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Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

The Non-Cash Food Assistance Program (BPNT) is a government policy aimed at reducing the expenditure burden of poor households through the provision of food assistance in non-cash form. However, its implementation at the village level still faces various challenges. This study aims to analyze the effectiveness of the BPNT Program in Tolotio Village, Tibawa District, Gorontalo Regency, and to identify the factors influencing its implementation. This research employed a qualitative approach with a descriptive method. Data were collected through in-depth interviews, field observations, and document analysis involving village government officials, the Social Affairs Office, BPNT facilitators, and beneficiary communities. The results indicate that the implementation of BPNT in Tolotio Village is relatively effective in supporting food needs and enhancing the independence of Beneficiary Families (KPM). Nevertheless, several issues remain, including inaccurate targeting of beneficiaries, limited technological literacy, and insufficient transparency and updating of beneficiary data. The factors affecting program effectiveness include the availability of implementing resources, the support of physical facilities and technology, the effectiveness of communication among stakeholders, and the level of community acceptance and response. The findings imply the need for regular data updating, improved socialization, and strengthened coordination and supervision to ensure that the BPNT Program is implemented more fairly, accurately targeted, and sustainably.

Patient safety is often viewed exclusively as the responsibility of clinical personnel. However, administrative errors occurring during the admission process and documentation stages constitute major contributors to medical risk. This study aims to explore the strategic role of administrative staff in strengthening patient safety culture and mitigating medical risks through effective communication coordination. A narrative literature review was conducted extensively using the Scopus, PubMed, and Web of Science databases, covering publications from 2014 to 2024. Data were analyzed using a thematic synthesis approach. The findings identify four main themes: administrative staff as information gatekeepers, the role of health information technologies (HIS/EMR) in bridging communication gaps, collaboration barriers arising from hierarchical structures, and the need for an Interprofessional Shared Governance framework. Administrative accuracy in the early phases of care is proven to be critical in preventing latent errors with potentially fatal consequences. Strengthening patient safety culture requires the integration of administrative staff through cross-departmental training, standardized communication protocols, and non-punitive incident reporting systems. Administrative staff are not merely bureaucratic support personnel but are key actors within the modern hospital patient safety ecosystem.

This study aims to design a web-based information system for vehicle management and monitoring at Bengkel Merdeka Motor, which previously relied on manual recording processes. Manual data management caused several problems, including data loss, inaccurate records, difficulties in monitoring service progress, and delays in report generation. This research applies the Object-Oriented Analysis and Design (OOAD) method supported by Unified Modeling Language (UML) to analyze system requirements and design the proposed system. The research methodology includes requirements analysis, analysis of existing and proposed systems, system design, and user interface design. The proposed system integrates vehicle data management, service offers, work orders (SPK), service progress monitoring, invoice generation, and automatic monthly reporting into a single web-based platform. The results show that the designed system improves data accuracy, operational efficiency, and transparency of service information. In addition, real-time monitoring features support better coordination between administrators and mechanics. The implementation of this system is expected to enhance service quality, reduce administrative errors, and support managerial decision-making at Bengkel Merdeka Motor.

Radiographic examination of the wrist plays a crucial role in diagnosing fractures, particularly in traumatic cases, and requires precise projection selection, proper immobilization, and adequate radiation protection to obtain high-quality diagnostic images. In theory, wrist radiography commonly includes anteroposterior (AP) and lateral projections; however, observations at the Radiology Installation of RSU PKU Muhammadiyah Delanggu showed that fracture examinations are generally limited to posteroanterior (PA) and lateral projections. This difference highlights a gap between theoretical recommendations and clinical practice, as several references emphasize the importance of projection variation to enhance diagnostic accuracy. This study aimed to describe the wrist joint radiographic examination procedures for fracture cases at RSU PKU Muhammadiyah Delanggu and to examine the immobilization techniques applied during the procedure. A descriptive qualitative design with a case study approach was used, conducted from May to June 2025. The subjects included three radiographers, while the object of study was wrist radiography in fracture cases. Data were obtained through direct observation, interviews, and documentation, and analyzed descriptively. The findings showed that PA and lateral projections were consistently used, with immobilization achieved using sandbags and foam pads to ensure stability and minimize motion artifacts. Radiation protection was implemented through lead aprons, collimation, and appropriate exposure adjustments. Overall, the procedures followed established radiography guidelines, particularly the Bontrager standard, ensuring both diagnostic quality and patient safety.

