ABSTRAK Cloud Computing atau komputasi awan adalah gabungan pemanfaatan teknologi komputer ('komputasi') dan pengembangan berbasis Internet ('awan'). Awan (cloud) adalah metafora dari internet, sebagaimana awan yang sering digambarkan pada diagram jaringan computer. Selain seperti awan dalam diagram jaringan komputer, awan (cloud) dalam  cloud computing juga merupakan abstraksi dari infrastuktur kompleks yang disembunyikannya. Private Cloud Storage dengan kolaborasi komponen Service Oriented Architecture mampu menyediakan antarmuka yang efisien untuk proses berjalannya lembaga dan berfungsi sebagai Virtualization Server dan Online Storage yang dapat diakses menggunakan Fixed Device dan Mobile Device seperti Smartphone Android, Komputer Pad, dan PC Tablet melalui koneksi jaringan lokal dan internet.Sebagai fungsi manfaat dari cloud sendiri bisa di manfaatkan kedalam kebutuhan E-Learning, mengingat dari segi kemudahan, kebutuhan, dan keamanan, cloud computing terbilang metode yang pas dalam menerapkan kebutuhan tersebut. Dimana setiap data berhak di simpan secara bebas, luas dan aman tentunya.   ABSTRACT Cloud Computing is a combination of the use of computer technology and cloud of Internet-based development. Clouds are metaphors of the Internet, as are clouds that are often depicted in computer network diagrams. In addition to clouds in computer network diagrams, clouds in computing technology are also an abstraction of the complex infrastructure it conceals. Private Cloud Storage with Service Oriented Architecture component collaboration is able to provide an efficient interface for the running process of the institution and serves as Virtualization Server and Online Storage that can be accessed using Fixed Devices and Mobile Devices such as Android Smartphone, Pad Computer and Tablet PC via local network connection and Internet.As function of the benefits of the cloud itself can be utilized into the needs of E-Learning, given the ease, needs, and security, cloud computing spelled out the right method in applying these needs. Where every data is entitled to save freely, broadly and safely of course.How to Cite : Santiko, I. Rosidi, R., Wibawa. S. A (2017). PEMANFAATAN PRIVATE CLOUD STORAGE SEBAGAI MEDIA PENYIMPANAN DATA E-LEARNING PADA  LEMBAGA PENDIDIKAN. Jurnal Teknik Informatika, 10(2), 137-146. doi:10.15408/jti.v10i2.6992Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i2.6992

No abstract

Based on Indonesia's health profile in 2008, Diabetes Mellitus is the cause of the ranking of six for all ages in Indonesia with the proportion of deaths of 5.7% under stroke, TB, hypertension, injury and perinatal. This is reinforced by WHO (2003), Diabetes Mellitus disease reached 194 million people or 5.1 percent of the world's adult population and in 2025 is expected to increase to 333 million inhabitants. In particular, in Indonesia, people with Diabetes Mellitus are increasing. In 2000, Diabetes Mellitus sufferers have reached 8.4 million people and it is estimated that the prevalence of Diabetes Mellitus in 2030 in Indonesia reaches 21.3 million people.This allows researchers and practitioners to focus their attention on detecting/diagnosing diabetes mellitus and to prevent it because the disease can cause complications. The method used in this research was problem identification, data collection, pre-processing stage, classification method, validation and evaluation and conclusion. The algorithm used in this research was CART and Naïve Bayes using dataset taken from UCI Indian Pima database repository consisting of clinical data ofpatients who detected positive and negative diabetes mellitus. Validation and evaluation method used was 10-crossvalidation and confusion Matrix for the assessment of precision, recall and F-Measure. The result of calculation has been done, got the accuracy result on CART algorithm equaled to 76.9337% with precision 0.764%, recall 0.769%, and F-Measure 0.765%. Whilethe diabetes dataset was tested with the Naïve Bayes algorithm, got an accuracy of 73.7569% with precision 0.732%, recall 0.738%, and F-Measure 0.734%. From these results it can be concluded that to diagnose diabetes mellitus disease it is suggested to use CART algorithm.

Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.

No abstract