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This study applies the Mean Variance model, which aims to form an optimal portfolio composition in the health, property, and cyclical consumer sectors and combine the three sectors into one portfolio, then visualize its efficient frontier. This study analyzes the return profiles and compares the risks of each portfolio using alternative risk measures such as the Coefficient of Variation (CV), Value at Risk (VaR), and Conditional Value at Risk (CVaR). Daily closing price data for the three sectors listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to March 3, 2025, were used in this study. Stock selection was conducted using purposive sampling, followed by selecting seven stocks for optimization based on the lowest Coefficient of Variation (CV) value. Portfolio optimization analysis was conducted using the Python programming language with Visual Studio Code software. The findings of this study indicate that the combined portfolio incorporating the three sectors is the most efficient, with an expected return of 0.104%, standard deviation of 0.007, and alternative risk measures such as Coefficient of Variation (CV) 6.9328, Value at Risk (VaR) of -0.99%, and Conditional Value at Risk (CVaR) of -1.44%, which are lower than those of single-sector portfolios. Visualization of the efficient frontier curve confirms that the combined portfolio offers better results in terms of risk and return. The results of this study indicate that cross-sector diversification can significantly reduce risk and prevent significant losses.

This study aims to analyze the effect of variations in screw conveyor speed and cutting blade on an automatic meatball molding machine in producing meatballs weighing between 15 and 20 grams. The research method used a design of experiments (DOE) approach with a factorial design, followed by a two-way ANOVA analysis to test the effect of each factor and their interactions. The screw conveyor speed variations used were 160 RPM, 140 RPM, and 124 RPM, while the cutting blade speed was varied at 224 RPM, 186 RPM, and 160 RPM. The speed variations were obtained by adjusting the pulley ratio on the machine. The testing process was carried out by molding meatballs using a combination of these speed variations, then boiling them until they float to ensure doneness. After that, the mass of each meatball was weighed with a precision scale. The weighing data were processed using Microsoft Excel and Minitab 21 software to obtain accurate statistical analysis. The results showed that increasing the screw conveyor speed tended to increase the meatball mass, while increasing the cutting blade speed actually decreased the mass of the meatballs produced. The interaction between screw conveyor speed and cutting knife speed was statistically significant with a p-value ≤ 0.05, indicating that the combination of the two plays an important role in determining the final meatball mass. Through Response Optimization analysis, the most optimal combination for producing meatballs with a mass in the range of 15–20 grams is a screw conveyor speed of 124 RPM and a cutting knife speed of 160 RPM. This setting can be achieved by using pulleys with diameters of 114.3 mm (4.5 inches) and 88.9 mm (3.5 inches). These findings are expected to be a reference for meatball industry players, especially MSMEs, in increasing production efficiency and maintaining product size consistency.

This study aims to analyze the optimality of a quadratic program model using the penalty function. The analysis is carried out in each case that has been made so that in each case optimal results are obtained. There are many methods that can be used to solve the quadratic program problem but in terms of the number of iterations, this research uses the penalty function and then implements it into the Matlab programming language. The results obtained in this study indicate that by using the penalty function as a parameter, the optimal value of a function can be obtained. The results of the analysis will also be more optimal by using the lagrange method depending on the parameter values obtained.