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The instability of the climate is becoming increasingly prominent across Southeast Asia, creating uncertainty in agricultural systems that are highly dependent on seasonal weather patterns. Indonesia, where rice remains the primary staple food, is particularly vulnerable to the effects of rising temperatures and rainfall deficits. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict rice production while incorporating indicators of extreme climate anomalies. Using publicly available datasets, including FAOSTAT production statistics, NOAA rainfall and temperature anomalies, and climate indices from the World Bank, this model was developed following the Box-Jenkins procedure. Among the configurations tested, the SARIMA model (1,1,1)(0,1,1)₁₂ showed the strongest performance, reflected in a MAPE of 4.62% and low RMSE values. The model indicates that significant El Niño events can reduce annual rice production by 3–7%, while wetter La Niña conditions may support production recovery. These findings highlight the importance of integrating climate-sensitive data into agricultural forecasting. The model presented here could support early warning systems, adaptive farming strategies, and long-term food security planning in Indonesia.

Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.  

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

Queueing systems play an important role in evaluating service performance, especially in small-scale businesses such as barbershops, where fluctuating customer arrival patterns and limited service capacity often lead to long waiting times. This study aims to analyze the performance of barbershop services using the M/M/1 queueing model and an analytical approach based on experimentally tested arrival (λ) and service (μ) rates. The model was selected because it represents a single-server system with Poisson arrivals and exponentially distributed service times, closely matching real barbershop operational characteristics. Using assumed realistic parameters, the analysis shows that when λ = 12 customers per hour and μ = 6 customers per hour, the system becomes unstable with a utilization rate (ρ) exceeding 1, indicating continuous queue growth. Further simulations with increased service rates demonstrate significant improvements: at μ = 15, the system achieves ρ = 0.8 with an average waiting time of 16 minutes, while at μ = 13, the system remains stable but experiences a long waiting time of approximately 55 minutes. These findings emphasize that barbershop performance is highly sensitive to service speed and that even small increases in μ can produce substantial improvements in queue stability and customer waiting times. The study concludes that barbershops must ensure adequate service capacity—either through optimizing service duration, improving worker efficiency, or adding servers—to maintain service quality and enhance customer satisfaction.

This research aims to design and implement a village MSME website as a centralized digital promotional medium to overcome the limitations of conventional marketing and expand the market reach of local products more effectively and sustainably. The system was developed using the waterfall method, encompassing requirements analysis, design, implementation, testing, and maintenance. The system was developed using the PHP programming language and the CodeIgniter framework based on the Model-View-Controller (MVC) architecture to ensure a structured, efficient, and maintainable development process. The implementation resulted in a responsive and user-friendly website equipped with key features such as an informative product catalog, village MSME profiles, and a content management system via an admin dashboard that allows MSMEs to update data independently and flexibly. Functional testing demonstrated that all features functioned well and reliably according to user needs. Therefore, this village MSME website can be concluded as an effective digital solution for increasing the visibility of local products, strengthening MSME competitiveness, and supporting village economic growth through sustainable and integrated online promotion.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

The scarcity of real-world data in Air-Conditioning (AC) fault diagnosis necessitates the use of synthetic data; however, rule-based synthetic datasets often suffer from a significant sim-to-real domain gap. To address this, we propose a Model-Data Coevolution (MDC) framework that employs a Simulated Annealing (SA) controller to optimize augmentation parameters. We introduce a novel technique, Stochastic Feature Decoupling (SFD), which applies independent noise to raw and derived features, contrasting it with traditional Logically-Consistent Augmentation (LCA). Empirical results show that SFD significantly outperforms LCA, achieving a weighted F1-score of 0.93 and increasing NORMAL class recall to 82%. We demonstrate that by breaking deterministic feature links, SFD acts as a robust regularizer, utilizing "physically impossible" data to enhance generalization in complex real-world environments.

This study analyzes the impact of artificial intelligence on youth digital political engagement through a Structural Equation Modeling approach. The development of algorithmic technology has changed the way young people access, assess, and react to political information, requiring an empirical understanding of its mechanisms of influence. This study explores how digital literacy, trust in AI, and perceived usefulness shape online political participation. Data was obtained from an online questionnaire targeting individuals aged 17-30 who are active with AI, then analyzed using the SEM-PLS 4 method. The main findings reveal that digital literacy and trust in AI have a strong influence on perceived usefulness, which acts as a key mediator in encouraging such participation. This indicates that the impact of AI is not direct, but rather occurs through cognitive processes that guide young people in assessing the benefits of technology. The implications of this research emphasize the importance of strengthening digital literacy, algorithm transparency, and responsible AI implementation to strengthen inclusive youth political participation in the digital environment.

