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Social and economic welfare reflects the quality of life in a region and is influenced by local social, economic, and environmental factors. East Java, as the second most populous province in Indonesia, faces challenges in improving the welfare of its residents, particularly due to varying regional characteristics such as employment, education, and population demographics. To understand the patterns of interrelationships among factors affecting welfare, this study conducted a klaster analysis to group regions based on similar characteristics. The klaster analysis employed both hierarchical (complete linkage) and non-hierarchical (K-means) approaches to determine the optimal number of klasters. The results revealed that the level of diversity across regions in East Java tends to be homogeneous in social and economic aspects, with average values exceeding standard deviations. Assumption tests for the klaster analysis confirmed that the data met the assumptions of multivariate normal distribution and dependency.Through hierarchical (complete linkage) and non-hierarchical (K-means) klaster analysis, two main klasters were formed, dividing districts/cities in East Java based on welfare characteristics. Using the complete linkage method, 27 regions were grouped into klaster 1, and 11 regions into klaster 2, while K-means grouped 26 regions into klaster 1 and 12 regions into klaster 2. Out of the six variables used, one variable was found to be insignificant in influencing the klastering results. Based on the mapping results, the grouping aligns with similar criteria, where urban areas predominantly fall into one klaster, and the other klaster is dominated by rural areas.

Damage that occurs to manufactured goods can occur due to several things, such as in the manufacturing process, in the packaging process or during the delivery process. To find out where when the goods were damaged and the factors causing the damage to the goods as well as the efforts made to reduce the damage to the goods, an investigation was carried out at the time of unloading the goods, to find out where the damage to the goods occurred so as not to harm the recipient of the goods if they received the goods in damage condition.Based on multiple linear regression analysis, the results obtained are Y = 3.133 + 0.044 X1, + 0.402 X2 + 0.299 X3 + . Partial test results of machine (X1), material (X2), man power (X3) have a positive and significant effect on damage goods. It is proven by the results of the comparison of the value of t count with t table and with a significant level comparison of 5% (0.05) where the machine variable t count (0.413) < t table (1.98609), material variable t count (3.142) > t table (1.98609), variable man power t count (2.990) > t table (1.98609). The R2 test of machines, materials and man power gave a significant effect on damage goods by 43.6%, while the remaining 56.4% was explained by other causes outside the model variables that were not examined.