Children under five are more at risk of experiencing nutritional disorders because during this period children will experience very rapid growth and development. Nutritional disorders can result in children's performance in activities not being optimal, hampering the growth and development process, and can even cause disease. Malnutrition sufferers in Indonesia are increasing every year, and the percentage of malnutrition in Indonesia is around 3.4%. An expert system is a computer-based system built based on facts, knowledge and reasoning that can help solve a problem. However, to measure the uncertainty and level of expert confidence in identifying and detecting malnutrition, an expert system can use the Dempster-Shafer method. The results of testing the system model using the Dempster-Shafer method on toddler nutritional diseases obtained quite good results with system accuracy of 84% with a precision value of 81.83% and also a recall value of 80%.

The process of identifying the ripeness level of cayenne peppers is an important step in cultivation and post-harvest handling. Dependence on the quality factors of farmers, such as visual diversity and differences in ripeness perception, results in subjective harvest outcomes. This manual process is also prone to inconsistent results, as humans have time limitations, fatigue, and sometimes lack concentration when sorting for long periods. To minimize these issues, technological intervention is needed to mechanically classify the ripeness level of cayenne peppers. This research aims to develop a classification model for the maturity level of cayenne pepper plants. This research proposes the use of the CNN method for feature extraction and KNN for data classification based on the features extracted by CNN. From the test scenarios carried out, the classification carried out by KNN based on CNN feature extraction got the best accuracy of 99.33%, while the CNN classification model got the best accuracy of 87.33%.

: This study aims to evaluate the sales information system at UMKM CKS using the COBIT 5 framework. Initial observations revealed several issues, including an unresponsive user interface and a complicated check-out process. The audit results showed an average maturity level of 1.56 or 156%, indicating a Fully Achieved (F) status with a systematic approach. However, on the capability model scale, it is at level 1, which is the Performed Process. The conclusion of this research indicates that although the sales information system has been successfully implemented, there is a need for innovation and enhancement of consumer services, as well as better communication between the company and consumers to improve the effectiveness of the information system.

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.