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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi Jurnal Teknologi Elektro JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah FIFO InComTech: Jurnal Telekomunikasi dan Komputer INOVTEK Polbeng - Seri Informatika Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Abdi Masyarakat (JAM) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Abdidas Journal of Telecommunication, Electronics and Control Engineering (JTECE)
Trie Maya Kadarina
Univeristas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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Analisis Metode SIFT dan SURF untuk Sistem Pendeteksi Gambar Termanipulasi Penyerangan Copy-Move Forgery Regina Lionnie; Trie Maya Kadarina; Mudrik Alaydrus
InComTech : Jurnal Telekomunikasi dan Komputer Vol 8, No 3 (2018)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v8i3.3074

Abstract

Pemalsuan gambar digital telah menjadi salah satu taktik penyebar hoax yang merupakan ajang penyebar provokasi, menimbulkan kerusuhan dan kebencian. Untuk dapat memerangi pemalsuan gambar digital perlu diciptakan sebuah sistem yang dapat mendeteksi gambar tersebut merupakan gambar hasil manipulasi apa tidak. Pada penelitian ini gambar termanipulasi jenis penyerangan copy-move forgery dengan variasi penyekalaan dan rotasi telah dianalisa oleh metode SIFT dan SURF. Hasilnya kedua metode dapat mendeteksi gambar termanipulasi jenis penyerangan copy-move forgery dengan SIFT memberikan hasil dua kali lebih banyak kecocokan dibandingkan SURF dan SURF memberikan hasil pemrosesan waktu 0.33 kali lebih cepat dibandingkan SIFT.
Pelacakan Lokasi Pasien berbasis Internet of Things untuk Sistem Pendukung Layanan Kesehatan Ibu dan Anak Rinto Priambodo; Trie Maya Kadarina
Jurnal Inovtek Polbeng Seri Informatika Vol 5, No 2 (2020)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v5i2.1509

Abstract

Aplikasi berbasis Internet of Things (IoT) di bidang kesehatan memberikan kemudahan dalam melakukan pemantauan terhadap kondisi pasien. Sejumlah perangkat sensor dapat mengukur dan mengirimkan data kondisi pasien beserta lokasinya. Dari data tersebut dokter maupun paramedis kemudian dapat melakukan analisis dalam waktu nyata dari jarak jauh sehingga kondisi pasien dapat selalu terpantau dan pendeteksian dini terhadap kondisi darurat dapat dilakukan. Rekomendasi tindakan terkait kondisi dan lokasi pasien dapat diberikan dengan lebih tepat. Begitu pula dalam kasus pelayanan kesehatan ibu dan anak, adanya informasi lokasi akan memudahkan tidak hanya dokter maupun paramedis dalam penentuan tindakan tapi juga membantu pasien dalam melakukan tindakan secara mandiri jika diperlukan. Dengan demikian pengolahan dan penyajian data lokasi pasien yang baik dalam pelayanan kesehatan ibu dan anak sangat dibutuhkan. Aplikasi Elasticsearch, Logstash, dan Kibana (ELK) merupakan sebuah teknologi yang memiliki performa yang sangat baik dalam mengumpulkan data log dan data lainnya yang berasal dari berbagai sumber secara kontinyu dalam jumlah yang sangat besar dan menampilkannya dalam bentuk grafik dan peta. Penelitian ini bertujuan untuk mengembangkan sebuah sistem yang dapat menampilkan hasil pencatatan kondisi dan lokasi dari sejumlah pasien dalam waktu yang nyata menggunakan aplikasi ELK untuk kebutuhan pelayanan kesehatan ibu dan anak.
Realistic image synthesis of COVID-19 chest X-rays using depthwise boundary equilibrium generative adversarial networks Zendi Iklima; Trie Maya Kadarina; Eko Ihsanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5444-5454

Abstract

Researchers in various related fields research preventing and controlling the spread of the coronavirus disease (COVID-19) virus. The spread of the COVID-19 is increasing exponentially and infecting humans massively. Preliminary detection can be observed by looking at abnormal conditions in the airways, thus allowing the entry of the virus into the patient's respiratory tract, which can be represented using computer tomography (CT) scan and chest X-ray (CXR) imaging. Particular deep learning approaches have been developed to classify COVID-19 CT or CXR images such as convolutional neural network (CNN), and deep convolutional neural network (DCNN). However, COVID-19 CXR dataset was measly opened and accessed. Particular deep learning method performance can be improved by augmenting the dataset amount. Therefore, the COVID-19 CXR dataset was possibly augmented by generating the synthetic image. This study discusses a fast and real-like image synthesis approach, namely depthwise boundary equilibrium generative adversarial network (DepthwiseBEGAN). DepthwiseBEGAN was reduced memory load 70.11% in training processes compared to the conventional BEGAN. DepthwiseBEGAN synthetic images were inspected by measuring the Fréchet inception distance (FID) score with the real-to-real score equal to 4.3866 and real-to-fake score equal to 4.4674. Moreover, generated DepthwiseBEGAN synthetic images improve 22.59% accuracy of conventional CNN models.
Sentiment classification of delta robot trajectory control using word embedding and convolutional neural network Zendi Iklima; Trie Maya Kadarina; Muhammad Hafidz Ibnu Hajar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp211-220

