Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

Era big data menjadi sebuah fenomena yang menarik untuk di bahas oleh kalangan peneliti dan pengembang perangkat lunak, pengembangan aplikasi dan konsep pengelolaan data semakin banyak varian dan dukungan menjadikan kerangka big data dapat masuk kesetiap lini kehidupan, data yang tersusun baik secara singkronus maupun asingkronus, melibatkan mesin dan manusia dalam pengumpulan data menjadikan teknologi ini semakin sejalan dengan konsep Revolusi Industri 4.0 Dalam berbagai kajian di sajikan konsep dan kerangka kerja Big Data, dari kajian tersebut beberapa peneliti menyajikan lapisan dalam arsitektur Big Data, di mana masing masing lapisan memberi input bagi lapisan lain untuk dapat di olah menjadi bentuk yang siap saji di masyarakat, lapisan yang tediri dari pengumpulan data, penyimpanan data, pemrosesan data serta Analisa data, sehingga pada lapisan aplikasi penggunaan data dapat lebih maksimal di rasakan oleh pengguna. Dalam makalah ini di sajikan beberapa bahan studi literature yang di rangkum untuk mendapatkan penjelasan mengenai lapisan arsitektur Big Data yang dapat di kembagkan dan di terapkan pada bidang bidang penelitian lain

CLASSIFICATION ACADEMIC DATA USING MACHINE LEARNING FOR DECISION MAKING PROCESS

One of the qualities of higher education is determined by the success rate of student learning. Assessment of student success rates is based on students' graduation on time. The university always evaluates the performance of its students to find out information related to the factors that cause students to become inactive so that they are more likely to drop out and what data affects students ability to graduate on time. The evaluation results are stored in an academic database so that the data can later be used as supporting data when the university makes decisions. The data was processed using the Decision Tree C4.5 method so as to produce a model in the form of a tree and rules. The data used in this study is the graduation data of Informatics Engineering students from 2011 to 2015, totaling 632 data records. Variables used are Nim, in-progress grades each semester, credit taken every semester, GPA, and graduation status. Tests were conducted using split data scenarios with comparison of training data: 90:10, 80:20, 70:30, 60:40, and 50:50. Based on the test results, it is known that the attribute that influences the success of student studies is the grade point average (GPA), where the accuracy of the maximum recognition rate is 88.19% is in the comparison of training data and test data (80%: 20%)