Article Per Year (5 Year)
p-Index From 2020 - 2025
4.56
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JMECS (Journal of Measurements, Electronics, Communications, and Systems) Teknika Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Algoritme Jurnal Mahasiswa Teknik Informatika Paradigma Internet of Things and Artificial Intelligence Journal eProceedings of Engineering
Satyawan, Arief Suryadi
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
PENGEMBANGAN MODEL CAPSULEGAN UNTUK PENGHAPUSAN HUJAN CITRA TUNGGAL Hidayat, Haryanto; Munawir, Munawir; Putra, Muhammad Taufik Dwi; Satyawan, Arief Suryadi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 2 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i2.5534

Abstract

Pengaruh cuaca hujan pada kualitas gambar sering menjadi masalah di bidang Computer Vision (CV), karena informasi-informasi penting yang diperlukan oleh algoritma CV menjadi hilang. Berbagai macam solusi telah diusulkan oleh para peneliti untuk menyelesaikan masalah tersebut, mulai dari menggunakan filter tradisional hingga penerapan metode Deep Learning. Penerapan algoritma Deep Learning, seperti Deep Convolutional Generative Adversarial Network (DCGAN) digunakan karena tingkat kualitas gambar yang diproduksi sangat baik, tetapi masih ada kekurangan yaitu hilangnya informasi spasial antar komponen hujan, sehingga tidak dapat mengidentifikasi dimana letak garis hujan yang menyebabkan tersisanya garis hujan pada gambar. Capsule Network (CapsNet) menjadi solusi dalam permasalahan tersebut, dengan memperhatikan hubungan antara detail parsial dengan objek global, informasi-informasi spasial pada gambar seperti posisi dan rotasi antar objek dapat dipertahankan, dengan begitu penggunaan CapsNet pada arsitektur akan memberikan pengaruh yang cukup signifikan. Dengan menggabungkan kedua metode tersebut akan didapatkan model de-raining yang dapat menghasilkan gambar lebih tajam sekaligus menghilangkan garis hujan secara efektif. Kami menggabungkan CapsNet pada bagian arsitektur Discriminator untuk pengklasifikasian yang lebih baik. Hasil perbandingan dengan model lain menunjukkan bahwa penggabungan kedua arsitektur tersebut menghasilkan gambar yang lebih baik dibandingkan dengan kebanyakan model Deep Learning lain. Meskipun begitu, masih terdapat kekurangan yaitu gambar yang dihasilkan masih memiliki efek blur dan sisa hujan akibat proses pelati-han yang tidak stabil.
Rancang Bangun Content Management System Pada Website Riset Fakultas Teknik Universitas Nurtanio Menggunakan Bahasa Pemrograman PHP Dan MySQL Ali, Abdul Latif; Satyawan, Arief Suryadi; Wulandari, Ike Yuni; Puspita, Heni
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.168

Abstract

The development of information and communication technology has had a major impact on human life along with the easier it is to obtain information through the internet. The need for the internet is very high, especially in the world of education and especially universities. The emergence of internet technology that exists today has become a tool used to ease human work, especially in the field of websites. Website is a form of implementation of a programming language. Hypertext Preprocessor (PHP) is a web-based programming language that has the ability to process and process website data that is run by the MySQL server as a database. One that can be used for dynamic website creation is content management system (CMS) technology. This website design aims to make it easier for admin managers and kontributors to provide more interactive information to research students and lecturers in developing an autonomous electric vehicle website. This study uses a system development method in the form of a Software Development Life Cycle (SDLC) with a prototype model, namely software development in the form of a physical system work model and functions as an initial version of the system. In testing the CMS using black-box testing with boundary value analysis techniques that focus on input and output data. Based on the research and testing process that has been carried out successfully, the CMS dashboard page on the engineering faculty research website is able to process data dynamically and provide convenience for admin managers and kontributors in the development of autonomous electric vehicles.
PENGOLAHAN DATA GPS UNTUK KENDARAAN LISTRIK OTONOM Putra, Nyoman Triyoga Arika; Satyawan, Arief Suryadi; Wulandari, Ike Yuni; Ariffin, Denden Mohamad
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.187

