Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

5.383

P-Index

This Author published in this journals

All Journal ELKHA : Jurnal Teknik Elektro Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal CoreIT 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 Jurnal Teknologi Terpadu STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Ilmu Komputer dan Informatika Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference Scientica: Jurnal Ilmiah Sains dan Teknologi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) INTECH (Informatika dan Teknologi)

M Ezar Al Rivan

Universitas Multi Data Palembang

Author-ID : 3646709

Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture 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 : 54 Documents Claim Missing Document

Claim Missing Document

Articles

2 3 4 5 6

Klasifikasi Isyarat Bahasa Indonesia Menggunakan Metode Convolutional Neural Network Muhammad Ezar Al Rivan; Suryanto Hartoyo
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

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 Muhammad Ricky; Muhammad Ezar Al Rivan
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

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%.

PELATIHAN PENGGUNAAN WORDPRESS UNTUK MEDIA INFORMASI KPCDI PALEMBANG Al Rivan, Muhammad Ezar; Irsyad, Hafiz; Meiriyama, Meiriyama; Yohannes, Yohannes; Devella, Siska; Wijaya, Novan; Rachmat, Nur
FORDICATE Vol 4 No 2 (2025): April 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i2.11572

Penguasaan teknologi informasi menjadi kebutuhan penting bagi organisasi berbasis komunitas dalam menyebarluaskan informasi secara cepat dan terstruktur. Komunitas Pasien Cuci Darah Indonesia (KPCDI) Palembang membutuhkan sarana digital yang dapat menunjang komunikasi dan edukasi antaranggota. Kegiatan pengabdian ini bertujuan untuk memberikan pelatihan penggunaan WordPress sebagai media informasi komunitas. Pelatihan dilaksanakan di Rumah Sakit RK Charitas Palembang dengan metode ceramah, demonstrasi, dan praktik langsung. Materi pelatihan mencakup pengelolaan konten situs, pengunggahan media, dan pengaturan tampilan dasar website. Peserta dibimbing secara bertahap agar mampu memahami penggunaan platform meskipun berasal dari latar belakang non-teknis. Hasil kegiatan menunjukkan bahwa peserta antusias dan mampu mengikuti alur pelatihan dengan baik. Kegiatan ini diharapkan dapat memperkuat kapasitas digital KPCDI Palembang dalam pengelolaan media informasi secara mandiri dan berkelanjutan

Facial Recognition Software for Employee Presence Using Convolutional Neural Network with InceptionV3 Architecture Nicholas, Nicholas; Al Rivan, Muhammad Ezar
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6769

Presence is a crucial aspect of human resource management that involves recording and monitoring employee attendance. It serves not only for tracking presence but also as the foundation for salary calculation, performance evaluation, and strategic decision-making. While many companies still adopt manual presence systems due to their simplicity, such methods are inefficient, prone to human error, and burdensome in administrative tasks, especially in the presence of growing operational complexity. Moreover, even digital systems like fingerprint scanners are often inflexible, as they require physical presence at designated devices, making them unsuitable for remote or mobile employees. This research developed an Android-based presence application utilizing facial recognition technology with the Convolutional Neural Network method using the InceptionV3 architecture. The system is designed to enable automatic, flexible, and accurate attendance recording both inside and outside the workplace. A website-based system has also been developed for centralized attendance data management. Implementation results show that the Android-based application successfully enables employees to perform attendance both inside and outside the office using facial recognition technology, eliminating the need for manual documentation. Additionally, the web-based system can automatically record and summarize attendance data, simplifying recapitulation processes and reducing administrative workload. The facial recognition model, trained using gradual transfer learning, achieved an accuracy of 97.86% and F1-Score of 97.55%. This application has significant potential to improve the efficiency and flexibility of corporate attendance systems.

