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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization J-Dinamika: Jurnal Pengabdian Kepada Masyarakat GUYUB: Journal of Community Engagement Automotive Experiences The Indonesian Journal of Computer Science Advance Sustainable Science, Engineering and Technology (ASSET)
Dadet Pramadihanto
Politeknik Elektronika Negeri Surabaya
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Journal : The Indonesian Journal of Computer Science

 1 
Implementasi Particle Swarm Optimization untuk Optimasi Fuzzy-Social Force Model pada Sistem Navigasi Robot Omnidirectional Anugerah Wibisana; Bima Sena Bayu Dewantara; Dadet Pramadihanto
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3076

Abstract

Particle Swarm Optimization (PSO) is a swarm-based optimization method that is easy to implement and requires only a few parameters to set. This study aims to implement PSO to optimize the Fuzzy-Social Force Model (FSFM). FSFM combines the Social Force Model (SFM) as a navigation algorithm and the Fuzzy Inference Rule (FIS) to produce adaptive gain on SFM to create a mobile robot navigation system that is more responsive to obstacles. The PSO implementation optimizes fuzzy rules to be more optimal when the mobile robot navigates into social spaces. From the experimental test results on the VREP simulation software, cognitive parameter c1 = 1 and social parameter c2 = 2 produced the best navigation performance compared to other test parameter values.
Analisis Kinematika Maju dari Tangan Robotik Berjari 4 yang Digunakan pada Robot Humanoid T-FLoW Apriandy, Kevin; Dewantara, Bima Sena Bayu; Dewanto, Raden Sanggar; Pramadihanto, Dadet
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3291

Abstract

Model kinematika merupakan bagian penting dalam pengembangan robot humanoid karena dapat merepresentasikan karakteristik dari robot, membuat pemahaman tentang robot menjadi lebih mudah. Mengingat perkembangan robot humanoid T-FLoW yang saat ini dilengkapi dengan sepasang tangan baru, maka perlu dibangun model kinematika untuk memahami lebih lanjut tentang tangan robot baru tersebut. Oleh karena itu, dalam pekerjaan ini, disajikan sebuah analisis kinematika maju untuk memperoleh model kinematika dari tangan berjari 4 baru robot humanoid T-FLoW. Dengan menggunakan pendekatan matriks transformasi homogen, model kinematika tangan robot diturunkan berdasarkan perkalian beberapa matriks rotasi dan matriks translasi yang tersusun dari frame koordinat pangkal ke frame koordinat tujuan. Model kinematika yang diturunkan disimulasikan dalam tugas gerak dasar tangan: menggenggam sebuah benda, dihitung dengan bantuan MATLAB, dan divisualisasikan menggunakan fitur plot 3D MATLAB. Hasil menunjukkan bahwa model tersebut memberikan berbagai karakteristik tangan robot seperti konfigurasi, posisi sendi, dan posisi end-of-effector, yang kemudian dapat divisualisasikan menjadi kerangka tangan. Kedepannya, pekerjaan kami dapat memfasilitasi pengembang T-FLoW dalam membangun pergerakan tangan dengan sistem umpan balik, yang kemudian dapat digunakan untuk menyelesaikan berbagai permasalahan desain gerakan tangan. Kinematics models are important part of humanoid robot development as they can represent the characteristics of the robot, making understanding the robot easier. Given the development of the T-FLoW humanoid robot which is currently equipped with a new pair of hands, it is necessary to build a kinematics model to understand more about the new robot hands. Therefore, in this work, a forward kinematics analysis is presented to derive the kinematics model of the new 4-fingered T-FLoW humanoid robot hand. Using a homogeneous transformation matrix approach, the kinematics model of the robot hand is derived based on the multiplication of several rotation and translation matrices arranged from the base coordinate frame to the goal coordinate frame. The derived kinematics model is simulated in a basic hand motion task: grasping an object, calculated with the help of MATLAB, and visualized using MATLAB's 3D plot feature. The results show that the model provide various characteristics of the robot hand such as configuration, joint positions, and end-of-effector positions, which then be visualized into a hand skeleton. In the future, our work can facilitate T-FLoW developers in building hand movement and feedback systems, which then can be used to solve various hand motion design problems.
Pengenalan Wajah 3D dengan menggunakan PointNet Arif Hidayah; Dewantara, Bima Sena bayu; Pramadihanto, Dadet
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3294