Abstract

Information and communication technology continues to show its development, and one of the technological developments in the information sector is the Global Positioning System (GPS). GPS technology is designed by utilizing satellites in space called the Global Navigation Satellite System (GNSS) whose application can be used in autonomous electric vehicles. Therefore, this study aims to design a GPS data processor on autonomous electric vehicles. The method used is using a Raspberry Pi 4 mini computer and a Neo 6M GPS module and the basic application is using the Python programming language. The application created has an output in the form of a display that shows the data obtained from the satellite and automatically the data will be stored in the form of an Excel file. The data in the Excel file will be compared with data from Google Maps. From the results of the research that has been done, the use of the Neo 6M GPS module in this application has an economical price, and the location data received is quite accurate, the GPS can be used indoors or outdoors, and even in rainy conditions the GPS can still receive signals well. these results can be applied to the autonomous electric vehicles technology.
KLASIFIKASI OBJEK BERBASIS GAMBAR THERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET 50) Nugroho, Agung; Satyawan, Arief Suryadi; Siswanti, Sri Desy
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.201

Abstract

The development of technology in the field of transportation is currently something that is very enthusiastically welcomed by the Indonesian people in general. But along with the development of transportation technology that exists today is much different from the past, with a lot of sophistication and improving quality and safety that is more innovative. An Autonomous Car was created that can make it easier for drivers and maintain safety while driving. This system was built using the Neural Network control method, as well as Image Processing as the input signal in the form of images, and with the Flir camera as the vehicle data input. This of course has a very positive impact on human life today, of course humans will be more efficient in time, maintain safety on the trip, and can be more productive when driving. The method that is currently developing rapidly is automatic extraction using deep learning technology. The method used is Fully Convolutional Network (FCN) with Residual Neural Network (ResNet) architecture.The method currently used in the research is automatic extraction using deep learning technology to detect objects in the classification that has been made, with Residual Neural Network 50 (ResNet) architecture. In this study, the extraction accuracy for automatic vehicle function training reached 97.1% for ResNet 50 and 96.7% for ResNet 101 with a resolution of 640x512 pixels.
REKONSTRUKSI OBJEK DUA DIMENSI MENGGUNAKAN MODIFIKASI RANSAC Christian, Yohanes Wahyu; Satyawan, Arief Suryadi; Wulandari, Ike Yuni; Ariffin, Denden Mohamad
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.203

Abstract

Abstract—RANSAC (Random Sample Consensus) can be used as mathematical model parameters of a set of observed data contains inlier and outlier data. In manufacture can also be used to build exact line equations from a number of data, by validating the data by looking at the inlier and outlier data of a project being worked on. To create a program in making this final project, the author uses two-dimensional data that is processed by LiDAR (Light Detection and Ranging), the author conducts data collection and research on the performance of two-dimensional object reconstruction using RANSAC modifications in Python. In the first experiment using data from LiDAR detection, the authors limit that the variable value of the residual threshold is above 3000, so it can be expressed as an imperfect image, from the first test it is produced with 92.85% perfect data, while for the second test it gets 52% data. Perfect. Reconstruction of 2D objects from LiDAR data can be validated using the RANSAC method. Ransac method is used to build the exact equation of the line from a number of data. So that the generated line can be a reference whether the amount of data to be observed is a correct 2-dimensional object reconstruction. How to validate by looking at the suitability of the inlier and outlier data from the RANSAC line equation. Inliers data will indicate whether the pattern of the reconstruction data is appropriate.
Peningkatan Kualitas Sistem Informasi Berbasis Website untuk Kendaraan Listrik Otonom Septiyanti, Riska Yucha; Satyawan, Arief Suryadi; Siswanti, Sri Desy
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.204

