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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika Perfecting a Video Game with Game Metrics JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknologi Pertanian Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JTET (Jurnal Teknik Elektro Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Creative Information Technology Journal JIKO (Jurnal Informatika dan Komputer) Compiler Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sains Komputer dan Teknologi Informasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Information Systems for Public Health
Agus Harjoko
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Medicine & Pharmacology Public Health

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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Facial Expression Recognition By Using Fisherface Methode With Backpropagation Neural Network Zaenal Abidin; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 1 (2011): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2010

Abstract

Abstract— In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15,  for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5.Keywords— facial expression, fisherface method, PCA, LDA, backpropagation neural network.
Reversible Watermarking dengan Metode Modifikasi Histogram pada Difference Image Yustina Retno; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 2 (2011): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2018

Abstract

Abstract— Reversible watermarking schemes are widely used to maintain the authenticity of the digital image. This research will discuss a method on histogram modification of difference image in which the difference image is created from the difference value of adjacent pixels of the image grayscale.  Embedding process begins by dividing the host image and watermark into b blocks, followed by making a difference image of the host image block. From the difference image histogram, determine the peak value, and modify the histogram based on the peak value. Then, insert each block of the watermark to the difference image that has been modified and transform back into the grayscale image.Extraction and recovery process is the inverted version of the embedding stage. This process begins by dividing the watermarked image into b blocks, followed by making a difference image of each block. Then, extract the data and shift difference image histogram using a peak value. That difference image is transformed back into a grayscale image. Experimental results demonstrate that the average insertion capacity is 14% greater than Xue's with PSNR value over 48 dB for 4 x 4 pixels and 23 % greater with PSNR over 46 dB for 8 x 8 pixels. From the comparison of robustness to line and salt n’ pepper on the density of 0.05 noises is obtained that the watermark with ECC is more robust than a watermark without ECC. JPEG compression in lossless mode may be applied to the watermarked image. Multiple insertion of watermark can be done with the consequence that the more insertions will result in lower PSNR values.Keywords—difference image, histogram modification, reversible watermarking
Klasifikasi Fase Retinopati Diabetes Menggunakan Backpropagation Neural Network Rocky Yefrenes Dillak; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 7, No 1 (2013): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3049

Abstract

AbstrakRetinopati diabetes (DR) merupakan salah satu komplikasi pada retina yang disebabkan oleh penyakit diabetes. Tingkat keparahan DR dibagi atas empat kelas yakni: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), dan macular edema (ME). Penelitian ini bertujuan mengembangkan suatu metode yang dapat digunakan untuk melakukan klasifikasi terhadap fase DR. Data yang digunakan sebanyak 97 citra yang fitur – fiturnya  diekstrak menggunakan gray level cooccurence matrix (GLCM). Fitur ciri tersebut adalah maximum probability, correlation, contrast, energy, homogeneity, dan entropy. Fitur – fitur ini dilatih menggunakan jaringan syaraf tiruan backpropagation untuk dilakukan klasifikasi. Kinerja  yang dihasilkan dari pendekatan ini adalah sensitivity 100%, specificity 100% dan accuracy  97.73%  Kata kunci— fase retinopati diabetes, GLCM, backpropagation neural network  Abstract Diabetic retinopathy (DR) is one of the complications on retina caused by diabetes. The aim of this studyis to develop a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). Ninenty-seven retinal fundus images in used in this study. Six different texture features such as maximum probability, correlation, contrast, energy, homogeneity, and entropy were extracted from the digital fundus images using gray level cooccurence matrix (GLCM). These features were fed into a backpropagation neural network classifier for automatic classification. The  proposed approach is able to classify with sensitivity 100%, specificity 100% and accuracy  97.73%  Keywords— diabetic retinopathy stages, GLCM,  backpropagation neural network
Pengenalan Karakter Plat Nomor Mobil Secara Real Time Ikhwan Ruslianto; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 7, No 1 (2013): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3050

