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All Journal ELKHA : Jurnal Teknik Elektro Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal Komputer Terapan Jusikom : Jurnal Sistem Komputer Musirawas JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Petir Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTIKA - Jurnal Sistem Komputer Jurnal Informatika Global STRING (Satuan Tulisan Riset dan Inovasi Teknologi) jurnal teknik informatika dan sistem informasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference
Muhammad Ezar Al Rivan
Universitas Multi Data Palembang
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : Petir

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Penggunaan Global Contrast Saliency dan Histogram of Oriented Gradient Sebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia Yohannes Yohannes; Muhammad Ezar Al Rivan
PETIR Vol 13 No 1 (2020): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (655.511 KB) | DOI: 10.33322/petir.v13i1.908

Abstract

Mammal type can be classified based on the face. Every mammal’s face has a different shape. Histogram of Oriented Gradient (HOG) used to get shape feature from mammal’s face. Before this step, Global Contrast Saliency used to make images focused on an object. This process conducts to get better shape features. Then, classification using k-Nearest Neighbor (k-NN). Euclidean and cityblock distance with k=3,5,7 and 9 used in this study. The result shows cityblock distance with k=9 better than Euclidean distance for each k. Tiger is superior to others for all distances. Sheep is bad classified.
Perbandingan Hybrid Genetic K-Means++ dan Hybrid Genetic K-Medoid untuk Klasterisasi Dataset EEG Eyestate Muhammad Ezar Al Rivan; Giovani Prakasa Gandi; Fendy Novianto Lukman
PETIR Vol 14 No 1 (2021): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v14i1.953

Abstract

K-Means++ and K-Medoids are data clustering methods. The data cluster speed is determined by the iteration value, the lower the iteration value, the faster the data clustering is done. Data clustering performance can be optimized to get more optimal clustering results. One algorithm that can optimize cluster speed is Genetic Algorithm (GA). The dataset used in the study is a dataset of EEG Eyestate. The optimization results before hybrid GA on K-Means++ are the iteration average values is 11.6 to 5,15, and in K-Medoid are the iteration average values decreased from 5.9 to 5.2. Based on the comparison of GA K-Means++ and GA K-Medoids iterations, it can be concluded that GA - K-Means++ better
Co-Authors Ahmad Farisi Alvin Setiawan Alwyn Giovri Riyadi Andree Fendiawati Angella Octavia Arta Tri Narta Bhagaskara Bhagaskara Billy Gunawan Busdin, Rusdie Devella, Siska Dini Amputri Elisa Putri Suwanto Fareza, Ivan Felix Antony Fendy Novianto Lukman Gabriela Repca Sung Gasim Giovani Prakasa Gandi Hafiz Irsyad Hafiz Irsyad Hafiz Irsyad Irsyad, Hafiz Irvan Wijaya Kevin kevin Molavi Arman Monica Rizki Ayustin Muhammad Fachrurrozi Syawalludin Muhammad Restu Alviando Narta, Arta Tri Ningrum Larasati Noviardy, Mochammad Trinanda Nur Rachmat Orlando Orlando Randy Andreo Sonaru Reynald Dwika Prameswara Rheza Rijaya Ricky Yohannes Risha Ambar Wati Sehat Martinus Surya Benediktus Siti Nadra Steven Steven Suherman, Jessica Tegar Juangkara Tommy Saputra William Tanzil Winston Kennedy Yoannita Yoannita Yohannes Yohannes