Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Semua inovasi yang berguna dan berkaitan secara langsung maupun tidak langsung terhadap inovasi nasional yang lebih bermutu dengan intisari tidak terbatas pada teknologi apapun, baik yang mengenai Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, e-Health, Animation dan lainnya yang serumpun dengan Inovasi Nasional lainnya. AERONAUTIKA antara lain: Aerodinamika, Material dan Komposit, Kelistrikan dan Instrumen Pesawat Terbang, Avionik, Flying Control, Teknologi Rekayasa Material, Termofluida, Fluida Mekanik, Material Komposit Maju, Teknologi Persenjataan, dsb. TEKNIK ELEKTRO antara lain: Sistem Mikroelektronik, Instrumentasi Industri, Optoelektronik, Perancangan Rangkaian Analog, Desain dan Teknologi ASIC, Desain dan Teknologi FPGA, Sensor dan Transducer, Sistem Pembangkit Energi Listrik, Sistem Transmisi dan Distribusi, Sistem Proteksi Listrik, Tegangan Tinggi, Mesin-mesin Listrik, Elektronika Daya, Energi Terbarukan, Modulasi dan Pemrosesan Sinyal Digital, Teori Informasi dan Pengkodean, Saluran Transmisi, Antena dan Propagasi Gelombang, Komunikasi Bergerak dan Nirkabel, Sistem dan Jaringan Komunikasi, Teknologi Jaringan Telekomunikasi, Sistem Otomatisasi, dsb. TEKNIK INDUSTRI antara lain: Ilmu Ergonomi Industri, Industri Pertahanan, dsb. TEKNIK DIRGANTARA antara lain: Ilmu-ilmu Aeronautika dan Astronautika, Aerodinamika, Struktur Pesawat Terbang, Propulsi, Mekanika Terbang, Material Pesawat Terbang, Astrodinamika, Aeroelastisitas, Aeroakustik, Sistem Pesawat Terbang, Proses Desain dan Produksi Pesawat Terbang, dan Sistem Operasi Penerbangan, dsb. TEKNIK KIMIA antara lain: Kimia bahan eksplosif, Kimia logam, Kimia Industri TEKNOLOGI INFORMASI & KOMUNIKASI antara lain: Sistem Informasi, Rekayasa Perangkat Lunak, Arsitektur Komputer, Jaringan Komputer, Security & Cryptography, Mobile & Cloud Computing, Quality of Service (QoS), Network Programming, Remote Sensing & GIS, Pemodelan dan Simulasi, Multimedia Processing, Teknologi Informasi dan Aplikasi, Big Data, dsb. DRONE & ROBOTIKA antara lain: Control theory and applications, embedded system, mechatronic and robotic, Unmanned Aerial System (UAS), mobile robotics, artificial intelligent, neural network, fuzzy logic, intelligent system, swarm intelligent, genetic algorithm, soft robotic, dsb. CYBER antara lain: Teknik Hacking, Cyber Defense, Cyber Security, Cyber Attack System, dsb.
