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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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Penggunaan Gray Level Co-Occurance Matrix Dari Koefisien Aproksimasi Wavelet untuk Deteksi Cacat Tekstil Raihan Islamadina; Fitri Arnia; Khairul Munadi
Jurnal Buana Informatika Vol. 6 No. 2 (2015): Jurnal Buana Informatika Volume 6 Nomor 2 April 2015
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v6i2.405

Abstract

Pendeteksian cacat tekstil saat ini masih dilakukan secara manualmengakibatkan seseorang sulit mendeteksi lebih dari 60% dari cacat yang ada.Untuk itu, penelitian ini menerapkan metode deteksi cacat tekstil secara otomatismenggunakan Gray Level Co-Occurance Matrix (GLCM) dari koefisienaproksimasi wavelet yang bertujuan untuk mengevaluasi analisis kinerja metode.Tahapannya, sampel citra tekstil dibagi menjadi delapan bagian untukmendapatkan tekstur cacat yang lebih jelas. Bagian tersebut didekomposisikedalam dua level. GLCM dihitung dari koefisien aproksimasi wavelet level satudan dua untuk dijadikan fitur. Penelitian ini dilakukan empat set simulasi citradengan orientasi latar berbeda. Setiap set terdiri dari satu citra noncacat dan duajenis citra cacat. Setiap bagian citra noncacat dihitung jaraknya dengan semuabagian pada citra cacat pertama dan kedua menggunakan jarak euclidean. Hasilsimulasi menunjukkan bahwa GLCM dari koefisien aproksimasi wavelet levelkedua mampu mendeteksi lebih dari 70% dari cacat yang ada.
Penggunaan Histogram dari Koefisien Aproksimasi Wavelet untuk Deteksi Cacat Tekstil Fitri Arnia; Andika Saputra; Khairul Munadi
JURNAL NASIONAL TEKNIK ELEKTRO Vol 3 No 1: Maret 2014
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1145.802 KB) | DOI: 10.25077/jnte.v3n1.57.2014

Abstract

Generally, textile defect inspection at textile industry is still conducted manually by human. This approach is susceptible to errors and tends to be inconsistent due to fatigue and inattentiveness. To guarantee the consistency and inspection quality, an automatic defect detection system is required. This research proposes the use of histograms generated from two-level wavelet’s approximation coefficients as features to detect textile defects. The Euclidian distance that is calculated between feature of reference textile (non-defective textile) and feature of defective one is used as an evaluation parameter. If the Euclidian distances of the features of textile images are higher than a predetermined threshold, the textiles are determined as defective ones, and vice versa. Simulations are conducted using four groups of textile defects. It turns out that the proposed method can achieve 100% detection rate for textile group with ink-spot and textile group with holes.Keywords : Wavelet coefficient histogram, Euclidean distance, Textile defect, industrial textiles, Image features AbstrakPada industri tekstil, cacat produksi umumnya masih diperiksa secara manual oleh manusia. Pemeriksaan secara manual rentan terhadap kesalahan dan kurang konsisten, karena sifat manusia yang dapat lelah, lupa dan lain sebagainya. Untuk menjamin konsistensi dan kualitas pemeriksaan cacat kain, sebuah sistem deteksi otomatis perlu ada. Penelitian ini mengusulkan penggunaan histogram dari koefisien aproksimasi wavelet dua tingkat sebagai fitur untuk deteksi cacat tekstil. Jarak Euclidian yang dihitung diantara fitur tekstil citra referensi (berasal dari citra tidak cacat) dengan fitur tesktil citra cacat digunakan sebagai parameter evaluasi. Jika jarak Euclidian dari fitur suatu citra tekstil berada di atas nilai ambang yang telah ditentukan sebelumnya, citra tersebut dinyatakan cacat, dan sebaliknya. Penelitian dilaksanakan dengan menjalankan simulasi deteksi cacat tekstil, menggunakan empat kelompok cacat tekstil yang berbeda. Ditemukan bahwa metode usulan mencapai tingkat kebenaran deteksi sebesar 100% untuk citra kelompok cacat tinta dan kelompok cacat lubang.             Kata Kunci : Histogram koefisien wavelet, Jarak  Euclidean, Cacat tekstil, Industri tekstil, Fitur citra  
Pengenalan Aksara Jawi Tulisan Tangan Menggunakan Freemen Chain Code (FCC), Support Vector Machine (SVM) dan Aturan Pengambilan Keputusan Safrizal .; Fitri Arnia; Rusdha Muharar
JURNAL NASIONAL TEKNIK ELEKTRO Vol 5 No 1: Maret 2016
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.515 KB) | DOI: 10.25077/jnte.v5n1.185.2016

