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All Journal International Journal of Electrical and Computer Engineering Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Kitektro JOURNAL OF INFORMATICS AND COMPUTER SCIENCE JURNAL NASIONAL TEKNIK ELEKTRO Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Rekayasa elektrika Jurnal Pengabdian Rekayasa dan Wirausaha
Fitri Arnia
Department Of Electrical And Computer Engineering, Universitas Syiah Kuala, Indonesia
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Transportation Other

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Penerapan Deskriptor Warna Dominan untuk Temu Kembali Citra Busana pada Peranti Bergerak Yustina Dhyanti; Khairul Munadi; Fitri Arnia
Jurnal Rekayasa Elektrika Vol 12, No 3 (2016)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v12i3.5701

Abstract

Nowadays, clothes with various designs and color combinations are available for purchasing through an online shop, which is mostly equipped with keyword-based item retrieval. Here, the object in the online database is retrieved based on the keyword inputted by the potential buyers. The keyword-based search may bring potential customers on difficulties to describe the clothes they want to buy. This paper presents a new searching approach, using an image instead of text, as the query into an online shop. This method is known as content-based image retrieval (CBIR).  Particularly, we focused on using color as the feature in our Muslimah clothes image retrieval. The dominant color descriptor (DCD) extracts the wardrobe's color. Then, image matching is accomplished by calculating the Euclidean distance between the query and image in the database, and the last step is to evaluate the performance of the DWD by calculating precision and recall. To determine the performance of the DCD in extracting color features, the DCD is compared with another color descriptor, that is dominant color correlogram descriptor (DCCD). The values of precision and recall of DCD ranged from 0.7 to 0.9 while the precision and recall of DCCD ranged from 0.7 to 0.8. These results showed that the DCD produce a superior performance compared to DCCD in retrieving a set of clothing image, either plain or patterned colored clothes.
Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning Ilal Mahdi; Kahlil Muchtar; Fitri Arnia; Tia Ernita
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i2.25224

Abstract

Moving object detection systems have been widely used in everyday life. Currently, research in the field of background subtraction is still being carried out to achieve maximum accuracy results. This study aims to model the background subtraction of an image using the mean value with the concept of non-overlapping block. Furthermore, the background abstraction results will be used in deep learning-based moving object detection. Specifically, the input image will be divided into several blocks, then the mean value of each block will be calculated to later produce a binary block (binary map). The binary blocks that have been generated will be used as input for background modeling. The background model aims to separate moving objects from the background in the input image. The resulting moving object (object localization) will be sent to the object classification stage using deep learning. The dataset used in this study is CDNet 2014. The results of the study were able to produce a more accurate moving object detection system. Quantitative tests carried out resulted in an accuracy of above 90%.
Simulasi Pelacakan Titik Daya Maksimum Modul Surya dengan Metode Grey Wolf Optimization Rizki Faulianur; Ira Devi Sara; Fitri Arnia
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.8973

Abstract

The photovoltaic module has a nonlinear current and voltage characteristic curve where there is a maximum power point to be tracked to avoid wasted energy. Some methods for tracking the maximum power points have been developed such as perturb and observe (P O), Incremental Conductance (IC), and Hill Climbing (HC). However, those methods were not so accurate to find the maximum power point and they were also slow to respond the changes in solar radiation and temperature. To overcome the shortcomings of the method, a new optimization approach was developed. This method is called Gray Wolf Optimization (GWO). It work based on the wolf behavior in capturing the prey. In this study, it will be determined to what extent the GWO method can track the maximum working point of solar modules that undergo changes in radiation and working temperature quickly and accurately. This research was conducted by simulation using Matlab/Simulink by comparing the extract of power GWO method with its power characteristics. The results obtained by the GWO method trace maximum power with an average accuracy rate of 99.14 % with time less than 0.1 second. From this data, it can be concluded that the GWO method successfully responds well and accurately to changes in radiation and temperature.
Fine Tuning CNN Pre-trained Model Based on Thermal Imaging for Obesity Early Detection Hendrik Leo; Fitri Arnia; Khairul Munadi
Jurnal Rekayasa Elektrika Vol 18, No 1 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i1.25100

