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All Journal Jurnal CoreIT jurnal teknik informatika dan sistem informasi
Al Rivan, M. Ezar
STMIK Global Informatika MDP
Computer Science & IT

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Perbandingan Kecepatan Gabungan Algoritma Quick Sort dan Merge Sort dengan Insertion Sort, Bubble Sort dan Selection Sort Al Rivan, Muhammad Ezar
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 2 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i2.675

Abstract

Ordering is one of the process done before doing data processing. The sorting algorithm has its own strengths and weaknesses. By taking strengths of each algorithm then combined can be a better algorithm. Quick Sort and Merge Sort are algorithms that divide the data into parts and each part divide again into sub-section until one element. Usually one element join with others and then sorted by. In this experiment data divide into parts that have size not more than threshold. This part then sorted by Insertion Sort, Bubble Sort and Selection Sort. This replacement process can be reduce time used to divide data into one element. Data size and data type may affect time so this experiment use 5 data sizes and 3 types of data. The algorithm dominates in experiment are Merge-Insertion Sort and Merge-Selection Sort.
Klasifikasi Isyarat Bahasa Indonesia Menggunakan Metode Convolutional Neural Network Al Rivan, Muhammad Ezar; Hartoyo, Suryanto
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 2 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i2.4863

Abstract

Indonesian Sign Language is word signs initially taken from the signs conveyed by deaf children. Sign language is common for the deaf and mute, but it is no stranger to ordinary people. For this reason, alternative intermediaries are needed who can become translators between deaf and speech impaired sufferers and ordinary people. This study aims to classify the Indonesian sign system using the Convolutional Neural Network method with VGG-16 and Alexnet architecture. The data divided by each letter from the letter A to the letter Z is 320 test data, 1600 train data, and 320 validation data, and the data will be resized to a size of 224 x 224 pixels, followed by grayscale and augmentation. The results of the VGG-16 test show that the classification using VGG-16 with the Adam optimizer gets the highest level of accuracy, which is 99.32% for each letter, 91.18% for the whole. While the classification results using VGG-16 with the SGD optimizer get the lowest level of accuracy, which is 98.85% for each letter and 84.96% for the whole. Meanwhile, from the AlexNet test results, it can be seen that the results of the classification using AlexNet with the Adam optimizer get the highest level of accuracy, which is 99.16% for each letter and 89.04% for the whole. While the classification results using AlexNet with the SGD optimizer get the lowest level of accuracy, which is 97.33% for each letter and 68.33% for the whole.
Implementasi Deep Convolutional Generative Adversarial Network untuk Pewarnaan Citra Grayscale Ricky, Muhammad; Al Rivan, Muhammad Ezar
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5218

Abstract

The process of adding color to a grayscale image is needed so that improvements to the image can be done quickly and without special knowledge. Image coloring using Deep Convolutional Generative Adversarial Network (DCGAN) and Generative Adversarial Network (GAN) methods. The model training uses the Places365 dataset, which contains 98,721 training data and 6,600 test data. The image is converted into the CIELAB color space, using the L channel as grayscale input and the AB channel as the other input. The test is done by comparing the accuracy values ​​using the Mean Absolute Error (MAE) and Structural Similarity Index Matrix (SSIM) methods. The calculation results of the MAE method show that the average MAE value of the DCGAN method is smaller than the GAN method, with a score of 10.18 and 10.81. The results of the calculation of the SSIM method show that the DCGAN method has a higher average with a score of 91.54% and 68.32% for the GAN method. The results of the questionnaire conducted on 30 respondents showed that the DCGAN method was chosen by more respondents than the GAN method, respectively 88.40% and 11.60%.
Pengenalan Iris Dengan Normalisasi Menggunakan LBP dan RBF Al Rivan, Muhammad Ezar; Devella, Siska; Saputra, Jordi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 6, No 2 (2020): Desember 2020
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (515.475 KB) | DOI: 10.24014/coreit.v6i2.9685

Abstract

Biometrik merupakan sistem yang menggunakan bagian tubuh manusia untuk dijadikan identitas pribadi seseorang. Iris merupakan salah satu bagian tubuh yang dapat digunakan dalam biometri. Setiap iris memiliki tekstur yang sangat detail dan unik bahkan berbeda antara mata kanan dan kiri. Iris mata juga tidak berubah dan stabil dalam waktu yang lama sehingga dapat digunakan dalam sistem identifikasi. Pada penelitian ini proses yang dilakukan untuk melakukan identifikasi iris mata adalah akuisisi data, preprocessing, ekstraksi ciri dan klasifikasi. Prepocessing yang dilakukan berupa normalisasi iris dengan mengubah bentuk iris. Local Binary Pattern digunakan sebagai ektraksi ciri tekstur iris mata sedangkan untuk mengklasifikasikan ciri dari tekstur iris mata digunakan Jaringan Syaraf Tiruan Radial Basis Function (RBF). Dari hasil pengujian diperoleh hasil akurasi tertinggi sebesar 80% dengan menggunakan spread 225 untuk data training berupa 8 citra iris kiri dan data testing berupa 2 citra iris kiri.
Co-Authors Busdin, Rusdie Devella, Siska Fareza, Ivan Hartoyo, Suryanto Irsyad, Hafiz Kevin kevin Narta, Arta Tri Noviardy, Mochammad Trinanda Ricky, Muhammad Saputra, Jordi