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The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.

A student's learning success is largely determined by their academic evaluation. Estimating a student's final grade can assist educational institutions in conducting initial assessments of academic achievement. This study aims to analyze the performance of the Multiple Linear Regression (MLR) and Random Forest (RF) algorithms in predicting students' final grades using Google Colab. This research method uses a quantitative approach using secondary data that includes age, mid-term exam scores, final exam scores, and categorical variables as independent variables, with the final grade as the dependent variable. The research process is carried out through data preprocessing steps, dividing training and test data, model training, and performance evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The results show that the Random Forest algorithm provides more accurate prediction accuracy compared to the Multiple Linear Regression algorithm, especially in identifying nonlinear relationships between variables. Therefore, the Random Forest algorithm is more recommended for predicting students' final grades with complex data characteristics.

This study aims to compare the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) in predicting student performance based on academic scores. Student performance is defined as the average of math scores, Reading Scores, and writing scores. This study uses a quantitative approach with a comparative design based on predictive modeling. The data used is secondary data from the Student Prediction dataset obtained through the Kaggle platform, which was processed using the Python programming language through the Google Colab platform. The analysis stages included the formation of performance variables, the separation of training and test data with a ratio of 80:20, model training, and evaluation using the Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The results show that the Multiple Linear Regression model produced an MSE value of 2.74 × 10⁻²⁸, an MAE of 1.51 × 10⁻¹⁴, and an R² of 1.000. Meanwhile, Random Forest Regression produced an MSE of 0.296, an MAE of 0.375, and an R² of 0.998. These findings indicate that both models have a very high level of accuracy, but Multiple Linear Regression provides the best performance. This is due to the strong linear relationship between the input variables and the target variables formed directly from the combination of academic values. Thus, the linear regression model is proven to be more suitable for use in data structures that have simple linear relationships compared to ensemble-based models.

The ability to read the Qur’an is a fundamental competence in Islamic education. However, many children still face difficulties in reading the Qur’an accurately and fluently in accordance with tajwid rules. This community service program (Pengabdian kepada Masyarakat/PKM) aimed to improve Qur’anic reading skills of students at Asy-Syakirin Qur’anic Learning Center (TPQ) in Nibung Village, Kapuas Hulu Regency, West Kalimantan. The program employed a Participatory Action Research (PAR) approach, actively involving teachers and students in all stages of the activities. The implementation consisted of planning, action, observation, and reflection stages. During the planning phase, students’ initial reading abilities were identified and grouped into basic (iqra’) and advanced (tahsin) levels, appropriate learning methods were selected, and supporting learning media were prepared. The action phase involved individual and small-group mentoring, guided Qur’an reading with direct correction of pronunciation (makharij al-huruf) and tajwid, application of memorization and dictation (imla’) techniques, and varied teaching approaches through instructor rotation. Evaluation was conducted through pre-tests and post-tests, direct observation of students’ reading performance, and assessment of participation and attendance. The results indicate a significant improvement in students’ Qur’anic reading skills, particularly in fluency, accuracy of pronunciation, and basic understanding of tajwid. This program is expected to serve as a sustainable model for improving Qur’anic literacy in Qur’anic learning institutions.

International student enrollment is a critical driver of financial sustainability for Higher Education Institutions (HEIs). While advanced forecasting is standard in the corporate sector, its application in educational planning remains limited. This study addresses this gap by comparing the predictive performance of ARIMA, Facebook Prophet, and Long Short-Term Memory (LSTM) models. Using a publicly available annual dataset from a US-based institution (2000–2022), the analysis employed a strategic partition training on 2000–2017 and testing on 2018–2019 to validate models on stable, pre-pandemic data. Empirical results revealed that the statistical ARIMA (2,1,0) model demonstrated superior accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 1.26%. Conversely, Prophet (11.81%) and LSTM (13.84%) struggled with the limited sample size, failing to generalize effectively compared to the linear approach. The findings suggest that for annual enrollment trends, parsimonious statistical models outperform complex deep learning architectures, providing administrators with a robust, accessible framework for data-driven strategic decision-making.

Background: Accurate detection of renal cysts on CT urography requires high diagnostic precision, while manual interpretation by radiologists is susceptible to inter-observer variability and potential delays in clinical decision-making. These challenges underscore the need for a reliable automated detection system to support radiological assessment. Objective: This study aims to develop and evaluate the performance of the Neo-ZasAI application based on the YOLOv8 algorithm for the automatic identification of renal cysts. Methods: Employing a Research and Development design using the ADDIE model, the study encompassed needs analysis, model design, software development, system implementation using 200 CT urography images, and diagnostic performance evaluation. Classification results generated by Neo-ZasAI were compared with radiologist readings through confusion matrix analysis and ROC–AUC assessment. Results: The findings indicate that Neo-ZasAI achieved an accuracy of 97,5%, sensitivity of 96%, specificity of 99%, positive predictive value of 98,9%, and negative predictive value of 96,1%. The ROC analysis yielded an AUC of 0.988 (p < 0.001), demonstrating excellent discriminative capability and high concordance with radiologist interpretations as the diagnostic gold standard. Conclusion: These results suggest that Neo-ZasAI is capable of performing rapid, consistent, and accurate renal cyst detection and is thus feasible for implementation as a clinical decision support system in radiology, with potential integration into PACS workflows and further development to enhance model generalizability.

