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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

Author-ID : 3646709

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

Published : 39 Documents Claim Missing Document

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Articles

Title

Klasifikasi Mamalia Berdasarkan Bentuk Wajah Dengan k-NN Menggunakan Fitur CAS dan HOG Muhammad Ezar Al Rivan; Yohannes Yohannes
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 5 No 2 (2019): JATISI
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Object classification has been done to various images. Animal classification has been done using segmentation and non-segmentation approach as initial stage. Context Aware Saliency (CAS) is a method that able to make the object area more dominant than the background in saliency mode so that it can be an alternative object segmentation process. The shape feature will taken based on saliency results using the Histogram of Oriented Gradient (HOG). The K-Nearest Neighbors (K-NN) used to classify mammal species based on HOG features from saliency images. The dataset used in this study is LHI-Animal-Faces. The results obtained show that animal species that can be recognized well are cats and tigers, while sheep, dogs, and pigs have not been able to be recognized properly.

Identifikasi Potensi Glaukoma dan Diabetes Retinopati Melalui Citra Fundus Menggunakan Jaringan Syaraf Tiruan Muhammad Ezar Al Rivan; Tegar Juangkara
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 6 No 1 (2019): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Glaucoma and diabetic retinopathy identification conduct form fundus image. Artificial Neural Network can be used as algorithm to identify glaucoma and diabetic retinopathy. Dataset contains 60 fundus image consist of 20 glaucoma fundus image, 20 diabetic retinopathy fundus images and 2- normal fundus image. The result are 86,6% for average recall, 86,6% for average precision and 91,06% for average accuracy.

Implementasi LDA pada fitur HOG untuk Klasifikasi ASL Menggunakan K-NN Muhammad Ezar Al Rivan; Hafiz Irsyad; Kevin Kevin; Arta Tri Narta
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 7 No 2 (2020): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v7i2.286

Sign Language is alternative way to communication using sign. One of sign language is American Sign Language (ASL). Image from Dataset processed using feature extraction HOG then reduce using Linear Discriminant Analysis (LDA). The reduced feature used to K-Nearest Neighbor classification. There are 3 distance used consist of euclidean, manhattan and chebyshev. The best accuracy obtain from manhattan distance using k=3 with 72,42% precision.

Pengenalan ASL Menggunakan Metode Ekstraksi HOG dan Klasifikasi Random Forest Ningrum Larasati; Siska Devella; Muhammad Ezar Al Rivan
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 2 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i2.456

Sign languages have many types, one of them is the American Sign Language (ASL). This study uses the ASL alphabet handshape image extracted with the Histogram of Oriented Gradient (HOG) feature and the resulting feature is used for the Random Forest classification. The test results show that using the HOG feature and the Random Forest classification method for ASL recognition gives a good accuracy rate, with an overall accuracy value of 99.10%, an average accuracy value per class of 77.43%, an average value of precision 88.81%, and an average recall value of 88.65%.

Perbandingan Metode K-Means dan GA K-Means untuk Clustering Dataset Heart Disease Patients Muhammad Ezar Al Rivan; Randy Andreo Sonaru
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.2799

Heart disease is a condition which heart as vital human organ is disordered and doesn’t function properly. Heart disease is one of deadliest diseases in the world and the leading cause of death globally, taking an estimated 17.9 million lives each year. In this study, heart disease patients’ data were clustered to see the characteristic and similarities of each patient. The dataset used in the study is Heart Disease Patients dataset, which consists of 303 patients’ medical data with 11 features. Clustering method used in the paper are K-Means and GA K-Means. Genetic Algorithm is used to optimized the initial centroid for K-Means clustering. The results were evaluated by noting the iteration, inter cluster, and intra cluster of each clustering method. Genetic algorithm is able to optimize the K-Means method which can be seen in iteration average, from 13,4 to 12,5 iteration with the decreasing of the maximum iteration from 21 to 17 iterations. Based on the calculation of inter cluster and intra cluster, the intra cluster results of GA K-Means tend to be better than K-Means and for inter cluster, there is a very little different result, where K-Means method inter cluster average slightly better than GA K-Means.

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

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