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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Matematika MATEMATIKA Jupiter Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Sinergi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Momentum: Physics Education Journal Jurnal Elektronika dan Telekomunikasi Jurnal Informatika dan Rekayasa Elektronik Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Engineering, Mathematics and Computer Science Journal (EMACS) Media Elektrik

Samsuryadi Samsuryadi

Universitas Sriwijaya

Author-ID : 208392

Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Articles

Title

Classification of Finger Spelling American Sign Language Using Convolutional Neural Network Anna Dwi Marjusalinah; Samsuryadi Samsuryadi; Muhammad Ali Buchari
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Sign language is a combination of complex hand movements, body postures, and facial expressions. However, only a limited number of people can understand and use it. A computer aid sign language recognition with finger spelling style utilizing a convolutional neural network (CNN) is proposed to reduce the burden. We compared two CNN architectures such as Resnet 50, and DenseNet 121 to classify the American sign language dataset. Several data splitting proportions were also tested. From the experimental result, it is shown that the Resnet 50 architecture with 80:20 data splitting for training and testing indicates the best performance with an accuracy of 0.999913, sensitivity 0.998966, precision 0.998958, specificity 0.999955, F1-score 0.999913, and error 0.0000898.

Detection of Diabetic Retinopathy Using Convolutional Neural Network (CNN) Rizq Khairi Yazid; Samsuryadi Samsuryadi
Computer Engineering and Applications Journal Vol 11 No 3 (2022)
Publisher : Universitas Sriwijaya

One of the complications of Diabetes Mellitus, namely Diabetic Retinopathy (DR) damages the retina of the eye and has five levels of severity: Normal, Mild, Medium, Severe and Proliferate. If not detected and treated, this complication can lead to blindness. Detection and classification of this disease is still done manually by an ophthalmologist using an image of the patient's eye fundus. Manual detection has the disadvantage that it requires an expert in the field and the process is difficult. This research was conducted by detecting and classifying DR disease using Convolutional Neural Network (CNN). The CNN model was built based on the VGG-16 architecture to study the characteristics of the eye fundus images of DR patients. The model was trained using 4750 images which were rescaled to 256 X 256 size and converted to grayscale using the BT-709 (HDTV) method. The CNN-based software with VGG-16 architecture developed resulted in an accuracy of 62% for the detection and classification of 100 test images based on five DR severity classes. This software produces the highest Sensitivity value in the Normal class at 90% and the largest Specificity value in the Mild class at 97.5%.

Optimization of Deep Neural Networks with Particle Swarm Optimization Algorithm for Liver Disease Classification Muhammad Nejatullah Sidqi; Dian Palupi Rini; Samsuryadi Samsuryadi
Computer Engineering and Applications Journal Vol 12 No 1 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i1.432

Liver disease has affected more than one million new patients in the world. which is where the liver organ has an important role function for the body's metabolism in channeling several vital functions. Liver disease has symptoms including jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, enlarged spleen and gallbladder and has abnormalities that are very difficult to detect because the liver works as usual even though some liver functions have been damaged. Diagnosis of liver disease through Deep Neural Network classification, optimizing the weight value of neural networks with the Particle Swarm Optimization algorithm. The results of optimizing the PSO weight value get the best accuracy of 92.97% of the Hepatitis dataset, 79.21%, Hepatitis 91.89%, and Hepatocellular 92.97% which is greater than just using a Deep Neural Network.

Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method Tri Kurnia Sari; Dian Palupi Rini; Samsuryadi Samsuryadi
Computer Engineering and Applications Journal Vol 12 No 2 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i2.429

The brain is one of the most important organs in the human body as a central nervous system which functions as a controlling center, intelligence, creativity, emotions, memories, and body movements. Epileptic seizure is one of the disorder of the brain central nervous system which has many symptoms, such as loss of awareness, unusual behavior and confusion. These symptoms lead in many cases to injuries due to falls, biting one’s tongue. Detecting a possible seizure beforehand is not an easy task. Most of the seizures occur unexpectedly, and finding ways to detect a possible seizure before it happens has been a challenging task for many researchers. Analyzing EEG signals can help us obtain information that can be used to diagnose normal brain activity or epilepsy. CNN has been demonstrated high performance on detection and classification epileptic seizure. This research uses CNN to classify the epilepsy EEG signal dataset. AlexNet and LeNet-5 are applied in CNN architecture. The result of this research is that the AlexNet architecture provides better precision, recall, and f1-score values on the epilepsy signal EEG data than the LeNet-5 architecture.

Brahmi Script Classification using VGG16 Architecture Convolutional Neural Network Vincen, Vincen; Samsuryadi, Samsuryadi
Computer Engineering and Applications Journal Vol 11 No 2 (2022)
Publisher : Universitas Sriwijaya

Many Indonesians have difficulty reading and learning the Brahmi script. Solving these problems can be done by developing software. Previous research has classified the Brahmi script but has not had an output that matches the letter. Therefore, letter classification is carried out as part of the process of recognizing Brahmi script. This study uses the Convolutional Neural Network (CNN) method with the VGG16 architecture for classifying Brahmi script writing. Training results from various amounts of image data. Smooth model. The requested image data is a 224x224 binary image. This study has the highest quality, accuracy is 96%, highest recall is 98% and highest precision is 98%.

Co-Authors Agus Mistiawan Ahmad Fali Oklilas Ahmad Heryanto Akbar, M. Agung Ali Firdaus Anna Dwi Marjusalinah Apit Fathurohman Apriansyah Putra Aprilisa, Shinta Archibald Hutahaean, Jerrel Adriel Ardina Ariani Ardina Ariani Ariani, Ardina Arnelawati, Arnelawati Astuti, Dwi Lydia Zuharah Ayu Luviyanti Tanjung Azhar Azhar Bambang Tutuko Barlian Khasoggi Buchari, Muhammad Ali Cahyadi, Gabriel Ekoputra Hartono Darmawahyuni, Annisa Darmawijoyo, Darmawijoyo Dedy Fitriady Fitriady Deris Stiawan Desty Rodiah Dewy Yuliana Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dwi Budi Santoso Dwi Lydia Zuharah Astuti Dwi Lydia Zuharah Astuti Dwi Meylitasari Tarigan Ermatita - Erni Erni Esti Susiloningsih Fatma Susilawati Mohamad Firdaus Firdaus Hadipurnama Satria Hadipurnawan Satria Hasby Rifky Islami, Anggun Jambak, Muhammad Ihsan Jayanti Jayanti Julian Supardi Khairun Nisa Kurniabudi, Kurniabudi Leni Marlina Lingga Wijaya, Harma Oktafia Lintang Auliya Kurdiati Lintang Auliya Kurdiati M. Nejatullah Sidqi Marlina Sylvia Meryansumayeka Meryansumayeka Mohamad, Fatma Susilawati Muhammad Fachrurrozi Muhammad Naufal Rachmatullah Mukhlis Febriady Murniati . Nur Rachmat Nusantara, Duano Sapta Primanita, Anggina Purnama, Benni Rahmat Budiarto Ramadhan, Muhammad Fajar Ratu Ilma Indra Putri Rifkie Primartha Risda Intan Sistyawati Riszky Pabela Pratiwi Rizq Khairi Yazid Rossi Passarella Rudi Kurniawan Rudi Kurniawan Saparudin Saparudin Sapitri, Ade Iriani Serrano, Philip Alger M. Sharipuddin, Sharipuddin Sisca Puspita Sepriliani Siti Nurmaini Sukemi Sukemi Sukemi Sukemi Sutarno Sutarno Tri Kurnia Sari Vincen, Vincen Willy, Willy Yesinta Florensia Yogi Tiara Pratama Yulia Hapsari Yundari, Yundari Zahra Alwi Zulkardi Zulkardi

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