Abstract

Current technological developments have made the website a means of disseminating information that offers various conveniences and speeds in its presentation. Speed and convenience are positive values of the existence of the internet. The website is more easily accessible by people in various regions just by using the internet. One of the benefits of the website is that in terms of marketing the products of a business, the website can be a means of promoting, marketing and conveying information effectively and efficiently to the public. Another benefit of website technology is for information purposes of autonomous electric vehicles. This technology in the future can be integrated on the website for the purpose of information on its existence and condition. In fact, it is possible to create applications for the use of autonomous electric vehicles by making calls through the website, as is the case with applications that are currently popular, namely Gojek or Grab.In designing a website-based information system, a markup language consisting of HTML and CSS is used which is synchronized so that it can run simultaneously, besides that, the PHP programming language is also used to make the website look more dynamic and responsive. The method used in improving the quality of the website- based information system for autonomous electric vehicles is the static website method. On static websites the content provided is constant or does not change and on each page is made with HTML code.In this study, a website-based information system was developed for the purposes of a simple autonomous electric vehicle, which operates in the Nurtanio University Bandung campus. This website design consists of 8 pages. Page 1 contains a homepage where general information on research activities for the development of simple autonomous electric vehicles is found. Page 2 contains object detection which displays more detailed information about the object detection technology used in autonomous electric vehicles. Information about the presence of autonomous electric vehicles in the campus environment is then shown on page 3, namely Av Tracker. Page 4 contains the results of the research that has been done. Page 5 contains research opportunities. This website design is also equipped with a contributor page on page 6 which contains researchers involved in autonomous electric vehicle research. Page 7 contains biodata of alumni who have contributed. At the end, page 8 is for contact information for those who are interested in communicating further about matters related to this research.The final form of a website-based information system is to provide convenience to convey information about autonomous electric vehicles, and also to provide convenience for website management according to the required data and for further development with a tracking system.
Pemanfaatan Metoda RANSAC Untuk Validasi Rekontruksi Objek 2 Dimensi Dengan Menggunakan LiDAR Mulyana, Tri; Satyawan, Arief Suryadi; Siswanti, Sri Desy
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.205

Abstract

This research is a development of Random Sample Consensus (RANSAC) technology which is one of the facilities in autonomous type electric vehicles. Random Sample Consensus (RANSAC) is a two-dimensional technology that detects the presence of objects so that the vehicle can respond in the form of braking or maneuvering to avoid these objects. In the application of RANSAC, it is often constrained by anomaly data which affects the accuracy in the detection of actual objects. In this study, it aims to overcome anomaly data. In this study, it was assisted by MATLAB software which was used for analysis, comparison and programming whose results were entered into Excel as a dataset for the Reconstruction of 2-Dimensional Objects Using RANSAC Modifications The result data will be processed using the Linear Regression method or prediction based on previous data and using the Least Square method or the Least Squares method. The results of the study used samplesize and maxdistance which varied, from the first test getting 92.8% of the data declared good and the second test getting 58.33% of the data declared good. The results of this study show that the reconstruction of 2-Dimensional objects from LiDAR data can be validated using the RANSAC method, the Robust Fit line and the Least Square Fit from the image can be changed by setting SampleSize and MaxDistance, in the test results that have been carried out the tested data are declared successful, and the amount of data and the determination of the amount of data can affect the results in the study.
SEGMENTASI OBJEK BERBASIS GAMBAR TERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNEXT 50) Fauzan, R. Aldam Dwi; Satyawan, Arief Suryadi; Siswanti, Sri Desy; Puspita, Heni
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.207

Abstract

The transportation sector at this time has experienced many technological developments which have been well receifed by the public, especially the people of Indonesia. Along with the development of transportation technology has undergone many developments, with sophistication and increased comfort and better security. So Autonomus Car technology was created that can help drivers to maintain safety while driving. Autonomus car was built using the Neural Network control method, and also Image Processing as signal processing with image input, and with a flip camera used for vehicle input data. Autonomous cars have many positive impacts on human life today, so humans can minimize time properly. Travel safety is maintained, and can be more productive when driving. The method that is currently developing rapidly is automatic extraction using deep learning. In this final project, automatic extraction method with deep learning technology used is Fully Convolutional Network (FCN) with Residual Neural Network Next (ResNext) architecture. In this study, the extraction accuracy for automatic vehicle function training reached 98% for ResNext 50 with a resolution of 640x540 pixels. Semantic segmentation will then test with 34030 image frames offline. In ResNext 50 architecture contains 20512 frames in good category, 7883 in adequate category and 5605 in poor category.
KLASIFIKASI BUMBU DAPUR DENGAN GAMBAR 360 DERAJAT (FISH EYE) MENGGUNAKAN TENSORFLOW Nurul P., Vethrea D. Gynandra; Satyawan, Arief Suryadi; Siswanti, Sri Desy
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.208