This community service activity was conducted at PT Danny Karya Sukses, a newly established distribution company specializing in stainless steel kitchen equipment in Batam City, which faced challenges in managing inventory due to the use of manual recording systems and the absence of standardized operational procedures. These conditions led to a high risk of data inaccuracies, stock discrepancies, and inefficiencies in operational coordination. The objective of this program was to design and implement a standardized Inventory Standard Operating Procedure (SOP) integrated with a digital inventory management system using Zoho Inventory. The methods employed included interviews, field observations, documentation studies, and literature reviews to identify operational needs and design appropriate solutions. The implementation process involved SOP development, system configuration, employee training, and operational assistance. The findings indicate significant improvements in inventory accuracy, real-time stock monitoring, work efficiency, and interdepartmental coordination between administration, warehouse, and sales divisions. The adoption of Zoho Inventory reduced manual errors, accelerated stock reporting, and strengthened internal control mechanisms. The implications of this activity demonstrate that the integration of digital inventory systems with clear SOPs can serve as a strong operational foundation for newly established distribution companies, supporting sustainable business growth and enhanced competitiveness.

Non-Cash Food Assistance (BPNT) is a government assistance program aimed at poor or underprivileged communities, distributed monthly through electronic accounts and used specifically to purchase basic necessities at designated E-Warong stores. However, in reality, the implementation of the BPNT program in the field still faces comprehensive challenges, such as issues related to targeting inaccuracy and the quality of food obtained by KPM not always meeting standards. The purpose of this study is to explore the evaluation of the Non-Cash Food Assistance (BPNT) program policy through a Systematic Literature Review (SLR) approach, so that researchers can trace various empirical findings regarding the implementation of the Non-Cash Food Assistance program policy in several regions. The policy evaluation model used is William N Dunn's model, which includes six indicators, namely effectiveness, efficiency, adequacy, equity, responsiveness, and accuracy. The results of this study are expected to provide a comprehensive and in-depth picture of the phenomenon being studied in an objective and systematic manner, and can be used as a reference for government agencies in evaluating and optimizing the implementation of the BPNT program policy so that it becomes a more effective and sustainable social assistance program.

The development of Artificial Intelligence (AI) in the 21st century has brought significant transformation to the field of education, including Islamic Religious Education (PAI). One of the most practical implementations of this technology is the chatbot an automated conversational system capable of providing quick and contextual responses to user queries. This study aims to analyze the utilization of AI-based chatbots in addressing Islamic-related questions among students of the Islamic Education Department at IAIN Kerinci. The research employs a descriptive qualitative approach, with data collected through interviews, observations, and documentation. The findings reveal that students use chatbots as an initial source of information to understand Islamic concepts such as fiqh, tafsir, and hadith. Chatbots serve as learning aids that promote active learning and enhance students’ digital religious literacy. However, the study also identifies limitations related to the accuracy and validity of the sources used by the system, indicating that students still need verification from lecturers and authoritative Islamic literature. Overall, AI-based chatbots hold great potential to support interactive and contextual Islamic learning, provided their use is guided by academic supervision rooted in Islamic values.

This study examines the effectiveness of problem-solving skills in enhancing decision-making processes among teachers at State Junior High Schools in Jambi Province. The research was motivated by the increasing demands placed on educators to make timely, accurate, and contextually appropriate decisions in academic, administrative, and student-related matters. A quantitative approach was employed using a survey method, involving teachers from several public junior high schools across the province. Data were collected through validated questionnaires measuring levels of problem-solving competence and decision-making quality. The results indicate a significant positive relationship between problem-solving skills and decision-making effectiveness. Teachers with higher levels of analytical thinking, alternative evaluation, and solution implementation were found to make decisions more systematically, responsively, and with greater accuracy. Moreover, the findings reveal that problem-solving skills contribute not only to improving daily pedagogical decisions but also to enhancing school governance and conflict resolution. This research highlights the importance of continuous professional development programs aimed at strengthening teachers’ cognitive and strategic abilities. The study concludes that integrating structured problem-solving training into teacher development initiatives can substantially improve decision-making quality in junior high schools, ultimately supporting better educational outcomes in Jambi Province.

This study analyzes and designs a dual-role web-based ordering information system to optimize order management at Sunrise Bakery. This SME currently faces inefficiencies due to manual recording. The system, developed using the SDLC Waterfall method with PHP and MySQL, serves two main actors: customers, who can order online, browse catalogs, track orders, and pay digitally; and administrators (admin, cashier, owner), who manage products, update stock, input in-store orders, generate daily/monthly sales reports, and manage user access. Black Box Testing confirms all core functions work correctly. The system successfully addresses manual process shortcomings by improving data accuracy and providing real-time monitoring for both customers and management. It offers a comprehensive digital solution to enhance operational efficiency and service quality. Limitations include the lack of integrated digital payment gateways and external messaging. Future development should incorporate payment gateways (e.g., OVO, GoPay), WhatsApp notifications, a mobile application, and predictive analytics for sales and stock forecasting.

