  Diabetes, especially T2DM, continues to be a significant health problem. One
of the major concerns associated with diabetes is the development of its
complications. Diabetic nephropathy, one of the chronic complication of
diabetes, adversely affects the kidneys, leading to kidney damage. Diagnosing
diabetic nephropathy involves considering various criteria, one of which is the
presence of a pathologically significant quantity of albumin in urine, known as
albuminuria. Thus, early prediction of albuminuria in diabetic patients holds
the potential for timely preventive measures. This study aimed to develop a
supervised learning model to predict the risk of developing albuminuria in T2DM
patients. The selected supervised learning algorithms included Na\"ive Bayes,
Support Vector Machine (SVM), decision tree, random forest, AdaBoost, XGBoost,
and Multi-Layer Perceptron (MLP). Our private dataset, comprising 184 entries
of diabetes complications risk factors, was used to train the algorithms. It
consisted of 10 attributes as features and 1 attribute as the target
(albuminuria). Upon conducting the experiments, the MLP demonstrated superior
performance compared to the other algorithms. It achieved accuracy and f1-score
values as high as 0.74 and 0.75, respectively, making it suitable for screening
purposes in predicting albuminuria in T2DM. Nonetheless, further studies are
warranted to enhance the model's performance.
