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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.

Corporate value plays a crucial role in evaluating the success and sustainability of a business entity, influenced by various internal factors such as special connections, transfer pricing practices, and the effective tax rate (ETR). This study aims to examine the impact of these variables on the value of companies in the property and real estate sector on the Indonesia Stock Exchange, and to examine the role of corporate social responsibility (CSR) as a moderating variable. The study's findings reveal that special relationships and less transparent transfer pricing practices generally decrease company value, while efficiency in ETR can increase it. CSR acts as a moderating factor, strengthening the positive relationship between internal variables and company value while reducing the negative risks of unethical practices. This study recommends the implementation of more solid governance and transparency in business activities to increase competitiveness and company value in the Indonesian property and real estate industry.

The development of sustainable tourism facilities is one of the important efforts in increasing the attractiveness of destinations while preserving the environment. This study aims to analyze the implementation of ecological concepts in the development of glamping facilities in the Pearl Beach tourist area. The method used is a descriptive qualitative approach, with data collection through field observations, interviews with managers and tourists, and literature studies related to ecotourism principles and sustainable design. The results of the study show that the application of ecological concepts in glamping facilities in Mutiara Beach includes the use of environmentally friendly materials, integrated waste management, the application of energy efficiency, and designs that integrate the natural landscape without damaging the coastal ecosystem. The application of this concept not only improves the comfort and experience of tourists, but also contributes to increasing environmental awareness and strengthening the positive image of tourist destinations. In addition, this ecologically-conceptual glamping development model is expected to be a reference for the development of sustainable tourism facilities in other coastal areas, which prioritizes nature preservation and the welfare of local communities.

Automatic motorcycle spare part sales have increased along with the high use of automatic two-wheeled vehicles in the community. To support optimal sales strategies and stock management, customer purchasing pattern analysis is required. This study uses the FP-Growth algorithm to identify association patterns between automatic motorcycle spare part products that are frequently purchased together. FP-Growth was chosen because of its ability to efficiently find frequent itemsets without the need to generate candidate itemsets as in the Apriori algorithm. Transaction data is processed to form an FP-Tree which is then extracted to find relationships between items. The analysis results show combinations of products that frequently appear together, such as brake pads and engine oil, which can be used as a basis for compiling sales packages, product placement, and product recommendations. By implementing the FP-Growth algorithm, spare part stores or workshops can improve service and efficiency in sales management.

The Community Service (PkM) activity aimed to enhance the capacity of civil society to conduct effective public oversight of the East Lombok Regency Regional Revenue and Expenditure Budget (APBD) for fiscal year 2025. The “Budget School” program, organized by the Pimpinan Daerah Pemuda Muhammadiyah in collaboration with the Indonesian Forum for Budget Transparency (FITRA) NTB and ITSKes Muhammadiyah Selong, provided participants with a comprehensive and critical analysis of the regional budget structure and allocation patterns. The key findings highlighted notable fiscal inefficiencies, particularly the disproportionately high allocation for Employee Spending (Belanja Pegawai), which indicates an urgent need for budget reallocation toward increasing Capital Expenditure (Belanja Modal). Such realignment is essential to accelerate infrastructure development, enhance public service delivery, and ensure broader socio-economic benefits for the community. The event, conducted on September 25, 2025, successfully improved fiscal literacy among youth and civil society actors by strengthening their understanding of fiscal governance and legal oversight mechanisms. Overall, the activity fostered collective awareness and encouraged active participation in promoting sustainable, transparent, and efficient regional financial management.

