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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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Comparative Analysis of Human Detection using Depth Data and RGB Data with Kalman Filter: A Study on Haar and LBP Methods Aulia, Fira; Oktavianto, Hary; Dewantara, Bima Sena Bayu
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2739

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. 
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.
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%.
Segmentasi Pertumbuhan Padi berbasis Aerial Image menggunakan Fitur Warna dan Tekstur untuk Estimasi Produksi Hasil Panen Arifin, Muhammad Jainal; Basuki, Achmad; Dewantara, Bima Sena Bayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0813438

Abstract

Pertumbuhan padi di daerah yang luas seringkali tidak ideal. Ini dapat disebabkan oleh faktor alam, jenis varietas padi, dan model perawatan yang digunakan. Ini juga akan mempengaruhi hasil panen. Luasnya lahan membuat petani sulit untuk memantau bagian yang tidak terjangkau. Seringkali pemantauan perkembangan padi dilakukan di tepi sawah tetapi tidak mencapai area tengah. Studi ini mengusulkan sistem pemantauan untuk pengembangan padi yang dapat menjangkau secara lebih luas dan memperkirakan hasil padi di setiap area lahan pertanian. Sistem ini menggunakan gambar udara untuk menjangkau area yang lebih luas dan kemudian memperkirakan produksi pertanian. Estimasi produksi dilakukan dengan mengelompokkan gambar kawasan pertanian menggunakan metode K-Means. Pengelompokan ini menggunakan parameter warna HSV dan tekstur Gabor sebagai fitur dari setiap bagian gambar. Hasilnya adalah segmen area padi berdasarkan pertumbuhannya. Jumlah segmen yang sesuai dengan usia Padi nyata akan menentukan nilai estimasi hasil. Penelitian menunjukkan bahwa tiga segmen pengembangan padi, dan memperkirakan produksi adalah 1.787 ton dengan perkiraan panen maksimum 1.924 ton dari data nyata 1,80 ton. Dan dengan skala kesalahan persentase rata-rata absolut 0,72% dan perbedaan 0,013 ton. AbstractPaddy growth in large areas is often not ideal. This can be caused by natural factors, types of rice varieties, and the treatment model used. This will also affect crop yields. The extent of land makes it difficult for farmers to monitor the unreachable part. Often monitoring of rice developments is done on the edge of the field but does not reach the middle area. This study proposes a monitoring system for rice development that can reach more broadly and estimate the yield of rice in every area of agriculture land. This system uses aerial images to reach a wider area and then estimates of agricultural production. Estimation of production is done by clustering images of agricultural areas using the K-Means method. This clustering uses HSV color parameters and Gabor textures as features of each part of the image. The result is a segment of the paddy area based on its growth. The number of segments corresponding to the age of the real Paddy will determine the estimated value of the yield. The research shows that three segments of rice development, and estimates the production is 1,787 tons with a maximum estimated harvest of 1,924 tons from the real data of 1, 80 tons. And with a mean absolute percentage error scale of 0.72% and a difference of 0.013 tons.
Studi Analisi Konsentrasi Warna Pada Cairan Pewarna Makanan Dengan Metode Pengukuran Optical Density Meiyanto, Onie; Gunawan, Agus Indra; Bayu Dewantara, Bima Sena
BRILIANT: Jurnal Riset dan Konseptual Vol 6 No 4 (2021): Volume 6 Nomor 4, November 2021
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1307.714 KB) | DOI: 10.28926/briliant.v6i4.718

Abstract

Metode Image Processing banyak diimplementasikan untuk mengidetifikasi suatu bentuk atau perubahan pada gambar untuk mendapatkan hasil identifikasi suatu percobaan. Dalam penelitian ini perpaduan Image Processing, optical density(OD) dan sensor rgb untuk menentukan kualitas campuran air yang didapatkan nilai komposisi cairan warna. Karakteristik warna dari sampel air diperoleh dari histogram pada gambar yang tertangkap oleh mikroskop digital, dari histogram warna dapat diperoleh nilai max dan mean dan hasil gambar dari difraksi oleh kamera digital serta nilai output sensor rgb. Dengan metode tersebut diperoleh hasil setiap sampel yang telah di encerkan memiliki karakteristik warna yang berbeda-beda, hal ini dapat dilihat dari setiap kanal warna dari output sensor. Pengolahan data dengan metode histogram untuk dilakukan proses pengambilan nilai rata-rata(mean) dan nilai maksimum(Max) diperoleh model untuk memprediksi jenis dan konsentrasi dari sampel, pengujian yang telah dilakukan, didapatkan hasil grafik yang sigifikan sesuai dengan komposisi kualitas air dengan pewarna makanan
Concept and Design of Anthropomorphic Robot Hand with a Finger Movement Mechanism based on a Lever for Humanoid Robot T-FLoW 3.0 Apriandy, Kevin Ilham; Ulurrasyadi, Faiz; Dewanto, Raden Sanggar; Dewantara, Bima Sena Bayu; Pramadihanto, Dadet
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.
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 APRIANDY, KEVIN ILHAM Arif Hidayah Arif Hidayah Arifin, Muhammad Jainal 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 Daffa, Muhammad Fariz Dewanto, Raden Sanggar Dewi Mutiara Sari Djoko Purwanto Edo Bagus Prastika Endra Pitowarno Fadhillah, Excel Daris 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 Kisron Kisron Linda Indrayanti Lusiana Lusiana M Udin Harun Al Rasyid, M Udin Harun Makarim, Ahmad Fauzi MARTINI, NI PUTU DEVIRA AYU Meiyanto, Onie Mohamad Walid Asyhari Mohamad Walid Asyhari Muhammad Abdul Haq Muhammad Anwar Sanusi Muhammad Faiz Naohiro Hozumi 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 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 ULURRASYADI, FAIZ Wahjoe Tjatur Sesulihatien Wahjoe Tjatur Sesulihatien Wibowo, Iwan Kurnianto