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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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Identifikasi Mutu Buah Pepaya California (Carica Papaya L.) Menggunakan Metode Jaringan Syaraf Tiruan Muhammad Ezar Al Rivan; Gabriela Repca Sung
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 10, No 1 (2021): MARCH
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v10i1.1105

Papaya is one of the fruits that grows in the tropics area, one of the kinds that people’s love the most is papaya California. The quality identification of papaya California fruit can be measured using color, defect, and size. Color, defect and size extracted from image of papaya. The dataset that used in this research are 150 images papaya California. The dataset consist of 3 quality there are good, fair and low. Identification of papaya using the backpropagation neural network method with 17 training function in each training data with 3 different neurons in the hidden layer. The best result of the test is using training function trainrp with 10 neurons is 81,33% for accuracy, 73,37% for precision, and 72% for recall, with 20 neurons is 82,67% for accuracy, 75,24% for precision, and 74% for recall, and with 25 neurons is 80,89% for accuracy, 74,42% for precision, and 71,33% for recall.

Klasifikasi Hewan Mamalia Berdasarkan Bentuk Wajah Menggunakan Fitur Histogram of Oriented dan Metode Support Vector Machine Muhammad Ezar Al Rivan; Molavi Arman; Hafiz Irsyad; Reynald Dwika Prameswara
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 11, No 1 (2022): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v11i1.1205

Mammals have several characteristics that can be distinguished, such as footprints, voice, and face shape. Mammals can be recognized. To classify the face shape of mammals, the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) methods can be used. This study uses the LHI-Animal-Faces dataset which is taken as many as 15 species of mammals, where each type of mammal is selected 60 images and resized to 150x150 pixels. The image is converted into a grayscale image for the HOG feature extraction process. Furthermore, the classification process uses SVM. The kernels used are Linear, Polynomial, and Gaussian kernels. The testing process uses K-Fold Cross Validation. The folds used are 3-fold, 4-fold, 5-fold, 6-fold, and 10-fold. The performance of the HOG feature and the SVM method that gives the best results is the Linear kernel using 10-fold with an accuracy value of 96.55%, precision of 77.92%, and recall of 74.11%. The sequence of kernels that give the best results in this test is the Linear kernel, Polynomial kernel, and Gaussian kernel.

PERBANDINGAN JARAK POTRET DAN RESOLUSI KAMERA PADA TINGKAT AKURASI PENGENALAN ANGKA KWH METERMENGGUNAKAN SVM Dini Amputri; Siti Nadra; Gasim Gasim; M. Ezar Al Rivan
Jurnal Informatika Global Vol 8, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Electricity meter is a tool used to measure the electricity power consumption. Electricity meter has the numeral part known as electricity meter number which load the electricity power consumption.Usually, the recording is done once a month, by recording the number on the electric meter into the book, and then the recording officer take pictures the power meter. Electricity meter number can be analized through an image using the knowledge of pattern recognition in image processing. In analyzing picture of electicity meter number, we used Histogram Of Oriented Gradients (HOG) as the feature extraction and Supply Vector Machine (SVM) as the classification method. The result using 100 train-set and 30 test-set for each combination of category shows that the best resolution is 10 MP and 14 MP and the picture capturing distance is at 30 cm byand 10 cm by 73,33% accuracy for each image and 86,67% for each number and confusion matrix shows that presentation of all number is 75,48%. Key words:Number Recognition, Histogram Of Oriented Gradients (HOG) , Support Vector Machine (SVM)

Klasifikasi Jenis Kacang-Kacangan Berdasarkan Tekstur Menggunakan Jaringan Syaraf Tiruan Muhammad Ezar Al Rivan; Nur Rachmat; Monica Rizki Ayustin
Jurnal Komputer Terapan Vol. 6 No. 1 (2020): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Classification of types of beans is done on red beans, green beans, and peanuts. Texture features are obtained using the Gray Level Co-occurrence Matrix (GLCM) algorithm. The algorithm used to do the classification is Artificial Neural Networks (ANN). Experiments carried out with 3 different numbers of neurons in the hidden layer. Also, there are 17 types of training functions used. Each experiment scenario was repeated 5 times. Based on the experimental scenario, the best results are 99.8% for accuracy, 99.6% for precision and 99.8% for recall using 20 neurons in the hidden layer.

Perbandingan Arsitektur LeNet dan AlexNet Pada Metode Convolutional Neural Network Untuk Pengenalan American Sign Language Muhammad Ezar Al Rivan; Alwyn Giovri Riyadi
Jurnal Komputer Terapan Vol. 7 No. 1 (2021): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

American Sign Language (ASL) is a sign language used to communicate for deaf people. The method used to identify ASL is Convolutional Neural Network (CNN). The architecture used by LeNet and AlexNet. The results of each architecture are then compared. The research was conducted with 2 schemes of the amount of data used, namely the first scheme of 100 data per letter and the second scheme of 1,000 data per letter to test the performance of the two architectures. The research results after being tested with new data, the first scheme for the LeNet architecture produces an overall accuracy of 48.332% and the AlexNet architecture produces an overall accuracy of 32.584%. The second scheme for the LeNet architecture produces an overall accuracy of 92.468% and the AlexNet architecture produces an overall accuracy of 91.618%. Overall comparison can be said that the LeNet architecture is the best architecture in this study.

