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Road preservation is a type of maintenance that keeps roads in satisfactory working order by using preventive, corrective, rehabilitation, and reconstruction methods. This approach makes sure that the road stays usable for the entire time it is planned to be used. In practice, preservation projects often run into delays that can lead to important contract conditions. To avoid this, there needs to be a structured way to evaluate things through a Show Case Meeting (SCM). This study looks at how SCM was used in the 2024 Girian–Kema–Rumbia–Buyat Road Preservation Project to find out what caused the delays and how well the agreed-upon fixes worked. The method included looking at physical progress, differences between planned and actual performance, and how well the contractor did during SCM Stages I and II. The results indicate that the contractor failed to provide enough workers and move the right equipment, which led to the critical contract condition. SCM Stage I did not meet the required test-case target, but SCM Stage II did, showing that the contractor was able to meet the required performance targets and finish the work on time. These results show that SCM is an important way to control contracts and fix problems, which helps construction projects get done on time and well.

This study aims to analyze the effect of workload, leadership style, and compensation on employee performance at PT Ungaran Sari Garment, Semarang Regency. This quantitative research involved 109 respondents selected using the Slovin formula and purposive sampling. Data were collected using Likert-scale questionnaires and analyzed through multiple linear regression with SPSS 21. The results show that workload and compensation positively and significantly influence employee performance, while leadership style has no significant effect. Simultaneously, all three variables significantly affect performance. These findings indicate that appropriate workload management and a fair compensation system are vital for improving employee performance.

This study analyzes a 10-floor multi-story lecture building in Lombok, focusing on the impact of concrete quality degradation on the building's performance. Due to limited material access, the actual on-site concrete quality changed from the design quality of fc 30 MPa to fc 24.9 MPa. The building structure was modeled in 3D using ETABS v22 software, and two structural models were compared: one with the design concrete quality (fc' 30 MPa) and one with the actual quality (fc' 24.9 MPa). The analysis evaluated dynamic performance, inelastic displacements, P-Delta effects, and reinforcement requirements. The comparison aimed to assess the impact of concrete degradation on structural stiffness, inter-story displacements, and reinforcement needs. The results show that concrete quality deterioration increases the structure's vibration period, inelastic displacement, and lateral forces due to P-Delta effects. While beam reinforcement requirements remain mostly unchanged, column reinforcement significantly increases, especially in columns with large axial forces. This study provides valuable insights into the technical consequences of concrete quality degradation and serves as a reference for evaluating structural redesigns in projects facing material limitations.

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.

Distribution transformers play a critical role in delivering electrical energy from medium-voltage networks to low-voltage consumers. At ULP Medan Kota, several distribution transformers have been operating with loads exceeding 80% of their nominal capacity, posing risks of overloading, efficiency reduction, and equipment failure. This study aims to analyze the performance of distribution transformers based on actual load data and evaluate mitigation strategies through the implementation of additional parallel transformers (trafo sisip). The methodology includes data collection, load and current calculation, and simulation of load distribution after transformer insertion. The results show that the installation of trafo sisip reduces the load on the main transformer by approximately 50% and significantly lowers the current to safer levels. Moreover, placing the trafo sisip at an optimal position minimizes voltage drop to as low as 0.0745 Volts. Therefore, the addition of trafo sisip is proven to enhance the reliability, efficiency, and operational life of the power distribution system at ULP Medan Kota.

This study aims to analyze the influence of work environment and job satisfaction on the performance of warehouse employees at PT J&T Express. Work environment and job satisfaction are important factors that are thought to increase productivity and work efficiency. The research method used is quantitative with a survey approach, data collection through questionnaires announced to 100 warehouse employees of PT J&T Express using a purposive sampling technique. The data obtained were analyzed using linear multiple regression to test the partial and simultaneous effects of independent variables on the dependent variable. The results of the study indicate that work environment and job satisfaction have a significant positive effect on the performance of warehouse employees. This finding indicates that improving the quality of the work environment and job satisfaction can be an effective strategy in improving employee performance at PT J&T Express. This study is expected to provide practical contributions to company management in managing human resources to increase company competitiveness.

Cocoa fruit is one of the crops with a long history in Indonesia. Cocoa powder and chocolate are among the export commodities processed from cocoa fruit. Currently, cocoa product exports fluctuate annually. The challenges faced in cocoa product exports are diverse. This study aims to analyze the performance of cocoa product exports in the global market. The data used in this study was obtained from UN Comtrade and the Central Statistics Agency (BPS) from 2006 to 2024. The methods used in this study include Revealed Symmetric Comparative Advantage (RSCA) and Export Competitiveness Index (ECI). The results show that the export performance of cocoa powder is relatively good, while that of chocolate is not so good. This is based on the RSCA and ECI values of cocoa powder, which are better than those of chocolate. The average RSCA value of cocoa powder is 0.62, while the average RSCA value of chocolate is -0.93. The average ECI value for cocoa powder is 0.95 and the average ECI value for chocolate is 0.97. One of the factors contributing to the good export performance of cocoa powder is that the export value of cocoa powder is higher than that of chocolate. Strict safety standards in export destination countries also pose a barrier to chocolate exports

