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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
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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SEGMENTASI OBJEK BERBASIS GAMBAR THERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET101 Harahap, Taufiq Hidayat; 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.v4.2022.211

Abstract

Currently, the car is one of the means of transportation that is widely used by many people and it has become a necessity to have a car to help users move more easily. Car technology continues to be developed by experts, including steering aid systems and safety for car users, such as automatic reading of objects and road boundaries that can be useful for both things. This system was built using the Fully Convolutional Network (FCN) method with Residual Neural Network (ResNet) architecture, and also Image Processing as signal processing with image input, and with a thermal Flir camera as vehicle input data. The data generated by this thermal camera is labeled first and then trained so that it can segment objects correctly according to their classification. In this study, the extraction accuracy of the training generated by the autonomous vehicle feature can reach 96.27% for ResNet 101 with a resolution of 640x480 pixels. As for suggestions for development to be even better in terms of segmentation, namely by using more training data than is used now and shooting locations for datasets in different places from the current research.
KLASIFIKASI JENIS TAS PADA GAMBAR 360 DERAJAT (FISH EYE) DENGAN MENGGUNAKAN TENSORFLOW Pangemanan, Agnes Novi Anna; 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.v4.2022.212

Abstract

Classification of objects is one of the studies that are currently being developed. The impact on the world of fashion is where women and men, teenagers to parents today cannot be separated from the bag as an addition to daily fashion. In the selection of bags, mistakes are often made, so as not to make a wrong choice, in this final project, I classify the types of bags, which is the method used to distinguish the characteristics of bags by type. The bag classification system based on type is a program that can identify a person's bag according to the type that has been trained and stored in the database of the program being run. Classification of bag types can be done in various ways, one of which is Deep Learning with the Convolutional Neural Network (CNN) method, CNN implementation using Tensorflow with the python programming language. This study was conducted using 5 classifications of bag type datasets totaling 6,720 images that have been trained with an image size of 180 x 180 using a 360o camera. It is hoped that this system is able to work well for classifying bag types in 360o (fish eye) image format. This study resulted in true detection rates of 55% and false detection of 45% where true detection is seen from the number of truths of accuracy in determining the output results, while false detection is the opposite of true detection from the number of 135 images that have been tested.
KLASIFIKASI WAJAH MANUSIA PADA GAMBAR 360 DERAJAT (FISH EYE) DENGAN MENGGUNAKAN TENSORFLOW Sutejo, Muhammad Fajar; 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.v4.2022.213

Abstract

Technology knows no boundaries, in fact it always shows new developments, one of which is classification in pictures. Human face classification is a method used to distinguish the characteristics of a person's facial pattern. The face classification system is an application that can find out a person's face according to the human face image that has been trained and stored in the machine's database. It is hoped that this application system can work well for classifying human faces in 360˚ image formats that have significant distortion Classification of human faces can be done in various ways, one of which is the Convolutional Neural Network (CNN) method using Tensorflow. This final project is carried out using 5 classifications of human face datasets totaling 6600 images that have been trained with an image size of 180 x 180 using a 360˚ camera and the Python programming language. The classification of human faces in 360˚ (fish eye) images was successfully carried out with a percentage of 65% true detection and 35% false detection from the total 135 images that have been tested. In further research, other deep learning methods can be used to obtain better classification accuracy
Pemodelan Dan Simulasi 6 Derajat Kebebasan Pesawat Udara Dengan Menggunakan Matlab/Simulink Prameswari, Aulia Widya; Satyawan, Arief Suryadi; Yuniorrita, Seszy
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.v4.2022.214

Abstract

Six degrees of freedom of the aircraft can be described by making a simulation using the MATLAB/Simulink 2021a software which is connected to FlightGear 2020.3 software for aircraft animation. The aircraft model is a twin engine civilian aircraft similar to the Boeing 757-200 made by the Group for Aeronautical Research and Technology (GARTEUR) in Europe whose longitudinal shape is non-linear while the lateral-directional is linear. This research is limited to the preparation of the aircraft motion model, entering the control surface that is carried out, namely the aileron, elevator, and rudder deflection. The motion response is described by using the input doublet on each control surface separately. Where the input is entered after the aircraft is in steady level flight at a certain speed. The simulation results show that the aircraft requires a more positive elevator deflection to be able to balance at higher speeds. With the elevator deflection input (longitudinal) it only affects the variables u (translational speed of objects on the x-axis), w (translational speed on the z-axis), q (rotational speed on the y-axis / pitch rate), and theta (euler angular position / pitch angle), while the others are 0 and stable. For input aileron and rudder deflection (directional lateral) affects the entire response, namely the variable v (translational speed of objects on the y-axis), p (aircraft rotational speed / roll rate), r (rotational speed on the z-axis / yaw rate), phi (position euler angle / roll angle), and psi (euler angle position / heading angle), as well as variables that affect longitudinal. So it can be stated that this plane shows stable dynamics.
SEGMENTASI OBJEK BERBASIS GAMBAR THERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET 152) Noviely, Isra Fanliv; Satyawan, Arief Suryadi; 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.v4.2022.215