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

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

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

PENENTUAN KUALITAS BUAH PEPAYA CALIFORNIA MENGGUNAKAN METODE K-NN Muhammad Ezar Al Rivan; Molavi Arman; Winston Kennedy
Jusikom : Jurnal Sistem Komputer Musirawas Vol 6 No 1 (2021): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v6i1.1175

Pepaya merupakan salah satu buah tropis yang banyak manfaat. Penentuan kualitas buah pepaya secara konvensional memiliki keterbatasan. Penelitian ini dilakukan untuk menentukan kualitas pepaya berdasarkan bentuk fisiknya. Metode yang digunakan untuk menentukan kualitas buah pepaya yaitu dengan menggunakan metode k-Nearest Neighbors (k-NN). Metode k-NN digunakan untuk melakukan klasifikasi dengan cara mencari kedekatan data uji dengan basis data yang sudah diberi label. Ciri fisik papaya dapat dilihat dari warna, ukuran dan defect. RGB merupakan salah satu fitur warna. Namun fitur warna yang digunakan yaitu fitur R (Red) dan G (Green) sedangkan untuk fitur ukuran yaitu minor axis dan major axis. Penelitian ini menggunakan jarak Euclidean dan Manhattan dengan nilai k = 3, 5, 7, dan 9. Citra buah pepaya yang digunakan dalam penelitian ini merupakan citra buah pepaya yang diambil sendiri yang terdiri dari baik, sedang, dan jelek dengan total sebanyak 150 citra yang terbagi menjadi 120 data training dan 30 data testing. Hasil terbaik yang diperoleh yaitu dengan menggunakan jarak Euclidean dan nilai k sebesar 7 dengan accuracy sebesar 86,67%, precision sebesar 87,50%, dan recall sebesar 80,00%. Nilai accuracy, precision dan recall menunjukkan perfoma penentuan kualitas yang baik.

Pelatihan Pembuatan Website Portal dengan Menggunakan Wordpress untuk Siswa/Siswi SMA Negeri 6 Palembang Ahmad Farisi; Nur Rachmat; Muhammad Ezar Al Rivan
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol 5, No 1 (2022): Januari 2022
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v5i1.1118

Website merupakan sarana yang digunakan sebagai media penyampaian informasi yang bisa diakses secara online. Website dapat dibuat dengan berbagai cara, salah satunya menggunakan Content Management System (CMS). CMS merupakan alternatif pembuatan website yang dapat digunakan bagi mereka yang tidak terbiasa dengan pemrograman. Salah satu platform CMS yang paling banyak digunakan adalah Wordpress. Pengabdian masyarakat ini memberikan pelatihan pembuatan website dengan menggunakan Wordpress bagi siswa/siswi SMA Negeri 6 Palembang. Adapun metode yang digunakan dalam pelatihan ini adalah pendidikan masyarakat dan pelatihan. Metode ini digunakan dalam bentuk pelatihan dan penyuluhan yang disertai dengan demonstrasi atau praktik bersama dalam pembuatan website menggunakan Wordpress. Kegiatan ini dimulai dengan melakukan survei kebutuhan peserta pelatihan untuk memetakan kebutuhan dan harapan dari peserta pelatihan. Peserta juga memberikan respon terhadap proses pelatihan melalui kuesioner pasca pelatihan. Berdasarkan data yang dikumpulkan dari kuesioner tersebut, sebagian besar peserta menyatakan bahwa materi yang disampaikan dalam pelatihan telah sesuai dengan kebutuhan peserta, disampaikan dengan sangat baik, dan peserta akan menggunakan Wordpress untuk membuat website setelah pelatihan dilaksanakan.

DESAIN MODEL FUZZY-TSUKAMOTO UNTUK PENENTUAN KUALITAS BUAH PEPAYA CALIFORNIA (CARICA PAPAYA L.) BERDASARKAN BENTUK FISIK Muhammad Ezar Al Rivan; Angella Octavia; Irvan Wijaya
Jurnal SAINTEKOM Vol. 11 No. 1 (2021): Maret 2021
Publisher : STMIK Palangka Raya

Papaya easily found in the local market with a relatively cheap price, adequate nutrient and vitamin content. The quality of California papaya can be measured by size, color and defect. This research discusses the topic of fuzzy model design regarding the measured of the quality of papaya using fuzzy tsukamoto with input variables major axis, minor axis, red and green intensity color, and defect variables along with the output as a result of determining the quality of California papaya. Based on the tests that have been carried out, the results of the quality is 75%.

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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