This paper was made to explain the results of research on how to obtain the most appropriate citric acid adhesive composition in the manufacture of randu wood fiber composites in order to obtain a strong and suitable composite material. The research was carried out by experimental methods in the laboratory through a series of mechanical tests, namely the bending strength test and the screw grip strength test. The sample specimen is 5 cm × 20 cm × 1 cm for flexural strength testing and 5 cm × 10 cm × 1 cm for screw grip strength test. Composite specimens were made with variations in the composition of citric acid adhesives of 2.5%, 5%, 7.5%, 10%, 12.5%, 15%, 17.5%, and 20% by weight of randu wood. The results showed that the composite of randu wood particles with a citric acid matrix had optimal strength at a certain ratio, which was 7.5%. At the same ratio, the test results of the screw grip strength test also provide the highest value. These findings confirm that the exact composition of the adhesive has a significant impact on the final performance of the resulting composite.

This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

The rapid development of information technology in today's digital era has significantly impacted organizational performance, particularly in data management and resource planning. One organization that heavily relies on accurate data availability is the Indonesian Red Cross (PMI), especially its Blood Donor Unit (UDD). UDD PMI of Asahan Regency faces challenges in determining monthly blood donor targets to maintain stable blood stock. A shortage of blood supply can be fatal for patients requiring transfusions. Therefore, a system is needed to forecast the number of blood donors, allowing for more accurate decision-making. This study utilizes the Weighted Moving Average (WMA) method to predict the number of blood donors for the following month based on historical data from March 2024 to March 2025. The WMA method is chosen for its ability to assign greater weight to recent data, making the forecast more relevant and accurate. The results of this research are expected to assist UDD PMI Asahan Regency in anticipating blood needs and maintaining optimal stock availability.

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 compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

This study aims to evaluate the effectiveness of the green water system in the nursery phase of Nile tilapia Oreochromis niloticus by observing growth performance, water quality, and survival rate. The experiment was conducted for 21 days using 450-liter circular tanks with two treatments: green water and clear water systems. The green water system was established by adding plankton starter to stimulate algal growth, while the clear water system used clean water with routine siphoning. Observations included absolute length, absolute weight, water quality parameters pH, temperature, dissolved oxygen, nitrite, and phosphate, and survival rate. The results indicate that the green water system provided superior nursery performance compared to clear water. Tilapia seeds reared in green water exhibited higher growth in length and weight, more stable water quality, and a greater survival rate 90% than those in the clear water system 80%. These improvements are attributed to the presence of microalgae, which serve as natural feed as well as bioremediation agents that reduce ammonia, nitrite, and phosphate toxicity. Therefore, the green water system proves to be more effective, economical, and environmentally friendly for tilapia nursery culture compared to the clear water system.

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.

Angklung is one of the traditional musical instruments originating from the Sundanese culture in West Java, Indonesia, and has been recognized as an Intangible Cultural Heritage by UNESCO since 2010. This research aims to analyze the functions, values, and development of angklung in West Java society. The study applies a qualitative descriptive approach through literature review, field observations, and interviews with cultural practitioners. The findings show that angklung plays an important role not only as a musical medium but also as a tool for social cohesion, educational development, and cultural identity preservation. Over time, angklung has experienced significant transformation in terms of performance, musical arrangements, and adaptation in modern contexts, such as tourism, creative industries, and international collaborations. However, challenges remain in maintaining authenticity amid commercialization and the declining interest of younger generations in traditional musical arts. This study highlights the need for continuous cultural education, community engagement, and government support to sustain angklung's legacy. The implications of this study contribute to cultural preservation strategies and provide insights into the sustainable development of traditional arts in the modern era.

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.

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.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

This study investigates the impact of GeoGebra-assisted collaborative learning on students' understanding of function graphs. Function graphs are fundamental in mathematics education, yet many students struggle to grasp the relationships between variables, primarily due to traditional teaching methods that focus on procedural skills rather than conceptual understanding. To address this challenge, the study incorporates GeoGebra, a dynamic mathematics software, alongside collaborative learning strategies. The research utilizes a quasi-experimental design involving high school students who had previously struggled with function graphs. The results demonstrate that the experimental group, which engaged in GeoGebra-assisted collaborative learning, showed a significant improvement of 27% in their post-test scores, compared to just a 6% improvement in the control group using traditional methods. The study highlights the effectiveness of GeoGebra in fostering a deeper conceptual understanding of mathematical functions by enabling students to visualize and manipulate graphs interactively. Additionally, collaborative learning encouraged peer interaction, reinforcing the learning process and promoting better problem-solving skills. The findings suggest that combining interactive tools like GeoGebra with collaborative learning techniques can enhance students’ mathematical comprehension, leading to improved engagement and performance in mathematics education.

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.