The baby’s condition is a condition that is vulnerable to environmental changes, especially weather changes. Knowledge of a mother in maintaining the health of baby also should be considered, especially in terms of nutritional intake. A healthy baby's condition affects the baby's growth and development. The development of a decision support system should be preceded by collecting and analyzing the data according to need. In this study, the variables were baby feeding items, namely Body Temperature (37.70c), Fuss (2.4), Restless (4.5), frequent bowel movements (3.7), watery bowel movements  (5.6), Bloating (3.5), Nausea (3.7), vomiting (3.2) , Stomachache (2.7) and Itchy Skin (2.8). The results of the calculations will result in defoliation as follows: Measles (1:48), septic (1:48), diarrhea (1:48), ISPA (7:36), enteritis (0.77), Miliary (1:48), OMP (1:48) and varicella (1:48). The range of fuzzy values ranges from 0 to 1, indicating the baby has enteritis or stomach problems. The calculation of defuzification obtained result of 8.1, so the condition of the baby is very sick and should be handled immediately by bringing to the medical personnel.

Sweet orange is very much consumed by humans because oranges are rich in vitamin C, sweet oranges can be consumed directly to drink. The classification carried out to determine proper (good) and unfit (rotten) oranges still uses manual methods, This classification has several weaknesses, namely the existence of human visual limitations, is influenced by the psychological condition of the observations and takes a long time. One of the classification methods for sweet orange fruit with a computerized system the Convolutional Neural Network (CNN) is algorithm deep learning to the development of the Multilayer Perceptron (MLP) with 100 datasets of sweet orange images, the classification accuracy rate was 97.5184%. the classification was carried out, the result was 67.8221%. Testing of 10 citrus fruit images divided into 5 good citrus images and 5 rotten citrus images at 96% for training 92% for testing which were considered to have been able to classify the appropriateness of sweet orange fruit very well. The graph of the results of the accuracy testing is 0.92 or 92%. This result is quite good, for the RGB histogram display the orange image is good

Riau province has Malay Arabic script as a traditional cultural heritage of ancient characters that should be preserved; this script is adapted from Arabic writing. This script from Malay Arabic has a unique form that is different from the original Arabic writing adaptation, which is read in a combination of letters forming latin meanings as an introduction to the everyday language of Riau Malay people in the earlier kingdom. Malay Arabic writing became an introduction to the local content of traditional languages in schools. To foster a love for preserving culture, in accordance with current technology that is able to recognize scripting patterns when written in paper, a knowledge base was created by using Matlab software by applying a convolutional Neural Network (CNN) artificial neural network algorithm capable of recognizing script patterns well. The result of image input in the form of handwriting written on paper then in the scanner in the form of JPEG image format. Testing was carried out on four Arabic Malay characters namely alif, ha, la, kho and nun. The result of training for the letter alif (a) epoch is obtained 98 out of 100 iterations with a training length of 3 seconds, furthermore, in validation performance with a result of 0.25013 on epoch 92 of 98 epoch for gradient letters with a value of 0.0071991 on the next epoch 98 in the extras produces an accuracy value of 0.6548 which states the correct result accordingness because it is close to the alif script. In the process of train input the letter kho obtained epoch 80 out of 100 iterations with a training process for 3 seconds, validation performance 0.25153 on epoch 74 out of 80 epoch for check validation with a value of 0.0011682 on the next epoch 80 in the extras obtained an extra value of 0.9326 stated the value is incorrect. Because the result of the extras results in an image that does not come close to the kho letter. Therefore, a study of how the system can recognize Malay Arabic writing patterns with the Convolutional Neural Network (CNN) method because it is very good at identifying image pattern features with an accuracy value of 4.12% of the 10 sample image patterns that have been inputted. With the introduction of imagery patterns from the extraction of features scanned Malay Arabic characters can help the findings of ancient Malay Arabic script as morphological learning of the validity of abstraction of Malay Arabic script is good

Pineapple fruit is included in the type of tropical fruit, which is quite popular because it contains a lot of Vitamin C, which is quite high. Pineapple is a local fruit in the Kampar area, this fruit can be consumed directly and become other local processed products. Therefore, the quality of pineapple ripeness must be maintained. The problem that occurs at this time is that the pineapple fruit selection process is still done manually, by looking at it visually, so mistakes can occur in the process of clarifying pineapple fruit identification according to standards. Therefore, it is necessary to research the ripeness of pineapples using the Color Space Algorithm Hue Saturation Intensity (HIS). The variables to be input are based on photos of ripe, half ripe, and raw pineapples using a smartphone camera or DSLR camera with a minimum resolution of 8 MP. Clarifying the results with image processing and Hue Saturation Intensity (HIS) transformation has an accuracy rate of 80% for the 20 image test data. So that the expected results can help pineapple farmers in detecting the level of maturity of pineapple fruit, which is difficult, can minimize errors in determining the ripeness of pineapple fruit

This research aims to determine the effect of the blended learning model on self-regulated learning of mathematics student. The design of this study is a quasi-experimental. The study population was all eighth-grade students at SMPN 1 Way Tenong in West Lampung which consisted of 7 classes. The experimental class was chosen randomly because the school has available internet networks and devices that can access the internet and students have the ability to access the internet. The instrument used was the self-regulated learning of mathematics questionnaire. Questionnaire of self-regulated learning is arranged on a Likert scale with categories always, often, sometimes, rarely, and never. The measured aspects of self-regulated learning are self-initiative, setting learning goals and strategies, as well as evaluating or self-reflection. The data analysis technique uses inferential analysis. Pretest and posttest data have been calculated and declared to be normally distributed. To find out the increasing selfregulated learning mathematics used the gain test. The results of the t-test between the pre-test and posttest obtained the t-test value of 10.744 and significant 0.000