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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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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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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.

Studi Awal Penggunaan Algoritma C4.5 dan Logika Fuzzy pada Klasifikasi Enkripsi Transaksi Keuangan Bebasis XML Nur Rachmat; Samsuryadi Samsuryadi
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

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XML (eXtensible Markup Language) telah digunakan secara luas dalam transportasi data baik dalam transaksi kebutuhan umum hingga transaksi keuangan. Penggunaan XML yang meningkat dalam pesan transaksi keuangan menciptakan ketertarikan yang selaras dengan protokol keamanan yang terintegrasi untuk melindungi pesan XML dalam pertukarannya dengan cara yang efisien namun kuat. Lembaga keuangan melakukan transaksi setiap harinya membutuhkan pengamananan pesan XML dalam skala besar. Mengamankan pesan yang besar akan menimbulkan masalah kinerja dan sumberdaya. Oleh karena itu, sebuah pendekatan dibutuhkan untuk mengamankan dan mengenkripsi bagian tertentu dari dokumen XML, sintaks dan membuat batasan yang merepresentasikan bagian yang harus diamankan.Dalam penelitiaan ini penulis mengajukan pendekatan untuk mengamankan transaksi keuangan dengan Logika Fuzzy dan algoritma C4.5 untuk optimasi rule fuzzy. Pada fase klasifikasi fuzzy, sebuah nilai dipasang pada atribut yang dinamakan "Importance Level". Nilai yang diberikan pada atribut tersebut mengindikasikan sensitifitas data untuk setiap tag XML. Algoritma C4.5 digunakan untuk mengurangi ketergantungan terhadap expert dalam pemilihan rule yang bertujuan untuk menyederhanakan rule dan meningkatkan performa komputasiPenelitain ini juga akan menerapkan pengklasifikasian enkripsi isi pesan XML dengan mengenkripsi elemen yang dipilih saja (element-wise), yang telah ditetapkan pada fase klasifikasi. Proses enkripsi menggunakan kunci simestris berupa algoritma AES dengan besaran kunci yang berbeda. Kunci 128-bit digunakan pada tag yang diklasifikasikan sebagai elemen yang ditandai sebagai "Medium" sedangkan kunci 256-bit pada tag "High".

Pengenalan Ciri Citra Mayat Didalam Danau/Kolam Akibat Kecelakaan dengan Algoritma Principal Component Analysis (PCA) Sukemi Sukemi; Yogi Tiara Pratama; Samsuryadi Samsuryadi; Rifkie Primartha
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 1 (2021): JUPITER Vol. 13 No. 1 April 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract Penelitian ini dilatarbelakangi dari kesulitan para penyelam untuk mencari mayat akibat kecelakaan yang berada didalam air pada daerah terbatas dan berbatas waktu. Pengganti penyelam yang bertindak sebagai pencari adalah sebuah boat yang telah berhasil diujicobakan pada penelitian sebelumnya. Boat ini akan merekam foto citra dibawah air sesuai dengan posisi mayat (yang diperagakan oleh manakin) tersebut diperkirakan berada. Dengan menggunakan metode principle component analysis, foto citra manakin tersebut dapat dikenali sesuai dengan penciriannya. Hasil akhir dari peneliian ini, bahwa manakin berhasil dikenali dengan nilai eigenvalue >1 pada data 1, data 2, data 3, data 4, data 5 dan data 6 dari 8 data yang digunakan dengan prosentasi keberhasilan sebesar 87,50 %. Keywords—Boat, Manakin, Principal Component Analysis Abstract This research is motivated by the difficulty of divers to find bodies due to accidents that are in the water in a limited and time-bound area. Substitute the diver who acts as a searcher is a boat that has been successfully tested in previous studies. This boat will record the image under the water according to the position of the corpse (which is displayed by the manakin) it is estimated to be. By using the principle component analysis method, the manakin images can be recognized according to their characteristics. The final result of this research is that manakin is successfully identified by the value of eigenvalue> 1 in data 1, data 2, data 3, data 4, data 5 and data 6 of 8 data used with a success percentage of 87.50%. Keywords — Boat, Manakin, Principal Component Analysis

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

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.

CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM Ardina Ariani; Samsuryadi Samsuryadi
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.009

The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data.

REAL-TIME CLASSIFICATION OF FACIAL EXPRESSIONS USING A PRINCIPAL COMPONENT ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK Dwi Lydia Zuharah Astuti; Samsuryadi Samsuryadi; Dian Palupi Rini
SINERGI Vol 23, No 3 (2019)
Publisher : Universitas Mercu Buana

Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction. It is employed in various applications such as digital entertainment, customer service, driver monitoring, and emotional robots. Moreover, it has been studied through several aspects related to the face itself when facial expressions change based on the point of view or perspective. Facial curves such as eyebrows, nose, lips, and mouth will automatically change. Most of the proposed methods have limited frontal Face Expressions Recognition (FER), and their performance decrease when handling non-frontal and multi-view FER cases. This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. The results of this study proved to be more accurate than that of previous studies. The combination of PCA and CNN methods in the Static Facial Expressions in The Wild (SFEW) 2.0 dataset obtained an accuracy amounting to 70.4%; the CNN method alone only obtained an accuracy amounting to 60.9%.

