Article Per Year (5 Year)
p-Index From 2021 - 2026
3.907
P-Index
This Author published in this journals
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

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network Zendi Iklima; Trie Maya Kadarina; Rinto Priambodo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 4 No 4 (2022): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v4i4.255

Abstract

A Convolutional Neural Network (CNN) classifier is generally utilized to classify an image tensor according to the mapped labels. The simplification of the classifier causes CNN to be often used to classify images, especially in the biomedical field. Thus, CNN is widely used to classify computer tomography (CT) and chest X-ray (CXR) images against the mapped labels. Several transfer learning models were implemented to classify CXR images for preliminary detection of COVID-19 infection, e.g., ResNet, Inception, Xception, etc. However, a transfer learning model has a maximum and minimum input resolution. Thus, the computational cost tends to be huge and unable to be optimized. Therefore, A custom CNN model can be a solution to reduce computational costs by configuring the feature extraction layers. This study proposed an efficient reduction of feature extraction for COVID-19 CXR namely Depthwise Separable Convolution Network. Furthermore, numerous strategies were adopted to lower the computational cost while retaining accuracy, including customizing the Batch Normalization (BN) layer and replacing the convolution layer with a separable convolution layer. The proposed model successfully reduced the feature extraction represented by the decreases in trainable parameters from 28.640 trainable parameters to 4.640 trainable parameters. The depthwise separable convolution effectively retains the performance accuracy 72.96%, loss 12.43%, recall 74.67%, precision 77.67%, and F1-score 75.33%. The CXR augmentation is also successfully increase the performance accuracy 74.55%, loss 11.37%, recall 77.67%, precision 79.56%, and F1-score 78.33%.
Impact of Moving Sign (Running Text) Implementation at PKBM Wiyata Utama Julpri Andika; Yudhi Gunardi; Triyanto Pangaribowo; Heru Suwoyo; Muhammad Hafizd Ibnu Hajar; Ketty Siti Salamah; Zendi Iklima; Rachmat Muwardi
Jurnal Abdi Masyarakat (JAM) Vol 8, No 1 (2022): JAM (Jurnal Abdi Masyarakat)-September
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jam.v8i1.16577

Abstract

The running information display board or Running Text is one of the information media or digital publications comprised of an ordered pattern of Light Emitting Diode (LED) lights, and each LED has a coordinate point that determines which LED position is on or off. This LED light is available in a range of colors, including red, yellow, green, blue, white, and blended hues. This running text is often used in Office Buildings, School Buildings, Shopping Buildings, and other locations where the general public must be informed. At this community service, running text has been installed in the PKBM Wiyata Utama school environment in Kembangan Utara, West Jakarta, which is suitable for school-related information media such as education level, school name, and school events
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%.
Dental caries classification using depthwise separable convolutional neural network for teledentistry system Trie Maya Kadarina; Zendi Iklima; Rinto Priambodo; Riandini Riandini; Rika Novita Wardhani
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4428

Abstract

Caries may be halted or reversed in their progression by early detection, better hygiene habits, and coadministered drugs. The major clinical procedures for identifying dental caries are visual-tactile examination and dental radiography. However, due to their location, approximate caries exceedingly difficult to detect and affect the clinical assessment. Incorrect interpretations may also hinder the diagnostic procedure. Computational approaches and technology can be used to help dentists assess caries. Teledentistry has the ability to improve dental health care by providing access to dental care services from a remote location. Teledentistry helps identifying various stages of caries lesions using neural network and devices connected to the internet. This research develops an image classification for teledentistry systems using depthwise separable convolutional neural network. The trainable parameters reduction of depthwise separable convolution (DSC) successfully reduces the computational cost of conventional convolutional neural networks (CNN). As a result, the DSC model is reduced by 91.49% when compared to the traditional CNN model. Several DSC models improve conventional CNN accuracies in the training, validation, evaluation, and testing stages.
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.
Collision avoidance of mobile robot using Alexnet and NVIDIA Jetson Nano B01 Kristanto, Ferryawan Harris; Iklima, Zendi
Journal of Integrated and Advanced Engineering (JIAE) Vol 4, No 1 (2024)
Publisher : Akademisi dan Saintis Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v4i1.118

