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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Masyarakat dan Riset Pendidikan
APRIANDY, KEVIN ILHAM
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Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Analisis Kinematika dan Pola Gerakan Berjalan pada Robot Bipedal Humanoid T-FLoW 3.0 WIJAYA, RYAN SATRIA; APRIANDY, KEVIN ILHAM; AL BANNA, M. RIZQI HASAN; DEWANTO, RADEN SANGGAR; PRAMADIHANTO, DADET
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 1: Published January 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i1.31

Abstract

ABSTRAKRobot humanoid merupakan robot menyerupai manusia dengan tingkat kompleksitas yang tinggi dan fungsi yang serbaguna. Pada penelitian ini dilakukan analisis model kinematika gerak pada robot bipedal humanoid TFLoW 3.0, serta menganalisis pola gerakan berjalannya. Pola pergerakan yang diimplementasikan pada robot bipedal TFLoW 3.0 merupakan hasil pendekatan dari teori cara berjalan manusia dengan menggunakan enam gerakan dasar manusia saat berjalan. Kemudian menganalisis model gerakan robot menggunakan kinematika terbalik dengan solusi geometri. Tujuan dari model kinematika terbalik adalah untuk mengubah data input berupa posisi kartesian menjadi nilai sudut untuk setiap parameter joint pada masing-masing Degrees of Freedom (DoF). Lalu dilakukan analisis model mekanik robot saat berjalan yang terbagi atas fase tegak dan fase berayun yang bertujuan untuk mengetahui hasil pengujian.Kata kunci: robot humanoid, gaya berjalan, kinematika, TFLoW, DoF. ABSTRACTHumanoid robots are human-like robots with a high level of complexity and versatile functions. In this study, kinematics analyze on TFLoW 3.0 humanoid bipedal robot is carried out, as well as analyzing the pattern of its walking movement. The implemented movement of TFLoW 3.0 bipedal robot is the result of an approach from human walk using six basic human movements when walking. the robot movement model is analyzed by inverse kinematics with geometric solutions. Invers kinematics model is to transform the input data in the form of a Cartesian position into an angle value for each joint parameter in each Degrees of Freedom (DoF). Then an analysis of the robot's mechanical model when walking is carried out which is divided into a stance phase and a swinging phase which aims to determine the test results.Keywords: humanoid robot, gait, kinematics, TFLoW, DoF.
The Impact of Image Pre-processing for Tuberculosis Prediction System Based on Chest X-ray Images Kurniawan, Rudi; Badriyah, Tessy; Apriandy, Kevin Ilham; Syarif, Iwan
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9086

Abstract

With the rapid development of automated detection system using deep learning techniques on Chest X-ray (CXR) image datasets to the subjective assessment performed by healthcare professionals. Preprocessing is critical in medical image analysis as it helps highlight important anatomical features while suppressing irrelevant information, thus enabling the model to focus on meaningful patterns. In this paper, we investigate the impact of image preprocessing techniques on the performance of a tuberculosis prediction system based on CXR images using a deep learning approach. We used the “Tuberculosis Chest X-rays (Shenzhen)” dataset, which contains 1,344 CXR images (672 TB cases and 672 normal cases). We propose a five-step preprocessing pipeline consisting of resizing, heavy sharpen filtering, CLAHE (Contrast Limited Adaptive Histogram Equalization), horizontal flip augmentation, and data normalization. The findings indicate that the model utilising preprocessing markedly surpasses the one lacking it, attaining an accuracy, precision, recall, and F1-score of 71%, in contrast to 51%, 50%, 50%, and 36% without preprocessing, respectively.  This study enhances the existing research on the application of deep learning in medical diagnostics and emphasises the significance of preprocessing for attaining dependable, high-performance systems.
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.
Instalasi Teknologi Hidroponik sebagai Upaya Pemberdayaan Masyarakat Desa Sukodono dalam Meningkatkan Kemandirian Pangan : Pengabdian Pradiza, Revvan Rifada; Siswantoro, Dwi Heru; Apriandy, Kevin Ilham; Asrofi, Mochamad; Dwilaksana, Dedi; Sutjahjono, Hary; Yani, Luluk Fitri
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.3996

Abstract

This community service activity aims to improve the understanding and skills of residents of RT 07, Sukodono Village, Sidoarjo, in applying hydroponic technology as a solution for managing limited land and supporting food security. This activity consists of lectures and discussions, as well as the installation of a hydroponic system. During the lectures and discussions, residents were given an understanding of the basic principles of hydroponics, its benefits, and how to apply it in limited land. Discussions were held to explore more deeply the challenges and potential of applying hydroponics in a household environment. The final stage, the installation of the hydroponic system, involved residents directly in the creation and installation of a simple but effective hydroponic system. Although there was no harvest to report at the time of the activity, residents successfully understood and installed the hydroponic system correctly. This success shows that residents of RT 07, Sukodono Village have the potential to manage hydroponic gardens independently. This activity made a significant contribution to increasing food self-sufficiency at the local level, even though challenges in terms of maintenance and limited resources remain.
A VGG16 CNN-based Method for Multiclass Lung Cancer Classification using CT Imaging Sari, Sekar; Muniroh, Muna Afdi; Apriandy, Kevin Ilham
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1483

Abstract

Lung cancer is the leading cause of death worldwide among all types of cancer. Early detection and accurate classification are essential to prevent disease progression and improve patient survival rates. One effective approach is the use of computer-aided diagnosis (CAD) systems based on medical imaging, particularly CT scans, which provide high-resolution and non-invasive visualization of lung structures, including blood vessels, soft tissues, and lesions or nodules. This study proposes a VGG16 CNN-based multiclass classification method for lung cancer. Unlike previous studies that primarily focus on binary classification, this research addresses four distinct classes of lung nodule CT images to better reflect complex clinical needs. The modified VGG16 architecture incorporates additional layers, including Flatten, Dense, and Dropout, along with the Softmax activation function, effectively improving classification performance and reducing overfitting risk. An ablation experiment was also conducted by replacing ReLU with LeakyReLU to address the potential “dying ReLU” issue. However, the results indicated that LeakyReLU did not provide significant improvement over the standard ReLU. The proposed model achieved an accuracy of 90.72%, precision of 91.5%, sensitivity of 89.25%, specificity of 96.76%, F1-score of 90%, and a low loss value of 0.37. Furthermore, the modified VGG16-CNN outperformed other CNN architectures, including ResNet50, EfficientNetB1, MobileNetV2, and AlexNet, in multiclass lung cancer image classification. The results demonstrate that the proposed method is effective for diagnosing lung nodules from CT scans and has the potential to support medical professionals in making accurate and timely diagnoses.
Co-Authors AL BANNA, M. RIZQI HASAN Bima Sena Bayu Dewantara Dadet Pramadihanto Dedi Dwilaksana Dewanto, Raden Sanggar Hary Sutjahjono iwan Syarif Mochamad Asrofi Muniroh, Muna Afdi Pradiza, Revvan Rifada Rudi Kurniawan Ryan Satria Wijaya Sari, Sekar Siswantoro, Dwi Heru Tessy Badriyah, Tessy ULURRASYADI, FAIZ Yani, Luluk Fitri