Kotak Belajar Ajaib (Kobela) is props for elementary school math class II which can help learn to calculate multiplication and division. Based on research conducted by Sugeng Harnanto, Kobela can improve concentration, increase creativity and student learning outcomes. This tool has been tested in low-grade learning and extracurricular learning activities. The average student success in learning without using teaching aid is 54.56 (56.77%), after using teaching aid the average learning success rate reaches 90.52 (94.19%). The level of mastery learning for Basic Competencies: 3.1 Doing Multiplication of Two Numbers have increased by 37.42. In previous studies, the application of Kobela teaching aid in all learning activities was still manual-based. Potential or opportunities for development, especially for reading assessments and automatic data storage are possible to be achieved by implementing the Internet of Things (IoT). In this study, Kobela was built which implements IoT technology for reading, assessment, and recording based on learning activities. Then evaluate the system by testing the functionality of all the learning activities. From the test results, it was found that the system was running 100% by the specified function. The results of system performance testing in terms of sensor readings are on average 3 seconds with 8 Watt room lighting conditions and the average value of the assessment accuracy is 84.
Hadoop merupakan sebuah framework software yang bersifat open source dan berbasis java. Hadoop terdiri atas dua komponen utama, yaitu MapReduce dan Hadoop Distributed File System (HDFS). MapReduce terdiri atas Map dan Reduce yang digunakan untuk pemrosesan data, sementara HDFS adalah tempat atau direktori dimana data hadoop dapat disimpan. Dalam menjalankan job yang tidak jarang terdapat keragaman karakteristik eksekusinya, diperlukan job scheduler yang tepat. Terdapat banyak job scheduler yang dapat di pilih supaya sesuai dengan karakteristik job. Fair Scheduler menggunakan salah satu scheduler dimana prisnsipnya memastikan suatu jobs akan mendapatkan resource yang sama dengan jobs yang lain, dengan tujuan meningkatkan performa dari segi Average Completion Time. Hadoop Fair Sojourn Protocol Scheduler adalah sebuah algoritma scheduling dalam Hadoop yang dapat melakukan scheduling berdasarkan ukuran jobs yang diberikan. Penelitian ini bertujuan untuk melihat perbandingan performa kedua scheduler tersebut untuk karakteristik data twitter. Hasil pengujian menunjukan Hadoop Fair Sojourn Protocol Scheduler memiliki performansi lebih baik dibandingkan Fair Scheduler baik dari penanganan average completion time sebesar 9,31% dan job throughput sebesar 23,46%. Kemudian untuk Fair Scheduler unggul dalam parameter task fail rate sebesar 23,98%.
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