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Economic empowerment of rural communities through the strengthening of Micro, Small, and Medium Enterprises (MSMEs) is a crucial pillar in achieving national economic independence. One of the fundamental obstacles faced by MSMEs in rural areas is financial exclusion due to low digital literacy and dependence on conventional cash transaction systems. The Community Service Program (KKN) of students from the State Islamic University of North Sumatra (UIN SU) in Pematang Tengah Village was designed to bridge this gap by optimizing the Indonesian Standard Quick Response Code (QRIS). Through an intensive participatory mentoring approach, students acted as agents of digital transformation who educated, trained, and facilitated 10 local MSME actors in adopting non-cash payment technology. The results of the program show a significant shift from digital skepticism to digital trust. The implementation of QRIS has been proven to increase operational efficiency, financial management accuracy, and strengthen the image of business modernity in the eyes of consumers. This article emphasizes that the role of students is not merely as information deliverers, but as catalysts of social capital that is crucial for the sustainability of digitalization at the village level.

This study aims to estimate coal resources using the kriging method at PT. Inti Bara Perdana, Taba Penanjung, Bengkulu. Geostatistical analysis was applied to evaluate spatial variability of coal quality parameters, including total moisture (TM), inherent moisture (IM), ash content (ASH), volatile matter (VM), fixed carbon (FC), and calorific value (CV). The research utilized drilling data with an average spacing of 80 meters. Variogram modeling was conducted using SGeMS software, employing spherical models to determine nugget, sill, and range parameters. The results show a low nugget effect (0%) indicating strong spatial continuity. The obtained ranges vary between 180–1296 meters depending on the parameter. Blo./ck kriging estimation was performed using block dimensions of 25 × 25 × 8 meters and a coal density of 1.3 ton/m³. The total estimated coal resource up to an elevation of 230 m is 253,500 tons. These findings demonstrate that kriging provides reliable resource estimation and supports mine planning optimization. This research contributes to improving coal resource evaluation accuracy and operational decision-making in open-pit mining.

This research try to examine how investment choices in property and real estate sector businesses listed on IDX (2020-2024) are impacted by the cost of debt and equity.For the accuracy of the analysis, this study also uses firm size and profitability as controler. Purposive sampling was used in the sampling process, which produced seven qualifying organizations with a total of 35 observations over a five-year period. Panel data regression was used for data analysis, and the Common Effect Model was shown to be the best estimation model. The findings show that investment decisions are significantly influenced by firm size, profitability, cost of debt, and cost of equity all at the same time. Nonetheless, investment choices are not much impacted by the cost of debt. Similarly, it has been demonstrated that the cost of equity has no appreciable effect on the capital expenditures of the businesses. Firm size has a favorable and substantial impact, making it the main motivator for investment activity in the real estate industry. During the study period, investment decisions were not significantly impacted by profitability. These results show that, especially in the post-pandemic economic recovery era, asset capacity and economies of scale are more important for the viability of real investment projects for property firms on the IDX than yearly variations in capital costs.

Drug stockouts in hospitals pose significant risks to service quality, patient safety, and operational efficiency. This study aimed to analyze how drug demand planning and procurement processes at Johar Baru Regional General Hospital contribute to stockout occurrences and to develop data-driven recommendations based on supply chain management principles. A qualitative descriptive design was employed using data triangulation. Data were collected through in-depth interviews with the Head of the Pharmacy Installation, procurement staff, and warehouse pharmacists, complemented by direct observation and analysis of 2024 planning and procurement documents. Thematic analysis was conducted with the support of NVivo software to identify patterns and relationships among key variables, including drug demand planning, procurement, and inventory management.Findings reveal that stockouts stem from interconnected weaknesses in planning accuracy, procurement coordination, and inventory control systems. Effective stock management depends not only on increasing supply but also on improving data quality, integrating inventory information systems with operational workflows, and enhancing cross-functional collaboration. Recommended strategies include implementing a minimum stock alert system integrated with the Hospital Management Information System (HMIS), strengthening standard operating procedures for stockout response and procurement confirmation, improving integration between HMIS, the National Formulary, and budgeting systems, and applying consumption based planning methods combined with ABC VEN analysis to optimize inventory control.