This study presents a high-accuracy real-time soft failure detection framework for large-scale fiber-to-the-home(FTTH) optical access network using a hybrid ensemble of Isolation Forest and One-Class Support Vector Machine (OCVSM). The proposed model was trainde and validated on a real-word multivariate performance dataset comprising more than 1.8 million samples collected at 5-minute intervals from 50 Optical Line Terminal (OLTs) and over 3,000 Optical Network Terminals (ONTs) across a five-month periode(June-October 2025). Ground-truth validation was performed using 111 confirmed network incidents in October 2025 affecting 12,990 customer. The hybrid ensemble achieved Precision 0.940, Recall 0.982, with an average detection delay of only 7.8 minutes-representing an 87.7% reduction compared to conventional manual response (63.5 minutes). The framework significantly outperforms traditional threesholding and recent ML-based methods while demonstrating practical deployability in live operational enviroments.

The development of information technology has encouraged various organizations, including cooperatives, to digitize their service systems. The Credit Cooperative (Kopdit) CU Mera Ndi Ate is one of the cooperatives that still uses a manual system in managing savings and loans, which causes the service process to be slow, inaccurate, and has a high risk of recording errors. This study aims to design and build a web-based savings and loans system that can be used by members of Kopdit CU Mera Ndi Ate. This system allows members to conduct transactions online, view transaction history, and monitor savings or loan balances. The research methods used are observation, interviews, and literature studies. The system development process uses a waterfall model with stages of analysis, design, implementation, and testing. The result of this study is a prototype of a web-based savings and loans information system that has main features such as member registration, transaction recording, financial data management, and automatic financial report generation. With the implementation of this system, it is hoped that the cooperative can improve work efficiency, speed up services, and provide easy access to information to all members.

The disparity in the quality of rehabilitation services across regional work units presents a significant challenge to effective public management. This study aims to bridge the gap between problem diagnosis and policy prediction by proposing a hybrid, data-driven approach. We integrate K-Means Clustering to map the current state of service quality and Stochastic Simulation to predict the impact of strategic interventions. Using the 2024 Public Satisfaction Index (IKM) dataset from the National Narcotics Agency (BNN), the K-Means algorithm initially identified 26 work units (15.7%) in the "Red Zone" (critical performance), highlighting urgent areas for improvement. Next, a stochastic simulation modeling a "Directed Priority Intervention" scenario was run. The results predicted a significant structural shift in the distribution of service quality, characterized by an 80.8% decrease in critical units (down to 5 units) and a 71.8% increase in excellent performing units (up to 67 units). These findings validate that the integration of clustering and simulation provides a comprehensive framework for evidence-based decision-making, enabling policymakers to optimize resource allocation and efficiently accelerate national service standardization.

This study aims to simulate the M/M/1 queueing system using Python through a Modeling and Simulation approach supported by the Discrete-Event Simulation (DES) method. The objective of the research is to analyze key performance indicators of queueing behavior, including arrival time, service time, waiting time, queue length, and server utilization. The methodology employs DES, which models system behavior based on discrete events such as customer arrivals, service initiation, and service completion. The simulation generates stochastic arrival and service times using Poisson and exponential distributions, respectively. The results indicate that the DES-based M/M/1 simulation accurately reflects theoretical queueing behavior, showing increases in waiting times and queue lengths when arrival rates approach service rates, while server utilization corresponds to system load intensity. The findings demonstrate that DES is an effective approach for analyzing queue performance and can be extended to more complex models such as multi-server systems, priority queues, and predictive simulations using artificial intelligence.

Security in e-commerce applications is a crucial factor that significantly affects user trust. Many users often feel anxious about the confidentiality of personal data, transaction security, and the potential for misuse of information. This study is a systematic literature review (SLR) using the PRISMA model, aiming to analyze in depth the influence of security on user trust in the context of e-commerce applications. Through this review, relevant previous studies on user security and trust were identified and evaluated to provide a more comprehensive understanding. The results of the analysis show that the improvement and implementation of superior security features, including strict data protection, multi-layered authentication, and transparent and robust privacy policies, have an essential role in growing and strengthening user trust. Guaranteed security not only creates a sense of convenience during the transaction process, but it is also very effective in increasing and maintaining user loyalty to the e-commerce platform in question. Improving security can be interpreted as a strategic investment for the sustainability of digital businesses.

