An entity primarily responsible for making the resource Rikardo Hotman Siahaan1*, Swingly Purba2, Jeremia Siregar3, Marvin Frans Sakti Hutabarat4, Rasmi Sitohang Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5814/972 Publisher An entity responsible for making the resource available Informatics Engineering Study Program, Institut Sains dan Teknologi TD Pardede, Medan, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/f8aecefe157b36d5c5caf251d8a4a79c.pdf f9d882b744b0ef1a897381bb65aef8d8 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 8 No 5 (2024) 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 Optimizing Book Stocktaking Process: Integration of Mobile Robot QRCode Commands with SLiMS Subject The topic of the resource library management;QR code;mobile robot;Stock Take Process;API;SLiM Description An account of the resource This study presents a novel approach to enhance efficiency and precision in library management through the utilization of QR code technology. Integration of a mobile robot equipped with a QR code reader into the stocktake process, interfaced with theSliMSframeworkvia an accessible API, lays the groundwork for an automated book inventory management system. This groundbreaking system enables the generation of dynamic QR code commands, facilitating seamless adjustments to bookshelf layouts. The autonomous and accurate movement of the mobile robot significantly reduces the time required for recording, allowing library staff to allocate more time to value-added tasks.The implementation of this method entailed configuring the mobile robot to navigate library aisles, scan QR codes on book spines, and transmit inventory data to the SliMS system in realtime. Research findings indicate a notable decrease in inventory processing time, accompanied by an improvement in accuracy resulting from the eradication of manual data entry errors. Specifically, the calculated efficiency gain of approximately 66.81% highlights the substantial benefits of integrating the mobile robot scan QR code process compared to manual methods.In conclusion, the deployment of this automated book inventory management system, driven by QR code technology, marks a positive shift in library management practices, enhancing the efficiency of the book inventory process and overall operational effectiveness. Creator An entity primarily responsible for making the resource Mohammad Harry Khomas Saputra1, Pipit Anggraeni2, Wahyu Adhie Candra Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5638/971 Publisher An entity responsible for making the resource available Teknologi Rekayasa Informatika Industri, Teknik Otomasi Manufaktur dan Mekatronika, Politeknik Manufaktur Bandung, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/9c00c7188023f735860fa6aba45c85d0.pdf e053fa46620a1184ec5cf3b8fb0bb4eb 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 8 No 5 (2024) 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 Improving Diabetes Prediction Accuracy in Indonesia: A Comparative Analysis of SVM, Logistic Regression, and Naive Bayes with SMOTE and ADASYN Subject The topic of the resource adaptive synthetic sampling;diabetes mellitus;logistic regression;naïve bayes;synthetic minority over-sampling technique;support vector machine Description An account of the resource This study aims to enhance the accuracy of diabetes prediction models in Indonesia by comparing the performance of Support Vector Machines (SVM), Logistic Regression, and Naïve Bayes algorithms, both with and without synthetic oversampling techniques such as SMOTE and ADASYN. The research addresses the issue of imbalanced datasets in medical diagnostics, specifically in predicting diabetes among Indonesian patients, where such imbalance often leads to biased predictions. A comprehensive dataset comprising 657 patient records from a Regional General Hospital in Indonesia was used, with 70%of the data allocated for training and 30% for testing. The results indicate that the SVM model combined with SMOTE achieved the highest accuracy of 95.8% and an AUC of 99.1, underscoring the effectiveness of these techniques in improving prediction performance. The findings of this study highlight the importance of selecting appropriate oversampling methods and algorithms to optimize diabetes prediction accuracy in the Indonesian context, providing valuable insights for future healthcare strategies Creator An entity primarily responsible for making the resource Selly Rahmawati1, Arief Wibowo2*, Anis Fitri Nur Masruriyah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5980/970 Publisher An entity responsible for making the resource available Department Information System, Faculty Information Technology, Universitas Budi Luhur, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/d40ab0b5cce9b6f3e17adbeb7f88e36b.pdf 53c52a773d630897675b2853bc2e25fd 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 8 No 5 (2024) 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 Cloud Node Auto-Scaling System Automation Based on Computing Workload Prediction Subject The topic of the resource Auto-scaling; Cloud Computing; Forecasting; Workload Description An account of the resource Auto-scaling systems in cloud computing are important for handling application workload fluctuations. This research usesmachine learning to predict resource requirements based on workload work patterns and design an automatic scaling system. The dataset used includes features of node name, time, CPU usage percentage, and RAM usage. The ML model is applied for prediction regression of CPU usage percentage, CPU load, and RAM usage, and then the server workload is classified into four categories: Very High, High, Low, and Very Low. The autoscaling system used is horizontal scaling. From the results of this research, it was found that the stacking algorithm with the base learner Random Forest and XGBoost had better performance in producing predictive regression. Then, after performing stability testing using K-Fold cross-validation by classifying based on workload status, it was found that the Gradient Boosting algorithm had better results compared to other algorithms, namely for the percentage of CPU usage with an accuracy of 0.998, precision 0.9, recall 0.878, f1score 0.888; CPU load average 15 minutes with accuracy 0.997, precision 0.854, recall 0.863, f1score 0.863; Meanwhile, the percentage of RAM usage is accuracy 0.992, precision 0.986, recall 0.986, and f1score 0.986. However, the XGBoost algorithm also has test results that are almost the same as Gradient Boosting Creator An entity primarily responsible for making the resource Tri Fidrian Arya1*, Reza Fuad