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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Sinergi Jurnal Teknologi Elektro Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Abdi Masyarakat (JAM) Jurnal Ilmu Teknik dan Komputer Indonesian Journal of Electrical Engineering and Computer Science Journal of Integrated and Advanced Engineering (JIAE)
Zendi Iklima
Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Sinergi

 1 
PERANCANGAN SCORE BOARD DAN TIMER MENGGUNAKAN LED RGB BERBASIS ARDUINO DENGAN KENDALI SMART PHONE ANDROID Fina Supegina; Zendi Iklima
SINERGI Vol 19, No 1 (2015)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.498 KB) | DOI: 10.22441/sinergi.2015.1.003

Abstract

Smart Phone merupakan salah satu kecanggihan teknologi dibidang telekomunikasi yang didalamnya terdapat fitur-fitur yang dapat mempermudah pekerjaan manusia. Banyak sekali jenis smart phone  diantaranya adalah smart phone dengan OS Android. Smart phone Android merupakan smart phone yang mudah penggunaannya, baik untuk keperluan bisnis, pendidikan, hiburan dan lain-lain. Dengan media komunikasi, pertukaran informasi, pertukaran data dan sebagaginya akan terasa lebih mudah dan cepat. Kemajuan teknologi tersebut tentunya belum dapat memenuhi kebutuhan jasmani seseorang khususnya dalam bidang olahraga. Namun kehadirannya mampu mendorong kemudahan dalam bidang olahraga tersebut. Misalnya, penggunaan sistem penskoran dan timer yang menggunakan seven segment sehingga dapat digunakan pada kondisi indoor ataupun outdoor. Score board dan timer digunakan guna mempermudah juri atau wasit menentukan score dan waktu pertandingan pada beberapa cabang olahraga. Karena diketahui setiap cabang olahraga mempunyai peraturan yang berbeda prihal mengenai sistem penskoran dan waktu nya. Hasil dari penelitian ini adalah menghasilkan suatu score board dan timer menggunakan LED RGB yang dapat dikontrol melalui smart phone android. Score board dan timer yang dibuat mampu digunakan dalam beberapa cabang olahraga seperti basket, badminton, footsal dan volley.
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).
SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS Zendi Iklima; Muhammad Imam Muthahhar; Asif Khan; Arifiansyah Zody
SINERGI Vol 25, No 3 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.3.001

Abstract

As known as Parallel-Link Robot, Delta Robot is a kind of Manipulator Robot that consists of three arms mounted in parallel. Delta Robot has a central joint constructed as an end-effector represented as a gripper. An Analysis of Inverse Kinematic (IK) used to convert the end-effector trajectory (X, Y) into rotations of stepper motors (ZA, ZB and ZC). The proposed method used Artificial Neural Networks (ANNs) to simplify the process of IK solver. The IK solver generated the datasets contain motion data of the Delta robot. There are 11 KB Datasets consist of 200 motion data used to be trained. The proposed method was trained in 58.78 seconds in 5000 iterations. Using a learning rate (α) 0.05 and produced the average accuracy was 97.48%, and the average loss was 0.43%. The proposed method was also tested to transfer motion data over Socket.IO with 115.58B in 6.68ms.
Defect classification of radius shaping in the tire curing process using Fine-Tuned Deep Neural Network Zendi Iklima; Bugi Nur Rohman; Rahmat Muwardi; Asif Khan; Zody Arifiansyah
SINERGI Vol 26, No 3 (2022)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2022.3.009

Abstract

The curing process or vulcanization process is the final stage of the tire manufacturing process, where the properties of the tire compound change from rubber-plastic material to become elastic by forming cross-links in its molecular structure. The green tire is formed in the curing process, which is placed on the bottom mould. The inside of the green tire surrounds the bladder. The top mould will close to carry out the next curing process. In closing the mould, there is a shaping process of forming a green tire placed on the bladder and given a proportional pressure. Improper or abnormal radius shaping results cause seventy percent of product defects. This paper proposed abnormal detection of radius shaping in the curing process using Fine-tuned Deep Neural Network (DNN). Several DNN models have been examined to analyze an optimized DNN model for abnormal detection of radius shaping in the curing process. The fine-tuned DNN architecture has been exported for the curing system. The DNN was trained with a training accuracy of 97.88%, a validation accuracy of 95%, a testing accuracy of 100%, and a loss of 4.93%.
Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family Mirna Yunita; Rachmat Muwardi; Zendi Iklima
SINERGI Vol 27, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023..1.004

Abstract

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.   
Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture Khoirul Aziz; Inggis Kurnia Trisiawan; Kadek Dwi Suyasmini; Zendi Iklima; Mirna Yunita
SINERGI Vol 27, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023.2.003

Abstract

Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.
A simplified dental caries segmentation using Half U-Net for a teledentistry system Kadarina, Trie Maya; Iklima, Zendi; Priambodo, Rinto; Riandini, Riandini; Wardhani, Rika Novita; Setiowati, Sulis; Jusoh, Mohd Taufik
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.005

