One of the issues of pregnant mothers in Indonesia is their access speed and accuracy services availability towards the prediction of fetus or baby conceived during pregnancy. Thus, the research aimed to obtain the ability to predict three ranges of a fetal target, namely normal, risk, and abnormal condition. This research emphasized the modeling aspect of supervised learning using seven different algorithms to obtain an optimal working score. Those are Decision Tree, Gradient Boosting, Random Forest, SVM, k-NN, AdaBoost, and Stochastic Gradient Descent (SGD). The structure process is mainly divided into two steps, pre-process model and the prediction model. An early data pre-process is needed before executing. Prediction output indicated that dataset test is valid, and can be proven by comparing between the testing data table and prediction and testing table diagram. The resulting model has described the sequence for simulating the training and testing data model to produce the highest working score from the seven selected algorithms. The simulated data based on the model created is proved its validity thru three main filter processes, which are missing data solution, outlier data control, and data normalization. The result obtained a working score that has data proximity with a low score range of 0.063 from model evaluation, confusion matrix, and prediction output.

Software testing is a significant activity of the Software Development Life Cycle (SDLC) that can overcome possibility of error in every single development step. Software testing is most often used technique for verifying and validating the quality of software. Software testing that concern with the internal mechanism of a systems and mainly focus on control flow or data flow a program is called White box testing. White box testing is classified into static and structural testing. Type of white box testing that is used in this paper is structural testing. Path coverage and code complexity is a sub testing within the scope of structural testing. Path coverage split a program into a number of distinct paths. In this paper, structural graph and data flow graph is used to ease the tester to find path in a program by mapping out the program and software testing metrics is used to describe the effectiveness and quality of that processes that produce the software product. This paper did manual testing for genetic algorithm program, such as mapping the genetic algorithm program into the control flow graph, create test cases to provide some possibility inputs that can be handled by program, execute test cases, analyze, and calculate the software manual test process metrics.