Abstract

Sentiment classification (SC) is an important research field in natural language processing (NLP) that classifying, extracting and recognizing subjective information from unstructured text, including opinions, evaluations, emotions, and attitudes. Human-robot interaction (HRI) also involves natural language processing, knowledge representation, and reasoning by utilizing deep learning, cognitive science, and robotics. However, sentiment classification for HRI is rarely implemented, especially to navigate a robot using the Indonesian Language which semantically dynamics when written in text. This paper proposes a sentiment classification of Bahasa Indonesia that supports the delta robot to move in particular trajectory directions. Navigation commands of the delta robot were vectorized using a word embedding method containing two-dimensional matrices to propose the classifier pattern such as convolutional neural network (CNN). The result compared the particular architecture of CNN, GloVe-CNN, and Word2Vec-CNN. As a classifier method, CNN models trained, validated, and tested with higher accuracy are 98.97% and executed in less than a minute. The classifier produces four navigation labels: right means 'kanan', left means 'kiri', top means 'atas', bottom means 'bawah', and multiplier factor. The classifier result is utilized to transform any navigation commands into direction along with end-effector coordinates.
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%.
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.
Design and Implementation of IoT-Based Monitoring Battery and Solar Panel Temperature in Hydroponic System Rizky Rahmatullah; Trie Maya Kadarina; Bagus Bhakti Irawan; Reza Septiawan; Arief Rufiyanto; Budi Sulistya; Arief Budi Santiko; Puput Dani Prasetyo Adi; Nicco Plamonia; Ravindra Kumar Shabajee; Suhardi Atmoko; Dendy Mahabror; Yudi Prastiyono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26729

Abstract

Hydroponics is currently widely used for the effectiveness of farming in narrow areas and increasing the supply of food, especially vegetables. This hydroponic technology grew until it collaborated with the internet of things technology, allowing users to monitor hydroponic conditions such as temperature and humidity in the surrounding environment. This technology requires electronic systems to obtain cost-effective power coverage and have independent charging systems, such as power systems using solar panels, where the power received by solar panels from the sun is stored in batteries. It must ensure that the condition of the battery and solar panels are in good condition. The research contribution is to create a solar panel temperature monitoring system and battery power using Grafana and Android Application. Apart from several studies, solar panels are greatly affected by temperature, which can cause damage to the panels. If the temperature is too high, the battery and panel temperature monitoring system can help monitor the condition of the device at Grafana and Android application with sensor data such as voltage, current, temperature and humidity that have been tested for accuracy. Accuracy test by comparing AM2302 sensor with Thermohygrometer and INA219 sensor with multimeter and clampmeter, both of which have been calibrated. The sensor data gets good accuracy results up to 98% and the Quality-of-Service value on the internet of things network is categorized as both conform to ITU G.1010 QOS data based on network readings on the wireshark application. QOS results are 0% Packet loss with very good category, 14ms delay with very good category and Throughput 71.85 bytes/s.  With the results of sensor accuracy and QOS, the system can be relied upon with a high level of sensor accuracy so that environmental conditions are monitored accurately and good QOS values so data transmission to the server runs smoothly.
Analysis of Power Quality in Low Voltage Switch Panels in Real-Time Based on IoT Using the Fuzzy Logic Method Rahmawati, Yosy; Fadhilla , Karima Langgeng; Kadarina , Trie Maya
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10750

Abstract

This far power quality analysis from the PHBTR monitoring system is still done manually. This condition creates several challenges in efficiency and accuracy in detecting and responding to changes in PHBTR power quality. With manual processes, collecting and analyzing data related to power quality can be time consuming, and there is the potential for delays in identifying disturbances or anomalies that could affect PHBTR performance. Therefore, in this research an innovative step was taken by applying the fuzzy logic method to simplify and increase the accuracy of automatic power quality analysis. From the analysis results, it was found that the power quality at the MCC4, GRL2, and CKG116 substations when viewed from voltage, current, frequency, and temperature, the three substations were at normal indications (91.32) and in good condition (76.32). However, the load balance quality of the three substations is still not balanced with load imbalance percentages of 110%, 52% and 17% respectively. This is because there are consumers in one phase using higher power than consumers in another phase, so a load imbalance will occur. This can be caused by differences in the use of electrical equipment or loads on each phase. Through this effort, it is hoped that significant improvements in the efficiency and responsibility of the PHBTR monitoring system can be achieved
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.
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 Adi, Puput Dani Prasetyo Agam Deska Purwanto Akbar, Ishlahuddin Akhmad Wahyu Dani Akhmad Wahyu Dani, Akhmad Wahyu Arief Budi Santiko Arief Budi Santiko, Arief Budi Arief Rufiyanto Atmoko, Suhardi Bagus Bhakti Irawan Budi Sulistya Dendy Mahabror Eko Ihsanto Eko Ihsanto Fadhilla , Karima Langgeng Fadli Sirait Fina Supegina Hasna Naufal Giarniasih Hendi Hermawan Irawan, Bagus Bhakti Jusoh, Mohd Taufik Ketty Siti Salamah Lathifah Alfat, Lathifah M Hamsy Romario Mahabror, Dendy Mochamad Miftakhul Huda Mudrik Alaydrus Muhammad Hafidz Ibnu Fajar Muhammad Hafidz Ibnu Hajar Muhammad Hafizd Ibnu Hajar Nicco Plamonia Plamonia, Nicco Prastiyono, Yudi Puput Dani Prasetyo Adi Rahmatullah, Rizky Ramadhan, Akbar Nicko Ravindra Kumar Shabajee Regina Lionnie Reza Septiawan Riandini Riandini Riandini, Riandini Rika Novita Wardhani Rika Novita Wardhani Rinto Priambodo Rizky Rahmatullah Rufiyanto, Arief Sentosa, Arrival Dwi Septiawan, Reza Shabajee, Ravindra Kumar Suhardi Atmoko Sulis Setiowati Sulistya, Budi Suwoyo, Heru Triyanto Pangaribowo Utami, Eriska Wahyu Yosy Rahmawati Yudhi Gunardi Yudi Prastiyono Yuliza Yuliza Zendi Iklima