Abstract

Classification of objects is one of the studies that are currently being developed. One of the object classification techniques that are widely used is to implement deep learning methods. Convolutional Neural Network (CNN) is included in the type of Deep Learning because of the depth of the network. CNN is a convolution operation that combines several processing layers, using several elements that operate in parallel. Often people make the mistake of distinguishing one object from another, such as kitchen spices. Similar colors and shapes can make an error in the taste of food, therefore this object classification takes kitchen spices as an example to recognize and classify kitchen spices objects in making it easier for humans to recognize objects. The objects used are Ginger, Candlenut, Salam Leaves, Coriander and Lemongrass which are used as ingredients for classifying kitchen spice objects based on shape. Classification of objects that are classified using the CNN and Tensorflow methods. CNN construction will be built for object classification applications on 360° camera images (fish eye). An image in the form of a video of kitchen spices will be taken using a 360° camera and used as a model for classifying objects using Tensorflow and Jupyter Notebook for training data. The results of this detection system are expected to be able to work well in classifying objects in 360° image format that have significant distortion.
SEGMENTASI OBJEK BERBASIS KAMERA TERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET 34) Laksono, Muhammad Fauzan Anggi Fathul; Satyawan, Arief Suryadi; Siswanti, Sri Desy
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54706/senastindo.v4.2022.210

Abstract

Technology is developing very rapidly in a pluralistic country, among these technologies are electric vehicles without human intervention, we are more familiar with this with autonomous electric vehicles. The purpose of this autonomous electric vehicle is to suppress human negligence when driving, besides being able to make it easier for the driver to travel without the need to drive it. Before all these things can operate properly, the autonomous electric vehicle needs a detection scheme that can distinguish objects, the segmentation method uses a thermal camera sensor based on Deep Learning that is trained with the required data set. This method uses a Fully Convolutional Network (FCN) with a Residual Network 34 (ResNet 34) architectural model with an image dimension of 640x512 pixels as its feature extraction. The advantage of ResNet 34 is that it is able to do quite a lot of dataset training even though the hardware used is not the most qualified. The design of this object detection system uses semantic segmentation, Neural Network, and Image Processing methods as input signals in the form of images, and a FLIR thermal camera which is like an eye for a vehicle which receives an input signal, the process from start to finish is processed using the Jetson AGX Xavier. The cability of semantic segmentation was tested offline with 40216 image frames, there are three categories, namely, good, sufficient, and not good. Which includes the good category as many as 23389 frames, 10706 frames is a sufficient category, and for not good category there are 6120 frames. The mean Intersection over Union (IoU) obtained at the time of this study was 78.56%.
Co-Authors Adi, Puput Dani Prasetyo Adiprabowo, Tjahjo Agus Subekti Akbar, Fabian Ali, Abdul Latif Aloysius Adya Pramudita Aptadarya, Harwin Arentaka, Fiendo Mahendra Argaloka, Aditya Adni Ariffin, Denden Mohamad Arifyandy, Rachmat Artemysia, Khaulyca Arva Arumjeni Mitayani Aurelia, Felicia Bunga Awalya, Silmi Christian, Yohanes Wahyu Fauzan, R. Aldam Dwi Fazri, Nurul Fiky Y. Suratman Firman Galura Muhammad Suranegara Hamdani, Nizar Alam Haqiqi, Mokh. Mirza Etnisa Harahap, Taufiq Hidayat Helfy Susilawati Hidayat, Haryanto Iswarawati, Ni Kadek Emy Jody H, Amadeus Evan Kitagawa, Akio Laksono, Muhammad Fauzan Anggi Fathul Latukolan, Merlyn Inova Christie Linggi, Rinda Safana Manullang, Yan Ario Eko Panca Marchellyn, Ferryn Marta Dinata, Mochamad Mardi Muhammad Yassir Mulyana, Tri Munawir Munawir Nasrullah Armi Noviely, Isra Fanliv Nugroho, Agung Nurdiana, Dian Nurul P., Vethrea D. Gynandra Pangemanan, Agnes Novi Anna Paramita, I Gusti Ayu Putri Surya Prameswari, Aulia Widya Praptawilaga, Muhamad Fadly Rizqy Purwoko Adhi Puspita, Heni Putra, Muhammad Taufik Dwi Putra, Nyoman Triyoga Arika Putri, Riza Ayu RR. Ella Evrita Hestiandari Saharuna, Saharuna Samie, Muhammad Ikbal Saputra, Adhitya Dwi Septiyanti, Riska Yucha Shamie, M. Ikbal Siburian, Sebastian Edward Siswanti, Ike Yuni Siti Helmyati Sopian, Sani Moch Sopian, Sani Moch. Sri Desy Siswanti Suratman , Fiky Y. Sutejo, Muhammad Fajar Utomo, Prio Adjie Wiwik Handayani Wulandari, Ike Yuni Yuniorrita, Seszy