Abstract

Obesity is a complex disease that causes serious impact health, such as diabetes mellitus, cardiovascular disease, cancer, and stroke. An early obesity diagnosis/ detection method is required to prevent the increasing number of obese people. This study aims to: (i) fine-tune the pre-trained Convolutional Neural Network (CNN) models to build an early detection of obesity and (ii) evaluate the model performance in terms of classifying performance, computation speed, and learning performance. The thermal images acquisition procedure was conducted with 18 normal subjects and 15 obese subjects to build a thermal images dataset of obesity. Pre-trained CNN models: VGG19, MobileNet, ResNet152V, and DenseNet201 were modified and trained using the acquired dataset as the input. The training results show that the DenseNet201 model outperformed other models regarding classifying accuracy: 83.33 % and learning performances. At the same time, the MobileNet model outperformed other models in terms of computation speed with training elapsed time: 12 seconds/epoch. The proposed DenseNet201 model was suitable for implementation as an early screening system of obesity for health workers or physicians. Meanwhile, the proposed MobileNet model was suitable for mobile applications' early detection/diagnosis of obesity.
Identifikasi Tingkat Kematangan Kelapa Sawit Berbasis Pencitraan Termal Khusnul Azima; Khairul Munadi; Fitri Arnia; Maulisa Oktiana
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i1.12963

Abstract

Indonesia is the biggest producer of palm oil (Elaeis guineenis jacq).  The palm tree is a primary commodity that posses a high economic value. Palm oil must be considered in terms of quality to produce optimal and high-quality oil. Previously, the stipulation of the palm tree characterization used manual and visual image utilization method; it may have weaknesses due to the dependency of individual sorting and coruscation factor. Therefore, this research is aimed to improve the performance of the previous method in identifying the ripeness of palm tree based on thermal imaging. The excess of thermal imaging was not related to the coruscation since the level of ripeness was both determined by the temperature and colour. The detection method of this research deployed the colour-based features that are Dominant Colour Descriptor and Color Moment. The DCD  and Color Moment was the input to the K-Nearest Neighbor (KNN) method.  The percentage of identification rate was 89%, and the identification of oil palm maturity level using thermal imaging is more efficient because it is done without human intervention and does not depend on lighting assistance compared to manual method and method of using RGB visual images.
Sistem Multi-Sensor Nirkabel Berbasis RFID Untuk Pemantauan Keaktifan Siswa Zakiah Zakiah; Yuwaldi Away; Fitri Arnia; Andri Novandri
Jurnal Rekayasa Elektrika Vol 15, No 3 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i3.14107

Abstract

This study aims to design a multisensor observation system, develop a microcontroller-based prototype with ESP8266 and RFID modules, and analyze the performance of the prototype. From the testing carried out by tagging each time students do activities, the client tag data will be sent to the server, and the results are displayed PLX-DAQ. With the 50 tag cards that have registered the IDs of each student, it shows that those who attend the schedule will be given the logic "1" (otherwise logic "0"), and attendance data will be obtained by calculating the number of attendance of students in four types of activities in four locations. The data is transmitted to Data-loggers through two configurations, namely Data-logger as the client (indirect), and Data-logger as a server (direct). From the two configurations, it was found that the configuration of Data-logger as a server had a performance of 19.08% better than Data-logger as a client. From the data processing, it was found that the highest activity of students was the interest in the activities of language institutions (95.92%), followed by religious activities (95.83%), teaching and earning activities (93.88%), and reading (79.59 %)
Adaptasi Model CNN Terlatih pada Aplikasi Bergerak untuk Klasifikasi Citra Termal Payudara Roslidar Roslidar; Muhammad Rizky Syahputra; Rusdha Muharar; Fitri Arnia
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i3.8754

