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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
Bima Sena Bayu Dewantara
Politeknik Elektronika Negeri Surabaya
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Concept and Design of Anthropomorphic Robot Hand with a Finger Movement Mechanism based on a Lever for Humanoid Robot T-FLoW 3.0 Kevin Ilham Apriandy; Faiz Ulurrasyadi; Raden Sanggar Dewanto; Bima Sena Bayu Dewantara; Dadet Pramadihanto
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1793

Abstract

This work described a concept and design of an anthropomorphic robot hand for the T-FLoW 3.0 humanoid robot, which featured a mechanism based on a lever as its finger movement. This work aimed to provide an affordable, modular, lightweight, human-like robot hand with a mechanism that minimizes mechanical slippage. The proposed mechanism works based on the push/pull of a lever attached to the finger to generate its finger flexion/extension movement. The finger’s lever is pushed/pulled through a servo horn and a rigid bar by the affordable TowerPro MG90S micro-servo. Our hand is developed only as necessary to become close to human hands by only applying five fingers and six joints, where each joint has its actuator. The combination of 3D printing technology with PLA filament accelerates and streamlines the manufacturing process, provides a realistic appearance, and achieves a lightweight, affordable, and easy maintenance product. Structural analysis simulations show that our finger design constructed with PLA material could withstand a load of about 30 N. We verified our finger mechanism by repeatedly flexing and extending the finger 30 times, and the results showed that the finger movements could be performed well. Our hand offered excellent handling for the mechanical issues brought on by finger movements, one of the issues that robot hand researchers have encountered. Our work could provide significant benefits to the T-FLoW 3.0 developers in enhancing the ability of humanoid robots involving hands, such as grasping and manipulating objects.
Deep Learning Models for Dental Conditions Classification Using Intraoral Images Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Bayu Dewantara, Bima Sena; Brahmanta, Arya
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1914

Abstract

This paper presents the digitalization of dentistry medical records to support the dentist in the patient examination process. A dentist uses manual input to fill out the evaluation form by drawing and labeling each patient’s tooth condition based on their observations. Consequently, it takes too long to finish only one examination. For time efficiency, using AI-based digitalization technology can be a promising solution. To address the problem, we made and compared several classification models to recognize human dental conditions to help doctors analyze patient teeth. We apply the YOLOv5, MobileNet V2, and IONet (proposed CNN model) as deep learning models to recognize the five common human dental conditions: normal, filling, caries, gangrene radix, and impaction. We tested the ability of YOLO classification as an object detection model and compared it with classification models. We used a dataset of 3.708 intraoral dental images generated by various augmentation methods from 1.767 original images. We collected and annotated the dataset with the help of dentists. Furthermore, the dataset is divided into three parts: 90% of the total dataset is used as training and validation data, then divided again into 80% training data and 20% validation data. 10% of the total dataset will be used as testing data to compare classification performance. Based on our experiments, YOLOv5, as an object detection model, can classify dental conditions in humans better than the classification model. YOLOv5 produces an 82% accuracy testing value and performs better than the classification model. MobileNet V2 and IONet only get 80% and 70% testing accuracy. Although statistically, there is not much of a difference between the test accuracy values for YOLOv5 and MobileNet v2, the speed in classifying dental objects using YOLOv5 is more efficient, considering that YOLOv5 is an object detection model. There are still challenges with the deep learning technique used in this research, but these can be addressed in further development. A more complex model and the enlargement of more data, ensuring it is varied and balanced, can be used to address the limitations. 
Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones Huda, Achmad Torikul; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Sigit, Riyanto
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1961

Abstract

Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.
Comparative Analysis of Human Detection using Depth Data and RGB Data with Kalman Filter: A Study on Haar and LBP Methods Aulia, Fira; Dewantara, Bima Sena Bayu; Oktavianto, Hary
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2739

Abstract

Accurate human detection in video streams with occlusions, illumination variances, and varying distances is crucial for various applications, including surveillance, human-computer interaction, and robotics. This study investigates the performance of two widely used object detection features, Haar-like and Local Binary Pattern (LBP), for detecting human upper bodies in color and depth images. The algorithms are combined with Adaptive Boosting Cascade classifiers to leverage the discriminative power of Haar-like features and LBP texture features. Extensive experiments were conducted on a dataset comprising color images and depth data captured from a Kinect camera to evaluate the algorithms' performance in terms of precision, recall, accuracy, F1-score, and computational efficiency measured in frames per second (fps). The results indicate that when tested on color images, the Haar-Cascade method outperforms LBP-Cascade, achieving higher precision (27.4% vs. 7.8%), recall (49.2% vs. 7.8%), accuracy (21.4% vs. 4.1%), and F1-score (35.2% vs. 7.8%), while maintaining a comparable computational speed (19.07 fps vs. 19.26 fps). However, when applied to depth data, the Haar-Cascade method, coupled with Kalman filtering, demonstrates significantly improved performance, achieving precision (79.3%), recall (79.3%), accuracy (65.8%), and F1-score (79.3%) above 70%, with a computational time of approximately 19.07 fps. The integration of Kalman filtering enhances the robustness and tracking capabilities of the system, making it a promising approach for real-world applications in human detection and monitoring. The findings suggest that depth information provides valuable cues for accurate human detection, enabling the Haar-Cascade algorithm to overcome challenges faced in color image analysis. information provides valuable cues for accurate human detection, enabling the Haar-Cascade algorithm to overcome challenges faced in color image analysis.
Classification of Intraoral Images in Dental Diagnosis Based on GLCM Feature Extraction Using Support Vector Machine Romadhon, Nur Rizky; Sigit, Riyanto; Dewantara, Bima Sena Bayu
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3051

Abstract

This study aims to develop an AI-based diagnostic tool for classifying dental conditions and tooth types to enhance the accuracy and efficiency of dental diagnostics. Manual documentation and diagnosis in dentistry are often prone to errors, inefficiencies, and delays, leading to adverse patient outcomes. Leveraging digital image processing and machine learning, this research addresses these challenges by automating the classification process. Dental imaging data were collected from the Dental and Mouth Hospital (RSGM) of Nala Husada Surabaya, Indonesia, comprising 3,910 images categorized into dental conditions (1,767 images) and tooth types (2,143 images). The dataset was preprocessed through resizing, grayscale conversion, histogram equalization, and median filtering. Texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM), and classification was performed using Support Vector Machine (SVM), K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest algorithms. The SVM algorithm achieved the highest accuracy of 54.24% for dental conditions and 41.49% for tooth types, outperforming other methods. However, the overall performance was suboptimal, primarily due to dataset limitations, reliance on GLCM for feature extraction, and insufficient preprocessing. The results highlight the potential of AI-based tools in dentistry but also underscore the need for improvements in dataset diversity, advanced feature extraction methods, and hyperparameter optimization. Future research should focus on expanding the dataset, exploring deep learning-based feature extraction, and employing robust evaluation strategies to enhance model performance. This study lays the groundwork for developing a more reliable and efficient AI-based diagnostic tool, ultimately improving patient outcomes and streamlining clinical workflows in dentistry.
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