This study aims to evaluate the level of user satisfaction with the user interface of the Academic Information System (SIAKAD) at Esa Unggul University using the End User Computing Satisfaction (EUCS) method. This method assesses user satisfaction based on five key dimensions: content, accuracy, format, ease of use, and timeliness. The study employed a quantitative descriptive approach by distributing questionnaires to active Esa Unggul University students as primary system users. The collected data were analyzed using SPSS software to test validity, reliability, and the relationships between variables that influence user satisfaction with the SIAKAD interface. The results show that, in general, users are quite satisfied with the SIAKAD interface, particularly in the ease of use and accuracy dimensions, which obtained the highest scores. This indicates that usability and information accuracy are the dominant factors in creating a positive user experience. However, the timeliness and content dimensions still require further improvement, as they were rated as less optimal in providing fast and comprehensive information. These findings highlight the importance of an intuitive, efficient, and informative interface design in enhancing user satisfaction. This research is expected to serve as a reference for Esa Unggul University in developing and improving its Academic Information System to become more effective, efficient, and user-friendly. Evaluating user satisfaction through the EUCS approach provides valuable insights for system developers to refine the interface, improve information quality, and enhance system responsiveness. Thus, the results of this study are expected to contribute to improving academic service quality and supporting the digitalization of education at Esa Unggul University.

English speaking skill is a crucial competence for university students, particularly those involved in student organizations focusing on language development. Members of English clubs are expected to actively use English in academic and organizational contexts; however, many still face challenges related to pronunciation accuracy, fluency, limited active vocabulary, and low confidence. These challenges are often caused by limited structured speaking practice and insufficient integration between digital learning tools and communicative pedagogy.This community service program aimed to enhance the English-speaking skills of members of the Bhamada English Club at Universitas Bhamada Slawi through the utilization of the Duolingo application integrated with a Task-Based Learning (TBL) approach. The program was implemented through three stages: preparation, implementation, and evaluation. Activities included needs analysis, speaking pre-test, guided Duolingo practice focusing on pronunciation and speaking features, task-based speaking activities, and post-test evaluation.The results demonstrated improvements in pronunciation accuracy, speaking fluency, and participants’ confidence in using English orally. The integration of Duolingo as a source of comprehensible input and Task-Based Learning as a communicative output strategy proved effective in enhancing speaking skills. This program indicates that technology-supported task-based instruction can serve as an effective and sustainable model for improving English speaking skills among university students.

The Papua Special Autonomy Fund (Dana Otonomi Khusus Papua) represents a key instrument of Indonesia’s asymmetric fiscal decentralization aimed at reducing historical inequalities, accelerating regional development, and promoting social justice for Indigenous Papuans. However, after more than two decades of implementation, concerns persist regarding its effectiveness in producing equitable welfare outcomes, particularly with respect to accountability, targeting accuracy, and distributive justice. This literature review critically examines existing scholarly research on the governance, implementation, and impacts of Dana Otsus Papua, with an emphasis on how institutional arrangements shape policy performance and equity outcomes. The study employs a narrative–critical literature review enriched with systematic elements, including transparent search procedures, explicit inclusion and exclusion criteria, and thematic synthesis. Peer-reviewed journal articles and reputable conference proceedings were analyzed using thematic analysis and conceptual mapping to identify dominant findings, methodological approaches, and research gaps. The synthesis reveals recurring patterns across the literature. Accountability mechanisms remain fragmented and weakly integrated across planning, budgeting, monitoring, and evaluation processes. Targeting accuracy is inconsistent, with fiscal benefits frequently failing to reach Indigenous Papuans as intended. Moreover, distributive justice outcomes depend more on institutional recognition, participation, and governance capacity than on the size of fiscal transfers alone. The review also highlights a critical gap in integrative evaluations that link governance arrangements, implementation processes, and equity outcomes. The article concludes that improving Dana Otsus Papua requires a shift from expenditure-focused assessments toward governance- and justice-oriented evaluation frameworks. The study contributes theoretically by integrating accountability, implementation, and distributive justice perspectives, and offers practical insights for strengthening oversight, refining targeting mechanisms, enhancing participatory governance, and embedding digital tools within accountability systems.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