CLASSIFICATION OF AMERICAN SIGN LANGUAGE USING SCALE INVARIANT FEATURE TRANSFORM FEATURES AND ARTIFICIAL NEURAL NETWORKS Alviando, Muhammad Restu; Al Rivan, Muhammad Ezar; Yoannita, Yoannita
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

American Sign Language (ASL) is a sign language in the world. This study uses the neural network method as a classification and the scale invariant feature transform (SIFT) as feature extraction. Training data and test data for ASL images were extracted using the SIFT feature, then ANN training was conducted using 17 training functions with 2 hidden layers. There are architecture used [250-5-10-24], [250-5-15-24] and [250-15-15-24] so there are 3 different ANN architectures. Each architecture is performed 3 times so that there are 9 experiments (3 x 3 trials run the program). Determination of the number of neurons concluded by the training function is selected by the best test results on the test data. Based on the training function and the extraction of SIFT features as input values in the neural network it can be concluded that from 17 training functions, trainb with neuron architecture [250-5-10-24] becomes the best training function producing an accuracy value of 95%, precision of 15 % and recall 5%.

KLASIFIKASI PNEUMONIA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Wati, Risha Ambar; Irsyad, Hafiz; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Pneumonia is a type of lung disease caused by bacteria, viruses, fungi, or parasites. One way to find out pneumonia is by x-ray. X-rays will be analyzed to determine whether there is pneumonia or not. This study aims to classify the x-ray results whether there is pneumonia or not on the x-ray results. The classification method used in this study were Support Vector Machine (SVM) and Gray Level Co-Occurrence (GLCM) for the extraction method. There are several stages before classification, namely cropping, resizing, contrast stretching, and thresholding then extracted using GLCM and classified using SVM. The results showed that the best accuracy of 62.66%.

KNN Dan Gabor Filter Serta Wiener Filter Untuk Mendiagnosis Penyakit Pneumonia Citra X-RAY Pada Paru-Paru Antony, Felix; Irsyad, Hafiz; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 2 (2021): April 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Pneumonia adalah salah satu jenis penyakit paru-paru yang disebabkan oleh bakteri, virus, jamur, ataupun parasit. Salah satu cara untuk mengetahui penyakit pneumonia adalah dengan rontgen atau x-ray. Hasil rontgen akan dianalisis untuk mengetahui apakah terdapat pneumonia atau tidak. Penelitian ini bertujuan untuk mengklasifikasi hasil rontgen apakah terdapat pneumonia atau tidak pada hasil rontgen. metode yang digunakan untuk klasifikasi adalah K-Nearest Neighbor (KNN) dan metode ekstraksi Gabor Filter serta Wiener Filter. Tahapan yang dilaukan pada citra sebelum di Klasifikasi yaitu Resize, selanjutnya dilakukan ekstraksi menggunakan Gabor Filter, Image Enhancement menggunakan Wiener Filter dan di klasifikasi menggunakan K-Nearest Neighbor (KNN) menghasilkan akurasi terbaik sebesar 79,62%.

Identifikasi Kerusakan Daun Tanaman Apel Menggunakan Fitur GLCM Dan JST Suwanto, Elisa Putri; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 1 (2021): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Identifikasi kerusakan daun tanaman apel berbintik hitam, kering dan sehat. Fitur yangdiperoleh dengan menggunakan alogitrma Gray Level Co-occurrence Matrix(GLCM).Algoritma yang digunakan untuk melakukan identifikasi yaitu Jaringan syaraf Tiruan(JST).Penelitian ini dilakukan dengan 3 jumlah neuron yang berbeda pada hidden layer. Selain itu,training function yang digunakan ada 17 jenis. Setiap skenario eksperimen diulang sebanyak 5kali percobaan run program. Berdasarkan skenario eksperimen yang telah dilakukan hasilterbaik terdapat pada 30 neuron hidden layeryaitu untuk akurasi pada training function trainrpsebesar 77,43%, untuk presisi pada training function traingda sebesar 69,35% dan untuk recallpada training function trainrp sebesar 70,06%.