Rachmadi2, Achmad Affandi3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5928/969 Publisher An entity responsible for making the resource available Department of Electrical Engineering, Faculty of Intelligent Electrical and Information Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 06-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/751c4ae2991d8123bcabba7b050ce6fb.pdf fb0b6e3b8dd1728cd9c1b5152b1393e1 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 8 No 5 (2024) 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 Quantum Perceptron:A Novel Approach to Predicting Unemployment Levels in North Sumatra Province Subject The topic of the resource Analysis;quantum perceptron;quantum computing;initial prediction Description An account of the resource The application of Quantum Computing to improve the perceptron algorithm in unemployment prediction is a new aspect ofthis research. This study focuses on unemployment, which is a big challenge for the young generation in Indonesia, especially in the North Sumatra region. This research applies the quantum perceptron method to provide an alternative solution forpredicting the unemployment rate. The data used in this analysis comes from the North Sumatra Central Statistics Agency and includes published unemployment rates (TPT) for individuals aged 15 years and over from 2017 to 2023. This research uses seven variables ranging from x1 to x7 to produce accurate data. Quantum perceptron methods offer specific advantages over traditional methods, including higher computing speeds and the ability to handle greater data complexity. This analysis aims to identify unemployment patterns and trends in North Sumatra and provide more accurate predictions byapplyingthe quantum perceptron method. Although the results of this research are still limited to analysis, this research shows promising resultsand opens up opportunities for further, more in-depth research. This research is limited to predicting unemployment rates in North Sumatra. The use of quantum computing using the quantum perceptron method shows great potential for application to various other socio-economic problems in the future. This research contributes by introducing a new approach that utilizes quantum technology to improve prediction accuracy in economic analysis Creator An entity primarily responsible for making the resource Solikhun1*, Dimas Trianda Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5815/968 Publisher An entity responsible for making the resource available Informatics Engineering Study Program, STIKOM Tunas Bangsa, Pematang Siantar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 06-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/e7fb33947be3eb233b0fb3a5b3a8fd47.pdf 1fa3f930c341eb130474cd774352030e 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 8 No 5 (2024) 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 Designing aKnowledge-Based Chatbot to Elevate Business Licensing Services in Indonesia Subject The topic of the resource knowledge-based chatbot;business licensing;user-centered design;public services Description An account of the resource The business licensing process in Indonesia often faces several challenges, including lack of information, unstable system, complicated procedure, and slow response to complain. These issues can hinder economic growth and limit access for businesses. This research aims to design a knowledge-based chatbot to elevate business licensing services in Indonesia. The proposed chatbot will utilize natural language processing (NLP) technology and a structured knowledge base to provide accurate information, assist in form filling, and offer step-by-step guidance to users. This research employes a User-Centered Design (UCD) approach to ensure that the developed chatbot meets the needs and preferences of its users. The research stages involve user requirements analysis, UML design, system design, and iterations based on feedback obtained. Data will be collected through questionnaires, interviews, and literature studies. Leveraging the proposed architecture, we demonstrate how the resulting knowledge-based chatbot is expected to enhance business licensing services. The findings identified 8 key features expected in the chatbot, including real-time information access, problem reporting, business licensing guidance, a tracking system, personalized simulation, a feedback mechanism, multilingual support, and the ability to connect with a contact center agent.By implementing these features, the proposed chatbot is anticipated to significantly reduce processing times, streamline user interactions, and enhance user satisfaction by providing real-time assistance and reducing errors in form submissions. This will contribute to a more efficient licensing process, fostering economic growth and improving the business environment in Indonesia Creator An entity primarily responsible for making the resource Husain1, Ridwan Afandi2, Dana Indra Sensuse3, Sofian Lusa4, Nadya Safitri5, Damayanti Elisabeth Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6069/984 Publisher An entity responsible for making the resource available Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 04-11-202 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/adf2178fc2983166baf62863c506d8de.pdf 819fbb07f43adda3483ffee1f2033ceb 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 8 No 5 (2024) 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 Prototype of SwiftletNest Moisture ContentMeasurement Using Resistance Sensor and Machine Learning Subject The topic of the resource swiftlet nest;moisture content;IoT; Machine Learning; Neural Network; PRORESKA Description An account of the resource Swiftletnests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive,andreal-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a neural network wereemployed to predict moisture content. Validation tests conducted on paper and swiftletnests indicated that the neural network model, enhanced through transfer learning, achieved superior accuracy. The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftletnests and similar applications. Creator An entity primarily responsible for making the resource Ratu Anggriani Tangke Parung1, Hanna Arini Parhusip2*, Suryasatriya Trihandaru Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5923/983 Publisher An entity responsible for making the resource available Master of Data Science, Faculty of Science and Mathematics, Satya Wacana Christian University Date A point or period of time associated with an event in the lifecycle of the resource 28-10-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT