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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Matematika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic
Samsuryadi Samsuryadi
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Physics

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Network anomaly detection research: a survey Kurniabudi Kurniabudi; Benni Purnama; Sharipuddin Sharipuddin; Darmawijoyo Darmawijoyo; Deris Stiawan; Samsuryadi Samsuryadi; Ahmad Heryanto; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 1: March 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.184 KB) | DOI: 10.52549/ijeei.v7i1.773

Abstract

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies.
Kajian Pengenalan Ekspresi Wajah menggunakan Metode PCA dan CNN Dwi Lydia Zuharah Astuti; Samsuryadi Samsuryadi
Annual Research Seminar (ARS) Vol 4, No 1 (2018): ARS 2018
Publisher : Annual Research Seminar (ARS)

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Abstract

Pengenalan ekspresi wajah secara cepat menjadi bagian penting dalam sistem komputer dan interaksi antar manusia dan komputer karena cara yang paling ekspresif dalam menunjukkan emosi sebagai manusia adalah melalui ekspresi wajah. Dalam kajian ini, pengenalan eskpresi wajah dipelajari melalui beberapa aspek yang berhubungan dengan wajah itu sendiri. Ketika eskpresi wajah berubah, lekukan pada wajah seperti alis, hidung, bibir dan mulut akan otomatis berubah. Dalam kajian ini, akan dibahas mengenai sistem pengenalan ekspresi wajah secara realtime menggunakan metode PCA dan CNN. Dimana metode PCA yang akan digunakan untuk pengektraksi fitur adalah metode Eigenfaces sedangkan untuk pengklasifiksian akan menggunakan metode CNN
Kajian Pengenalan Gerakan Tangan Menggunakan Hidden Markov Model Agus Mistiawan; Khairun Nisa; Dewy Yuliana; Hasby Rifky; Samsuryadi Samsuryadi
Annual Research Seminar (ARS) Vol 1, No 1 (2015)
Publisher : Annual Research Seminar (ARS)

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Abstract

In the recent few years, hand gesture recognition system received great attention to be researched because of its ability to create human computer interaction. In this paper a survey on recent research about hand gesture recognition is provided. A review of hand gesture and implementation of Hidden Markov Model (HMM) also highlighted.
Ear Image Recognition using Hyper Sausage Neuron Samsuryadi Samsuryadi; Anggina Primanita
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.036 KB) | DOI: 10.11591/eecsi.v2.777

Abstract

It is important to distinguish an individual from a group of other individuals to ensure information security an d integrity. One of human body parts that has distinguishable characterics is the ear. Prior attempts on identification of hum an ear image has been implementing statistical pattern recogni tion which focusing more on classification between sample sets . This research attempts to build a robust ear image recognitio n system using Hyper Sausage Neuron (HSN) that concetrates on cognition process rather than classification. A recognition s oftware has been built and tested to recognize ear images. Ear images presented into the software has its geometrical moment invariants extracted. These moments is then used to build a se ven dimensional feature vector which will construct a network of HSN of each individual it represents. Different ear images f rom the same individual is presented into the software to test i ts accuracy. The experiment result shows that ear recognition using HSN has better accuracy and faster training time than p revious recognition attempts using statistical pattern recogniti on.
Pengidentifikasian Pembuat Tulisan Tangan Dengan Pengenalan Pola Biomimetik samsuryadi samsuryadi
Generic Vol 4 No 2 (2009): Vol 4, No 2 (2009)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Artikel ini membahas kerangka kerja baru untuk mengidentifikasi kepemilikan tulisan tangan yang sah berdasarkan Pengenalan Pola Biomimetik (PPB). Cara kerja PPB menggunakan Prinsip Keberlanjutan Homogen (PKH), yaitu perbedaan antara dua sampel dari kelas yang sama harus berubah secara bertahap. Serta menggunakan syaraf dua bobot untuk membentuk ruang ciri yang dinamakan Hyper Sausage Neuron (HSN). HSN diterapkan sebagai pelingkup ruang karakteristik wilayah distribusi dari titik-titik sampling di kelas yang sama. Pengujian kerangka kerja yang dikembangkan menggunakan data sederhana untuk mengidentifikasi pembuat tulisan tangan diperoleh hasil yang memuaskan dengan persentase rata-rata sebesar 94,8%.
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. 
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.
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.
Co-Authors Agus Mistiawan Ahmad Fali Oklilas Ahmad Heryanto Apit Fathurohman Ayu Luviyanti Tanjung Azhar Azhar Darmawijoyo, Darmawijoyo Deris Stiawan Dewy Yuliana Dian Palupi Rini Duano Sapta Nusantara Dwi Lydia Zuharah Astuti Esti Susiloningsih Fatma Susilawati Mohamad Hasby Rifky Jayanti Jayanti Khairun Nisa Kurniabudi, Kurniabudi Leni Marlina Lintang Auliya Kurdiati Meryansumayeka Meryansumayeka Muhammad Nejatullah Sidqi Primanita, Anggina Purnama, Benni Ratu Ilma Indra Putri Risda Intan Sistyawati Riszky Pabela Pratiwi Rudi Kurniawan Sharipuddin Sharipuddin Shinta Aprilisa Sisca Puspita Sepriliani Tri Kurnia Sari Yundari, Yundari Zahra Alwi Zulkardi Zulkardi