Missing and incomplete observations in Environmental IoT sensor networks reduce data reliability and disrupt analyses, especially for temperature and humidity time series exhibiting strong diurnal seasonality. This study develops and evaluates a seasonal imputation method to address missing data in IoT-based environmental monitoring, using a workflow of anomaly detection, outlier removal, time-of-day-aware imputation, and performance evaluation under varying missing-rate scenarios. Key challenges include sensor noise, connectivity issues, and intermittent hardware failures, which degrade data integrity and affect trend analysis, forecasting, and anomaly detection. To mitigate these, the method uses hourly and minute-level seasonal patterns after filtering out physically unrealistic values. Experimental results show high accuracy and robustness in reconstructing temperature and humidity data: temperature imputation achieves MAE values of approximately 0.86–0.87°C, and humidity yields MAE values of 3.92–4.01%RH, with no performance drop even at 50% data loss. The imputed series preserves natural diurnal dynamics without introducing distortions, effectively restoring continuity and structural consistency in environmental IoT time series for reliable modeling, feature extraction, and decision support.

Marriage registration at the Office of Religious Affairs (KUA) is still largely carried out using conventional methods, which gives rise to various problems such as document accumulation, the risk of file loss, data recording errors, and delays in the verification process, particularly for prospective couples who live far from the KUA office. This process requires prospective couples to visit the KUA office in person and submit physical documents, making it inefficient and ineffective. This study aims to develop a website-based marriage registration system that facilitates online registration for prospective couples and regulates the service order in an orderly and fair. This study applies the First Come First Served (FCFS) method to regulate the order of marriage registration based on the time of registration, ensuring a fair and orderly service process. Furthermore, the system development is carried out using the Waterfall approach, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The results show that the website-based marriage registration system operates according to the designed workflow. Alpha testing indicates that all main features function as specified, while beta testing obtained an average score of 4.18 with a percentage of 83.6%, which falls into the good category. These results indicate that the system is well accepted by users and suitable for implementation at the Office of Religious Affairs (KUA).

Subjectivity in manual quality control for traditional roof tiles poses a significant challenge, as the current process relies on manual, visual inspection and subjective judgment. This research proposes an automatic system to classify tile quality from images using a Convolutional Neural Network (CNN), specifically the EfficientNet-B0 model enhanced with transfer learning. The study utilized a primary dataset comprising 616 local roof tile images collected directly from producers in Berjo Kidul, Godean, Yogyakarta. These images were manually labeled based on producer criteria into three distinct classes: 'Finished' (203 images), 'Underbaked' (213 images), and 'Broken/Cracked' (200 images). The methodology involved resizing all images to 224x224 pixels and applying data augmentation, including random rotation, horizontal flipping, and color jitter, to mitigate overfitting. The EfficientNet-B0 model, pre-trained on ImageNet, was implemented in PyTorch and trained for 10 epochs using an 80/20 train/validation split with the Adam optimizer. The model demonstrated outstanding performance, reaching 99.70% accuracy in validation. Further evaluation confirmed this robustness; the model perfectly identified the 'Underbaked' class and recorded only a single misclassification error on the test set. Qualitative analysis via a Flutter mobile application showed the system is resilient to changes in background and viewing angles, although its accuracy is compromised by poor lighting and strong shadows. This study validates the proposed system as a highly efficient and objective tool for a more reliable quality control process.

The rapid advancement of information and communication technology is now increasingly utilized by the public in all sectors of activity, one of which is archival data management. The importance of archival data management today, increases awareness in agencies and organizations by utilizing information technology. Conventional archival data management can result in the possibility of errors that can be caused by several factors, namely, the age of archival data storage, loss of archival data due to work negligence and damage to archival data. Therefore, the existence of a website-based information system that can be used by organizations that can minimize errors in archival data management. The purpose of this study is to design an information system with the implementation of the Levenshtein algorithm on zakat recipient archives at BAZNAS Tapanuli Tengah Regency, where the performance of the Levenshtein algorithm here functions to measure the difference or distance between two strings in order to be efficient in time when searching for stored archival data.