Abstract

The latest technological developments in the field of Artificial Intelligence have very rapid capabilities and are able to produce systems that facilitate human activities, especially in the field of transportation, especially driving cars or autonomous electric cars. Artificial Intelligence technology itself is able to support success for object detection by detecting objects using semantic segmentation. Neural Network and Image processing are methods used to detect objects semantically as input signal processing in the form of images, and the FLIR thermal camera is used as input from the vehicle. The deep learning method uses a Fully Convolutional Network (FCN) with a Residual Network (ResNet) architectural model as its feature extraction. ResNet is an architectural model from FCN that works from this architectural model not to decline even though the architecture is getting deeper, so it can help humans to drive more productively. The method used in this final project is automatic extraction using deep learning technology with Residual Neural Network 152 (ResNet) architecture. The performance of the semantic segmentation system was tested with 3040 image frames offline using 800 labeled data sets. This method has an extraction accuracy for autonomous vehicle function training reaching 96% with a resolution of 640x512 pixels. The performance of the segmentation system resulted in 18576 image frames in good category, 9333 image frames in sufficient category and 6121 image frames in poor category.
PEMROSESAN GAMBAR 360° UNTUK APLIKASI SURVEILLANCE SISTEM KENDARAAN LISTRIK OTONOM Manullang, Yan Ario Eko Panca; Satyawan, Arief Suryadi; Siswanti, Ike Yuni
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.v4.2022.216

Abstract

Surveillance serves to monitor the security of a camera-based area, therefore, the need for technology in the 4.0 era where technology is growing and increasingly sophisticated. Currently the development and availability of surveillance cameras with the need to cover a wider area, the need for clarity on the aspect of supervision is therefore made developments based on conventional cameras as 360° cameras. Some of the external factors that are difficult to avoid based on 360° cameras are distortion of the image and where this distortion refers to geometric distortion. Can cause the layout of the captured image information 360° camera distortion causes disinformation, there needs to be a solution based on these geometric deviations. In conclusion, after the Matlab program is run and works well using 360° image processing software using the Matlab programming language and the information can also be conveyed very clearly without reducing the function of the image based on the initial image. The suggestion is that every 360° image processing into a cube grayscale form and the marcator can be developed into realtime form, so that it can be applied directly to autonomous electric vehicles.
Improved autocorrelation method for time synchronization in filtered orthogonal frequency division multiplexing Suyoto, Suyoto; Subekti, Agus; Satyawan, Arief Suryadi; Marta Dinata, Mochamad Mardi; Mitayani, Arumjeni; Adhi, Purwoko
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6538-6546

Abstract

Time synchronization is essential in multicarrier systems such as filtered orthogonal frequency division multiplexing (F-OFDM) because it determines the whole system’s performance. Differ with OFDM, where subcarrier allocation is not flexible. In F-OFDM, the subcarrier allocation is more flexible, and the whole subcarrier in one symbol can be grouped into several subbands. The use of subcarriers that are limited to only one subband can reduce the performance of time synchronization based on autocorrelation (AC) methods. In this study, we first compare the performance of the AC-based time synchronization algorithms used in F-OFDM when training symbols are limited to one subband. Secondly, we made improvements to the AC-based time synchronization with the averaging technique of its timing metric, thus increasing the accuracy of time estimates in the F-OFDM system. The averaging technique of the timing metric improved the performance of the AC method in cases where the training symbol is limited to one subband, as shown in the simulation results.
Random sample consensus-based room mapping using light detection and ranging Latukolan, Merlyn Inova Christie; Pramudita, Aloysius Adya; Armi, Nasrullah; Hamdani, Nizar Alam; Susilawati, Helfy; Satyawan, Arief Suryadi
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.6932