Abstract

The model development for breast thermal image classification can be done using deep learning methods, especially the convolutional neural network (CNN) architecture. This article focuses on adapting a trained CNN (trained model) on a mobile application for binary classification of breast thermal images into normal and abnormal classes. The CNN model applied in this study was based on ShuffleNet, called BreaCNet, with a learning weight of 1028 filters generated from training on images downloaded from the Database for Mastology Research (DMR) and a model size of 22 MB. The model must be converted into a mobile application to enable a trained model to be adapted into a mobile platform. The BreaCNet model was built using MatLab; thus, the stages in the adaptation process consisted of converting the model into ONNX file format, converting ONNX files into Tensorflow files, and Tensorflow files into Tensorflow Lite format. However, not all nodes are fully supported by MATLAB. The shuffle node on ShuffleNet cannot be fully exported using ExportToOnnx, so it needs to be re-defined with a placeholder named “MATLAB PLACEHOLDER”. In addition to the model conversion process, this article describes the user interaction process with the application using UML diagrams and application feature menu designs. The application was also tested on 20 thermal images of the breast. The testing results show that the application can perform the image classification process on mobile devices in less than 1 second with an accuracy rate of 85%. Finally, the breast thermal image screening application has been successfully built by directly interpreting the thermal image of the breast on a mobile device to keep the user data private.
Aplikasi Histogram Discrete Cosine Transform (DCT) Untuk Sistem Temu Kembali Citra Termal Berbasis Konten Faridah Faridah; Khairul Munadi; Fitri Arnia
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 2, No 1 (2019): APRIL 2019
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v2i1.1055

Abstract

Content Based Image Retrieval (CBIR) merupakan sistem yang digunakan untuk menemukan kembali gambar dari sebuah arsip gambar yang besar (database) berdasarkan isi (content) query gambar. Salah satu bidang yang telah menerapkan teknologi pencarian citra adalah bidang medis. Pada penelitian ini, CBIR diterapkan untuk menemukan kembali citra termal tangan dan kaki, tangan dan kaki dipilih karena banyaknya pasien yang memiliki masalah kesehatan pada bagian anggota tubuh tersebut, seperti patah tulang dan penyakit kulit. Kinerja CBIR dievaluasi dengan mengukur nilai recall, precision, dan f-measure dari hasil temu kembali citra query tangan dan query kaki. Hasil temu kembali terbaik diperoleh pada citra termal kaki dengan nilai recall mencapai 100% sedangkan citra termal tangan hanya 90%.
Cross-Spectral Cross-Distance Face Recognition via CNN with Image Augmentation Techniques Rahmatika, Nisa Adilla; Arnia, Fitri; Oktiana, Maulisa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5929

Abstract

Facial recognition is a critical biometric identification method in modern security systems, yet it faces significant challenges under varying lighting conditions, particularly when dealing with near-infrared (NIR) images, which exhibit reduced illumination compared to visible light (VIS) images. This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. Three CNN models—VGG16, ResNet50, and EfficientNetB0—were assessed using a dataset comprising 800 facial images from 100 individuals, captured at four different distances (1m, 60m, 100m, and 150m) and across two wavelengths (NIR and VIS). The Multi-task Cascaded Convolutional Networks (MTCNN) algorithm was employed for face detection, followed by image preprocessing steps including resizing to 224x224 pixels, normalization, and homomorphic filtering. Two distinct data augmentation strategies were applied: one utilizing 10 different augmentation techniques and the other with 4 techniques, trained with a batch size of 32 over 100 epochs. Among the tested models, VGG16 demonstrated superior performance, achieving 100% accuracy in both training and validation phases, with a training loss of 0.55 and a validation loss of 0.612. These findings underscore the robustness of VGG16 in effectively adapting to the CSCD setting and managing variations in both lighting and distance.
Analisis Laju Pembelajaran untuk Pengenalan Nyeri Melalui Metode Viola-Jones dan Pembelajaran Mendalam Raihan Islamadina; Khairun Saddami; Fitri Arnia; Taufik Fuadi Abidin; Rusdha Muharar; Muhammad Irwandi; Aulia Syarif Aziz
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i2.9466

