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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Edutech Semantik Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Ilmiah Kursor Jurnal Transformatika International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Data Science: Journal of Computing and Applied Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL SCIENTIFIC OF MANDALIKA (JSM) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Teknologi Informasi Cyberku Studies in English Language and Education Moneter : Jurnal Keuangan dan Perbankan Scientific Journal of Informatics Journal on Pustaka Cendekia Informatika

Pulung Nurtantio Andono

Universitas Dian Nuswantoro

Author-ID : 2398859

Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Perspektif Baru Enterprise Architecture Pemerintahan Kota Mataram Berbasis TOGAF ADM Husain Husain; Pulung Nurtantio Andono; M. Arif Soeleman
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 16 No 2 (2017)
Publisher : LPPM Universitas Bumigora

TIK salah satu penentu keberhasilan sebuah organisasi dalam mencapai visi dan misinya. Terpilihnya pemimpin yang baru, terbentuknya SKPD baru dengan visi misi baru sehingga master plan yang lama di anggap sudah tidak relevan lagi, sehingga persoalan yang muncul diselesaikan dengan cara reaktif dan memungkinkan persoalan yang sama akan muncul kembali pada masa yang akan datang. Arsitektur enterprise adalah cara untuk membangun arsitektur TIK dari sebuah organisasi yang berfokus pada arsitektur bisnis, arsitektur data, arsitektur aplikasi dan arsitektur teknologi. Penelitian ini merupakan penelitian deskriptif kualitatif dengan pendekatan studi kasus. Metodologi yang digunakan adalah Enterprise Architecture TOGAF ADM sebagai kerangka acuan untuk perencanaan strategis TIK Pemerintahan Kota Mataram. Subyek pada penelitian ini adalah responden yang memiliki kewenangan dalam pengambilan keputusan terkait TIK dan pengguna TIK di Dinas Komunikasi dan Informatika (DISKOMINFO). Kebutuhan bisnis yang terdiri dari Arsitektur Data, Aplikasi dan Teknologi diidentifikasi dan diusulkan untuk mendukung aktivitas bisnis demi pencapaian tujuan organisasi. Hasil dari penelitian ini dengan menganalisa penggunaan penerapan teknologi informasi dan komunikasi(TIK) Seperti Sumber daya Manusia yang terlibat, kebutuhan aplikasi dan infrastruktur jaringan komputer dalam untuk mendukung proses bisnis dalam pelaksanaan roda pemerintahan Kota Mataram, dengan menggunakan metode scorecard uji kelayakan dengan rata-rata perolehan 76%.

Analisa Perbandingan Adaptif Median Filter Dan Median Filter Dalam Reduksi Noise Salt & Pepper Ivan Maulana; Pulung Nurtantio Andono
CogITo Smart Journal Vol 2, No 2 (2016): CogITo Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Suatu data atau informasi disajikan tidak hanya berupa data teks tetapi juga dapat berupa audio, video, dan gambar. Pada zaman sekarang informasi sangatlah penting dan diperlukan, begitu juga informasi yang terdapat pada citra. Citra (image) atau istilah lain untuk gambar merupakan salah satu komponen multimedia yang berperan penting sebagai bentuk informasi visual. Dibandingkan dengan data teks, citra memiliki banyak informasi. Namun terkadang citra juga dapat mengalami penurunan yaitu degradasi atau penurunan kualitas yang disebabkan oleh derau / noise, warna terlalu kontras, kabur, dan lain-lain. Ada beberapa jenis noise dalam pengolahan citra salah satunya yaitu Salt & Pepper noise. Noise Salt & Pepper berbentuk seperti bintik hitam dan putih pada citra. Untuk mengurangi noise ini dibutuhkan suatu metode, salah satunya yaitu median filter. Metode yang digunakan pada penelitian ini adalah median filter dan adaptif median filter. Perbedaan mendasar antara kedua metode ini yaitu pada besarnya windows pada adaptif median filter adalah variabel. Dari hasil penelitian, citra yang menggunakan metode adaptif median filter lebih baik daripada median filter. Dari perhitungan Peak Signal to Noise Ratio (PSNR) citra yang menggunakan adaptif median filter mendapatkan 29,2495 dB sedangkan median filter mendapatkan 23,8181 dB.Kata Kunci: Median filter, Adaptif Median filter, Noise salt & pepper, PSNR

OPTIMASI KLASIFIKASI STATUS GIZI BALITA BERDASARKAN INDEKS ANTROPOMETRI MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFICATION ADABOOST Achmad Ridwan; Catur Supriyanto; Pulung Nurtantio Andono
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 2 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (310.243 KB)

