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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology Padjadjaran Journal of Dentistry Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization JURNAL INTEGRASI J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Mantik Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Electrical Engineering and Computer Science GUYUB: Journal of Community Engagement Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science
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Politeknik Elektronika Negeri Surabaya
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Journal : The Indonesian Journal of Computer Science

 1 
Lip Movement Recognition using Histogram of Oriented Gradient (HOG) and Support Machine Vector (SVM) for Arabic Word Rabbani, Fahmi Muhammad Rabbani; Bima Sena Bayu Dewantara; Endra Pitowarno
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3596

Abstract

This research aims to develop a lip gesture recognition system in Arabic words by utilizing Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) classification. The evaluation was conducted on a dataset of 1749 videos with male and female participation using Modern Standard Arabic. The 10 cross-fold validation method was used to measure the performance of the system. By applying a polynomial kernel, this study achieved an accuracy rate of 95.63%, while the word recognition rate reached 96%. These results confirm the system's ability to recognize lip movements with precision, confirming the effectiveness of the approach used in visual recognition for Arabic.
Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Wajah Menggunakan Penyematan FaceNet Catoer Ryando; Riyanto Sigit; Setiawardhana; Bima Sena Bayu Dewantara
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4323

Abstract

In recent years, face recognition has grown significantly in importance and popularity. Google created FaceNet, a deep learning system, in 2015, and it performs very well in creating extremely precise and personalised numerical representations of faces, or embeddings. In order to swiftly and effectively identify people, this study evaluates FaceNet's effectiveness in producing face embeddings and applies it to a variety of classification techniques, including support vector machine (SVM), decision tree, random forest, and k-nearest neighbours (KNN). A dataset with a wide range of positions, facial expressions, and lighting settings is used for the assessment. The findings of the experiment demonstrated that SVM with an radial basis function (RBF) kernel outperformed the other assessed classification techniques, achieving the maximum accuracy of 95%. These findings demonstrate the wide range of applications that face recognition technology may be used for, including identity management and security in different settings.
Online Terrain Classification Using Neural Network for Disaster Robot Application Sanusi, Muhammad Anwar; Dewantara, Bima Sena Bayu; Setiawardhana; Sigit, Riyanto
The Indonesian Journal of Computer Science Vol. 12 No. 1 (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.v12i1.3132

Abstract

A disaster robot is used for crucial rescue, observation, and exploration missions. In the case of implementing disaster robots in bad environmental situations, the robot must be equipped with appropriate sensors and good algorithms to carry out the expected movements. In this study, a neural network-based terrain classification that is applied to Raspberry using the IMU sensor as input is developed. Relatively low computational requirements can reduce the power needed to run terrain classification. By comparing data from the Accelerometer, Gyroscope, and combined Accelero-Gyro using the same neural network architecture, the tests were carried out in a not moving position, indoors, on asphalt, loose gravel, grass, and hard ground. In its implementation, the mobile robot runs over the field at a speed of about 0,5 m/s and produces predictive data every 1,12s. The prediction results for online terrain classification are above 93% for each input tested.
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 (IJCS)
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 (IJCS)
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.
Deteksi Kondisi Gigi Manusia pada Citra Intraoral Menggunakan YOLOv5 Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Dewantara, Bima Sena Bayu; Brahmanta, Arya
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3355

Abstract

Proses identifikasi dan pencatatan rekam medis pada praktik kedokteran gigi masih dilakukan secara manual. Akibatnya, proses tersebut memakan waktu yang cukup lama. Pada penelitian ini metode deteksi objek dimanfaatkan untuk membantu dokter melakukan identifikasi pada gigi pasien. YOLOv5 dipilih untuk dilatihkan pada dataset citra intraoral dengan lima kelas kondisi gigi (normal, karies, tumpatan, sisa akar, dan impaksi). Dataset yang digunakan berjumlah 1.767 data citra intraoral yang diambil dan dilabeli oleh dokter gigi. Dataset dibagi menjadi tiga bagian, 10% digunakan untuk data testing dan 90% digunakan untuk data training dan validation. Dilakukan komparasi performa berdasarkan nilai metrik evaluasi terhadap tiga jenis model YOLOv5 (S, M, L). Dari hasil pelatihan, YOLOv5 M sebagai model terbaik mendapatkan nilai mAP sebesar 84%, dan 82% nilai akurasi testing. Penelitian ini telah memenuhi tujuan utama untuk membangun sebuah model deep learning yang robust untuk mendeteksi dan mengklasifikasi beberapa kondisi gigi pada manusia.
Perancangan dan Integrasi Smart Touch Presenter Kit-Portable Interactive Surface dalam pembelajaran Hybrid Learning System Ashadi, Imam; Basuki, Achmad; Dewantara, Bima Sena Bayu
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.v11i2.3053

Abstract

The health crisis caused by the covid-19 virus outbreak has given birth to online learning in all parts of the world until the covid-19 pandemic ends, so that in this condition the government urges the government to innovate and adapt related to the use of available technology to support the learning process. All elements in learning such as teachers, lecturers and students are required to carry out a large-scale transmission that has never been done before from conventional learning (offline) to online-based learning. Some of the obstacles that often occur are caused by inadequate IT learning support infrastructure so that there is a range of learning losses. For this reason, learning IT devices that are cheap, ergonomic and fulfill all aspects of learning are carried out to solve the problems in this research, thus this paper proposes a device, namely smart projection low-cost interactive surface (SP-LCIS) as one of the solutions for supporting devices in implementation of Hybrid Learning System learning. In this research observation conducted at SMKN 3 Jember it was found that 99% of the devices run very well and provide added value in learning and 1% feel that they increase their preparation time, such as having to learn how to operate them effectively.
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.
New Method For Classifying Heart In Multiview Echocardiographic Images Mohamad Walid Asyhari; Riyanto Sigit; Bima Sena Bayu Dewantara; Anwar
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.v11i2.3078

