Publication Search

The growing demand for affordable transportation has significantly expanded the used car market. This study aims to develop optimal sales strategies by analyzing seller types and vehicle categories through exploratory data analysis and linear regression techniques. Using a cleaned public dataset of over 150,000 used car listings in Germany, key variables such as registration year, mileage, vehicle type, and seller type were examined. Results indicate that individual sellers dominate the market, although dealers set higher and more stable prices. SUVs and limousines typically hold higher market value. The linear regression model achieved an R-squared value of 0.34, suggesting that registration year and mileage account for 34% of the price variance. These findings offer practical insights for stakeholders in the used car business to tailor pricing strategies based on vehicle attributes and seller classification

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.

Many companies require good employee performance in order to achieve company goals, business competition is a trigger for companies to improve the performance of their employees. The variables used to determine the effect of the company's employee performance are discipline, compensation, work environment and training. This research was conducted on employees of PT. XYZ involved 60 respondents. This study uses a saturated sampling technique and uses a Likert scale. The analysis used was multiple linear analysis, with the regression equation Y = 16.349 + 0.068X1 + 0.001X2 + 0.159X3 + 0.287X4. The conclusions of this study are, (1) employee performance is not significantly affected by discipline, (2) employee performance is not significantly affected by compensation, (3) employee performance is not significantly affected by the work environment, (4) employee performance is significantly influenced by training, (5) Employee performance is influenced simultaneously by discipline, compensation, work environment and training.