Articles
<![CDATA[Studi Komparasi Kinerja Teknologi Near Field Communication Pada Sistem Berbasis Android dan Embedded System ]]>
<![CDATA[Muhammad Haries]]>;
<![CDATA[Yuwaldi Away]]>;
<![CDATA[Fitri Arnia]]>
<![CDATA[ Techno.Com Vol 20, No 2 (2021): Mei 2021 ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/tc.v20i2.4369
<![CDATA[Penelitian ini membahas perbandingan kinerja dua perangkat berbasis Android dan Embedded system dalam melakukan pembacaan tag Near Field Communication. Android dengan modul NFC NXP PN544 dan Embedded system dengan modul MFRC522. Studi kasus yang dilakukan untuk penelitian ini dengan menerapkannya pada manajemen aset. Penelitian ini bertujuan mengetahui kinerja dari kedua perangkat tersebut terdapat perbedaan signifikan atau tidak. Untuk mengetahui hal tersebut, penelitian ini menggunakan metode statistik uji Mann Whitney yang menghitung variabel waktu dan jarak ketika tag NFC berhasil terdeteksi, membaca, dan menampilkan informasi. Dengan metode uji Mann Whitney tersebut dapat diketahui terdapat perbedaan signifikan dari kedua perangkat tersebut berdasarkan hasil yang didapatkan. Setelah dilakukan pengujian, jarak efektif pada kedua alat didapatkan pada jarak 15 mm, 10 mm, dan 5 mm dengan pengujian pembacaan 15 tag NFC pada setiap jarak. Kemudian hasil tersebut diuji dengan metode Mann Whitney pada jarak 15 mm didapatkan nilai signifikasi sebesar .001, kemudian pada jarak 10 mm didapatkan nilai signifikasi sebesar .000, serta jarak 5 mm didapatkan nilai signifikasi sebesar .000 dan dari ketiga pengujian tersebut membuktikan bahwa kedua perangkat terdapat perbedaan signifikan dalam melakukan pembacaan tag NFC.]]>
<![CDATA[Pengenalan Aksara Jawi Tulisan Tangan Menggunakan Freemen Chain Code (FCC), Support Vector Machine (SVM) dan Aturan Pengambilan Keputusan ]]>
<![CDATA[Safrizal .]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Rusdha Muharar]]>
<![CDATA[ JURNAL NASIONAL TEKNIK ELEKTRO Vol 5 No 1: Maret 2016 ]]>
Publisher : <![CDATA[Jurusan Teknik Elektro Universitas Andalas ]]>
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DOI: 10.25077/jnte.v5n1.185.2016
<![CDATA[Jawi is one variant of Arabic script consists of 35 characters. Some of Jawi characters have the same main shape, but different number of dots in different location. Thus, recognition process of Jawi characters can be done by performing a classification based on the main shape. In recognition process, feature extraction plays an important role. In this research, Freeman Chain Code (FCC) was used as feature extraction and Support Vector Machine (SVM) as classifier. Then we apply the decision rules to classifySVMresult into Jawi characters. FCC is used to represent the boundary of Jawi characters into a chain code. Then the chain code is used bySVMto classify the characters into 19 groups. Feature of location and the number of dots are used by decision rules to classify the groups into Jawi characters. The Jawi characters are handwritten and generated by 10 writers from different backgrounds and ages. The recognition rate of this research was 80.00%.Keywords : Jawi script, handwriting, FCC, SVM, decision rules.Abstrak—Aksara Jawi merupakan salah satu varian dari aksara Arab yang terdiri dari 35 aksara. Dari 35 aksara Jawi tersebut terdapat beberapa aksara dengan bentuk bagian utama yang sama namun memiliki letak dan jumlah titik yang berbeda. Karena perbedaan tersebut maka proses pengenalan aksara Jawi dapat dilakukan dengan melakukan klasifikasi berdasarkan perbedaan bentuk bagian utama. Pada penelitian ini Freeman Chain Code (FCC) digunakan sebagai ekstraksi fitur dan Support Vector Machine (SVM). FCC digunakan untuk merepresentasikan garis batas (boundary) aksara Jawi kedalam kode rantai. Kode rantai tersebut diklasifikasi dengan menggunakan SVM kedalam 19 kelompok. Fitur letak titik dan jumlah titik digunakan sebagai aturan pengambilan keputusan terhadap 19 kelompok hasil klasifikasi SVM kedalam aksara Jawi. Aksara Jawi yang digunakan merupakan tulisan tangan dari 10 orang penulis dari berbagai latar belakang dan umur. Tingkat keberhasilan klasifikasi penelitian ini mencapai 80,00%.Kata Kunci : aksara Jawi, tulisan tangan, FCC, SVM, aturan pengambilan keputusan]]>
<![CDATA[Perbandingan Kinerja Support Vector Machine (SVM) Dalam Mengenali Wajah Menggunakan SURF DAN GLCM ]]>
<![CDATA[Syamsul Bahri]]>;
<![CDATA[Khairun Saddami]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Kahlil Muchtar]]>
