Accurate Control Mechanism Research Articles

School Operational Assistance Fund (BOS) Accounting Information System as an Internal Control Tool

This study examines the accounting information system as an internal control tool over cash deposits and disbursements on the School Operational Assistance Fund or in Indonesian called Bantuan Operasional Sekolah (BOS). To enforce accountability and transparency in the use of the BOS Fund, which is one of the important sources of funds for education in Indonesia, it is very important to implement a strict monitoring mechanism. This research approach uses qualitative and descriptive research methods, which combine primary data collection, document analysis, interviews with stakeholders, and direct observation of the accounting information system that regulates the process of receiving and spending BOS funds. The framework includes elements of planning, in this case including the initial stage of making the RKAS, then implementation by carrying out the RKAS by procedures and paying attention to internal controls. Furthermore, the reporting stage leads to the realization of the RKAS, where all stages use an integrated information system. Based on the research results, SMK Darurrohman already has a computerized information system that is available to the public. This system is designed to receive and distribute BOS funds by the guidelines outlined in the BOS guidebook and adequate and accurate control mechanisms. Accounting information systems play a key role in supporting the implementation and maintenance of effective internal controls in an organization. Suggestions for the sustainability of BOS fund management for SMK Darurrohman Sukawangi include utilizing BOS to maintain and improve the quality and quantity of education, emphasizing routine, and implementing job rotation.

Identifying animal species in NIR hyperspectral images of processed animal proteins (PAPs): Comparison of multivariate techniques

The use of PAPs in animal feed has several advantages over other feed ingredients, but requires rigorous and accurate control mechanisms that ensure the absence of ruminant meal. In order to differentiate between animal species while simultaneously offering the capacity to inspect PAPs in large volumes, a hyperspectral imaging (HSI) system operating in the NIR spectral range is proposed. This study investigates the sensitivity, specificity and other parameters with which HSI can discriminate between different animal species (ruminants, swine and poultry), making use of various classification methods. Diffuse reflectance spectra were acquired from 125 rendered meal samples in the 1000–1700 nm wavelength range; measured PAPs included particles of scale, hair, feather, blood, grease, skin, muscle and bone from both ruminant and non-ruminant animals, obtained in a rendering plant. Various classification methods were then applied to the dataset to determine the accuracy with which different animal species could be discriminated from each other. Support Vector Machine classification performed best in discriminating between animal species, with a sensitivity and specificity of around 90% and a Matthew's correlation coefficient of around 0.7 for non-ruminant species and higher than 0.95 for ruminant species. Other methods, such PLS-DA and Subspace Discriminant, also produced acceptable results and required less computational time. This study showed that spectral analysis of PAPs, based on diffuse reflectance spectroscopy, is a promising technique for differentiating between ruminant species and other terrestrial animal species. The technique may therefore offer accurate and fast analysis of large volumes of feed products, a necessary prerequisite for the lifting of the EU ban on non-ruminant processed animal proteins.