No abstract

No abstract

No abstract

Classification is one method of data analysis in data mining that is used to form a model in order to describe the appropriate data class or model that predicts data trends. The Usage of classification has been applied in various areas, including in health areas. One of the classification methods used is Naive Bayes. This study aims to predict the weight of infants in maternal hypertensive and nonhypertensive conditions with Naive Bayes method. Data were taken as many as 219 data from pregnant women based on the medical record in Obstetrics and Gynecology of Muhammadiyah Palembang Hospital from January 1017 until September 2017. Data is divided into two groups, 188 for training data and 31 data for testing data. The performance data analysis was using WEKA and the results showed that the Naive Bayes’s accuracy is 80.372%. the accuracy score means Naive Bayes works well to predict the weight of infants in maternal hypertensive and nonhypertensive mothers. The result is expected to be a reference for others research by comparing it with other classification methods and incorporating other factors in pregnancy and multiple births or other factors.

Research in the field of Natural Language Processing (NLP) is currently increasing especially with the arrival of a new term that is "big data". The needs of the programming library that ready-touse becomes very important to speed up the phases of research. Some libraries that have already been mature is available but generally for English language and its dependently. So, it can't be used for other languages. Stemming is one of the basic processes that exist in NLP. Indonesian stemming algorithm that often used is ECS (Enhanced Confix-Stripping). One of the libraries that already implemented the algorithm is Sastrawi 1. Results from the experiment show that the time of stemming processing by Sastrawi is still slow. Therefore, this research will optimize the speed of stemming processing using multiprocessing (MP). The data test are used in this research has manually taken from Wikipedia 2. The experiment results show that the MP technique can decrease the average time of stemming processing about 98.45%.

No abstract

No abstract

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels.