Decision Support System (DSS) is a system used to assist decision makers in determining the best alternative based on certain criteria. Determining retirement eligibility for ASN (State Civil Apparatus) employees is one of the important aspects in human resource management, where this decision affects the future of employee careers, their welfare, and state budget management. This study proposes the development of a decision support system for determining retirement eligibility for ASN employees using the Multi-Attribute Decision Making (MADM) method. The MADM method is a decision-making technique that considers several criteria and alternatives to produce optimal decisions. In this study, the criteria used to determine retirement eligibility include retirement age, length of service, work performance, health, and other factors relevant to the applicable pension policy. The method used in this system includes several steps, namely identifying criteria, assigning weights to each criterion, and evaluating alternatives based on these criteria using calculation techniques such as AHP (Analytical Hierarchy Process) or TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The results of this evaluation will provide recommendations on whether an employee is eligible for retirement or can still continue his career. The developed system is expected to facilitate the decision-making process regarding ASN employee retirement by providing objective and transparent analysis, so that the decisions taken are more appropriate and in accordance with applicable policies. Thus, this system can improve efficiency and effectiveness in human resource management in government agencies.

The purpose of this research is to create a JST (artificial neural network) model that can forecast population growth at the Population and Civil Registration Office of West Sumba Regency. population growth at the Population and Civil Registration Office of West Sumba Regency. Regency. Regional development planning must consider the increasing number of population, therefore proper forecasting is essential to encourage sustainable policies and initiatives. sustainable policies and initiatives. Because it can identify complex patterns in past data and produce more accurate forecasts than traditional techniques, an ANN model is used. traditional techniques, the ANN model is used. The data used in this study is the population growth of Southwest Sumba Regency over the past including characteristics such as birth and death rates and population movements. deaths and population movements. The backpropagation algorithm is used to optimize the multilayer perceptron (MLP) architecture for ANN training. Separating the data into training and testing sets and assessing the models model using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) based on the error. Error (RMSE) based on the prediction error are the steps involved in the training process. involved in the training process. The research findings show that, with a low level of error, the artificial neural network model can estimate the population increase in Southwest Sumba Regency with a reasonable level of accuracy. reasonable level of accuracy. The model is expected to serve as a reference for relevant authorities to better manage population data and as a tool to create more focused and successful population policies.