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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Information System
Edi Faisal
Dian Nuswantoro University
Computer Science & IT

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Perancangan Sistem Seleksi Lomba Gita Dian Nuswa Menggunakan Metode SAW dan TOPSIS Tan Ivan Agustian; Edi Faisal
JOINS (Journal of Information System) Vol 9 No 2 (2024): Edisi November 2024
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v9i2.11538

Abstract

Paduan Suara Mahasiswa Gita Dian Nuswa Adalah adalah sebuah Unit Kegiatan Mahasiswa (UKM) Universitas Dian Nuswantoro. dalam mengikuti perlombaan, terdapat beberapa hal yang menjadi permasalahan, terutama pada saat menentukan anggota mana saja yang mengikuti tim lomba.Simple Addictive Weighting (SAW) merupakan suatu metode penentuan pilihan optimal dari beberapa pilihan berdasarkan kriteria tertentu. sedangkan TOPSIS merupakan metode pengambilan keputusan multi kriteria yang dapat menentukan solusi terhadap kasus yang ada berdasarkan proses minimalisasi secara simultan dari titik jarak ideal dan proses maksimalisasi jarak dari titik terendah.Penggunaan metode SAW untuk melakukan perhitungan terhadap nilai normalisasi matriks keputusan terbobot. sedangkan metode TOPSIS digunakan untuk meningkatkan akurasi dari perhitungan nilai preferensi. nilai preferensi yang dihasilkan nantinya akan berada pada rentang 0 hingga 100 dan dapat digunakan untuk melakukan penyeleksian terhadap anggota tim lomba.
Brain tumor segmentation using multi-level Otsu thresholding and Chan-Vese active contour model Heru Pramono Hadi; Edi Faisal; Eko Hari Rachmawanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.21679

Abstract

Research on brain tumor segmentation has been developed, ranging from threshold-based methods to the use of the deep learning algorithm. In this study, we proposed a region-based brain tumor segmentation method, namely the active contour model (ACM). Tumor segmentation was carried out using fluid attenuated inversion recovery (FLAIR) modality magnetic resonance imaging (MRI) image data obtained from the multimodal brain tumor image segmentation benchmark (BRATS) 2015 dataset of 86 images. The initial stage of our segmentation method is to find the initial initialization point/area for the ACM algorithm using multi-level Otsu thresholding, with the level used in this study is 3 levels. After the initial initialization area has been obtained, the segmentation process is continued with ACM which explores the tumor area to obtain a full and accurate tumor area result. The results of this study obtained dice similarity (DS) for our study of 0.7856 with a total time required of 28.080722 seconds, which better than other method that we also compared with ours, 0.75 compared to 0.78 in term of DS.
Co-Authors Eko Hari Rachmawanto Heru Pramono Hadi Tan Ivan Agustian