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This research aims to implement the C4.5 algorithm in predicting bad credit in digital loan systems in the FinTech industry. The C4.5 algorithm was chosen because of its ability to handle numeric and categorical attributes, as well as produce a decision tree that can be interpreted easily. This research uses a dataset containing customer transaction and profile information, such as employment status, income and payment history. Test results show that the C4.5 algorithm is able to achieve an accuracy of 89.6% in predicting the possibility of bad credit, so it can help FinTech companies manage credit risk more effectively.

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

The shift in global paradigm and threat perspective has led to a wide variety of possible risks and uncertainties. This situation also occurs in the defense economy, so understanding the basic principles of risk and uncertainty is important, especially in a decision-making process. There are several elements and concepts that are usually used in all decision models. Almost all models, whether complex or simple, can be formulated using a standard structure and solved by using general evaluation procedures. For decisions involving a series of decisions and relating to various basic sequential conditions, the decision tree is an appropriate conceptual and schematic modeling tool. A decision tree is a schematic representation of a decision problem. A decision tree is a diagram made like a tree with branches and branches in a chronological order of events, with each having a choice and possibility of occurrence, as well as the results of each choice. The term decision tree is taken from the form of diagrams that have branches and twigs, just like a tree.

Infant health can be known one of them through the assessment of nutritional status. In general, Body Mass Index (BMI) has been used as a method for measuring the nutritional status of children. If there are two children who have same body weight and height, they may have different nutritional status. Whenever this occurs, the use of BMI for measuring the nutritional status shall be deemed less accurate. The anthropometry will be vital in measuring the nutritional statuss. The guidelines for determining the nutritional status Anthropometry parameters are selected and recommended which includes an assessment of the age, weight, body length or height. This research aims to build a model of C4.5 adaboost so it can recognize patterns and be able to classify the nutritional status of children into five classes: normal, fat, very fat, thin and very thin. The variables used in this classification is Gender, Age (Months), Weight (kg) Height (cm). C4.5 (decision tree) Method has a good performance in dealing with the classification of nutritional status but the C4.5 has a weakness in the class imbalance. Adaboost isone ofboosting methods that could reduce imbalances class by giving weight to the level of classification error which may alter the distribution of data. Experiments carried out by applying the adaboost method C4.5 to obtain optimal results and a good degree of accuracy. The experimental results obtained from C4.5 method show that accuracy is 89.53%, the error rate is 10.47%, while the results of C4.5 with adaboost show 90.23% accuracy and 9.77% error rate. It can be concluded in the classification of nutritional status of children with C4.5 and adaboost proven method to solve problems of class imbalance and improve the high accuracy and can reduce the level of classification error.