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This study is grounded in the Marketing Mix Theory (4P) proposed by McCarthy, which explains that marketing performance is influenced by the effective integration of product, price, place, and promotion strategies. The purpose of this study is to analyze and empirically examine the effect of the 4P marketing mix—product strategy, pricing strategy, place strategy, and promotion strategy—on increasing sales of mobile coffee businesses using electric bicycle carts in Semarang City. A quantitative research approach was employed using an accidental sampling technique, with respondents consisting of consumers who met the research criteria and were encountered directly in the field. Primary data were collected through questionnaire distribution, resulting in 85 valid respondents. Data analysis was conducted using SPSS version 26, including validity tests, reliability tests, and classical assumption tests (normality, multicollinearity, and heteroscedasticity). Hypothesis testing was performed using multiple linear regression analysis, partial tests (t-test), simultaneous tests (F-test), and the coefficient of determination (R²). The results indicate that product strategy and pricing strategy have a positive and significant effect on increasing mobile coffee sales. Place strategy also shows a significant influence on sales performance, highlighting the importance of mobility and strategic selling locations. Furthermore, promotion strategy has a positive and significant effect on sales increase. Simultaneously, the F-test results confirm that product, price, place, and promotion strategies collectively have a significant effect on increasing mobile coffee sales using electric bicycle carts in Semarang City. These findings support the applicability of the Marketing Mix Theory (4P) in explaining sales performance in mobile and small-scale coffee businesses.

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.

Distribution transformers play a crucial role in delivering electrical energy from the distribution system to consumers to ensure power quality and supply continuity. However, in practice, overload conditions often occur due to increasing demand and load growth that exceed the transformer’s rated capacity. This situation can lead to reduced efficiency, increased power losses, and accelerated equipment aging. This study aims to analyze the performance of the CMY distribution transformer at PT PLN (Persero) ULP Labuan, which operates beyond its nominal capacity, and to propose an alternative solution through transformer mutation, namely the replacement of the existing unit with a transformer of more appropriate capacity based on load analysis results. The Least Square Method is employed to predict future load growth and determine the projected time when the transformer will again experience overload after the mutation. The results indicate that the existing 100 kVA transformer is overloaded and should be replaced with a 160 kVA unit. After the mutation, the loading percentage decreases significantly, the transformer’s lifespan is extended, and the reliability of the distribution system improves. Furthermore, the Least Square prediction suggests that the new transformer may experience overload again in future years if no further planning is carried out. Therefore, transformer mutation can be considered an effective and medium-term solution to enhance and maintain the reliability of the electrical distribution system within the operational area of PT PLN (Persero) ULP Labuan.

This study aims to analyze the effect of teamwork, workload, and work discipline on the work productivity of employees at PT Sejin Fashion Indonesia Pati. The study used a sample of 94 respondents determined using proportionate stratified random sampling. Data collection was conducted using questionnaires and analyzed using multiple linear regression through SPSS version 25, including validity and reliability tests, t-tests, F-tests, and determination coefficients. The results show that: 1) teamwork has a positive and significant effect on employee productivity; 2) workload has a positive and significant effect on employee productivity; and 3) work discipline has a positive and significant effect on employee productivity; 4) teamwork, workload, and work discipline have a simultaneous effect on employee productivity. Work discipline is the most dominant variable in increasing employee work productivity. These findings imply that strengthening teamwork, adjusting workloads, and improving employee discipline are important strategies for companies to increase employee work productivity.

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.