Islamic boarding schools (pondok pesantren) are religious-based educational institutions that emphasize not only Islamic teachings but also the development of character and student discipline, particularly in maintaining environmental cleanliness. However, managing cleanliness often poses a challenge due to low student awareness and the suboptimal implementation of sanitation systems. This study aims to identify behavioral patterns related to student cleanliness through a data mining approach utilizing the Apriori algorithm. This method enables the analysis of students’ habitual behaviors using collected data to uncover hidden patterns related to cleanliness violations. The research was conducted at Salafiyah Syafi'iyah Sukorejo Islamic Boarding School and provides valuable insights for pesantren administrators to design more effective, data-driven cleanliness management strategies. The findings are also expected to enhance student awareness of cleanliness as an essential aspect of Islamic values. Overall, the results serve as a foundation for strategic decision-making to foster a healthy, clean, and supportive environment conducive to optimal learning in Islamic boarding schools.

Libraries play a crucial role in supporting the improvement of public literacy by providing reading materials tailored to users' needs and interests. One of the challenges faced by the Bukit Batu District Public Library is that the collection acquisition analysis process is not yet based on comprehensive borrowing patterns, potentially resulting in inaccurate results. This study aims to predict book borrowing patterns and classify collections into popular and unpopular categories using the Random Forest algorithm. Historical book borrowing data from 2019 to 2024 was used as the primary source in the model training and testing process. Testing was conducted with three data sharing ratios, namely 70:30, 80:20, and 90:10, which resulted in prediction accuracy of 89.19%, 88.69%, and 86.74%, respectively. Based on the analysis results, mathematics books were identified as the most popular collection with 146 borrowings, while social studies books were categorized as unpopular with 122 borrowings. These findings are expected to serve as a reference for libraries in formulating more effective, efficient, and data-based collection management strategies, thereby increasing the relevance and attractiveness of collections for users and supporting the optimization of library services.

The selection of outstanding students in the school environment is an important process that requires objective and structured assessment. However, in practice, this process is still often done manually so that it has the potential to cause subjectivity and inaccuracy in decision making. This study aims to build a Decision Support System (DSS) in the selection of prospective outstanding students at SMKS Al-Washliyah 2 Merbau using the Profile Matching method. This method works by comparing the competencies or criteria possessed by students with the ideal profile that has been determined so that the gap (difference) between the two can be identified. The criteria used in the assessment include academic aspects, organizational activity, personality and discipline. This system is implemented in the form of a computer-based application to facilitate the selection process and improve assessment accuracy. The implementation of Decision Support using the Profile Matching method can be concluded that the system built is able to provide an effective solution in the selection process of prospective outstanding students at SMKS Al-Washliyah 2 Merbau.

An insurance company is a type of non-bank financial institution that protects clients from risks and collects premiums over a certain period, these facts provide an overview of the insurance business and highlight its role in the economy, this study evaluated the performance difference between the Decision Tree Regressor and Support Vector Regression (SVR) in predicting insurance payer income. The Decision Tree model demonstrated strong predictive accuracy, achieving a Mean Absolute Error (MAE) of approximately 57 million and an R-squared (R²) value of 0.896, meaning it could explain around 89.6% of the variance in the data. Additionally, the model maintained high consistency, as evidenced by 5-fold cross-validation scores ranging from 0.908 to 0.967, indicating strong generalization and low risk of overfitting. In contrast, the SVR model significantly underperformed. It recorded a much higher MAE of over 237 million and a large Mean Squared Error (MSE), reflecting substantial deviations from the actual values. Its R² score of -0.299 suggests that SVR performed worse than a naive mean predictor, failing to identify meaningful patterns. This poor performance was consistent across all cross-validation folds, which also produced negative R² scores. The SVR model’s inadequacy is likely due to the large scale of the income data and the lack of proper preprocessing, such as normalization, or parameter tuning. Overall, these findings clearly demonstrate that the Decision Tree Regressor is a more suitable, accurate, and stable model for predicting insurance payer income.

Changes in global economic policy, such as Donald Trump's import tariff policy in 2025, have generated various public responses recorded through social media such as Twitter. Analysis of this public opinion is important to understand public perception of the dynamics of gold prices as a strategic commodity. This study aims to analyze public sentiment towards the issue of tariff policies and gold using TF-IDF feature extraction. To overcome class imbalance in tweet data, the Synthetic Minority Over-sampling Technique (SMOTE) technique was used. The dataset was obtained from Twitter with the keywords "trump", "tariffs", and "gold", then preprocessing and sentiment labeling (positive, negative, neutral) were carried out. The results of the analysis showed that 88.8% of tweets contained positive sentiment, 6.9% negative, and 4.1% neutral. The model evaluation produced an accuracy of 81.23%, with the highest precision in the positive class (0.81) and a recall of 1.00. These findings indicate that the issue of tariff policies is associated optimistically by the public because it is considered beneficial to gold prices.