Klasifikasi Jenis Kanker Kulit Menggunakan CNN-SVM Yohannes, Ricky; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 2 (2022): April 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Kanker kulit merupakan pertumbuhan yang berlebihan pada jaringan kulit yang mengenai beberapa atau seluruh lapisan kulit. Untuk dapat mendiagnosa kanker kulit dapat digunakan metode biopsi dimana jaringan kulit diambil lalu diperiksa. Penggunaan biopsi mengeluarkan biaya yang mahal dan merusak kulit. Penelitian ini menerapkan metode CNN SVM untuk mengklasifikasi jenis-jenis kanker kulit. CNN yang sebagai ekstraksi fitur dengan arsitektur VGG-19 dan ResNet-50. SVM digunakan sebagai pengklasifikasi dengan menggunakan kernel linear dan RBF kemudian dioptimasi menggunakan random dan grid. Dataset terdapat 300 citra per jenis lalu dibagi menjadi 240 data latih, 60 data uji, dan dengan jumlah total 1500 citra. Penelitian ini melakukan 2 skenario pada citra yaitu menggunakan preprocessing resize dan preprocessing patch lalu diterapkan pada model, sehingga terdapat 16 skenario total. Hasil terbaik penelitian ini didapatkan pada preprocessing patch arsitektur VGG-19 menggunakan kernel linear optimasi random dan grid dengan nilai accuracy sebesar 65,33%, nilai recall sebesar 65,33%, nilai precision sebesar 68,51%, dan nilai f1-score sebesar 65,77%.

Perbandingan Penempatan Pivot Pada Quick Sort Berdasarkan Ukuran Pemusatan Data Rijaya, Rheza; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.5735

Sorting is one of the basic algorithm that executed frequently in a program. The most popular sorting algorithm is Quick Sort because it is faster in most scenarios than other algorithms. However, pivot selection on Quick Sort algorithm is very important to avoid the worst case scenario. This study aims to test commonly used pivot selection methods (first, middle, last) and pivot selection based on central tendency of data (mean, median, mode). The data that is tested are random data (repeated), random data (permutation), sorted, reverse-sorted, and almost sorted. The size of data that is tested are 1.000, 10.000, 100.000, dan 1.000.000. The best result is achieved by selecting middle element as pivot based on the execution time of each scenario.

Co-Authors Alviando, Muhammad Restu Alvin Setiawan Alwyn Giovri Riyadi Andreas Saputra, Andreas Andree Fendiawati Antony, Felix Arta Tri Narta Arta Tri Narta Awalludin, Nur Bhagaskara Bhagaskara Billy Gunawan Budi, Raden George Samuel Busdin, Rusdie Caroline, Fionna Daniel Daniel Devella, Siska Dicko Dini Amputri Fareza, Ivan Farisi, Ahmad Fendy Novianto Lukman Fernando Fernando, Fernando Gabriela Repca Sung Gasim Giovani Prakasa Gandi Hafiz Irsyad Hafiz Irsyad Hartoyo, Suryanto Ivan Fareza Juliana Nasution Kevin kevin Kevin Kevin Meiriyama, Meiriyama Mochammad Trinanda Noviardy Molavi Arman Monica Rizki Ayustin Muhammad Ricky Nadimsyah, Ahmad Zaky Narta, Arta Tri Nicholas, Nicholas Ningrum Larasati Novan Wijaya Noviardy, Mochammad Trinanda Nur Rachmat Octavia, Angella Orlando Orlando Pratama, M Fikri Rizki Randy Andreo Sonaru Reynald Dwika Prameswara Ricky, Muhammad Rijaya, Rheza RR. Ella Evrita Hestiandari Rusdie Busdin Saputra, Jordi Sehat Martinus Surya Benediktus Siti Nadra Steven Steven Suherman, Jessica Suryanto Hartoyo Suwanto, Elisa Putri Tanzil, William Tegar Juangkara Tommy Saputra Wati, Risha Ambar Wijaya, Irvan Winston Kennedy Wiwik Handayani Yoannita, Yoannita Yohannes Yohannes Yohannes, Ricky

Title

Found 54 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 54 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 54 Documents
Search