Abstract

AbstrakPengenalan plat nomor di Indonesia biasanya digunakan pada sistem parkir yang masih dilakukan secara manual, yaitu dengan mencatat karakter plat nomor oleh petugas jaga parkir. Padahal pengenalan plat nomor tidak hanya dilakukan untuk system perparkiran tetapi dapat digunakan untuk menemukan kendaraan yang melanggar peraturan lalu lintas dijalan raya secara real time, misalnya pelaku tabrak lari pada kecelakaan maupun kendaraan yang melanggar rambu-rambu lalu lintas.Penelitian ini memberikan alternatif pengenalan karakter plat nomor mobil menggunakan metode connected component analysis dan matching sehingga dapat menyelesaikan permasalahan dengan background yang kompleks dan mobil yang bergerak dijalan raya.Metode connected component analysis berhasil melakukan proses segmentasi plat dan segmentasi karakter dengan kondisi background yang kompleks secara tepat terhadap 67 sampel citra dengan tingkat keberhasilan 95,52% untuk segmentasi plat dan 94,98% untuk segmentasi karakter dan metode template matching berhasil melakukan proses pengenalan karakter secara akurat dengan tingkat keberhasilan 87,45%. Kata kunci— real time, connected component analysis, template matching  Abstract Indonesia’s number plat recognition system are typically used in parking lots that are still done manually, by recording the license plate characters by parking guard. Though number plate recognition system is not only for parking but can be used to find vehicles that violate traffic rules highway street in real time, such as actors on the hit and run accident and the vehicles that violate traffic signs.This study provides an alternative car number plate character recognition using connected component analysis and matching so as to solve problems with complex background and a moving car on the road.Connected component analysis method successfully to the plates segmentation and character segmentation in complex background condition are appropriate to the 67 sample images with the success rate of 95.52% for the plate segmentation and 94.98% for plate character segmentation and template matching method successfully perform the character recognition process accurately with a success rate of 87.45%. Keywords— real time, connected component analysis, template matching
Video Retrieval Berdasarkan Teks dan Gambar Rahmi Hidayati; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 7, No 1 (2013): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3054

Abstract

AbstrakVideo retrieval digunakan untuk melakukan pencarian video berdasarkan query yang dimasukkan oleh user yaitu teks dan gambar. Sistem ini dapat meningkatkan kemampuan pencarian terhadap video dan diharapkan dapat mengurangi waktu temu-kembali video. Tujuan dari penelitian ini adalah merancang dan membuat sebuah aplikasi perangkat lunak video retrieval berdasarkan teks dan gambar yang ada dalam video. Proses indeks untuk teks adalah proses tokenizing, filtering (stopword), stemming. Hasil stemming disimpan dalam tabel indeks teks. Proses indeks untuk gambar adalah  membuat histogram warna dan menghitung nilai rata-rata serta standar deviasi pada setiap warna dasar red, green dan blue (RGB) dari setiap gambar. Hasil ekstraksi fitur disimpan pada tabel gambar. Proses retrieval video menggunakan query yaitu  teks, gambar atau keduanya. Untuk query teks sistem memproses query teks dengan melihat query teks pada tabel indeks teks. Jika query teks ada pada tabel indeks teks sistem akan menampilkan informasi video sesuai dengan teks query. Untuk query gambar sistem memproses query gambar dengan mencari nilai dari fitur ekstraksi yaitu means red, means green, means blue, standar deviasi red, standar deviasi green dan standar deviasi blue. Jika nilai hasil ekstraksi ke enam fitur  query gambar ada pada tabel indeks gambar  sistem akan menampilkan informasi video sesuai dengan gambar query. Untuk query teks dan query gambar, sistem akan menampilkan informasi video jika query teks dan query gambar memiliki keterkaitan yaitu  query teks dan query gambar mempunyai judul film yang sama.  Kata kunci—  video, indeks, retrieval, teks, gambar AbstractRetrieval video has been used to search a video based on the query entered by user which were text and image. This system could increase the searching ability on video browsing and expected to reduce the video’s retrieval time. The research purposes were designing and creating a software application of retrieval video based on the text and image on the video. The index process for the text is tokenizing, filtering (stopword), stemming. The results of stemming to saved in the text index table. Index process for the image is to create an image color histogram and compute the mean and standard deviation at each primary color red, green and blue (RGB) of each image. The results of feature extraction is stored in the image table The process of video retrieval using the query text, images or both. To text query system to process the text query by looking at the text index tables. If there is a text query on the index table system will display information of the video according to the text query. To image query system to process the image query by finding the value of the feature extraction means red, green means, means blue, red standard deviation, standard deviation and standard deviation of blue green. If the value of the six features extracted query image on the index table image will display the video information system according to the query image. To query text and query images, the system will display the video information if the query text and query images have a relationship that is query text and query image has the same film title.  Keywords—  video, index, retrieval, text, image
Identifikasi Barcode pada Gambar yang Ditangkap Kamera Digital Menggunakan Metode JST Salman Aliaji; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 7, No 2 (2013): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3351