<![CDATA[ JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 2: July 2019 ]]>
Publisher : <![CDATA[Jurusan Teknik Elektro Universitas Andalas ]]>
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DOI: 10.25077/jnte.v8n2.620.2019
<![CDATA[Face recognition is one part of the biometrics research. Face recognition is widely used in identification and recognition process. Speed-up Robust Feature (SURF) is one of feature extraction method used in face recognition system. This research aims to compare face recognition performance between SURF and Gray Level Co-occurence Matrix (GLCM) methods for perspective rotation. In this study, the image features were extracted using SURF and GLCM. Each feature was used on classification stage using Support Vector Machine (SVM). The dataset was obtained from National Cheng Kung University (NCKU). The NCKU dataset has more variation of rotation angle. The dataset used in this study consists of 10 classes that showed 10 of the subject. The results show that SURF method obtained 85% of accuracy and GLCM method reached 50% of accuracy. Therefore, we concluded that SURF method has better performance on implementing on face recognition system.Keywords : SURF, GLCM, Face Recognition, SVM Abstrak Pengenalan wajah merupakan salah satu bagian dari penelitian biometrika. Pengenalan wajah banyak digunakan dalam proses identifikasi manusia. Metode ekstraksi fitur Speed-Up Robust Feature (SURF) merupakan salah satu metode yang digunakan untuk mengenali wajah. Penelitian ini bertujuan untuk membandingkan kinerja sistem pengenalan wajah dengan menggunakan metode ekstraksi fitur SURF dan Gray Level Co-occurence Matrix (GLCM). Pada penelitian ini, data input wajah akan diekstraksi fiturnya menggunakan SURF dan GLCM. Setiap fitur digunakan pada tahapan klasifikasi menggunakan Support Vector Machine (SVM). Data yang digunakan merupakan data yang didapatkan dari National Cheng Kung University (NCKU). Data wajah NCKU mempunyai sudut rotasi yang lebih banyak. Dataset yang digunakan pada penelitian ini terdiri dari 10 kelas yang menunjukkan 10 subjek penelitian. Pengenalan wajah menggunakan metode SURF dan SVM mempunyai akurasi 85%, sedangkan menggunakan metode GLCM mempunyai akurasi 50%. Hasil menunjukkan bahwa metode SURF mempunyai kinerja yang lebih baik dari metode GLCM.Kata Kunci : SURF, GLCM, pengenalan wajah, SVM]]>
<![CDATA[PENGENALAN GERAKAN ISYARAT BAHASA INDONESIA MENGGUNAKAN ALGORITMA SURF DAN K-NEAREST NEIGHBOR ]]>
<![CDATA[Nur Amalia Hasma]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Rusdha Muharar]]>
<![CDATA[ Jurnal Komputer, Informasi Teknologi, dan Elektro Vol 7, No 1 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.24815/kitektro.v7i1.23262
<![CDATA[Berkomunikasi adalah kebutuhan dasar setiap manusia untuk berinteraksi satu sama lain. Dalam kehidupan sehari-hari, manusia menggunakan komunikasi verbal untuk berinteraksi. Namun tidak setiap orang mampu menggunakan komunikasi secara verbal, seperti tuna rungu dan tuna wicara. Terdapat keterbatasan ketika melakukan komunikasi antara orang normal dengan tuna rungu dan tuna wicara dikarenakan kurangnya pemahaman mengenai bahasa isyarat. Pada penelitian ini dilakukan pengenalan bahasa isyarat, berupa isyarat huruf dan angka (SIBI) dengan memanfaatkan teknik pengolahan citra. Proses pengenalan dilakukan dengan menggunakan algoritma Speeded Up Robust Features (SURF) sebagai metode ekstraksi fitur dan algoritma K-Nearest Neighbor (K-NN) sebagai metode klasifikasi. Pengujian akurasi digunakan metode k-fold Cross Validation. Uji akurasi menggunakan 10-fold Cross Validation untuk menentukan nilai K. Dengan menggunakan nilai K = 7 didapatkan hasil akurasi tertinggi untuk pengenalan Gerakan Isyarat Bahasa Indonesia dengan persentase 90%.]]>
<![CDATA[On Reducing ShuffleNets’ Block for Mobile-based Breast Cancer Detection Using Thermogram: Performance Evaluation ]]>
<![CDATA[Rizka Ramadhana]]>;
<![CDATA[Khairun Saddami]]>;
<![CDATA[Khairul Munadi]]>;
<![CDATA[Fitri Arnia]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022 ]]>
Publisher : <![CDATA[IAES Indonesian Section ]]>
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DOI: 10.52549/ijeei.v10i4.4062
