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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
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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The Development of PISA-based Numerical Problem Using the Context of Religious Day during the Pandemic Sisca Puspita Sepriliani; Zulkardi Zulkardi; Ratu Ilma Indra Putri; Samsuryadi Samsuryadi; Zahra Alwi; Meryansumayeka Meryansumayeka; Jayanti Jayanti; Duano Sapta Nusantara; Risda Intan Sistyawati; Ayu Luviyanti Tanjung; Shinta Aprilisa; Riszky Pabela Pratiwi
Jurnal Pendidikan Matematika Vol 16, No 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jpm.16.2.16010.157-170

Abstract

This study aims to produce valid and practical PISA-based numerical problems in the context of the pandemic period and to find out the role of the questions in the form of potential effects on the mathematical literacy skills of secondary school students. This research uses developmental research design, which has 2 stages, namely preliminary and formative evaluation (self evaluation, expert review, one-to-one and small group validation, and field test). The participants in this study were students in Grade 8 who were under the age of 15 and different levels of skills. Data analysis was done descriptively by conducting observations, tests, interviews, and document analysis. The research was conducted face-to-face and via Zoom and WhatsApp Group (WAG) to produce valid and practical PISA-like arithmetic questions. Based on the students' responses, it can be stated that the questions presented are in the practical category because they can be completed quickly by students, they can be understood well by students, and they have the potential effect on students' mathematical literacy skills. In addition, there is a diversity of answers between one students and another according to the level of difficulty that is appropriate for Grade 8 students. This proves that a PISA-like numeration problem in the context of the religious day during a pandemic can help improve students' mathematical literacy.
Automated handwriting analysis based on pattern recognition: a survey Samsuryadi Samsuryadi; Rudi Kurniawan; Fatma Susilawati Mohamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp196-206

Abstract

Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction. Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it. The features of handwriting use as input to classify a person’s personality traits. This paper discusses a pattern recognition point of view, in which different stages are described. The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model. Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system. 
Efficient mobilenet architecture as image recognition on mobile and embedded devices Barlian Khasoggi; Ermatita Ermatita; Samsuryadi Samsuryadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp389-394

Abstract

The introduction of a modern image recognition that has millions of parameters and requires a lot of training data as well as high computing power that is hungry for energy consumption so it becomes inefficient in everyday use. Machine Learning has changed the computing paradigm, from complex calculations that require high computational power to environmentally friendly technologies that can efficiently meet daily needs. To get the best training model, many studies use large numbers of datasets. However, the complexity of large datasets requires large devices and requires high computing power. Therefore large computational resources do not have high flexibility towards the tendency of human interaction which prioritizes the efficiency and effectiveness of computer vision. This study uses the Convolutional Neural Networks (CNN) method with MobileNet architecture for image recognition on mobile devices and embedded devices with limited resources with ARM-based CPUs and works with a moderate amount of training data (thousands of labeled images). As a result, the MobileNet v1 architecture on the ms8pro device can classify the caltech101 dataset with an accuracy rate 92.4% and 2.1 Watt power draw. With the level of accuracy and efficiency of the resources used, it is expected that MobileNet's architecture can change the machine learning paradigm so that it has a high degree of flexibility towards the tendency of human interaction that prioritizes the efficiency and effectiveness of computer vision.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (382.052 KB) | DOI: 10.18495/comengapp.v11i3.406

Abstract

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

Abstract

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.
Developing Mobile Learning of Physics (MOBLEP) with android-based problem-based learning approach to improve students’ learning independence Apit Fathurohman; Murniati Murniati; Sukemi Sukemi; Esti Susiloningsih; Erni Erni; Lintang Auliya Kurdiati; Samsuryadi Samsuryadi
Momentum: Physics Education Journal Vol. 7 No. 1 (2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/mpej.v7i1.7980

Abstract

This development research aims to produce Mobile Learning of Physics (Moblep) by applying Android-based Problem Based Learning Approach to Increase Student Learning Independence which is valid and practical. The development model used is the Rowntree model modified with Tessmer's formative evaluation method. The tessmer's formative evaluation stages in this study include self-evaluation, expert review, one-to-one, and small group. At the expert review stage, data were collected through interviews, expert tests, and questionnaires using nine material experts, nine design experts, and eleven language experts. The one-to-one stage and the small group stage were carried out at SMA Negeri 1 Suak Tapeh. The results showed that the Mobile Learning of Physics (Moblep) with the Android-based Problem-Based Learning Approach that was developed, based on the results of the expert review, obtained a total percentage score of 94.73% from the validator and was included in the "very valid" category. Based on the results of the student response questionnaire at the one-to-one evaluation stage, the average percentage was 83.5%, and at the small group stage, the average percentage was 95.2%, so this Moblep was included in the "very practical" category.The implication of this research is that the results of this study can be applied as reference material and considered as additional references for further research.
Efektifitas Penggunaan Mobile Learning App Materi Fisika SMA Berbasis STEM sebagai Sumber Belajar Siswa Bahasa Indonesia terhadap Hasil Belajar Apit Fathurohman; Ahmad Fali Oklilas; Leni Marlina; Lintang Auliya Kurdiati; Esti Susiloningsih; Azhar Azhar; Samsuryadi Samsuryadi
Jurnal Penelitian Pendidikan IPA Vol 9 No 3 (2023): March
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i3.2991

Abstract

This study aims to determine the effectiveness of using the STEM-Based Physics Mobile Learning App as a learning resource for students in Indonesia on learning outcomes. The method used in this research is the experimental method. To describe the experimental results, statistical analysis techniques were used, namely the N-Gain technique. The research was conducted at SMAN 1, Air Sugihan, Ogan Komering Ilir Regency. The analysis of the data reveals that the improvement in learning outcomes in the experimental class compared to the control class is evidence of the usage of STEM-based high school physics learning applications as a learning resource for teachers and students. The experimental class's average post-test score was 81.1, while the control class' average post-test score was 72.22
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

Abstract

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.
Multi-scale input reconstruction network and one-stage instance segmentation for enhancing heart defect prediction rate Sutarno, Sutarno; Nurmaini, Siti; Sapitri, Ade Iriani; Rachmatullah, Muhammad Naufal; Tutuko, Bambang; Darmawahyuni, Annisa; Firdaus, Firdaus; Islami, Anggun; Samsuryadi, Samsuryadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3404-3413

Abstract

Artifacts and unpredictable fetal movements can hinder clear fetal heart imaging during ultrasound scans, complicating anatomical identification. This study presents a new medical imaging approach that combines one-stage instance segmentation with ultrasound (US) video enhancement for precise fetal heart defect detection. This innovation allows real-time identification and timely medical intervention. The study acquired 100 fetal heart US videos from an Indonesian Hospital featuring cardiac septal defects, generating 1,000 frames for training, validation, and testing. Utilizing a combination of the multi-scale input reconstruction network (MIRNet) for image enhancement and YOLOv8l-seg for real-time instance segmentation, the method achieved outstanding validation results, boasting a 99.50% mAP for bounding box prediction and 98.40% for mask prediction. It delivered a remarkable real-time processing speed of 68.4 frames per second. In application to new patients, the method yielded a 65.93% mAP for bounding box prediction and 57.66% for mask prediction. This proposed approach offers a promising solution to early fetal heart defect detection using ultrasound, holding substantial potential for enhancing healthcare outcomes.
Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased Fachrurrozi, Muhammad; Darmawahyuni, Annisa; Samsuryadi, Samsuryadi; Passarella, Rossi; Archibald Hutahaean, Jerrel Adriel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1679-1686

Abstract

Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.
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