Body Mass Index (BMI) is commonly used as a measure to assess the nutritional status of infants. If there are two babies whose weight and height are the same may have different nutritional status. If it happens then the use BMI to measure nutritional status less relevant. Anthropometric measurement tool to be very instrumental for determining the nutritional status. The guidelines for determining the nutritional status Anthropometric parameters are selected and recommended which includes an assessment of the age, weight, height. On the contrary, along with the development of technology, increasing the amount of data that requires some methods to process and draw conclusions from such data and information. NBC algorithm is an algorithm of decision tree method has good performance in dealing with the classification of Toddler Nutritional Status based index Anthropometry, but NBC has a weakness in the class imbalance. Adaboost one boosting methods that could reduce imbalances class by giving weight to the level of classification Error which may alter the distribution of data. The use of Adaboost with reason this method can improve the accuracy in the process of classification and prediction by means generate a combination of a model, select the model that has the greatest weight. These experiments will apply the NBC algorithm used for classification of Toddler Nutritional Status based index Anthropometry and will be increased again by Adaboost method for being able to overcome the imbalance class thus increasing the probability value of each class and improve accuracy, it also lowers Error Classification. While that would be classified are five classes: normal, fat, very fat, thin, or very thin. The results of the experiment were obtained from NBC method to an accuracy of 88.60% and a classification Error of 11.40%, while the method by Adaboost (NBC + Adaboost) to an accuracy of 88.84% and 11.16% of the classification Error. So we can conclude NBC with Adaboost algorithm implementation on the Classification of Toddler Nutritional Status based index Anthropometry proved capable of overcoming the class imbalance and improve accuracy also lowers Error Classification.

Prediksi Pendapatan Penjualan Obat Menggunakan Metode Backpropagation Neural Network dengan Algoritma Genetika Sebagai Seleksi Fitur Nur Azise; Pulung Nurtantio Andono; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.593 KB)

The hospital is one of the means of health services for the community, in which there are multiple units, one of which was the installation of a pharmaceutical is a source of revenue for the hospitals contributed by 40 – 60%. Each month the sale of drugs on pharmaceutical erratic installation (fluctuating) and have an impact on earning spharma ceutical installations specifically and at hospitals in General, i.e. against the determination of the lead in policy development and the development of hospitals inthe future. Therefore the forecast or prediction about the drug's sales revenue is urgently needed. Forecasting technique commonly used is the technique of forecastingwith Artificial neural Network method or so-called Artificial neural Network which hasthe best accuracy with the value error. However, the method of Artificial neural Network has a number of shortcomings, so it takes an optimization method, one of themwith Genetic Algorithm optimization methods. In this study using data on drug sales revenue installation hospital Elizabeth Situbondo. In the process of training and testing data in this study using the method of Backpropagation Neural Network and Genetic Algorithm to feature selection. On this panelitian proves that the method of Backpropagation Neural Network with genetic algorithm as a selection of the best RMSE value generating features of 0115. While the test results with the method of Backpropagation Neural Network without Genetic Algorithms as a value generating features selection RMSE 0152.

Persepsi Mahasiswa Terhadap Formatif Evaluation Model (FEM) Sebagai Model Evaluasi Pembelajaran Berbasis Learning Management System (LMS) Sri Winarno; Asih Rohmani; Denny Senata; Pulung Nurtantio Andono
Jurnal Sistem Informasi Vol 14, No 1 (2022)
Publisher : Universitas Sriwijaya