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<![CDATA[STRATEGI PEMERINTAH DAERAH GUNA MEWUJUDKAN NILAI-NILAI MULTIKULTURALISME DALAM PERSPEKTIF KETAHANAN WILAYAH (Studi Kasus Di Daerah Istimewa Yogyakarta) ]]>
<![CDATA[Herlambang, Purnomo]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.200
<![CDATA[Abstract – The values of multiculturalism in Indonesia were born along with the awareness of the nation’s children to expel the invaders from ths archipelago with the spirit of togetherness, sharing the same fate, and the desire to leave regional traits that have been proven unable to expel the invaders from this motherland. It ie from this spirit of togetherness rooted in diversity that brings the Indonesian people to gates of Indonesian. Independence, United, sovereign, just and prosperous. The spirit of multiculturalism values that are still being guided by all Indonesian people are the value of mutual respect, the value of mutual tplerance, the value of unity, the value of cooperation, and the value of solidarity that must be nurtured ang maintaied. The Special Region of Yogyakarta is part of the unity state of the Republic of Indonesia, which has an attarction for the Indonesian people themselves ang the world community to visit and get to know Yogyakarta up close. The city which is attached to the nickname as a city of culture, tourism city. And student city is supported by a culture of inhabitants who are always grateful and accept the nature that has been given by the Almighty, to the low cost of living. Yogyakarta has its own magnet as must-visit area ini Indonesia. The present of foreign culture ini Yogyakarta that is able to blend in with local culture ang grow and develop in the surrounding community, so it is not an exaggeration if Sri Sultan Hamengkubuwono X (Governor of DIY), said that “Yogyakarta is a miniatur of Indonesia”. The negative side of the presence of foreign culture to Ypgyakarta is the inability of local culture to blend in with the culture of immigants, which is precisely the diversity or plurality in Yogyakarta that can lead to conflict that lead to social unrest. Migrant communities who still bring the original culture of the region to Yogyakarta which may not necessarily be accept by culture of the people of Yogyakarta. There needs to be stategy from the Yogyakarta Regional Government in order to realize the values contained in Multiculturalism in the perspective of region resilience in Yogyakarta.]]>
<![CDATA[KLASIFIKASI OBJEK BERBASIS GAMBAR THERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET 50) ]]>
<![CDATA[Nugroho, Agung]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Siswanti, Sri Desy]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.201
<![CDATA[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.]]>
<![CDATA[REKONSTRUKSI OBJEK DUA DIMENSI MENGGUNAKAN MODIFIKASI RANSAC ]]>
<![CDATA[Christian, Yohanes Wahyu]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Wulandari, Ike Yuni]]>;
<![CDATA[Ariffin, Denden Mohamad]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.203
<![CDATA[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.]]>
<![CDATA[Peningkatan Kualitas Sistem Informasi Berbasis Website untuk Kendaraan Listrik Otonom ]]>
<![CDATA[Septiyanti, Riska Yucha]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Siswanti, Sri Desy]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.204
<![CDATA[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.]]>
<![CDATA[Pemanfaatan Metoda RANSAC Untuk Validasi Rekontruksi Objek 2 Dimensi Dengan Menggunakan LiDAR ]]>
<![CDATA[Mulyana, Tri]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Siswanti, Sri Desy]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.205
<![CDATA[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.]]>
<![CDATA[SEGMENTASI OBJEK BERBASIS GAMBAR TERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNEXT 50) ]]>
<![CDATA[Fauzan, R. Aldam Dwi]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Siswanti, Sri Desy]]>;
<![CDATA[Puspita, Heni]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.207
<![CDATA[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.]]>
<![CDATA[KLASIFIKASI BUMBU DAPUR DENGAN GAMBAR 360 DERAJAT (FISH EYE) MENGGUNAKAN TENSORFLOW ]]>
<![CDATA[Nurul P., Vethrea D. Gynandra]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Siswanti, Sri Desy]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.208
<![CDATA[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.]]>
<![CDATA[SEGMENTASI OBJEK BERBASIS KAMERA TERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET 34) ]]>
<![CDATA[Laksono, Muhammad Fauzan Anggi Fathul]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Siswanti, Sri Desy]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.210
<![CDATA[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%.]]>
<![CDATA[SEGMENTASI OBJEK BERBASIS GAMBAR THERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET101 ]]>
<![CDATA[Harahap, Taufiq Hidayat]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Wulandari, Ike Yuni]]>;
<![CDATA[Puspita, Heni]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.211
<![CDATA[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.]]>
<![CDATA[KLASIFIKASI JENIS TAS PADA GAMBAR 360 DERAJAT (FISH EYE) DENGAN MENGGUNAKAN TENSORFLOW ]]>
<![CDATA[Pangemanan, Agnes Novi Anna]]>;
<![CDATA[Satyawan, Arief Suryadi]]>;
<![CDATA[Siswanti, Sri Desy]]>
<![CDATA[ Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol. 4 (2022): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) ]]>
Publisher : <![CDATA[Akademi Angkatan Udara ]]>
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DOI: 10.54706/senastindo.v4.2022.212
<![CDATA[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.]]>