Abstract

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
Deteksi Pemalsuan Citra dengan Teknik Copy-Move Menggunakan Metode Ordinal Measure dari Koefisien Discrete Cosine Transform Zulfan .; Fitri Arnia; Rusdha Muharar
JURNAL NASIONAL TEKNIK ELEKTRO Vol 5 No 2: Juli 2016
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1014.985 KB) | DOI: 10.25077/jnte.v5n2.230.2016

Abstract

This article discusses a new method for the detection of forgery images generated by copy-move technique. Copy-move technique is one of image forgery techniques which taking a particular object from its original image and add it on that image for the purpose of increasing the number of or changing the same object in the original image. This study aims to detect the forged image generated by the copy-move techniques and copy-move forged image that has been modified by the rotation operation and histogram equalization. Detection feature used is Ordinal Measure of Discrete Cosine Transform coefficient (OM-DCT). Detection starts with division of the image into a block size of BXB (B = 16x16, 32x32 and 64x64) and two-dimensional DCT was performed to each of blocks. The feature distance from the original to the fake image, was calculated by the Euclidian distance and each feature has a distance of less than or equal to the threshold value (T) according to the observations will be marked as a forged part. The results show that there are blocks detected on the copy-move image, whether on the unmodified copy-move forge image or those which modified by the rotation operation and histogram equalization. The number of blocks that are found in the copy-move object varies according to the size of the detection block used.Key words: Discrete Cosine Transform (DCT), ordinal measure of DCT Coefficient, copy-move, rotation, histogram equalization.Abstrak— Artikel ini membahas tentang metode baru untuk deteksi citra palsu yang dihasilkan dari teknik copy-move. Teknik copy-move merupakan salah satu teknik pemalsuan citra dengan cara mengambil objek tertentu dari citra asli dan menambahkannya pada citra tersebut dengan tujuan untuk menambah jumlah atau merubah objek yang sama pada citra asli. Penelitian ini bertujuan untuk mendeteksi citra palsu yang dihasilkan oleh teknik copy-move dan citra palsu copy-move yang telah dimodifikasi dengan operasi rotasi dan ekualisasi histogram. Fitur deteksi yang digunakan adalah Ordinal Measure dari koefisien Discrete Cosine Transform (OM-DCT). Pendeteksian dimulai dengan membagi citra ke dalam blok berukuran BxB (B = 16x16, 32x32 dan 64x64) dan DCT 2 dimensi dilakukan pada setiap blok tersebut. Jarak fitur citra asli dengan palsu dihitung dengan persamaan jarak Ecluidian dan setiap fitur yang memiliki jarak lebih kecil atau sama dengan nilai threshold (T) menurut pengamatan akan ditandai sebagai bagian yang dipalsukan. Hasil pendeteksian menunjukkan bahwa ada blok-blok yang terdeteksi pada objek citra yang di-copy-move baik pada citra palsu copy-move yang tidak dimodifikasi ataupun yang telah dimodifikasi dengan operasi rotasi dan ekualisasi histogram. Jumlah blok yang ditemukan pada objek copy-move bervariasi sesuai ukuran blok pendeteksian yang digunakan.Kata kunci : Discrete Cosine Transform (DCT), ordinal measure dari koefisien DCT, copy-move, rotasi, ekualisasi histogram.
Karakterisasi Kematangan Buah Kopi Berdasarkan Warna Kulit Kopi Menggunakan Histogram dan Momen Warna Hendri Syahputra; Fitri Arnia; Khairul Munadi
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 1: March 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.311 KB) | DOI: 10.25077/jnte.v8n1.615.2019