This research is grounded in the growing use of social media as a digital communication tool for companies to strengthen engagement with their audiences, including user communities. The study aims to explain the content management practices of the Instagram account @daihatsuofficialclub, which is managed by the Marketing Support team of PT Astra Daihatsu Motor (ADM). The research focuses on how content is planned, managed, and utilized as part of ADM’s digital communication strategy to build stronger connections with 21 official Daihatsu user communities. A descriptive qualitative method was employed through internship-based observation and interviews with the account management team. The findings show that the content management process applies the AIDA model through visually appealing posts, relevant information, community-centered storytelling, and interactive calls-to-action that encourage member participation. The implementation of the SOME model further supports a structured workflow across the stages of Share, Optimize, Manage, and Engage. Content marketing analysis indicates that the content fulfills key indicators of relevance, accuracy, value, readability, discoverability, and consistency. These results highlight that the management of @daihatsuofficialclub operates effectively, reflects a human-centered communication approach, and aligns with ADM’s digital communication strategy, contributing to stronger engagement, community closeness, and user loyalty.

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.

The rapid advancement of Artificial Intelligence (AI) is reshaping public governance, including legislative processes. In the United Arab Emirates (UAE), AI is being actively utilized to enhance law-making through faster drafting, improved consistency, and greater transparency. This study examines the role of AI in the UAE’s legislative functions, focusing on how AI tools assist in analyzing legal data, formulating policy recommendations, and drafting legislation. It explores how AI impacts the speed, accuracy, and legitimacy of law-making, while also addressing the ethical and legal challenges of delegating legislative tasks to intelligent systems. Using a qualitative case study method, the paper evaluates government initiatives, expert insights, and regulatory structures that frame AI's integration into the UAE’s law-making system. While AI offers opportunities for data-driven governance and increased legislative productivity, it also presents risks such as algorithmic bias, reduced human oversight, and accountability gaps. The study emphasizes that AI must be governed by strong regulatory frameworks to safeguard democratic values, fairness, and legal integrity. By analyzing a pioneering national model, this research contributes to global discussions on AI in governance and offers key insights for policymakers, technologists, and legal scholars seeking to balance innovation with ethical and legal standards.

This literature review explores how entrepreneurs act as pragmatic experimenters when navigating uncertainty, focusing on three interrelated cognitive processes: causal inference, feedback interpretation, and heuristic adaptation. Drawing on recent empirical and theoretical studies, the review synthesizes how entrepreneurs learn through iterative experimentation, adjust mental models based on ambiguous feedback, and develop heuristics to guide decision-making in unpredictable environments. The findings suggest that entrepreneurial success is often linked to adaptive learning strategies rather than predictive accuracy. This research contributes to a deeper understanding of bounded rationality and cognitive flexibility in entrepreneurial contexts, offering insights for future inquiry into how entrepreneurs balance action and learning under uncertainty.

This study aims to analyze the innovation of the use of e-SDM Applications in improving the quality of digital employee data management at the Surabaya City Human Resources Development Agency (BKPSDM). This study uses a qualitative approach with a case study method through data collection techniques such as in-depth interviews, direct observation, and supporting documentation. The research analysis refers to the success factor model of e-Government innovation according to Maulidhia J.P., which includes aspects of leadership, stakeholders, resources, technology and information, processes, goals and values, and laws and regulations. The results of the study indicate that the implementation of e-SDM Applications can improve work efficiency, data accuracy, transparency, and ease of access to employee information through an integrated digital system. This success is supported by leadership commitment, collaboration between stakeholders, and the availability of adequate resources. However, this study also found several challenges, including technical system and network constraints, the need to increase human resource capacity, and the need for continuous regulatory and SOP updates. Overall, e-SDM innovations have made a positive contribution to improving the quality of employee data management in government environments.

This study aims to develop an Indonesian traffic sign detection system using a transfer learning approach to improve road safety and traffic efficiency. The dataset was obtained from Kaggle and consists of 2,100 images across 21 traffic sign classes. The research stages include data collection, preprocessing to reduce noise and normalize image brightness, object detection using YOLOv5, and classification based on transfer learning with ResNet, VGG-16, and MobileNet architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the YOLOv5 model is capable of detecting traffic sign objects; however, the classification performance remains relatively low, with a mean Average Precision (mAP) value of 0.17. These findings suggest that further optimization is required in data preprocessing, dataset quality, and model parameter tuning to achieve better performance. This study demonstrates that transfer learning has significant potential for developing computer vision-based traffic sign detection systems, although further improvements are necessary to ensure robustness under real-world Indonesian traffic conditions.