Abstract

In this research, an intelligence collision avoidance system on a mobile robot was designed using the AlexNet image classifier method. AlexNet is a convolutional neural network architecture that managed to win the ImageNet Large Scale Visual Recognition Challenge in 2012. The dataset consists of three categorical labels: blocked right, blocked left, and free. Images of 224 x 224 pixels were trained into two CNN architectures: AlexNet and ResNet-18. The performance of both architectures was examined in a testing environment. The system was built without real-time obstacles, instead using the side boundaries of the test lane. Analogously, if the mobile robot moves either through the side lane or off track, then these conditions are defined as a crash. From the entire research that was done, it was determined that intelligence collision avoidance models based on AlexNet were the most reliable models, with an average accuracy deviation rate of 6,00%. The true pre-trained AlexNet adopted from PyTorch Transfer Learning had 92.22% overall accuracy, while the non-trained AlexNet achieved 90.81% accuracy. It is also supported by the evidence that Intelligence Collision Avoidance Model-1 and Model-3 based on AlexNet didn’t lead the mobile robot to spin out and were stable in the test lane.
Dental Caries Segmentation using Deformable Dense Residual Half U-Net for Teledentistry System Iklima, Zendi; Trie Maya Kadarina; Priambodo, Rinto; Riandini, Riandini; Wardhani, Rika Novita; Setiowati, Sulis
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.511

Abstract

Clinical practitioners’ workload and challenges are significantly reduced by classifying, predicting, and localizing lesions or dental caries. In recent research, a high-reliability diagnostic system within deep learning models has been implemented in a clinical teledentistry system. In order to construct an efficient, precise, and lightweight deep learning architecture, it is dynamically structured. In this paper, we present an efficient, accurate, and lightweight deep learning architecture for augmenting spatial locations and improving the transformation modeling abilities of fixed-structure CNNs. Deformable Dense Residual (DDR) enhances the efficacy of the residual convolution block by optimizing its structure, thereby mitigating model redundancy and ameliorating the challenge of vanishing gradients encountered during the training stages. DDR Half U-Net presents notable advancements to the simplified U-Net framework across three pivotal domains: the encoder, decoder, and loss function. Specifically, the encoder integrates deformable convolutions, thereby enhancing the model's capacity to discern features of diverse scales and configurations. In the decoder, a sophisticated arrangement of dense residual connections facilitates the fusion of low-level and high-level features, contributing to comprehensive feature extraction. Moreover, the utilization of a weight-adaptive loss function ensures equitable consideration of both caries and non-caries samples, thereby promoting balanced optimization during training.
Segmentasi Hasil Potret Cacat Produk pada Spring Washer menggunakan Metode Mask RCNN Iklima, Zendi; Ningrum, Dinar Sakti Candra
Jurnal Ilmu Teknik dan Komputer Vol 8, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jitkom.v8i2.005

Abstract

Spring washer adalah salah satu komponen yang digunakan dalam industri otomotif dan bertindak sebagai sistem penggerak atau retensi untuk unit perakitan. Dalam penelitian ini dilakukan analisa terhadap kegagalan fungsi yaitu karat pada spring washer. Analisa dilakukan untuk mengetahui perbedaan antara spring washer yang berkarat dan tidak berkarat. Terjadinya sebuah karat atau korosi pada logam disebabkan oleh reaksi kimia atau elektrokimia dengan lingkungan. Pada penelitian ini, permasalahan tersebut bertujuan untuk memisahkan antara spring washer berkarat dan tidak berkarat menjadi kelas label yang dipadukan dengan metode teknologi pengolahan citra model klasifikasi. Pada beberapa penelitian yang sudah ada menggunakan algoritma klasisifikasinya dengan Convolutional Neural Network (CNN). Pada penelitian ini menggunakan metode deep learning untuk segmentasi dengan memilih arsitektur Mask R-CNN karena dikenal cukup handal untuk penanganan jumlah data yang banyak. Serta hasil pengujian serta analisa yang diperoleh pada sistem yang telah diuji pada penelitian yaitu setiap pengolahan dataset dapat memilih serta melakukan anotasi pada platform makesense.ai dan pengguna akan mendapat dataset untuk melakukan training atau pelatihan untuk segmentasi citra dataset spring washer yang berkarat sesuai dengan algortima yang sudah dibuat. Resolusi citra dan model arsitektur memiliki pengaruh dalam penentuan parameter pengujian objek yang berkarat pada spring washer. Metode Mask R-CNN dapat diaplikasikan deteksi karat pada citra spring washer. Dari proses ini didapat akurasi terbaik yaitu pada iterasi 1000 dengan loss sebesar 0,0921 pada ResNet50 dengan waktu 4.278 detik dan loss 0,1143 pada ResNet101 dengan perolehan waktu 4.456 detik untuk melakukan segmentasi serta deteksi citra. Pretrain Mask R-CNN dalam segmentasi pada dataset memudahkan pengguna sehingga bisa langsung digunakan tanpa harus menyusun layer per layer.
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.
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