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Sinergi
Published by Universitas Mercu Buana
ISSN : 14102331     EISSN : 24601217     DOI : https://dx.doi.org/10.22441/sinergi
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high quality theoretical and empirical original research papers, review papers, and literature reviews that are closely related to the fields of Engineering (Mechanical, Electrical, Industrial, Civil, and Architecture). The theme of the paper is focused on new industrial applications and energy development that synergize with global, green and sustainable technologies. The journal registered in the CrossRef system with Digital Object Identifier (DOI). The journal has been indexed by Google Scholar, DOAJ, BASE, and EBSCO.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 14 Documents
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Search results for , issue "Vol 25, No 2 (2021)" : 14 Documents clear
MOTION CONTROL ANALYSIS OF TWO COLLABORATIVE ARM ROBOTS IN FRUIT PACKAGING SYSTEM Tresna Dewi; Citra Anggraini; Pola Risma; Yurni Oktarina; Muslikhin Muslikhin
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.013

Abstract

As robots' use increases in every sector of human life, the demand for cheap and efficient robots has also enlarged. The use of two or more simple robot is preferable to the use of one sophisticated robot. The agriculture industry can benefit from installing a robot, from seeding to the packaging of the product. A precise analysis is required for the installation of two collaborative robots. This paper discusses the motion control analysis of two collaborative arms robots in the fruit packaging system. The study begins with the relative motion analysis between two robots, starting with kinematics modeling, image processing for object detection, and the Fuzzy Logic Controller's design to show the relationship between the robot inputs and outputs. The analysis is carried out using SCILAB, open-source software for numerical computing engineering. This paper is intended as the initial analysis of the feasibility of the real experimental system.
SUSTAINABLE LANDSCAPE DESIGN CONCEPTS FOR GREEN OPEN SPACE MANAGEMENT Silia Yuslim; Hendrik Sulistio
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.012

Abstract

To achieve the target of 30% of the total area's green space area, causing Jakarta and other big cities to make efforts to accelerate the provision of green space. Efforts to provide and manage it did not go as expected. The problem lies in the planning, implementation, utilization, and control relating to institutions, financing, and community participation. That causes the management of Green Open Space (GOS) is not running effectively and efficiently. The main problem is the unavailability of clear guidelines on implementing green space management and ineffective phasing related to the determination of terms of reference with budget constraints. Based on previous research, the concept of sustainable landscape design in harmony with sustainable development is the right way to solve this problem. This study aims to discuss how the concept of sustainable landscape design can be bundled by governance involved in managing green space using action research based on soft systems methodology. It will provide a conceptual model of packaging efforts so that it becomes a reference in various actions to provide and manage green space to achieve effective and efficient GOS management.
SELF-COLLISION AVOIDANCE OF ARM ROBOT USING GENERATIVE ADVERSARIAL NETWORK AND PARTICLES SWARM OPTIMIZATION (GAN-PSO) Zendi Iklima; Andi Adriansyah; Sabin Hitimana
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.005

Abstract

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of  3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator  always gets the same output point which mapped to different input  values. The discriminator  produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis).  The PSO was successfully reduced the number of training epochs of the generator  only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE).
CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM Ardina Ariani; Samsuryadi Samsuryadi
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.009

Abstract

The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data.

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