Abstract

Echocardiography is a test that uses high-frequency sound waves to describe the structure of the heart. Echocardiography is used by doctors to analyze the movement of the walls in the heart chambers and identify heart disease. Several images, including the long-axis, short-axis, 2-chamber and 4-chamber left ventricle, can be used to check heart function. Many studies that have been carried out, including cardiac evaluation, are still carried out conventionally and require a certain level of accuracy. In this research, several methods proposed to achieve object extraction are used to build a classification system, the steps start with image enhancement, segmentation, tracking, extraction, output characteristics, validation and classification. Imaging enhancement aims to improve the echocardiographic image, thereby clarifying the edges of the heart wall. In addition, the images are reprocessed to separate the left ventricle from the heart wall and generate ventricular contours, at the segmentation stage. The contours are obtained by looking for the good features on each heart wall. In this approach, good features are identified only on the first image of the left ventricular slice. The good feature points used are 24 point which will be grouped into 6 segments. In addition, all images will be processed using the optical flow method to track the movement of the walls of the heart. Optical flow tracing will generate direction and distance feature extraction values that can be used to describe the resulting data features and find a suitable classification algorithm that is combined using different validation techniques, namely K-fold and Leave-one-out. In its implementation, Classifier Support Vector Machine (SVM) with rbf core achieves the highest accuracy. The SVM classification algorithm with validation techniques, namely k-fold cross-validation and leave-one-out cross-validation, reaches an accuracy value of 100% and 100%.
Co-Authors Achmad Basuki Achmad Basuki Achmad Basuki Afifah, Izza Nur Agus Indra Gunawan Agus Indra Gunawan Agus Indra Gunawan Ahmad Fauzi Makarim Alfan Rizaldy Pratama Pratama Ali Ridho Barakbah Alif Wicaksana Ramadhan Amang Sudarsono, Amang ANUGERAH WIBISANA Anwar Anwar Apriandy, Kevin Arif Hidayah Arif Hidayah Arna Fariza Arya Brahmanta Arya Brahmanta, Arya Ashadi, Imam Asmarany, Anja Aulia Dwi Maharani Aulia, Fira Bagus Nugraha Deby Ariyadi Bambang Sumantri Bambang Sumantri Catoer Ryando Chandra Edy Prianto Dadet Pramadihanto Dadet Pramadihanto Dadet Pramadihanto Dadet Pramadihanto Daffa, Muhammad Fariz Dewanto, Raden Sanggar Dewi Mutiara Sari Djoko Purwanto Edo Bagus Prastika Endra Pitowarno Fadhillah, Excel Daris Faiz Ulurrasyadi Fatekha, Rifqi Amalya Ferry Astika Saputra Fikri Aulia Fikri Aulia Fildzah Aure Gehara Zhafirah Fithrotul Irda Amaliah Gunawan, Agus Indra Gunawan, Agus Indra Hamida, Silfiana Nur Hary Oktavianto Hozumi, Naohiro Huda, Achmad Thorikul Huda, Achmad Torikul Husein Aji Pratama Idris Winarno Idris Winarno Ihwan Dwi Wicaksono Ilham Iskandariansyah Imam Ashadi IMANUDDIN, ACHMAD ILHAM Insivitawati, Era iwan Syarif Iwan Syarif Jun Miura Jun Miura, Jun Junaedi Ispianto Kamaluddin, Muhammad Wafiq Kevin Apriandy Kevin Ilham Apriandy Kisron Kisron Linda Indrayanti Lusiana Lusiana M Udin Harun Al Rasyid, M Udin Harun Makarim, Ahmad Fauzi MARTINI, NI PUTU DEVIRA AYU Mohamad Walid Asyhari Mohamad Walid Asyhari Muhammad Abdul Haq Muhammad Anwar Sanusi Muhammad Faiz Muhammad Jainal Arifin Naohiro Hozumi Onie Meiyanto Oskar Natan Prastika, Edo Bagus Pratama, Ariesa Editya Prianto, Chandra Edy Prima Kristalina Puspasari Susanti Rabbani, Fahmi Muhammad Rabbani Rachmawati, Oktavia Citra Resmi Raden Sanggar Dewanto Raden Sanggar Dewanto Ricky Afiful Maula Rika Rokhana Riyanto Sigit Riyanto Sigit, Riyanto Romadhon, Nur Rizky Rudi Kurniawan Sanusi, Muhammad Anwar Sesulihatien, Wahjoe Tjatur Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana, Setiawardhana Sholahuddin Muhammad Irsyad Sigit Riyanto Susanti, Puspasari Taufiqurrahman Taufiqurrahman Tessy Badriyah Tessy Badriyah, Tessy Tita Karlita Tita Karlita Titon Dutono Tri Harsono Tri Harsono Wahjoe Tjatur Sesulihatien Wahjoe Tjatur Sesulihatien Wibowo, Iwan Kurnianto