Abstract

Deep learning is growing and widely used in various fields of life. One of which is the recognition of pain through facial expressions for patients with communication difficulties. Viola-Jones is a simple algorithm that has real-time detection capabilities with relatively high accuracy and low computational power requirements. The learning rate is a significant number that has an impact on the deep learning result. This study recognized pain using the Viola-Jones and deep learning methods. The dataset used was a thermal image from the Multimodal Intensity Pain (MIntPAIN) database. The steps taken consisted of segmentation, training, and testing. Segmentation was conducted using the Viola-Jones method to get the significant area of the face image. The training process was carried out using four deep learning benchmarks model, which were DenseNet201, MobileNetV2, ResNet101, and EfficientNetb0. Besides that, deep learning has a very important number to determine that is learning rate, which impact the deep learning results. There were five learning rates, which were 10-1, 10-2, 10-3, 10-4, and 10-5. Learning rate values were then compared with four deep models learning to obtain high accuracy results in a short time and simple algorithm. Finally, the testing process was carried out on test data using a deep learning benchmark model in accordance with the training process. The research results showed that a learning rate of 10-2 from the MobileNetV2 method produced an optimal performance with a training validation accuracy of 99.60% within a time of 312 min and 28 s.
Co-Authors . Melinda . Roslidar ., Safrizal ., Zulfan Abbas Adam AzZuhri Akhyar Bintang Andika Saputra Arsy Febrina Dewi Aulia Syarif Aziz Bahri, Syamsul Cut Mutia Cut Mutia Devi Sara, Ira Dwipayana, Mahendar Elizar Elizar Fardian Fardian Fardian Fardian Faridah Faridah Fathurrahman Fathurrahman Fery Irianda Fikri, Rizal Hardian Saputra Hayatun Maghfirah Hendra Hidayat Hendri Syahputra Hendrik Leo Hubbul Walidainy Ilal Mahdi Iqbal, TWK Muhammad Kahlil Muchtar Khairul Fajri Khairul Munadi Khairul Munadi Khairul Munadi Khairul Munadi Khairul Munadi Khairul Munadi Khairun Saddami Khairun Saddami Khairun Saddami Khusnul Azima Laila Nujmi Burhan Lina Marlina Listia Sukma Putri Maghfirah, Hayatun Maulisa Oktiana Maya Fitria Maya Muthia Muchtar, Kahlil Muhammad Haries Muhammad Irhmasyah Muhammad Irwandi Muhammad Rizky Syahputra Muharar, Rusdha Muharar, Rusdha Munadi, Khairul Nailul Mustaqim Abdi Nargaza, Juanda Nasaruddin Nasaruddin Novandri, Andri Nur Amalia Hasma Nuriza Pramita Nuriza Pramita Nuriza Pramita Oktiana, Maulisa Oktiana, Maulisa Putri Rizkiah Rahmatika, Nisa Adilla Raihan Islamadina Raihan Islamadina Raihan Islamadina Ramadhani Ramadhani Ramiady, Luthfiar Ramzi Adriman Risnaty Utami Marsal Rizal Fikri Rizka Ramadhana Rizki Faulianur Roslidar Roslidar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar, Rusdha Rusdha Muharrar Saddami, Khairun Safrizal Razali Saputra, Andika Siti Aisyah Siti Aisyah Syahputra, Hendri Syahrul Wahyudi Syamsul Bahri Tata Arsatria Taufik Fuadi Abidin Taufik Fuadi Abidin Taufik Fuadi Abidin Tia Ernita TWK Muhammad Iqbal Yunida, Yunida Yunidar Yusni, Y Yustina Dhyanti Yuwaldi Away Zakiah Zakiah Zharifah Muthiah Zuhar Musliyana Zuhar Musliyana, Zuhar Zul Syukri Zulfan .