Abstract

Light detection and ranging (LiDAR) is a high-accuracy data source for geospatial providers that is displayed in two dimensions (2D) or three dimensions (3D). It is used to measure the distances or 2D or 3D maps of the environment. This study examines a random sample consensus (RANSAC)-based room mapping approach utilizing LiDAR. The RANSAC is used to achieve line fitting as a solution to acquire missing or incomplete point cloud data during the process of room scanning. The maximum x-y distance is proposed to achieve a proper model to fix the missing line during the LiDAR scanning process. Data retrieval uses ground-based LiDAR located in the middle of a certain room with the dimension of 5.76×4.95 m2. To explore a room mapping, a 2D LiDAR YDLIDAR G4 with an operating frequency of 7 Hz is used. The derived raw data is then visualized with MATLAB. The results show that the RANSAC can perform line-fitting for missing or illegible LiDAR point cloud data during the scanning process due to reflection or obstacles. The increase in the amount of data used is then directly proportional to the probability of the number of correct models.
PENGEMBANGAN SISTEM KOMBINASI KERJA REM, STEER, DAN TRAKSI BERBASIS LiDAR 3D UNTUK KENDARAAN LISTRIK OTONOM RODA TIGA Akbar, Fabian; Satyawan, Arief Suryadi; Wulandari, Ike Yuni; Utomo, Prio Adjie; Putri, Riza Ayu; Paramita, I Gusti Ayu Putri Surya; Iswarawati, Ni Kadek Emy; Linggi, Rinda Safana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 3: Juni 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.938134

Abstract

Selama beberapa waktu terakhir, pengembangan sistem deteksi objek berbasis LiDAR telah menjadi fokus utama bagi para pengembang kendaraan listrik otonom. Banyak upaya telah dilakukan untuk meningkatkan dan mengoptimalkan teknologi ini guna mencapai mobilitas otonom yang lebih canggih dan aman. Begitu pula yang dilakukan oleh BRIN, Pengembangan sistem deteksi objek berbasis LiDAR telah berhasil dilakukan hingga rekonstruksi dan posisi objek dapat ditemukan. Namun demikian pemanfaatannya belum mencakup sistem safety dan guidance dalam hal ini mengendalikan gerak laju kendaraan, rem, dan kemudi. Untuk memaksimalkan hasil sistem pendeteksian objek berbasis LiDAR yang telah diperoleh sebelumnya, maka pada penelitian tugas akhir ini akan di kembangkan sistem tersebut sehingga dapat digunakan untuk mengkombinasikan kerja laju kendaraan, rem, dan kemudi secara otomatis. Sistem safety dan guidance ini dilakukan dengan mengembangkan metoda maneuver untuk menghindari objek yang pendekatannya dapat dilakukan berdasarkan metoda fuzzy mamdani. Adapun algoritma di kembangkan dengan menggunakan python pada Jetson AGX Xavier, sedangkan untuk memproses gerak kendali maneuver yang dihasilkan dilakukan pada Mikrokontroller Teensy 4.1. Sistem safety dan guidance ini telah diterapakan pada kendaraan listrik roda tiga sederhana, dan dapat membantu kendaraan tersebut dapat ber manuver menghindari objek di depannya hingga 5 meter.
A Method of Anti-Windup PID Controller for a BLDC-Drive System Argaloka, Aditya Adni; Aptadarya, Harwin; Arentaka, Fiendo Mahendra; Suratman, Fiky Yosef; Satyawan, Arief Suryadi
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol. 10 No. 2 (2023): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v10i2.7209

Abstract

This research aims to enhance control systems for Brushless DC (BLDC) motors by introducing Proportional-Integral-Derivative (PID) control as a straightforward yet reliable solution, known for its precision, quick responsiveness, and stability. Emphasizing its suitability for BLDC motor speed control, the study addresses PID controller windup challenges, highlighting anti-windup techniques crucial for maintaining system stability. The primary focus is on improving the performance of an anti-windup PID controller for BLDC motor speed control in electric vehicles. Through simulations and analyses, the research aims to minimize steady-state error and overshooting, contributing to overall operational efficiency. Practical implementation involves optimizing the PID anti-windup controller's gain using the MATLAB PID Tuner and implementing it in the Arduino IDE. Experimental tests, which cover constant and varying step function scenarios, are conducted on the designed hardware. Results show the PID anti-windup controller's superiority, exhibiting significantly lower overshoot values of 5.42% and 3.35% compared to 13.26% and 23.76%, respectively. Despite its higher control action, the traditional PID controller displays a more pronounced overshoot. This research is a significant step toward advancing control systems for electric vehicles and optimizing BLDC motor performance in practical applications.
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