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All Journal Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika
Kusprasapta Mutijarsa
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering Engineering

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Obstacle Avoidance Method for a Group of Humanoids Inspired by Social Force Model Sadiyoko, Ali; Trilaksono, Bambang Riyanto; Mutijarsa, Kusprasapta; Adiprawita, Widyawardana
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 2 (2015)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (873.965 KB) | DOI: 10.14203/j.mev.2015.v6.67-74

Abstract

This paper presents a new formulation for obstacle and collision behavior on a group of humanoid robots that adopts walking behavior of pedestrian crowd. A pedestrian receives position information from the other pedestrians, calculate his movement and then continuing his objective. This capability is defined as socio-dynamic capability of a pedestrian. Pedestrian’s walking behavior in a crowd is an example of a sociodynamics system and known as Social Force Model (SFM). This research is trying to implement the avoidance terms in SFM into robot’s behavior. The aim of the integration of SFM into robot’s behavior is to increase robot’s ability to maintain its safety by avoiding the obstacles and collision with the other robots. The attractive feature of the proposed algorithm is the fact that the behavior of the humanoids will imitate the human’s behavior while avoiding the obstacle. The proposed algorithm combines formation control using Consensus Algorithm (CA) with collision and obstacle avoidance technique using SFM. Simulation and experiment results show the effectiveness of the proposed algorithm.
Videoconference System for Rural Education: Issues, Challenges, and Solutions a Title is Fewest Possible Words Kusprasapta Mutijarsa; Yoanes Bandung; Luki Bangun Subekti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.6291

Abstract

In this paper, we presented issues, challenges, and solutions of videoconference system for rural education. First, we discussed several issues on the implementation of videoconference system for education, particulary in rural area in Indonesia, which covered videoconference requirement, rural condition, and education needs. Second, we presented several challenges consisted of choosing videoconference technology, choosing compression method, system and application development, ensuring quality of services, and ensuring quality of experiences. Based on the issues and challenges, we proposed a solution of videoconference system which is specifically deployed in rural education. The solution was based on WebRTC technology implemented in Intel i5 core miniPC choosen to increase portability of the system. A STUN server was built on Javascript to facilitate communication between each client terminal. A simple and intuitive user interface was designed to facilitate the use of application by rural people. The system was deployed at two elementary schools in Cianjur, West Java, representing rural education in Indonesia. From the experiment, we obtained video sent data rate 82 kbit/s, video received data rate 245 kbit/s, average delay 316 ms and packet lost rate 1.32%. The experiment results showed that the audio and video quality can be accepted by users to implement distance learning.
Obstacle Avoidance Method for a Group of Humanoids Inspired by Social Force Model Ali Sadiyoko; Bambang Riyanto Trilaksono; Kusprasapta Mutijarsa; Widyawardana Adiprawita
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 2 (2015)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2015.v6.67-74

Abstract

This paper presents a new formulation for obstacle and collision behavior on a group of humanoid robots that adopts walking behavior of pedestrian crowd. A pedestrian receives position information from the other pedestrians, calculate his movement and then continuing his objective. This capability is defined as socio-dynamic capability of a pedestrian. Pedestrian’s walking behavior in a crowd is an example of a sociodynamics system and known as Social Force Model (SFM). This research is trying to implement the avoidance terms in SFM into robot’s behavior. The aim of the integration of SFM into robot’s behavior is to increase robot’s ability to maintain its safety by avoiding the obstacles and collision with the other robots. The attractive feature of the proposed algorithm is the fact that the behavior of the humanoids will imitate the human’s behavior while avoiding the obstacle. The proposed algorithm combines formation control using Consensus Algorithm (CA) with collision and obstacle avoidance technique using SFM. Simulation and experiment results show the effectiveness of the proposed algorithm.
SIMULASI SISTEM KENDALI BERBASIS PERILAKU PADA AUTONOMOUS MOBILE ROBOT DENGAN METODA Q-LEARNING Casi Setianingsih; Kusprasapta Mutijarsa; Muhammad Ary Murti
TEKTRIKA Vol 4 No 2 (2019): TEKTRIKA Vol.4 No.2 2019
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v4i2.2879

