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The design of a village fund assistance information system using the C4.5 algorithm aims to optimize the selection process for prospective aid recipients at the Ogan Ilir Regency Social Service (DINSOS). Currently, the village fund assistance selection process often takes a long time and is prone to inaccuracy and unfairness due to the limitations of the manual system. The C4.5 algorithm was chosen to build an effective decision tree in classifying based on predetermined criteria, such as income, number of dependents, employment, housing status, and expenses. By utilizing the Gain or Gain Ratio value of each attribute, the C4.5 algorithm is able to produce a clear decision tree, which makes it easier for DINSOS to make decisions objectively and transparently. This information system is designed with an easy-to-use user interface and a structured database to facilitate the management of aid recipient data. The results of the implementation of this system show increased accuracy in determining prospective aid recipients and time efficiency in data processing, thus supporting efforts to evenly distribute village fund assistance in Ogan Ilir Regency in a targeted manner.

This research evaluates and optimizes the operational efficiency of Bank Negara Indonesia (BNI) during the 2020-2023 period with a focus on the cost of funds ratio. Using a quantitative approach and qualitative interpretation, this research analyzes BNI financial data to identify trends and factors that influence fund expense ratios. The results provide insight into BNI's performance compared to industry standards and competitors. It is hoped that the recommendations from this research can help BNI management improve operational efficiency, support competitiveness in the banking industry, and make a positive contribution to the national economy.