Abstract

Pengenalan wajah tiga dimensi (3D) telah menjadi topik penelitian yang menarik karena mampu mengatasi keterbatasan pengenalan wajah dua dimensi (2D) dalam menghadapi perubahan pose, pencahayaan, dan pemalsuan. Penelitian ini mengusulkan sebuah pipeline pengenalan wajah 3D yang invarian terhadap perubahan cahaya, dengan menggunakan teknik segmentasi euclidean clustering dan Convolutional Neural Network (CNN) PointNet. Data wajah diambil menggunakan kamera Time-of-Flight yang menghasilkan titik awan (point cloud). Proses segmentasi euclidean clustering berhasil memisahkan area wajah dengan akurat, membantu dalam pengenalan wajah 3D. Melalui pelatihan dengan 217 dataset dan 2048 titik per wajah, sistem mencapai akurasi pelatihan sebesar 99% dan akurasi validasi sebesar 84,4%, dengan loss pelatihan sebesar 1% dan loss validasi sebesar 15,6%. Evaluasi pada tiap kelas menunjukkan rata-rata akurasi 0.9887471867966992, presisi 0.8255813953488372, recall 0.8255813953488372, dan F1-score 0.8255813953488372. Hasil menunjukkan bahwa pipeline pengenalan wajah 3D ini memiliki potensi besar dalam aplikasi keamanan, pengawasan, dan pengenalan objek di lingkungan yang kompleks. Three-dimensional (3D) face recognition has emerged as an intriguing research topic, addressing the limitations of two-dimensional (2D) face recognition in handling pose variations, lighting changes, and spoofing. This study proposes an illumination-invariant pipeline for 3D face recognition, utilizing the euclidean clustering segmentation technique and Convolutional Neural Network (CNN) PointNet. Facial data is captured using a Time-of-Flight camera, generating point clouds. The euclidean clustering segmentation effectively isolates facial regions, aiding in 3D face recognition. After training with 217 datasets and 2048 points per face, the system achieved 99% training accuracy and 84.4% validation accuracy, with 1% training loss and 15.6% validation loss. Class-wise evaluation yielded an average accuracy of 0.9887471867966992, precision of 0.8255813953488372, recall of 0.8255813953488372, and F1-score of 0.8255813953488372. The results highlight the significant potential of this 3D face recognition pipeline in security, surveillance, and object recognition in complex environments.
Co-Authors Abrari, Arya Rafi Achmad Basuki Adi Sucipto, Adi Adnan Rachmat Anom Besari Afifah, Izza Nur AL BANNA, M. RIZQI HASAN Aldi Bayu Kreshnanda Ismail Alfan Rizaldy Pratama Alfan Rizaldy Pratama Pratama Ali Ridho Barakbah Amang Sudarsono, Amang ANUGERAH WIBISANA Apriandy, Kevin APRIANDY, KEVIN ILHAM Arif Hidayah Arif Hidayah Arini, Nu Rhahida Arna Fariza Bambang Sumantri Besari, Adnan Rachmat Anom Bima Sena Bayu Dewantara Darmawan, Adytia Darmawan, Adytia Dewanto Sanggar Dewanto, Raden Sanggar Dewanto, Raden Sanggar Dewanto, Sanggar Dewanto, Sanggar Dewi Mutiara Sari Djoko Purwanto Eko Henfri Binugroho Eko Henfri Binugroho Eko Purbo Wahyono Endah Suryawati Ningrum Febby Ronaldo Hamida, Silfiana Nur Hary Oktavianto Huda, Achmad Thorikul Idris Winarno Ihsan Fikri Abdurahman Muharram Ismail, Aldi Bayu Kreshnanda iwan Syarif Kevin Apriandy Marta, Bayu Sandi Maulana, Himmawan Sabda Mentari Putri Jati Miyara, Akio MOCHAMAD ARI BAGUS NUGROHO Muhammad Ramadhan Hadi Setyawan Muharram, Ihsan Fikri Abdurahman Neny Kurniati Nurul Fahmi Pamenang, M. Unggul Pamenang, M. Unggul Puspita Sari, Wulandari R Sanggar Dewanto R. Dimas Pristovani R. Sanggar Dewanto Rachmawati, Oktavia Citra Resmi Raden Sanggar Dewanto Raden Sanggar Dewanto Riyanto Sigit Roziqin, M. Choirur Roziqin, M. Choirur Rudi Kurniawan Rully Sulaiman Rusli, Muhammad Rizani Ryan Satria Wijaya Samsul Huda Samsul Huda Sari, Dewi Mutiara Sesulihatien, Wahjoe Tjatur Setiawardhana Setiawardhana Setiawardhana, Setiawardhana Sigit Riyanto Sritrusta Sukaridhoto Subhan Khalilullah, Achmad Sukma Meganova Effendi Susanti, Puspasari Syadza Atika Rahmah Syahputra, Dimas Novian Aditia Teguh Hady Ariwibowo Tessy Badriyah, Tessy Tri Harsono ULURRASYADI, FAIZ W., Andri Permana W., Andri Permana Wahjoe Tjatur Sesulihatien Wahyu Widodo Widodo, Edi Wahyu Wina Rachmawan, Irene Erlyn Wina Rachmawan, Irene Erlyn Wulandari Puspita Sari Yanto, Luky Yanto, Luky Zainal Arief