Abstract

Autonomous robot adalah suatu robot yang mampu bekerja secara mandiri tanpa pengendalian langsung dari manusia. Robot bekerja berdasarkan sensor-sensor yang dimilikinya, mengambil keputusan sendiri untuk menyelesaikan misi dalam lingkungan kerjanya. Dalam dunia nyata, lingkungan kerja robot sangat dinamis, selalu berubah, dan tidak terstruktur. Membuat suatu model lingkungan yang tidak terstruktur sangat sulit. Memperoleh model matematik yang tepat dari lingkungan seperti ini hampir tidak mungkin dilakukan. Untuk membuat suatu autonomous mobile robots yang mampu bekerja pada lingkungan yang tidak terstruktur dan dinamis,diperlukansuatumetodatertentuyangadaptifdanmampubelajar. Berdasarkan permasalahan tersebut maka pada riset ini dirancang suatu autonomous mobile robot dengan arsitektur berbasis perilaku yang dapat belajar dan bekerja secara mandiri pada lingkungan yang tidak terstruktur, menggunakan metoda Reinforcement Learning. Tujuan metoda ini diterapkan agar robot mampu belajar dan beradaptasi terhadap lingkungan yang tidak terstruktur. Selanjutnya robot dikembangkan agar mampu menyelesaikan misi menemukan target pada posisi tertentu berdasarkan informasi yang diperoleh dari sensor sensor yang ada. Hasil simulasi menunjukan bahwa algoritma pembelajaran Reinforcement Learning berhasil diterapkan pada arsitektur kendali berbasis perilaku di autonomous mobile robot dengan akurasi sebesar 85,71%.
Survei Penelitian Metode Kecerdasan Buatan untuk Mendeteksi Ancaman Teknologi Serangan Siber Fitria, Eza Yolanda; Mutijarsa, Kusprasapta
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107341

Abstract

Keamanan siber merupakan isu penting di era modern seperti sekarang ini. Serangan siber yang semakin beragam terus bermunculan. Teknik dan metode baru machine learning dan deep learning terus dikembangkan oleh banyak peneliti untuk menangani serangan siber. Selain teknik baru, berbagai jenis dataset baru terkait serangan siber juga turut berkembang. Permasalahan muncul ketika banyaknya teknik atau metode yang ada belum tentu tepat menangani berbagai jenis serangan siber. Begitupun sebaliknya, belum tentu berbagai jenis serangan siber dapat ditangani hanya dengan menggunakan teknik atau metode tertentu saja. Tujuan penelitian ini adalah memetakan teknik-teknik dan metode kecerdasan buatan untuk mendeteksi ancaman teknologi serangan siber dalam bentuk Systematic Literature Review (SLR). Pada penelitian ini teknik dan metode machine learning maupun deep learning dievaluasi untuk dapat menangani jenis serangan siber tertentu dengan tepat. Berbagai dataset yang dapat digunakan untuk eksperimen juga dieksplorasi. Jenis serangan siber yang dibahas pada penelitian ini difokuskan jenis serangan pada sistem host dan serangan pada lapisan keamanan jaringan. Pada penelitian SLR sebelumnya, hal-hal tersebut dibahas secara terpisah atau bahkan salah satunya saja sehingga dalam penelitian ini perlu dibangun kembali SLR yang bisa mengisi kekurangan pada penelitian SLR sebelumnya. Originalitas penelitian ini terletak pada analisis teknik atau metode kecerdasan buatan yang secara spesifik tepat untuk menangani jenis serangan siber tertentu. Terdapat total 44 paper survei yang diulas, diterbitkan antara tahun 2018 hingga 2023. Dari keseluruhan paper tersebut, 30 paper membahas penggunaan teknk machine learning dan deep learning. Kemudian, 19 paper yang membahas penggunaan dataset dan 13 paper membahas peluang penelitian masa depan. Terakhir, 5 paper yang membahas terkait tools. Hasil dari penelitian ini diharapkan dapat berkontribusi dalam memberikan wawasan baru di dunia keamanan siber untuk membuka peluang penelitian masa depan, terutama bagi para peneliti pemula yang ingin melakukan riset di bidang keamanan siber.   Abstract Cybersecurity is an essential issue in today's modern era. An increasingly diverse range of cyberattacks continues to emerge. Many researchers continue to develop new techniques and methods for machine learning and deep learning to deal with cyberattacks. In addition to new techniques, various types of new datasets related to cyberattacks are also developing. Problems arise when the many existing techniques or methods are not appropriate for dealing with various types of cyberattacks. Vice versa, it is not certain that various types of cyberattacks can be handled only using specific techniques or methods. This research aims to map the techniques and methods of artificial intelligence to detect cyber-attack technology threats in the form of a Systematic Literature Review (SLR). In this research, machine learning and deep learning techniques and methods are evaluated to be able to handle certain types of cyberattacks properly. Various datasets that can be used for experiments are also explored. The types of cyberattacks discussed in this study focus on attacks on the host system and the network security layer. In previous SLR research, these matters were discussed separately or even just one of them. In this study, it was necessary to rebuild the SLR, which could fill the deficiencies in the previous SLR research. The originality of this research lies in the analysis of artificial intelligence techniques or methods that are specifically appropriate for dealing with certain types of cyberattacks. A total of 44 reviewed survey papers were published between 2018 and 2023. Of all these, 30 papers discuss machine learning and deep learning techniques. Then, 19 papers examine the use of datasets, 13 papers discuss future research opportunities, and five papers discuss developing tools. The results of this research are expected to contribute to providing new insights into the world of cybersecurity to open future research opportunities, especially for novice researchers who wish to conduct research in the field of cybersecurity.
Co-Authors Ali Sadiyoko Bambang Riyanto Trilaksono Casi Setianingsih Fitria, Eza Yolanda Luki Bangun Subekti Muhammad Ary Murti Widyawardana Adiprawita Widyawardana Adiprawita, Widyawardana Yoanes Bandung