The development of information technology has encouraged restaurants and cafés to function not only as dining places, but also as venues for hosting various events. However, the event reservation process at Rumah Makan Ny. Hartini and Café Rain is still carried out manually through logbooks, telephone calls, and WhatsApp, resulting in problems such as unorganized data, delayed confirmations, and miscommunication with customers. In addition, the manual system limits access to information regarding venue availability, reservation schedules, and additional facilities required by customers. This study aims to develop a web-based event reservation information system using the prototyping method. The system design was carried out using Unified Modeling Language (UML), including use case diagrams, activity diagrams, and class diagrams to model user interactions, process flows, and system structure. The results of the study show that the developed system is able to automate the reservation process, customer data recording, reservation confirmation, schedule management, and additional facilities management. This system improves operational efficiency, data accuracy, and service quality, while also making it easier for customers to make reservations independently and obtain information quickly and accurately.

The rise of financial technology (fintech) has significantly reshaped global investment over the last decade. Fintech innovations are increasingly applied in areas such as digital investment platforms, robo-advisors, blockchain-based assets, and cryptocurrency trading. The adoption of fintech in investment continues to grow due to the rising demand for accessibility, transparency, and efficiency in financial markets. Fintech has the potential to democratize investment by lowering entry barriers, expanding financial inclusion, and offering diverse investment instruments for retail investors. Therefore, research on fintech and investment has become an essential topic in recent years. This study uses a qualitative approach with data obtained from the Scopus database, which includes a total of 4,794 articles on fintech and investment published in the last decade (2020–2025). In addition, several software tools such as R Studio, VOSViewer, and Publish or Perish were used for data processing and bibliometric visualization. This study aims to analyze the development of research trends in fintech-driven investment, explore how technology is changing investor behavior, and provide insights for policymakers and practitioners in strengthening a sustainable and inclusive investment ecosystem.

This study examines the impact of Market Value Added (MVA), Economic Value Added (EVA), and Financial Value Added (FVA) on stock returns in energy-sector mining companies listed on the Indonesia Stock Exchange (IDX) during 2018–2023. A quantitative approach with multiple linear regression was applied to 23 purposively selected firms based on data availability. Secondary data were obtained from annual reports and stock prices published on the IDX website. The findings show that EVA has a significant effect on stock returns (p = 0.048 < 0.05), while MVA (0.075) and FVA (0.080) are not significant individually. However, the three variables collectively influence stock returns (p = 0.031 < 0.05). The adjusted R² of 0.396 indicates that 39.6% of return variability is explained by the model, with the rest influenced by other factors. Overall, EVA emerges as the key indicator for investors in evaluating return potential, while market-based measures such as MVA are less decisive, and historical value indicators (FVA) are less statistically relevant as predictors of stock returns. From a managerial perspective, firms are encouraged to focus on capital efficiency and sustainable economic value creation to enhance their investment appeal.

The COVID-19 pandemic has increased public interest in investing, especially in the banking sector, which is known for its stability. However, many investors still lack an understanding of fundamental analysis. This study aims to examine the effect of Return on Asset (ROA), Return on Equity (ROE), and Net Profit Margin (NPM) on stock prices of banking companies listed on the Indonesia Stock Exchange during the 2011–2023 period. The research used a quantitative approach with purposive sampling and multiple linear regression analysis using SPSS. The results show that ROA has no significant effect on stock prices. In contrast, ROE has a significant negative effect, while NPM has a significant positive effect on stock prices. These findings indicate that investors tend to consider net profit margins more than asset efficiency, and that high ROE may be perceived as a signal of high leverage risk. This research is expected to provide insights for investors in assessing banking performance before making investment decisions.

Student selection for the National Student Sports Olympiad (O2SN), particularly in the karate category, is still largely conducted manually, which may lead to subjectivity and inconsistency in the assessment process. This study aims to design and develop a web-based Decision Support System (DSS) to assist schools in selecting the best students for the O2SN karate competition by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method at SMA Negeri 2 Muaro Jambi. The system was developed using the waterfall model, which consists of requirement analysis, system design, implementation, and testing stages. The student evaluation process is based on criteria referring to the official O2SN karate standards, including stance accuracy, techniques, movement transitions, timing and harmony, breathing control, focus (kime), conformity (consistency with ryu-ha/style), strength, speed, and balance. The developed system processes assessment data to generate preference values and student rankings, which are separated by male and female categories. The results indicate that the application of the TOPSIS method is able to support more objective decision-making and improve transparency and efficiency in the O2SN student selection process within the school environment.

Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.

Hypertension is a major global health risk that requires accurate early detection, yet conventional methods struggle with complex and imbalanced health datasets. This study aims to optimize hypertension prediction using a Logistic Regression model integrated with Borderline-SMOTE to enhance recall and provide model transparency through SHAP (Shapley Additive Explanations). The method utilizes the BRFSS dataset, applying Borderline-SMOTE to address class imbalance at the decision boundary and XAI techniques for global and local interpretation. The findings show that the model achieved an accuracy of 0.719, an AUC of 0.800, and a significantly improved recall of 0.756. SHAP analysis identified age, high cholesterol, and BMI as the most influential risk factors, while waterfall plots successfully clarified individual risk extremes, ranging from 1.72% to 99.43% probability. These results imply that the proposed approach provides a sensitive and transparent screening tool for public health practitioners, effectively balancing statistical efficiency with clinical accountability.

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

Toko Jahit SA’aminah is a business engaged in tailoring services and the sale of sewing supplies that still manages data manually using record books. This condition causes several problems, such as slow data recording, the risk of data loss or damage, difficulties in monitoring the status of tailoring work and inventory, as well as obstacles in preparing tailoring service and sales reports. This study aims to design and develop a web-based tailoring service and sales information system to optimize the effectiveness and efficiency of operational performance. The system development method used is the waterfall method, which includes the stages of requirements analysis, system design using UML (Use Case Diagram, Activity Diagram, and Class Diagram), implementation using the Laravel framework with the PHP programming language and MySQL database, as well as system testing using the Black Box Testing method. The results show that the developed system is able to facilitate the management of tailoring service and sales data, monitor the status of tailoring work, check the availability of sewing supplies, and accelerate the preparation of tailoring service and sales reports to be submitted to the owner of Toko Jahit SA’aminah.

The JKN Mobile application is an electronic-based service innovation introduced by BPJS Health to facilitate JKN participants in accessing healthcare services and managing their membership information. This study aims to explore the experiences and perspectives of outpatient patients regarding the effectiveness of the JKN Mobile online registration system at Baubau City Regional General Hospital. A qualitative phenomenological approach was employed, with informants selected through purposive sampling. Data were collected to capture the meanings of patients’ experiences in using the application. The findings show that most patients are satisfied with the practicality, speed, and flexibility of the online registration system. However, technical problems, particularly unstable internet connectivity, remain a major challenge. Furthermore, the online registration system has contributed to improved service efficiency and patient satisfaction. These findings indicate the importance of continuous evaluation and improvement of the JKN Mobile online registration system to enhance service quality and health information system development at Baubau City Regional General Hospital.

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

Optimizing human resource (HR) performance through a business process reengineering (BPR) approach is a crucial strategy in a competitive and dynamic industrial environment. This study qualitatively examines through a literature review how BPRs radically redesign business processes to improve productivity, quality, time efficiency, and reduce operational costs, with a focus on HR aspects such as recruitment, training, performance appraisal, and career development. The BPR theory by Michael Hammer and James Champy emphasizes the principles of results-based workflow reorganization, the integration of information technology such as ERP and AI, and the empowerment of HR to eliminate task redundancy. In the Indonesian industrial context, the integration of BPR with digital transformation supports Industry 4.0, where HR acts as a catalyst for innovation through adaptive skills and cross-functional collaboration. The study results show that BPR implementation strategies including as-is process analysis, to-be design, change management, and continuous evaluation increase employee motivation, retention, and sustainable competitive advantage. Case studies such as PT Telkom Indonesia and PT Cahaya Mega Group demonstrate efficiency increases of up to 100%. This approach not only streamlines operations but also builds an organization resilient to market and technological disruption.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

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 rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.