Abstract

AbstrakDewasa ini hampir setiap produk konsumen memiliki label barcode. Namun alat pembaca barcode jenis laser memiliki kelemahan karena tidak dapat mengenali barcode yang mengalami goresan atau noise. Namun telah dikembangkan teknik lain dengan memanfaatkan kamera digital untuk identifikasi barcode. JST telah banyak digunakan untuk identifikasi berbagai macam pola. Proses identifikasi barcode dalam JST terdiri dari proses training dan proses identifikasi. Proses training menggunakan metode LVQ (Learning Vector Quantization). Proses identifikasi terdiri dari beberapa tahap, yaitu akuisisi citra, preprocessing, locating barcode, proses pengujian dan verifikasi. Berdasarkan hasil pengujian metode LVQ dapat digunakan untuk identifikasi foto barcode dengan kinerja yang baik. Hasil pengujian menunjukkan tingkat akurasi sebesar 73,6 % dari 72 citra yang diuji dengan waktu rata-rata adalah 0.5 detik. Sementara waktu yang dibutuhkan untuk menemukan lokasi barcode adalah sekitar 6 detik menggunakan blok dengan ukuran 32x32 pixel. Kata kunci— Barcode, Learning Vector Quantization, Jaringan Syaraf Tiruan AbstrakIn today’s modern society, almost every consumer product has a barcode label. But the barcode reader with laser type has the disadvantage of not being able to recognize the barcode has a scratch or noise. However, other techniques have been developed by using a digital camera for barcode identification. ANN has been widely used for identification of various patterns. Barcode identification process consists of the ANN training process and the identification process. Training process using the LVQ (Learning Vector Quantization). Identification process consists of several stages: image acquisition, preprocessing, locating barcode, testing and verification process. Based on test results LVQ method can be used for photo identification barcode with good performance. The test results showed an accuracy of 73.6% rate of 72 images were tested with an average time is 0.5 seconds. While the time required to find the location of the barcode is about 6 seconds using a block size of 32x32 pixels. Keyword— Barcode, Learning Vector Quantization, Artificial Neural Network
Penyembunyian Data pada File Video Menggunakan Metode LSB dan DCT Mahmuddin Yunus; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 1 (2014): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3498