<![CDATA[In this paper, we proposed a reduced-block-Shufflenet (RB-ShuffleNet) for thermal breast cancer detection. RB-ShuffleNet is a modification of Shufflenet obtained by reducing blocks from the original architecture. The images for training and testing were obtained from Database for Mastology Research (DMR). First, we detected and cropped the image based on the region of interest (ROI), in which the ROI is determined by using the red intensity profile. Then, the ROI images were trained using RB-ShuffleNets. In the experiments, we built eight architectures, based on ShuffleNet, each with a different number of reduced blocks. The result showed that RB-Shufflenet with four reduced blocks had fewer than 50% of the learning parameters of the original Shufflenet, without compromising its performance. The RB-ShuffleNet with up to four reduced blocks could achieve 100% testing accuracy. Furthermore, The RB-ShuffleNets performed better than MobileNetV2 and resulted in higher accuracy when fed with ROI images. Due to its light structure and good performance, we recommend RB-ShuffleNet as mobile-based CNN model which is preferable to implement in breast cancer detection.]]>
<![CDATA[Improved Classification of Handwritten Jawi Script Based on Main Part of Script Body ]]>
<![CDATA[Safrizal Razali]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Rusdha Muharrar]]>;
<![CDATA[Kahlil Muchtar]]>;
<![CDATA[Akhyar Bintang]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v7i1.4600
<![CDATA[Since the entry of Islam, many ancient relics in the archipelago were written using Jawi script. Due to human or natural factors, these ancient relics will be damaged or destroyed. To avoid the loss of this ancient heritage data, the data must be stored in digital documents. In order to convert digital documents into machine-readable text format, the use of Optical Character Recognition (OCR) technology is inevitable. In this research, OCR technology is implemented on isolated Jawi scripts. Freeman Chain Code (FCC) is used to extract the isolated Jawi script features. Subsequently, the FCC feature is fed into Support Vector Machine (SVM) in order to classify the character. The decision rule classification is applied to the class of SVM classification in the Jawi script form. The results of the SVM classification into 19 classes reached 81.58%, while the results for merging into 15 classes produced better results with the accuracy 84.21%. Feature extraction of dot location is divided into the top, middle, and bottom. Feature extraction of the number of dotss is done by counting the number of dots, while feature extraction of the presence of holes is carried out by detecting the presence of holes in the characters. These features are applied to the class of results from SVM classification with decision-making rules. The percentage of success in applying the decision rules to the results of the classification of incorporation into 15 classes by SVM reached 92.86%. Further research will be conducted to determine the effect of the feature of the location of the dot and the number of dots on the shape of the main part of the character.]]>
<![CDATA[Penerapan Deskriptor Warna Dominan untuk Temu Kembali Citra Busana pada Peranti Bergerak ]]>
<![CDATA[Yustina Dhyanti]]>;
<![CDATA[Khairul Munadi]]>;
<![CDATA[Fitri Arnia]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 12, No 3 (2016) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v12i3.5701
<![CDATA[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.]]>
<![CDATA[Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning ]]>
<![CDATA[Ilal Mahdi]]>;
<![CDATA[Kahlil Muchtar]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Tia Ernita]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i2.25224
<![CDATA[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%.]]>
<![CDATA[Fine Tuning CNN Pre-trained Model Based on Thermal Imaging for Obesity Early Detection ]]>
<![CDATA[Hendrik Leo]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Khairul Munadi]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 18, No 1 (2022) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v18i1.25100
<![CDATA[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.]]>
<![CDATA[Identifikasi Tingkat Kematangan Kelapa Sawit Berbasis Pencitraan Termal ]]>
<![CDATA[Khusnul Azima]]>;
<![CDATA[Khairul Munadi]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Maulisa Oktiana]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 15, No 1 (2019) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
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DOI: 10.17529/jre.v15i1.12963
<![CDATA[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.]]>