Abstract

Conventionally, the coffee maturity level is determined by observing the fruit colour, and it is done manually. This approach may result in inconsistency in colour classification. Thus, an automatic colour classification method based on colour of coffee maturity level is required. This paper presents the characterization of coffee maturity level based on two colour features: colour histogram and colour moment. Characterization of coffee maturity level was grouped into four class: green for unripe coffee, greenish-yellow for half ripe coffee, red for ripe coffee, and dark red for too ripe coffee. The purpose of the research is to determine the colour features that can characterize the coffee maturity level based on computer simulation in extracting and calculating the statistical values of the colour histogram and colour moments. It turned out from 200 coffee images that the statistical values of colour histogram are more suitable for characterising the coffee maturity. The kurtosis values of hue histogram for each maturity level of coffee were different: kurtosis value of unripe coffee was 17.2-28.3, those of half ripe coffee, ripe coffee and too ripe coffee were 29.2-31.4, 32.7-83.5, and more than 84.2 respectively..Keywords : colour histogram kurtosis, colour moment, image processing.AbstrakSecara tradisional, tingkat kematangan buah kopi ditentukan dari warna kulitnya yang dikelompokan secara manual. Cara ini menghasilkan pengelompokan warna yang kurang konsisten, sehingga diperlukan sebuah metode otomatis pengelompokan buah kopi berdasarkan warna dari tingkat kematangannya. Penelitian ini memaparkan hasil karakterisasi kematangan buah kopi arabika menggunakan dua fitur warna citra, yaitu histogram dan momen warna. Karakterisasi kematangan dibagi menjadi empat kelompok: hijau untuk kopi muda, hijau kekuningan untuk kopi setengah masak, merah untuk kopi masak, dan merah tua untuk kopi tua. Tujuan penelitian ini adalah menentukan fitur warna yang dapat mewakili karakter kematangan buah kopi dengan melakukan simulasi komputer untuk mengekstrak dan menghitung nilai statistik dari histogram warna dan nilai momen warna dari empat kelompok buah kopi.  Hasil penelitian menggunakan 200 citra kopi menunjukkan bahwa nilai statistik dari histogram warna lebih menggambarkan karakter kematangan buah kopi, dibandingkan dengan momen warna. Nilai kurtosis dari histogram hue memiliki nilai berbeda untuk setiap kategori kematangan buah kopi: kopi muda memiliki nilai kurtosis 17.2-28.3, kopi setengah masak 29.2-31.4, kopi masak 32.7-83.5dan kopi tua lebih dari 84.2.  Kata Kunci : kurtosis histogram warna, momen warna, pengolahan citra.
Perbandingan Kinerja Support Vector Machine (SVM) Dalam Mengenali Wajah Menggunakan SURF DAN GLCM Syamsul Bahri; Khairun Saddami; Fitri Arnia; Kahlil Muchtar
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 2: July 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (524.644 KB) | DOI: 10.25077/jnte.v8n2.620.2019

Abstract

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
Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG Cut Mutia; Fitri Arnia; Rusdha Muharar
Bulletin of Electrical Engineering and Informatics Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.666 KB) | DOI: 10.11591/eei.v6i3.657

Abstract

The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories.
Effectiveness of MPEG-7 Color Features in Clothing Retrieval Arsy Febrina Dewi; Fitri Arnia; Rusdha Muharar
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (797.276 KB) | DOI: 10.11591/eei.v6i2.619

Abstract

Clothing is a human used to cover the body. Clothing consist of dress, pants, skirts, and others. Clothing usually consists of various colors or a combination of several colors. Colors become one of the important reference used by humans in determining or looking for clothing according to their wishes. Color is one of the features that fit the human vision. Content Based Image Retrieval (CBIR) is a technique in Image Retrieval that give index to an image based on the characteristics contained in image such as color, shape, and texture. CBIR can make it easier to find something because it helps the grouping process on image based on its characteristic. In this case CBIR is used for the searching process of Muslim fashion based on the color features. The color used in this research is the color descriptor MPEG-7 which is Scalable Color Descriptor (SCD) and Dominant Color Descriptor (DCD). The SCD color feature displays the overall color proportion of the image, while the DCD displays the most dominant color in the image. For each image of Muslim women's clothing, the extraction process utilize SCD and DCD. This study used 150 images of Muslim women's clothing as a dataset consistingclass of red, blue, yellow, green and brown. Each class consists of 30 images. The similarity between the image features is measured using the eucludian distance. This study used human perception in viewing the color of clothing.The effectiveness is calculated for the color features of SCD and DCD adjusted to the human subjective similarity. Based on the simulation of effectiveness DCD result system gives higher value than SCD.
Features for Cross Spectral Image Matching: A Survey Maulisa Oktiana; Fitri Arnia; Yuwaldi Away; Khairul Munadi
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.648 KB) | DOI: 10.11591/eei.v7i4.843

Abstract

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.
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 Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (305.158 KB) | 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%.
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 .