The development of information technology requires public institutions to provide accessible and transparent information services. Polsek Kota Kisaran still faces limitations in delivering information to the public through digital media. This study aims to design and implement a profile website as a public information medium that supports service transparency. The research uses a qualitative descriptive approach with data collection through observation, informal interviews with police officers, and documentation. System development includes requirement analysis, system modeling using Unified Modeling Language (UML), interface design, and website implementation. The results indicate that the developed website is able to present structured information such as institutional profiles, organizational structures, SKCK procedures, SPKT services, news, and activity documentation. The implementation of this website facilitates public access to police information and improves the effectiveness of information dissemination. This study is expected to support digital-based public services at the local police level and strengthen public trust through transparent information delivery.

This study aims to evaluate the performance of Digilib services at Amikom University Purwokerto using the Information Technology Infrastructure Library (ITIL) version 3 framework in the Service Operation domain. The phenomenon underlying this study is the indication of a decline in the operational effectiveness of Digilib services as seen from the inconsistency of system performance, access speed, and limited service features. This study uses an evaluative quantitative approach by collecting data through a questionnaire compiled based on five main processes in the Service Operation domain, namely Event Management, Incident Management, Request Fulfillment, Problem Management, and Access Management. The research respondents were library staff directly involved in the management and operation of Digilib services. Data were analyzed using a process maturity model (Process Maturity Model) to assess service capabilities based on a scale of 0–5. The results of the analysis indicate that the overall maturity level of Digilib services is at Level 4 (Managed Process), with an average value of 4.07. This indicates that the operational process has been controlled and measured through certain performance indicators, although there are still opportunities for improvement towards Level 5 (Optimized), especially in the Request Fulfillment and Problem Management domains. These findings contribute to strengthening IT service governance in the higher education sector and provide strategic recommendations for improving automation, system integration, and data-driven service management. The implications of this research encourage the development of policies to improve the quality of digital services and serve as a reference for further research on ITIL implementation in academic settings.

Production quantity planning is a crucial component in the bottled water industry (AMDK) to ensure that consumer demand is met efficiently. Inaccuracies in determining the amount of production can lead to overproduction and supply shortages, which ultimately leads to increased operational costs and decreased customer satisfaction. This study applies the Sugeno fuzzy logic method to predict the amount of production based on two main variables, namely weekly demand and raw material stock. The analysis stages include the fuzzification process, the preparation of the rule base, inference using the zero-order Sugeno method, and defuzzification using the Weighted Average (WA) method. The data used is synthetic data that represents the operational conditions of the medium-scale bottled water industry. The results show that the Sugeno fuzzy system is able to produce production predictions that are adaptive and responsive to fluctuations in demand and stock availability. This model provides consistent and stable output, so it can help companies in determining the optimal production amount. These findings confirm that Sugino's fuzzy approach can be an effective decision support tool in bottled water production management, especially in the face of uncertainty and variability in market demand.

The rapid advancement of information technology has increased the adoption of digital service applications, including AxisNet as Axis’s official platform for purchasing data packages, checking balances, and accessing promotional features. However, varying levels of user satisfaction particularly among users in Purwokerto highlight the need for a comprehensive evaluation of the application's service quality. This study aims to measure user satisfaction with AxisNet by applying the End User Computing Satisfaction (EUCS) model, which consists of five key dimensions: content, accuracy, format, timeliness, and ease of use. Data were collected from 114 respondents through a structured questionnaire and analyzed using IBM SPSS, including validity testing, reliability testing, and simultaneous testing. The results indicate that all instruments are valid and reliable. Simultaneously, the five EUCS dimensions significantly influence user satisfaction. Partially, the dimensions of content, accuracy, timeliness, and ease of use demonstrate a positive effect, while the format dimension shows a negative effect, suggesting that improvements are needed in the application's visual design. These findings provide empirical insight into the factors shaping user satisfaction with AxisNet and serve as a strategic reference for enhancing the quality and effectiveness of digital service applications.

This research aims to design and build a letter archive information system at the KOFIPINDO Law Office to improve the effectiveness and efficiency of document management. The manual filing system that has been used so far poses various obstacles, such as search delays, the risk of losing documents, and low storage accuracy. To overcome these problems, this study applies the Waterfall System Development Life Cycle (SDLC) model in the process of designing and building the system. Web-based technologies used include PHP, HTML, CSS, Bootstrap, and MySQL. The results of the study show that the developed letter archive information system is able to simplify the process of storing, searching, and managing incoming and outgoing letters in a faster, structured, and safer manner. The implementation of this system not only improves administrative performance, but also strengthens accountability and supports the need for professional legal documentation within the KOFIPINDO Law Office. Thus, this web-based mail archive information system can be a strategic solution in modernizing legal document management.