Abstract

High-reliability diagnostic equipment efficiently supported by a computer-based diagnostics system. For instance, a computational approach establishes a model that can diagnose diseases. Artificial intelligence has been applied to aid in the field of medical imaging. Classification, prediction, and localisation of lesions or dental caries greatly minimise the load and difficulties for clinical practitioners. In this study, U-Net architectures are simplified to propose the feature reduction of the decoder layers. This simplification of U-Net architectures is utilised for segmented dental caries images. This paper simplified the U-Net decoder layers into the level of blocks Half-UNet () and Half-UNet (). The Half-UNet structural model surpasses the U-shaped structural model in terms of efficiency and segmentation capabilities. The simplification of the UNet architecture outperformed using Half-UNet 0.83% of the dice coefficient. The Half-UNet design is able to preserve model performance in segmenting actual images and ground truth against expected ground truth.
Performance of speech enhancement models in video conferences: DeepFilterNet3 and RNNoise Maulana, Muhammad Iqbal; Raisul Akbar, Muhammad Fadhlillah; Iklima, Zendi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.001

Abstract

As remote work and online education continue to gain prominence, the importance of clear audio communication becomes crucial. Deep Learning-based Speech Enhancement has emerged as a promising solution for processing data in noisy environments. In this study, we conducted an in-depth analysis of two speech enhancement models, RNNoise and DeepFilterNet3, selected for their respective strengths. DeepFilterNet3 leverages time-frequency masking with a Complex Mask filter, while RNNoise employs Recurrent Neural Networks with lower complexity. The performance evaluation in training revealed that RNNoise demonstrated impressive denoising capabilities, achieving low loss values, while DeepFilterNet3 showed superior generalization. Specifically, "DeepFilterNet3 (Pre-Trained)" exhibited the best overall performance, excelling in intelligibility and speech quality. RNNoise also performed well in subjective quality measures. Furthermore, we assessed the real-time processing efficiency of both models. Both RNNoise variants processed speech signals almost in real-time, whereas DeepFilterNet3, though slightly slower, remained efficient. The findings demonstrate significant improvements in speech quality, with "DeepFilterNet3 (Pre-Trained)" emerging as the top-performing model. The implications of this study have the potential to enhance video conference experiences and contribute to the improvement of remote work and online education.
Real-time dental caries segmentation with an efficient Deformable U-Net (DU-Net) for teledentistry system Iklima, Zendi; Kadarina, Trie Maya; Salamah, Ketty Siti; Sentosa, Arrival Dwi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.015

Abstract

Digital technology has greatly improved teledentistry by facilitating telediagnostics and teleconsultations, particularly benefiting those in remote areas. Additionally, AI advancements enhance diagnostic accuracy and streamline clinical decision-making, reducing costs and resource disparities in dental care. This study presents an improved U-Net architecture, Deformable U-Net (DU-Net), for semantic dental caries segmentation, leveraging deformable convolutions to dynamically adjust sampling points for improved feature extraction and reduced computational redundancy. By connecting encoder-decoder blocks via skip-connections, the DU-Net architecture enables efficient real-time segmentation and balance accuracy while reducing computational demands. The deformable block in DU-Net and DDR U-Net shows a balanced performance and efficiency while maintaining accuracy despite reduced FLOPs. The proposed architecture was implemented in real-time dental caries segmentation on a Dual Core Cortex A72 system and web server. It shows a significant improvement in Dice score, reducing CPU and memory usage compared to conventional U-Net models. Moreover, the DU-Net and its half variants achieved competitive performance with much lower computational demands makes suitable for web servers and embedded applications. The result highlights the DU-Net capability to optimize both computational efficiency and segmentation accuracy, offering a promising solution for real-world applications where speed and resource management are critical, particularly in the medical imaging field.
Co-Authors Andi Adriansyah Andi Adriansyah Annisa Ayu Wardani Arifiansyah Zody Asif Khan Bugi Nur Rohman Eko Ihsanto Fina Supegina Hitimana, Sabin Inggis Kurnia Trisiawan Jody Maulana Anggi Julpri Andika Jusoh, Mohd Taufik Kadek Dwi Suyasmini Ketty Siti Salamah Khan, Asif Khoirul Aziz Kristanto, Ferryawan Harris Maulana, Muhammad Iqbal Mirna Yunita Muhammad Hafidz Ibnu Hajar Muhammad Hafizd Ibnu Hajar Muhammad Imam Muthahhar Muthahhar, Muhammad Imam Ningrum, Dinar Sakti Candra Rachmat Muwardi Raisul Akbar, Muhammad Fadhlillah Ramadhani, Nandiko Riandini Riandini Riandini, Riandini Rika Novita Wardhani Rika Novita Wardhani Rinto Priambodo Sabin Hitimana Sentosa, Arrival Dwi Sulis Setiowati Suwoyo, Heru Trie Maya Kadarina Triyanto Pangaribowo Yananto Mihadi Putra Yudhi Gunardi Zody Arifiansyah Zody, Arifiansyah