Abstract

AbstrakPenyembunyian data pada file video dikenal dengan istilah steganografi video. Metode steganografi yang dikenal diantaranya metode Least Significant Bit (LSB) dan Discrete Cosine Transform (DCT). Dalam penelitian ini dilakukan penyembunyian data pada file video dengan menggunakan metode LSB, metode DCT, dan gabungan metode LSB-DCT. Sedangkan kualitas file video yang dihasilkan setelah penyisipan dihitung dengan menggunakan Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR).Uji eksperimen dilakukan berdasarkan ukuran file video, ukuran file berkas rahasia yang disisipkan, dan resolusi video.Hasil pengujian menunjukkan tingkat keberhasilan steganografi video dengan menggunakan metode LSB adalah 38%, metode DCT adalah 90%, dan gabungan metode LSB-DCT adalah 64%. Sedangkan hasil perhitungan MSE, nilai MSE metode DCT paling rendah dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan metode LSB-DCT mempunyai nilai yang lebih kecil dibandingkan metode LSB. Pada pengujian PSNR diperoleh databahwa nilai PSNR metode DCTlebih tinggi dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan nilai PSNR metode gabungan LSB-DCT lebih tinggi dibandingkan metode LSB.  Kata Kunci—Steganografi, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)                           AbstractHiding data in video files is known as video steganography. Some of the well known steganography methods areLeast Significant Bit (LSB) and Discrete Cosine Transform (DCT) method. In this research, data will be hidden on the video file with LSB method, DCT method, and the combined method of LSB-DCT. While the quality result of video file after insertion is calculated using the Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experiments were conducted based on the size of the video file, the file size of the inserted secret files, and video resolution.The test results showed that the success rate of the video steganography using LSB method was 38%, DCT method was 90%, and the combined method of LSB-DCT was 64%. While the calculation of MSE, the MSE method DCT lower than the combined method of LSB and LSB-DCT method. While LSB-DCT method has asmaller value than the LSB method. The PNSR experiment showed that the DCT method PSNR value is higher than the combined method of LSB and LSB-DCT method. While PSNR combined method LSB-DCT higher compared LSB method.  Keywords—Steganography, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)
Klasifikasi Varietas Tanaman Kelengkeng Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network dan Probabilistic Neural Network Hermawan Syahputra; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 3 (2011): November
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.5206

Abstract

Pengenalan daun memainkan peran penting dalam klasifikasi tanaman dan isu utamanya terletak pada apakah fitur yang dipilih stabil dan memiliki kemampuan yang baik untuk membedakan berbagai jenis daun. Pengenalan tanaman berbantuan komputer merupakan tugas yang masih sangat menantang dalam visi komputer karena kurangnya model atau skema representasi yang tepat. Fokus komputerisasi pengenalan tanaman hidup adalah untuk mengukur bentuk geometris berbasis morfologi daun. Informasi ini memainkan peran penting dalam mengidentifikasi berbagai kelas tanaman. Pada penelitian ini dilakukan pengenalan jenis tanaman berdasarkan fitur yang menonjol dari daun seperti fisiologis panjang (physiological length), lebar (physiological width), diameter,  keliling (leaf perimeter), luas (leaf area), faktor mulus (narrow factor), rasio aspek (aspect ratio), factor bentuk (form factor), rectangularity, rasio perimeter terhadap diameter, rasio perimeter panjang fisiologi dan lebar fisiologi yang dapat digunakan untuk membedakan satu sama lain. Berdasarkan hasil pengujian, ditunjukkan bahwa hasil pencocokkan daun kelengkeng dengan menggunakan neural network lebih baik dibandingkan dengan hasil pencocokkan daun kelengkeng dengan menggunakan probabilistic neural network. Akan tetapi ekstraksi fitur dengan menggunakan morfologi belum dapat memberikan informasi pembeda yang signifikan bagi pengenalan tanaman varitas kelengkeng berdasarkan daunnya.Keywords— klasifikasi, morfologi daun, neural network, probabilistic neural network
Penentuan Klas Sidik Jari Berdasarkan Arah Kemiringan Ridge Sri Suwarno; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 3 (2011): November
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.5208

Abstract

Researches on  fingerprint  classification are generally based on its features such as core and delta. Extraction of these features are generally preceded by a variety of preprocessing. In this study the classification is done directly on the fingerprint image without preprocessing. Feature used as the basis for classification is the direction of the ridge. The direction of the ridge  is determined by the slope of the blocks that are exist on every ridge. Fingerprint image is divided into blocks of size 3x3 pixels and the direction of each block is determined. Direction of the slope of the block are grouped into 8, these are  north, north-east, east, south-east, south, south-west, west and north-west. The number of blocks in each direction form the basis of classification using Learning Vector Quantization network (LVQ). This study used 80 data samples from the database of FVC2004. This model obtained classification accuracy of up to 86.3%. Keywords—fingerprint, classification, ridge, LVQ
Klasifikasi Lagu Berdasarkan Genre pada Format WAV Nurmiyati Tamatjita; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 2 (2014): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6542

