This literature study examines the influence of teacher competence on student learning outcomes at various levels of Senior High Schools (SMA) in Indonesia. To improve student learning outcomes, teachers must be able to develop their competence in every learning process. The teacher competence in question includes pedagogical, professional, social, and personality competence. This study applies the literature review method through analysis of various relevant scientific sources. The results of the study revealed that teacher competence influences the improvement of student learning outcomes, although in some cases there are other variables such as student learning motivation and learning facilities that also contribute. Therefore, improving teacher competence needs to be a concern in efforts to improve the quality of education.

In the online buying and selling process, the seller becomes the name of the store and the buyer does not know the identity of the seller such as name, address, and even age. Buyers will only know from the name of the store selling the item. The concept of online buying and selling is that you only have information about the name and address of the store, but do not know who the owner of the store is. In the buying and selling process, the buyer chooses the product available in the store and if there are any questions about the product, they can ask the seller via chat. The seller then uses a robotic or human system to answer the buyer's questions. The second pillar and condition is ma'qud 'alaih or a medium of exchange and goods sold. The medium of exchange used in the buying and selling process, both online and offline, still uses the same tool, namely money. The purchase or payment process for online buying and selling can be done through bank transfer through the payment code number that has been ordered. The majority of online stores also offer a wider selection of different types of goods.

This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0.
 
Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.