The Lamongan Regency Communication and Informatics Office plays a strategic role in supporting regional government decision-making through the implementation of the Megilan Innovation and Technology Program. The role of the Lamongan Regency Communication and Informatics Office in this program is realized as the main manager of digital information and archive systems that serve as the basis for providing data and information for regional leaders. Through the Megilan Innovation and Technology Program, the Communication and Informatics Office is responsible for everything from formulating archive digitization policies, implementing technology-based incoming mail management techniques, to providing fast, accurate, and integrated information to support the decision-making process. This study uses a descriptive qualitative method with interview and observation techniques carried out in the context of internship activities at the Lamongan Regency Communication and Informatics Office. Data analysis was carried out using the Miles and Huberman model which includes data collection, data condensation, data presentation, and drawing conclusions. The results of the study indicate that archive digitization through the Megilan Innovation and Technology Program can accelerate information access, improve the accuracy of letter disposition, and strengthen transparency and accountability of regional government. However, obstacles are still found in the form of limited human resources, technological infrastructure, and synchronization of physical and digital archives. Therefore, strengthening organizational capacity and supporting policies is necessary to ensure the Communication and Informatics Agency's role in decision-making can be optimal and sustainable

This study aims to develop a predictive model using linear regression to identify potential arrhythmias in the elderly based on electrocardiogram (ECG) data. Data were collected through observations at healthcare facilities from elderly patients with indications of arrhythmia, then preprocessed such as cleaning, normalization, feature selection, and outlier checking were carried out. The features used include PR interval, QRS duration, QT interval, and heart rate. The dataset was divided into training data (80%) and test data (20%) to build and evaluate the model. The training results showed that the model was able to predict the risk of arrhythmia with a Mean Squared Error (MSE) value of 0.15 and a coefficient of determination (R²) close to 1. Evaluation using a confusion matrix showed an accuracy of 76.19%, precision of 82.80%, recall of 76.19%, and F1 score of 72.70%. These results prove that linear regression can be used as an initial approach in the early detection of arrhythmias non-invasively in the elderly. This study provides a foundation for the development of ECG data-based clinical decision support systems and suggests future exploration of more complex models and integration with real-time monitoring technologies.

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

The Motor Operated Valve (MOV) is a critical component in fluid control systems at Steam Power Plants (PLTU). Training new technicians is often hindered by limited access to actual equipment and operational safety risks. This research aims to design and develop an Arduino-based MOV control module simulator capable of simulating basic functions such as open, close, stop, and limit switch responses. The method used is Research and Development (R&D) with an experimental approach. The simulator was tested using a DC motor as the simulated valve actuator, equipped with push buttons, relays, limit switches, and indicator lamps for visual feedback. The test results showed that the simulator successfully represented control functions with 100% accuracy in limit switch responses and consistent operation. User evaluations involving ten new technicians indicated an 85% satisfaction rate in terms of ease of understanding and operational safety. This simulator has proven to be an effective, interactive, and safe learning medium for new technicians at PLTU Nagan Raya.  

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.

This study compares two service management models to evaluate the governance of the Wi-Fi network in Dusun Gita Nagari Baru. The main objective is to measure user satisfaction and service quality following the implementation of the COBIT 2019 framework, particularly the DSS02 domain (Manage Service Requests and Incidents). The research employed a mixed methods approach, using historical-comparative document analysis and Likert scale questionnaires distributed to 21 active users. The data were analysed through gap analysis, capability level mapping, and descriptive statistical analysis to identify performance differences between two periods. The results indicate that most indicators in the COBIT 2019 capability model are at Level 4 (Predictable), one indicator reaches Level 5 (Optimising), and another indicator is at Level 3. Indicators directly related to the DSS02 domain, such as ease of reporting, response speed, schedule accuracy, and repair time, demonstrate the most significant improvements. These findings support the hypothesis that implementing COBIT 2019-based governance for DSS02 can enhance user satisfaction and the quality of Wi-Fi network services in rural areas. This study also provides practical recommendations for the sustainable management of digital infrastructure in areas with limited access.

This study aims to evaluate and compare the performance of two neural network architectures under the Recurrent Neural Network (RNN) category, namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), in predicting earthquake magnitude in Indonesia. The dataset used consists of daily earthquake magnitude records from 2008 to 2023, preprocessed into time series format and normalized using the MinMax method. The training process was conducted using various combinations of batch size and epoch, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and relative prediction accuracy. The evaluation results show that LSTM with a batch size of 32 and 50 epochs provides the best prediction performance, achieving a MAE of 0.2227 and 93.65% accuracy. Meanwhile, GRU performed optimally at a batch size of 64 and 50 epochs, with a MAE of 0.2229 and 93.66% accuracy. The prediction visualization shows that LSTM offers greater stability and precision in tracking actual data patterns. These findings indicate that LSTM holds stronger potential for supporting earthquake prediction systems based on time series data.

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms