ANALISIS ONTOLOGIS PERFORMA METODE EUCLIDEAN, MANHATTAN, DAN MINKOWSKI UNTUK IDENTIFIKASI KEMIRIPAN CITRA

Sofyan Pariyasto

Abstract


Abstract: Information about algorithm performance is one of the reasons for this research. Choosing an algorithm that suits your needs will certainly increase the effectiveness of the computational process. The ontology in this study focuses on finding information about the comparison of the performance of the Euclidean, Manhattan, and Minkowski methods. In the research process, testing was carried out using a dataset consisting of 1,000 images. From the dataset used, there are ten categories, namely African Tribes, Beaches, Buildings, Buses, Dinosaurs, Elephants, Flowers, Horses, Mountains, and Food. Each category consists of 100 images. The study was conducted by comparing the performance of each method to identify image similarities. From the results of the study, information was obtained on the average execution time to perform computations from each method, namely Euclidean 0.55 seconds, Manhattan 0.56 seconds, and Minkowski 0.58 seconds. Then the average memory usage for the computational process of each method, namely Euclidean 2.29 MB, Manhattan 2.28 MB, and Minkowski 2.22 MB. The peak memory usage during the computation process of each method is Euclidean 3.60 MB, Manhattan 3.59 MB, and Minkowski 2.39 MB. Euclidean distance offers the speed of the computation process but requires the highest resources. Minkowski distance offers low resource usage but the computation process becomes slow.

 

Keywords: Euclidean Method, Manhattan, Minkowski, Image Similarity Identification.

 

Abstrak: Informasi mengenai performa algoritma menjadi salah satu alasan dilakukan penelitian ini. Pemilihan algoritma yang sesuai dengan kubutuhan tentu akan meningkatkan efektifitas dalam proses komputasi. Ontologi dalam penelitian ini fokus dalam mencari keberadaan informasi mengenai perbandingan performa metode Metode Euclidean, Manhattan, dan Minkowski. Dalam proses penelitian dilakukan pengujian dengan menggunakan dataset yang terdiri dari 1.000 citra. Dari dataset yang digunakan terdapat sepuluh kategori yaitu Suku Afrika, Pantai, Bangunan, Bus, Dinosaurus, Gajah, Bunga, Kuda, Gunung, dan Makanan. Setiap kategori terdiri dari 100 citra. Penelitian dilakukan dengan membandingkan kinerja masing-masing metode metode untuk melakukan identifikasi kemiripan citra. Dari hasil penelitian didaptkan informasi rata-rata waktu eksekusi untuk melakukan komputasi dari tiap-tiap metode yaitu Euclidean 0.55 detik , Manhattan 0.56 detik, dan Minkowski 0.58 detik. Kemudian rata-rata penggunaan memori untuk proses komputasi dari masing-msing metode yaitu Euclidean 2.29 MB, Manhattan 2.28 MB, dan Minkowski 2.22 MB. Puncak penggunaan memori saat melakukan proses komputasi dari masing-masing metode yaitu Euclidean 3.60 MB, Manhattan 3.59 MB, dan Minkowski 2.39 MB. Eculidean distance menawarkan kecepatan proses komputasi namun membutuhkan resource paling tinggi. minkowski distance menawarkan penggunaan resource yang rendah namun proses komputasi menjadi lambat.

 

Kata kunci: Metode Euclidean, Manhattan, Minkowski, Identifikasi Kemiripan Citra


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DOI: https://doi.org/10.54314/jssr.v8i1.2480

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