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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

Author-ID : 233702

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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Articles

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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

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.

Pengenalan Karakter Plat Nomor Kendaraan Bermotor Menggunakan Zoning dan Fitur Freeman Chain Code Taufik Fuadi Abidin; Abbas Adam AzZuhri; Fitri Arnia
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

A license plate is one of the vehicle identities. It consists of alphabetic characters and numbers and represents provincial and area code where the vehicle is registered. This article discusses the character recognition of plate number using zoning and Freeman Chain Code (FCC). Zoning divides character image into several zones i.e. 4, 6, and 8, and then, the pattern of each character in the zone is extracted using FCC as the numerical features. The character is then classified using Support Vector Machines (SVM). It is a multi-class classification problem with 36 categories. The results show that FCC features with 8 zones give the best accuracy (87%) when compared to the other two zones.

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

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.

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

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

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 %)

Implementasi Haar-Like Feature Dan SVM Untuk Pengenalan Wajah Dari Citra Thermal Khairul Fajri; Fitri Arnia; Khairul Munadi
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Pengenalan wajah berbasis citra tampak atau visual menimbulkan permasalah iluminasi yang tidak merata. Iluminasi yang tidak merata ini tidak terjadi pada citra termal. Penelitian ini bertujuan untuk membangun suatu sistem pengenalan wajah berbasis citra termal dengan menggabungkan metode deteksi dan segmentasi wajah menggunakan haar-like feature, metode ekstraksi fitur berbasis tekstur dan klasifikasi menggunakan support vector machine (SVM). Citra termal terlebih dahulu dideteksi dan disegmentasi bagian wajah menggunakan metode haar-like feature. Setelah proses tersebut, citra diberikan noise atau gangguan untuk menambah variasi citra training. Noise yang ditambahkan adalah gaussian, salt & pepper, poison, blur dan kombinasi blurring dan gaussian noise. Selanjutnya, citra termal tersebut diekstraksi fiturnya menggunakan metode ekstraksi fitur berbasis tekstur, yaitu gray level co-occurrence matrix (GLCM), local binary pattern (LBP), dan Gabor-wavelet. Kemudian citra termal wajah dilatih menggunakan metode SVM dan menggunakan kernel radial basis function (RBF). Pada eksperimen, nilai gamma yang digunakan untuk kernel RBF adalah 10, 1, 10-1, 10-2, dan 10-3. Hasil penelitian menunjukkan bahwa metode ekstraksi fitur terbaik dalam mengenali wajah menggunakan citra termal adalah metode GLCM dengan tingkat akurasi mencapai 100%. Akurasi ini dicapai dengan menggunakan nilai gamma sebesar 10-1 untuk kernel RBF. Hasil terbaik untuk metode LBP adalah 70% akurasi dan untuk metode Gabor-wavelet adalah 40% akurasi. Hal ini menunjukkan bahwa metode GLCM memberikan hasil terbaik dalam ekstraksi fitur citra termal pada sistem klasifikasi pengenalan wajah berbasis citra termal.

Pengukuran Badan Ikan Berupa Estimasi Panjang, Lebar, dan Tinggi Berdasarkan Visual Capture Raihan Islamadina; Nuriza Pramita; Fitri Arnia; Khairul Munadi; TWK Muhammad Iqbal
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 1: Februari 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1308.794 KB)

Currently, fish measurement process is done manually using the gauge which can cause inappropriate, ineffective result, and requires long time to finish, especially for a great amount of fish. Therefore, an automatic fish body measurement technique in the form of estimation of length, width, and height of fish based on visual capture is needed to facilitate fish body measurement to become more effective and efficient. This research uses five samples of fish in measuring length, width, and height manually to obtain the average data/value as the calibration of reference value for calculation process in the system and to be stored in the database. The stages begin with capturing fish image using a digital camera. Then, preprocessing stage was carried out to get the grayscale image of the fish. The object of the grayscale image was then segmented to separate the important and unimportant part of the object. Lastly, feature extraction process of the fish body from the calibration average value was carried out, and the estimated value of length, width, and height of fish are obtained automatically. The results show that the automatic measurement technique of fish body based on visual capture was able to produce the truth degree of accuracy of 80% to 95%.

Temu Kembali Citra Busana Muslimah Berdasarkan Bentuk Menggunakan Curvature Scale Space (CSS) Hayatun Maghfirah; Fitri Arnia; Khairul Munadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 1: Februari 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1361.781 KB)

Nowadays in Indonesia, Islamic woman's clothing has been popular and follows the latest trend. Clothing with various color, texture, and shape are available. Furthermore, the online clothing trading system is becoming more attractive, which facilitates the users the apparel images through the websites. These images can be retrieved by querying a text to the retrieval system. However, the users face difficulties in describing the clothes precisely. Thus, a retrieval method based on content, which is known as content-based image retrieval (CBIR), is developed. Here, the content is represented by color, texture, and shape. This paper aims to present and discuss an application of Curvature Scale Space (CSS) as a shape feature for Islamic woman's clothing retrieval system. The performance of retrieval results of three clothing categories is analyzed: blouse-pant, long dress, and tunic, and used different feature length. The simulations run with as many as 300 images from the three categories, 100 images from each. Performance is measured in recall and precision. The results are compared by applying another shape feature; that is the histogram of gradient (HOG). The blouse-pant group achieves the highest performance, followed by tunic and long dress categories. The different feature length affects the retrieval performance; the longer the features, the lower recall and precision values. The feature of length 4 achieves the highest performance. The CSS is applied as the feature in CBIR of Islamic clothing, results in higher performance than the HOG.

Pengenalan Karakter Tulisan Tangan Jawi Menggunakan Metode New Relative Context dan SVM Rizal Fikri; Fitri Arnia; Rusdha Muharar
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 5 No 3: Agustus 2016
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1237.171 KB)

Dot is an important attribute in character recognition. Similarly in Jawi characters, a dot becomes a special characteristic that distinguish different characters with the same basic shape. Most of feature extraction methods only recognize the characters based on their basic shape and ignore the dots, such as Relative Context (RC). RC classifies characters with the same basic shape into a group. Therefore, the result recognition of RC is not individual characters, but the name of group character. To identify individual character, a new method for RC enhancement is introduced. The method is called New Relative Context (NRC). NRC works by separating characters into some areas. The wider area is defined as the basic shape, while other areas are defined as dot attribute. In this paper Support Vector Machine (SVM) is used to classify eleven sets of isolated Jawi characters. Eight sets of character images are used in the training phase, while in the testing phase three sets of images are used. The recognition rate of this method achieves 80%.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.8932

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 .

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