Abstract

AbstrakDalam dunia yang berkembang pesat, media audio semakin komplek. Karena itulah diperlukan sebuah mekanisme penentuan jenis lagu (genre) yang tepat secara efektif dan efisien.  Pencarian secara manual sudah tidak efektif dan efisien lagi karena banyaknya data yang tersimpan.          Zero Crossing Rate (ZCR), Average Energy (E) dan Silent Ratio (SR) adalah 3 Feature Extraction yang digunakan untuk klasifikasi pencarian 12 genre.Tiga dimensi adalah bentuk visualisasi pengukuran tingkat kemiripan sebuah data berdasarkan hasil klasifikasi yang diinput oleh user.            Dalam penelitian ini pengujian klasifikasi menggunakan metode 3, 6, 9 dan 12 genre melalui jarak terdekat (Euclidean Distance). Hasil pengujian yaitu menunjukkan bahwa 3 genre yaitu Balada, Blues dan Classic menunjukkan = 96,67%, 6 genre yaitu Balada, Blues, Classic, Harmony, Hip Jop dan Jazz menunjukkan = 70% dan 9 genre yaitu Balada, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin dan Pop menunjukkan = 53,33% serta 12 genre = 33,33% Kata Kunci— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance  Abstract            Music genre is getting complex from time to time. As the size of digital media grows along with amount of data, manual search of digital audio files according to its genre is considered impractical and inefficient; therefore a classification mechanism is needed to improve searching.            Zero Crossing Rate (ZCR), Average Energy (E) and Silent Ratio (SR) are a few of features that can be extracted from digital audio files to classify its genre. This research conducted to classify digital audio (songs) into 12 genres: Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop, Electronic, Reggae and Rock using above mentioned features, extracted from WAV audio files. Classification is performed several times using selected 3, 6, 9 and 12 genres respectively.            The result shows that classification of 3 music genres (Ballad, Blues, Classic) has the highest accuracy (96.67%), followed by 6 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz) with 70%, and 9 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop) with 53.33% accuracy. Classification of all 12 music genres yields the lowest accuracy of 33.33%.   Keywords— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance
Co-Authors Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Ashari Anak Agung Istri Ngurah Eka Karyawati Andi Sunyoto Aniati Murni Arymurthy Anny Kartika Sari Ashar Punto Nurwendo Azhari, Azhari Bakhtiar Alldino Ardi Sumbodo Bernard Renaldy Suteja Budi Rahardjo Budi Rahmani Edy Winarno Elizabeth Nurmiyati Tamatjita Enny Itje Sela Feri Wibowo Gamma Kosala Hadi Santoso Helna Wardhana Hermawan Syahputra I Gede Aris Gunadi I Putu Adi Pratama Ika Arfiani Ika Candradewi Ika Sudirahayu Ikhwan Ruslianto Iwan Budi Nugroho Khabib Mustofa Kusrini Kusrini La Ode Hasnuddin S. Sagala Lilik Sutiarso Lukman Awaludin Mahmuddin Yunus Maimunah Maimunah Maura Widyaningsih Muhammad Anis Al Hilmi Muhammad Shahid Ardi Munakhir Mudjosemedi Murinto Murinto Mursid Wahyu Hananto Nicodemus Mardanus Setiohardjo Nora Idiawati Norman Yazid Novrido Charibaldi Pujiastuti, Asih Raden Sumiharto Rahmad Hidayat Rahmi Hidayati Retantyo Wardoyo Rocky Yefrenes Dillak Rudiati Evi Masithoh Salman Aliaji Septia Rani Setyo Nugroho Slamet Santosa Slamet Santoso Sri Hartati Sri Hartati Sri Hartati Sri Kusumadewi Sri Suwarno Tia Widiana Tri Kuntoro Priyambodo Tri Wahyu Supardi Wawan Kurniawan Winarko, Edi Yustina Retno Zaenal Abidin