Decision Tree is a classification technique in data mining that aims to predict behaviour from database. This goal is supported by several algorithms, one of which is Iterative Dichotomiser 3 (ID3) that displays predictions in a tree structure. With the application of decision trees, warehouses or heaps of data can be processed so as to produce rules or decision trees as decision support in solving problems faced by agencies. In fact, the information or rules produced by decision trees are limited to rules using the logic of propositions. The challenge in making decisions on decision trees is how to determine algorithms with a high degree of accuracy from various algorithms in the decision tree and how to find support and confidence for each rule produced by the decision tree to add support value and confidence level of each rule produced. The resulting rule has weaknesses, namely the unavailability of support and confidence, all rules are considered equal in strength based on data before being processed, found records that vary or different amounts of data. By making support and confidence, it will be easier to make decisions based on the results obtained.

The quality of education can be achieved by measuring how big the level of success of learning outcomes and achievements obtained by each student. Students who have high achievement are students who have learning motivation and broad knowledge base. By reading, students are expected to have the ability to absorb various knowledge which is mostly conveyed through writing.To get a relationship or correlation between the relationship of one variable with another variable of course required a method in the process of completion. There is a method used by several previous researchers, namely using the association rule method, which is a data mining technique to find associative rules between a combination of items. The algorithm used is a priori which is a step for the process of finding frequent-itemsets by iterating over the data. Where the itemset is the set of items that are in the set processed by the system, while the frequent-itemset shows the itemset that has an occurrence frequency of more than a predetermined minimum value.The objectives of this research are: 1) Build a system that can correlate between learning motivation and interest in reading with student achievement. 2) To find out the minimum support and maximum confidence and the variation between learning motivation and interest in reading and student achievement. 3) To obtain the best rules and produce the latest information.  Keywords: Reading Interest, Correlation, Apriori, Data Mining

Artificial Neural Network (ANN) and time series data can be used for forecasting methods well. Artificial Neural Network is a method whose working principle is adapted from a mathematical model in humans or biological nerves. Neural networks are characterized by; (1) the pattern of connections between neurons (called architecture), (2) determining the weight of the connection (called training or learning), and (3) the activation function. The research objective was to obtain the best artificial neural network architecture, comparing the two methods of Backpropogation Neural Networks with the Radial Base Function Artificial Neural Network (RBF) method. This research is a research using real data (true experimental). This research was conducted at SMK Harapan Bangsa Kuala, which was obtained from 2015 to 2019. The results showed that for one iteration using the backpropagation method the result was 0,378197657 with a squared error 0.143033468, then the results achieved were not in accordance with the target.

Red chili is one type of vegetable that has a high economic value. Chili is usually used in the form of fresh or dried or cooked, for ingredients for kitchen spices, medicinal ingredients, the needs of the food industry, and home industries. . However, it is common for farmers to harvest red chilies that are not yet ripe or even ripe, such as reddish-brown, red-black (rotten). This can lead to a lack of maturity level of red chili and the quality of the red chili. From the input pattern/image of chili as training data and training targets, it is possible to identify the ripeness of chilies using the backpropagation method. Based on the image data of chilies, it can recognize the pattern of chilies that have a maturity level that is in accordance with the digital image by using the backpropagation method and using the MATLAB application, with an accuracy level of training and testing data that is 97,78%.