Identifikasi relasi objek komposisi dengan metode growing menggunakan standard template library (STL) = Identification object composition relation with growing method using standard template library (STL)

Alexander, Andrew (2018) Identifikasi relasi objek komposisi dengan metode growing menggunakan standard template library (STL) = Identification object composition relation with growing method using standard template library (STL). Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Sekumpulan objek dari domain yang sama dapat memiliki kesamaan berdasarkan fiturnya. Himpunan fitur suatu objek dapat dimiliki objek lain melalui relasi proper superset membentuk relasi objek komposisi. Pembentukan relasi objek komposisi dapat mengelompokan objek berdasarkan fiturnya. Namun relasi antar objek dapat berjumlah lebih dari satu karena memiliki sifat transitive. Relasi tersebut tidak diperlukan untuk mengkoneksikan objek ke dalam suatu kelompok dan dapat dihindari jika pembentukannya dilakukan secara bertahap dengan DFS. Metode growing merupakan metode untuk membentuk relasi antar objek secara bertahap yang disimpan dalam graph. Pembentukan relasi tersebut dimulai dengan mengidentifikasi superset pada dua objek yang berbeda menggunakan algoritma STL. Setelah itu identifikasi dilakukan pada objek yang lain dengan menelusuri relasi dalam graph menggunakan DFS. Hasil metode growing dengan DFS dapat mereduksi objek identik dan relasi yang bersifat transitive menjadi hasse diagram. Selanjutnya hasse diagram dapat membentuk transitive closure dengan algoritma DFS dan Warshall yang dilanjutkan dengan identifikasi komponen dalam graph. Pengujian dilakukan dengan white box testing menggunakan test case dari objek Geometri dan Durga agar program berjalan sesuai dengan yang diharapkan. Selain itu program juga diuji dengan dummy data untuk mengukur waktu eksekusi program seiring bertambahnya objek dan fitur. Pengujian dengan white box testing membuat program dapat digunakan untuk identifikasi relasi objek komposisi tanpa membentuk relasi yang transitive dengan metode growing. Hasil eksekusi program pada dummy data dengan jumlah 2000 objek membutuhkan total waktu 1769 ms, lebih lama dibandingkan dengan 1000 objek yang hanya 442 ms. Sedangkan pembentukan transitive closure dengan DFS dari 1000 objek diselesaikan dalam waktu 67 ms, lebih cepat dibandingkan algoritma Warshall yang membutuhkan 166 ms. / Collection of object from the same domain can have similarities based on its features. Features of an object can belong to another object through a proper superset relation called object composition relation. Object composition relation can group objects based on features. However relationships between objects can be more than one because it has transitive properties. This relation is not required to connect objects into a group and can be avoided if identification is done one by one with DFS. The growing method is a method to construct relationship between objects one by one in graph. This construction begins by identifying superset on two different objects using an algorithm of STL. Identification is continued by accessing another object with DFS to trace the graph that has been made before. The result of growing method with DFS can reduce identical object and transitive relation to minimum spanning subgraph. After that hasse diagram can form transitive closure with DFS and Warshall algorithm followed by component identification in graph. This program is tested with white box testing using test case from object Geometry and Durga to run the program as expected. Besides, the test is also done with dummy data to get time execution for program as the object and its features increase. Testing with white box testing makes program can be used to identify object composition relation without transitive in growing method. Time execution in dummy data with 2000 objects takes 1769 ms, longer than 1000 objects that only 442 ms. Meanwhile the formation of transitive closure with DFS of 1000 objects completed within 67 ms, faster than Warshall algorithm that requires 166 ms.

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