Hospital nutrition services are an integral component of improving the quality of patient health. This descriptive observational study aims to evaluate the achievement of Minimum Service Standards (MSS) for nutrition at RSUD Dr. H. Slamet Martodirdjo Pamekasan, specifically focusing on the accuracy of diet types, timeliness of food distribution, and patient food waste. Data were collected over a single menu cycle (10 days) from November 24 to December 4, 2025, involving 44 patients per day. Research instruments included observation sheets and the Comstock visual method, with data analyzed based on the Decree of the Minister of Health (Kepmenkes) No. 129 of 2008. The results indicated that the timeliness of food distribution reached 96% and the accuracy of diet types reached 100%, both of which met the MSS requirements (≥ 90% and 100%, respectively). However, the average patient food waste was 25%, which does not yet meet the maximum standard of ≤ 20%. In conclusion, while logistical aspects have fulfilled the established standards, the hospital needs to conduct further evaluations regarding food flavor factors and patients' clinical conditions to reduce food waste in accordance with the applicable standards.

This study evaluates the implementation of spot-check testing for road preservation works in Manado City, specifically on the Kairagi–Mapanget segment, the Manado City–Wori boundary, and the Liwas Terminal Access Road, to identify existing problems and assess the effectiveness of engineering-based mitigation efforts. The objective is to examine construction quality compliance with technical specifications and identify operational challenges encountered on site. The methodology encompasses the measurement of asphalt layer thickness and density via a core drill, the examination of contract documents, the analysis of laboratory test results, and the execution of field observations. The findings show that most samples meet the required standards, although several locations require corrective action. Challenges arise from weather conditions, heavy traffic, equipment limitations, and the need for adaptation to updated technical regulations. Operational mitigation strategies, staff training, the use of core drill technology, and digital documentation were found to enhance accuracy and efficiency in evaluation. The research demonstrates the value of stakeholder collaboration and capacity building in supporting quality control for road preservation works. A multidimensional approach is effective in resolving technical and operational complexities in urban road projects.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

This study aims to analyze the strategies of tahfidz teachers in improving students’ Qur’anic memorization quality through a literature review approach. Memorization quality is not merely measured by the quantity of verses memorized, but also includes accuracy of recitation, fluency, consistency in muroja’ah (revision), and long-term memory retention. This research employed a literature review method by examining national and international journal articles as well as relevant academic books published between 2020 and 2025. Data were analyzed using content analysis techniques to identify themes, patterns, and research gaps related to teachers’ strategies in tahfidz learning. The findings reveal that effective tahfidz teaching strategies can be classified into four main aspects: (1) structured and consistent implementation of repetition (tikrar), (2) reinforcement of muroja’ah and periodic evaluation, (3) motivational strategies and character development, and (4) innovative learning approaches integrating collaboration and educational technology. The tikrar method is proven effective in strengthening memorization retention when supported by systematic program planning. Furthermore, intrinsic motivation, a conducive learning environment, and varied instructional methods significantly contribute to maintaining students’ memorization stability. In conclusion, improving the quality of Qur’anic memorization depends not only on repetition frequency but also on the integration of pedagogical strategies, affective-spiritual approaches, and instructional innovation. This study provides a conceptual contribution to the development of more comprehensive and sustainable tahfidz learning strategies.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.