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in driving economic growth; however, their production activities frequently face uncertainty in achieving predetermined targets. Such uncertainty arises from fluctuating market demand, delays in raw material supply, labor limitations, variations in processing time, and other technical constraints. Conventional deterministic production planning methods often fail to capture these real-world risks and variations, leading to less accurate and suboptimal decisions. Therefore, a more adaptive analytical approach that incorporates probability and uncertainty is required. This study aims to analyze the probability of achieving MSME production targets using the Monte Carlo Simulation method. This method models random production conditions by generating data based on probability distributions derived from historical records. Simulations are repeated through numerous iterations to estimate possible variations in production output and measure the likelihood of meeting targets. The results indicate that Monte Carlo simulation provides more realistic and comprehensive production forecasts compared to traditional planning approaches. By understanding both the probability of success and potential risks, MSMEs can design adaptive strategies, optimize resource allocation, manage inventory more effectively, and improve overall production planning accuracy to ensure long-term business sustainability in a dynamic environment.

Stock monitoring is a critical phase that must be performed regularly to maintain the accuracy and efficiency of inventory management. Continuous monitoring ensures that all items remain under proper oversight, thereby making stock management processes simpler, more controlled, and highly accurate. PT XYZ operates in the general contracting sector and provides a range of services, including land transportation, crude oil rental, heavy equipment and light vehicle rental, material supply, and well maintenance services. At present, stock monitoring at PT XYZ still relies on a general-purpose application designed solely for numerical calculations. This approach leads to several limitations in the current system, most notably the lack of a login feature and the requirement to recreate reports using a separate application. Based on these problems, this research aims to design a prototype of a stock monitoring information system at PT XYZ. The system is developed using the waterfall development model and documented using use case diagrams. The main output of this study is a prototype information system that allows users to change their own password, perform CRUD operations on data entities including users, items, categories, brands, units, vehicles, suppliers, incoming goods, and outgoing goods, generate various reports related to inventory/stock at PT XYZ.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

In demographic studies, population growth refers to changes in the number of inhabitants over a given period, measured by calculating numerical differences in population size and expressing them within specific time units to illustrate patterns of increase or decline. Based on data from Tembalang District in Figures 2025, the population growth rate in Kedungmundu Village rose by 0.51% between 2016 and 2025, with most residents relying on clean water services provided by PDAM Tirta Moedal as their primary source of drinking water. This study aims to estimate clean water demand based on projected population growth and to design a pipeline network system capable of meeting future needs. The analysis of water demand applies population projection methods, including arithmetic, geometric, and least square approaches, to compare their levels of accuracy, while the clean water distribution network is modeled using EPANET 2.0. One of the main challenges faced by PDAM is that service coverage has not yet been fully optimized. The ten-year projection results indicate that the arithmetic method provides a correlation value closest to 1, estimating a future population of 14,904 people with a total clean water requirement of 3.48 liters per second. To support this projected demand, the proposed network design utilizes High Density Polyethylene (HDPE) pipes with diameters of 12, 10, and 8 inches to ensure efficient and sustainable water distribution.

This study aims to evaluate the implementation of Sidoarjo Regent Regulation Number 69 of 2017 concerning the Civil Servant Code of Ethics in the Family Planning Sector at the Sidoarjo Regency Women's Empowerment, Child Protection, and Family Planning Office. A qualitative approach with a case study was used to explore the implementation of the policy through interviews, observation, and documentation. The analysis was conducted using William Dunn's six policy evaluation indicators, namely effectiveness, efficiency, adequacy, equity, responsiveness, and accuracy. The results show that the policy has provided clear behavioral guidelines and is applied evenly in the work environment. However, the effectiveness and efficiency of implementation are not optimal due to disciplinary violations, weak supervision, and unstructured communication between superiors. The aspects of adequacy and accuracy are considered relevant to the needs of the organization, but have not been able to fully overcome obstacles such as high workloads and low internalization of ethical values ​​among employees. In general, this policy contributes to shaping the professionalism of civil servants, but still requires strengthening through continuous supervision and more systematic coaching. The originality of the study lies in the use of Dunn's evaluation model in the context of the implementation of the civil servant code of ethics at the regional level, as well as identifying gaps between normative policies and field practices. These findings confirm that the success of a code of ethics depends heavily on organizational communication, work culture, and consistency of oversight.

Design and Development of Fire Monitoring System Based on Internet of Things Using Flame Sensor and Smoke Sensor (Case Study at Balai Desa Dlimas). Fire is a disaster that can cause significant losses in both material and human life. Early fire detection is crucial to minimize the potential impact. Balai Desa Dlimas, located in Banyuputih District, Batang Regency, currently lacks an adequate fire detection and monitoring system. Therefore, this study aims to design and implement a fire monitoring system based on the Internet of Things (IoT) using an ESP32 microcontroller, a flame sensor, an MQ-2 smoke sensor, and a DHT22 temperature sensor. The research method used is Research and Development (R&D) with a prototyping model. The system is designed to detect the presence of fire, smoke, and room temperature in real time. Data from the sensors is processed by the ESP32 and sent to the Blynk application to notify users. Warning outputs are also provided through a buzzer and LED lights as on-site alerts. The test results show that the system can detect fire, smoke, and temperature increases with high accuracy. Users can receive real-time notifications via the Blynk application within less than 3 seconds of fire detection. This system is expected to help Balai Desa Dlimas minimize fire risk and can be further developed for implementation across various public facilities.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.