Seleksi Fitur pada Klasifikasi Penyakit Gula Darah Menggunakan Particle Swarm Optimization (PSO) pada Algoritma C4.5 Dwi Meylitasari Tarigan; Dian Palupi Rini; Samsuryadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Diabetes Mellitus (DM) is a disease caused by blood sugar level increased were higher than the maximum limit. Food consumed tends to contain uncontrolled sugar which could cause the drastic increase of blood sugar level. It is necessary to efforts, to increasing the public awareness to controlling blood sugar and the risks of increasing blood sugar level so as to determine of preventive and early detection measures One of used of data mining technique is information technology in the health sector which used a lot as a decision maker to predicting and diagnosing a several disease. This research aims to optimizing the features on classification of the data mining with the C4.5 algorithm using Particle Swarm Optimization (PSO) to detect the blood sugar level in patient. The dataset used is the effect of physical activity to the Blood Sugar Level at H. Abdul Manan Simatupang Kisaran Regional Public Hospital. The amount of dataset used is 42 record with 10 attributes. The result of this research obtained that the Particle Swarm Optimization (PSO) may increasing the accuracy performance of C4.5 from 86% to 95%. Whereas the evaluation result of the AUC Value increasing from 0,917 to 0,950. From those 10 attributes which are then selection with using PSO into 7 attributes used to determine the prediction of sugar level. Therefore the Algorithm C4.5 using the Particle Swarm Optimization (PSO) may provide the best solution to the accuracy of detection blood sugar levels.

Klasifikasi Transaksi Penipuan Pada Kartu Kredit Menggunakan Metode Resampling Dan Pembelajaran Mesin Mukhlis Febriady; Samsuryadi Samsuryadi; Dian Palupi Rini
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3515

The high number of credit card fraud causes a lot of losses for both users and credit service providers. Because the rate of credit card transactions is very fast, it is necessary to detect credit card fraud as early as possible. However, another challenge that is no less important is the amount of data that is imbalanced between valid and invalid transactions. One solution to the problem of data imbalance is to use a resampling method that can improve the quantity of data so that the accuracy results are good. In this study, three types of resampling methods were implemented, SMOTE, bootstrap, and jackknife. Furthermore, to validate the success of the resampling method, three types of machine learning methods were used. The machine learning methods are SVM, ANN, and random forest. From the test results, it was found that the combination of resampling SMOTE and random forest methods produced the best performance with values of accuracy, precision, recall and F1-score of 99.95%, 81.63%, 90.91% and 86.02%, respectively.

Pengujian Validitas dan Reliabilitas Model UTAUT 2 dan EUCS Pada Sistem Informasi Akademik Shinta Aprilisa; Samsuryadi Samsuryadi; Sukemi Sukemi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3074

The research instrument is used to collect data or measure the object of a study. The purpose of this study was to determine which instruments were declared valid and reliable and to find out the variables with the highest validity and reliability values. Validity testing is carried out to determine the effectiveness of an instrument, while reliability testing is carried out to show the level of reliability of the indicators used. Testing the validity and reliability using software SmartPLS version 3.3.2 with a measurement scale that is Likert scale. Validity testing is done by looking at the average variance extracted (AVE) value and the comparison of the latent variable correlations values, while reliability testing is done by looking at the composite reliability value. The population in this study were active students at the State Islamic University (UIN) Raden Fatah Palembang, totaling 19,260 students with the determination of the sample using the Slovin formula with a level of significance = 5%. Data collection in this study was carried out by distributing online questionnaires. The questionnaire was made based on indicators on the model used, where the models used were UTAUT 2 and EUCS. The UTAUT 2 model can be used to measure the level of user acceptance of the system consisting of the variables of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, perceived value, habit, and behavioral intention. Furthermore, the EUCS model is used to measure the level of user satisfaction which consists of content, accuracy, format, ease of use, timeliness, and user satisfaction variables. The results of the validity and reliability testing state that all indicators are valid and reliable with an AVE value > 0.50 in the validity test and the composite reliability value > 0.70 in the reliability test. The validity test with the highest value is found in the ease of use variable with an AVE value of 0.826 and reliability testing with the highest value is found in the performance expectancy variable with a composite reliability value of 0.924. With this research, it is expected to obtain variables in the model to evaluate user acceptance and satisfaction with academic information systems

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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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%.

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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