PENGEMBANGAN GAME UNTUK TERAPI MEMBACA BAGI ANAK DISLEKSIA DAN DISKALKULIA

Kesulitan belajar spesifik adalah suatu keadaan pada seorang anak yang mengalami ketidakmampuan dalam belajar, keadaan ini disebabkan gangguan proses belajar di dalam otak, yang dapat berupa gangguan persepsi (visual atau auditoris), gangguan dalam proses integratif atau gangguan ekspresif. Disleksia merupakan gangguan dalam kesulitan membaca dan Diskalkulia merupakan gangguan dalam kesulitan berhitung. Masalah muncul karena kurangnya perhatian terhadap anak yang mengalami kedua gangguan tersebut. Serta kurangnya pemahaman akan kedua gangguan tersebut terhadap anak dan belum adanya media khusus yang dapat dijadikan sebagai metode terapi terhadap kedua gangguan tersebut.Dari permasalahan diatas dibuat media pembelajaran sebagai terapi untuk anak yang mengalami gangguan dalam kesulitan membaca dan kesulitan berhitung. Media belajar yang cocok untuk menarik anak disleksia dan diskalkulia  adalah dalam bentuk aplikasi game.  Konsep permainan yang dikembangkan  adalah anak diajak untuk mengenal huruf dan angka serta dapat membedakannya juga. Kemudian anak diajak untuk menghafalkan huruf dan angka tertentu yang menurut mereka sulit untuk dihafalkan. Dan yang terakhir anak diajak untuk menjawab pertanyaan dan menghitung dengan soal matematika dasar.Pengembangan aplikasi game ini menggunakan metodologi analisa terhadap anak disleksia dan diskalkulia. Kemudian mencari konsep game yang cocok bagi anak disleksia dan diskalkulia. Konsep tersebut digunakan untuk menentukan dalam pembuatan desain game dan background untuk diimplementasikan di unity3d. Terakhir adalah building game pada perangkat dan testing untuk kelayakan game. Aplikasi game yang telah berhasil dibuat diberi nama game “Two Dis”. Hasil yang diharapkan dari Pembuatan game “Two Dis” berbasis android ini adalah dapat menjadi salah satu media terapi dan media pembelajaran baru bagi anak disleksia dan diskalkulia dengan metode bermain game

RESTORE: Automated Regression Testing for Datasets

In data mining, the data in various business cases (e.g., sales, marketing, and demography) gets refreshed periodically. During the refresh, the old dataset is replaced by a new one. Confirming the quality of the new dataset can be challenging because changes are inevitable. How do analysts distinguish reasonable real-world changes vs. errors related to data capture or data transformation? While some of the errors are easy to spot, the others may be more subtle. In order to detect such types of errors, an analyst will typically have to examine the data manually and assess if the data produced are "believable". Due to the scale of data, such examination is tedious and laborious. Thus, to save the analyst's time, it is important to detect these errors automatically. However, both the literature and the industry are still lacking methods to assess the difference between old and new versions of a dataset during the refresh process. In this paper, we present a comprehensive set of tests for the detection of abnormalities in a refreshed dataset, based on the information obtained from a previous vintage of the dataset. We implement these tests in automated test harness made available as an open-source package, called RESTORE, for R language. The harness accepts flat or hierarchical numeric datasets. We also present a validation case study, where we apply our test harness to hierarchical demographic datasets. The results of the study and feedback from data scientists using the package suggest that RESTORE enables fast and efficient detection of errors in the data as well as decreases the cost of testing.Comment: 10 pages, 2 figure

Test Case Selection for Black-Box Regression Testing of Database Applications

Context: This paper presents an approach for selecting regression test cases in the context of large-scale, database applications. We focus on a black-box (specification-based) approach, relying on classification tree models to model the input domain of the system under test (SUT), in order to obtain a more practical and scalable solution. We perform an industrial case study where the SUT is a large database application in Norway’s tax department. Objective: We investigate the use of similarity-based test case selection for supporting black box regression testing of database applications. We have developed a practical approach and tool (DART) for functional black-box regression testing of database applications. In order to make the regression test approach scalable for large database applications, we needed a test case selection strategy that reduces the test execution costs and analysis eort. We used classification tree models to partition the input domain of the SUT in order to then select test cases. Rather than selecting test cases at random from each partition, we incorporated a similarity-based test case selection, hypothesizing that it would yield a higher fault detection rate. Method: An experiment was conducted to determine which similarity-based selection algorithm was the most suitable in selecting test cases in large regression test suites, and whether similarity-based selection was a worthwhile and practical alternative to simpler solutions. Results: The results show that combining similarity measurement with partition-based test case selection, by using similarity-based test case selection within each partition, can provide improved fault detection rates over simpler solutions when specific conditions are met regarding the partitions. Conclusions: Under the conditions present in the experiment the improvements were marginal. However, a detailed analysis concludes that the similarity-based selection strategy should be applied when a large number of test cases are contained in each partition and there is significant variability within partitions. If these conditions are not present, incorporating similarity measures is not worthwhile, since the gain is negligible over a random selection within each partition