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Diabetes is a serious chronic disease that occurs because the pancreas does not produce enough insulin (a hormone that regulates blood sugar or glucose), or when the body cannot effectively use the insulin it produces. WHO data shows that the incidence of non-communicable diseases in 2004 reached 48 , 30% is slightly higher than the incidence rate of infectious diseases, namely 47.50% [1]. According to the Ministry of Health in 2012 diabetes caused 1.5 million deaths. Some Indonesian people, this disease is better known as diabetes or blood sugar. This research was developed through secondary data processing from the Pima Indians Diabetes Dataset health database which was taken from the Kaggle dataset and can be accessed through https://www.kaggle.com/uciml/pima-indians-diabetes-database. Where the data itself consists of 768 records with several medical predictor variables (Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age and Outcome). Then the data will be processed using the Particle Swarm Optimization (PSO) feature selection to increase the accuracy value and the Naive Bayes algorithm to determine the accuracy results of the diagnosis of diabetes. From the results of research that has been done for the accuracy of the classification algorithm Naive Bayes is 74.61%, while the accuracy of the classification algorithm with Particle Swarm Optimization is 77.34% with an accuracy difference of 2.73%. So it can be concluded that the application of the Particle Swarm Optimization technique is able to select attributes in the Naive Bayes Algorithm, and can produce a better level of diabetes diagnosis accuracy than using only the individual method, namely the Naive Bayes algorithm. Keywords: Diabetes, Particle Swarm Optimization, Naive Bayes Algorithm

A house for every human being is the main and most important need compared to others needs in general. A financial institution is an institution engaged in the financial sector where its customers are people from various walks of life with various behaviors. Lending is a business activity that carries a high risk and affects the business continuity of a banking company. The problem that is often faced in providing home loans is determining the decision to extend credit to prospective customers, while another problem is that not all home loan payments by customers can run well or commonly known as bad credit. One of the causes of bad credit is an assessment error in making credit decisions. Data mining is a process used to analyze cases in order to find the best performance of an algorithm being tested. One way to get information or patterns from a large data set is to use techniques in data mining. There are many classification methods that can be used to produce precise accuracy values. In this study, two classification algotihm methods are used in classifying the home crediting dataset, namely the C4.5 decision tree algorithm and the Naïve Bayes algorithm. The comparison of the two algorithms produces an accuracy value fo the Naïve Bayes algorithm of 36.36% and the Decision Tree C4.5 algorithm has an accuracy rate of 59.54%.

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

Classification is one of the most widely used techniques in machine learning. Text classification is the process of classifying data according to pre-determined groups or classes. Where in most cases, text classification uses labeled training data to obtain the rules used to classify test data into predefined groups. In this study, it is proposed to use CountVectorizer for Indonesian text classification which will be compared with TF-IDF Term Weighting and its three feature levels, namely Character Level, Word Level and N-gram Level as feature extraction which is implemented together with Naive Bayes classification and the BPPPTIndToEngCorpusHalfM dataset. To compare the classification performance, this study uses 10-Fold Cross Validation and Split Data using a ratio of 90:10, while to evaluate the accuracy of the authors using the F1-Score and AUC with the hope that this study will get good accuracy results so that it can be used as a reference to be developed using another method. The F1-Score accuracy obtained in this study was 0.93 and the AUC score was 0.95.

Chronic kidney failure is the failure of kidney function in maintaining metabolism and fluid and electrolyte balance in the body. Chronic kidney disease initially does not show significant symptoms and signs but can develop rapidly into kidney failure. Kidney disease can be prevented and treated if known earlier. One way to find out chronic kidney failure is to detect using data mining. Iterative Dichotomiser 3 (ID3) algorithm is one of the classification methods and is a type of method that can map or separate two or more different classes. Based on the measurement of performance classification of 80% of training data from 400 data used, it shows that the accuracy value reached 96.25%. It can be concluded that the ID3 Algorithm method is feasible to be used in research predictions for chronic kidney failure.

Infant health can be known one of them through the assessment of nutritional status. In general, Body Mass Index (BMI) has been used as a method for measuring the nutritional status of children. If there are two children who have same body weight and height, they may have different nutritional status. Whenever this occurs, the use of BMI for measuring the nutritional status shall be deemed less accurate. The anthropometry will be vital in measuring the nutritional statuss. The guidelines for determining the nutritional status Anthropometry parameters are selected and recommended which includes an assessment of the age, weight, body length or height. This research aims to build a model of C4.5 adaboost so it can recognize patterns and be able to classify the nutritional status of children into five classes: normal, fat, very fat, thin and very thin. The variables used in this classification is Gender, Age (Months), Weight (kg) Height (cm). C4.5 (decision tree) Method has a good performance in dealing with the classification of nutritional status but the C4.5 has a weakness in the class imbalance. Adaboost isone ofboosting methods that could reduce imbalances class by giving weight to the level of classification error which may alter the distribution of data. Experiments carried out by applying the adaboost method C4.5 to obtain optimal results and a good degree of accuracy. The experimental results obtained from C4.5 method show that accuracy is 89.53%, the error rate is 10.47%, while the results of C4.5 with adaboost show 90.23% accuracy and 9.77% error rate. It can be concluded in the classification of nutritional status of children with C4.5 and adaboost proven method to solve problems of class imbalance and improve the high accuracy and can reduce the level of classification error.

Heart failure has an impact not only on one's physical condition but also on the psychological impact of anxiety. Patients with heart disease who experience psychosocial problems, especially anxiety, will have a slower recovery process and more severe physical symptoms they experience. Thus, an intervention is needed to provide back massage to overcome anxiety. The purpose of this study was to describe the application of back massage towards anxiety scores in heart failure patients.This study belongs to descriptive research using the case study approach method. The sample in this study were 2 subjects who suffered classification II and III heart failure according to NYHA who experienced low to moderate anxiety. Both subjects were given back massage therapy. Hamilton Anxiety Rating Scale (HARS) was used as the instrument to measure the level of anxiety. The results showed that there was a significant decrease in the two subjects who initially fell into the anxiety category into the category of low anxiety after being given back massage therapy for 15 minutes at 24-hour intervals for 3 days. In conclusion, back massage is recommended to be applied in the hospital as a nursing intervention to overcome anxiety in heart failure patients.

One institution that requires an accounting information system and budget plan is an educational institution. Education funding includes all expenditures that have been budgeted through the School Income and Expense Plan (RAPBS) or School Budget Activity Plan (RKAS) to support the process of providing education in the form of services or goods. Madrasah Aliyah Sabilul Ulum has a duty to manage finances based on financial management principles so that the money in circulation can be utilized optimally. Break even point is the point where total income equals total cost or the point where profit equals zero. The computer-based accounting information system that is designed includes account data recording, student data recording, accounting transaction records, cost classifications, BEP calculations, income budgets, account data reports, general journals, financial accountability reports, expense classification reports, BEP reports, income budget reports and school budget plans.