AbstrakKondisi pandemi Covid19 ini memaksa para pemangku kebijakan khususnya dibidang pendidikan harus saling terkait dan berpadu untuk dapat memberikan solusi yang tepat agar supaya pendidikan tetap berjalan dengan baik. Pendidikan merupakan sektor yang terdampak cukup besar dimana Proses Belajar Mengajar (PBM) yang sebelumnya menggunakan model tatap muka langsung dihentikan. Perguruan tinggi dituntut lebih cepat dan tepat dalam memilih model pembelajaran dan melakukan evaluasi dalam pelaksanaannya sehingga PBM berjalan dengan baik. Namun demikian, capaian pembelajaran mata kuliah harus tetap diperhatikan dan dijaga supaya capaian pembelajaran lulusan tidak mengalami perubahan. Inovasi model pembelajaran yang melibatkan dosen dan mahasiswa harus dilakukan supaya pembelajaran lebih efektif, efisien dan sesuai dengan perkembangan situasi dan kondisi saat ini. Model evaluasi pembelajaran menjadi penting dalam mengukur capaian pembelajaran saat ini. Formatif Evaluation Model (FEM) dipilih sebagai model evaluasi pembelajaran alternatif untuk meningkatkan ketercapaian capaian pembelajaran. Oleh karena itu, dalam penelitian ini melakukan pengukuran sejauh mana persepsi mahasiswa terhadap FEM sebagai model evaluasi pembelajaran. Hasil pengukuran menunjukkan bahwa mayoritas mahasiswa menyatakan setuju dan sangat setuju jika FEM digunakan sebagai model evaluasi pembelajaran. Kata Kunci: FEM, PBM, Evaluasi Pembelajaran AbstractThe condition of the Covid-19 pandemic forces policy makers, especially in the field of education, to be interrelated and work together to be able to provide the right solution so that education continues to run well. Education is a sector that is heavily affected where the Teaching and Learning Process (PBM) which previously used the face-to-face model was immediately stopped. Universities are required to be faster and more precise in choosing learning models and evaluating their implementation so that PBM runs well. However, the learning outcomes of subjects must be considered and maintained so that the learning outcomes of graduates do not change. Learning model innovations that involve lecturers and students must be carried out so that learning is more effective, efficient and in accordance with the development of the current situation and conditions. The learning evaluation model is important in measuring the current learning achievement. Formative Evaluation Model (FEM) was chosen as an alternative learning evaluation model to improve learning outcomes. Therefore, in this study to measure the extent to which students' perceptions of FEM as a learning evaluation model. The measurement results show that the majority of students agree and strongly agree if FEM is used as a learning evaluation model. Keywords: FEM, PBM, Learning Evaluation

PERSEPSI MAHASISWA TERHADAP FORMATIF EVALUATION MODEL (FEM) SEBAGAI MODEL EVALUASI PEMBELAJARAN BERBASIS LEARNING MANAGEMENT SYSTEM (LMS) Sri Winarno; Asih Rohmani; Denny Senata; Pulung Nurtantio Andono
EDUTECH Vol 21, No 2 (2022)
Publisher : Prodi Teknologi Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/e.v21i2.45568

Kondisi pandemi Covid19 ini memaksa para pemangku kebijakan khususnya dibidang pendidikan harus saling terkait dan berpadu untuk dapat memberikan solusi yang tepat agar supaya pendidikan tetap berjalan dengan baik. Pendidikan merupakan sektor yang terdampak cukup besar dimana Proses Belajar Mengajar (PBM) yang sebelumnya menggunakan model tatap muka langsung dihentikan. Perguruan tinggi dituntut lebih cepat dan tepat dalam memilih model pembelajaran dan melakukan evaluasi dalam pelaksanaannya sehingga PBM berjalan dengan baik. Namun demikian, capaian pembelajaran mata kuliah harus tetap diperhatikan dan dijaga supaya capaian pembelajaran lulusan tidak mengalami perubahan. Inovasi model pembelajaran yang melibatkan dosen dan mahasiswa harus dilakukan supaya pembelajaran lebih efektif, efisien dan sesuai dengan perkembangan situasi dan kondisi saat ini. Model evaluasi pembelajaran menjadi penting dalam mengukur capaian pembelajaran saat ini. Formatif Evaluation Model (FEM) dipilih sebagai model evaluasi pembelajaran alternatif untuk meningkatkan ketercapaian capaian pembelajaran. Oleh karena itu, dalam penelitian ini melakukan pengukuran sejauh mana persepsi mahasiswa terhadap FEM sebagai model evaluasi pembelajaran. Hasil pengukuran menunjukkan bahwa mayoritas mahasiswa menyatakan setuju dan sangat setuju jika FEM digunakan sebagai model evaluasi pembelajaran.Kata Kunci: FEM, PBM, Evaluasi Pembelajaran

Texture Feature Extraction in Grape Image Classification Using K-Nearest Neighbor Pulung Nurtantio Andono; Siti Hadiati Nugraini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4137

Indonesian Grapes are a vine. This fruit is often found in markets, shops, and the roadside. Along with the development of computer technology today, computers can solve problems by classifying objects and objects. How to apply GLCM and K-NN methods for the classification of grapes. The purpose of this study is to apply the GLCM and K-NN methods in the classification of grapes. The dataset used from kaggle.com sources, the data tested are 3 types of grapes, and the number of images is 2624. The fruit that will be used for the data collection and classification process is limited to three types of grapes, namely grape blue, grape pink, and grape white. How to apply GLCM and K-NN methods for the classification of grapes. The feature extraction of GLCM used in this study is the feature contrast, energy, correlation, and homogeneity. From testing the test data, the highest accuracy value is 99.5441% with k = 2 at level 8, while the lowest accuracy value is 24.924% at each k level 2. The GLCM level value is very influential on the accuracy results, namely, the higher the GLCM level value, the higher the GLCM value. accuracy is getting better.

Deteksi Karakter Hiragana Menggunakan Metode Convolutional Neural Network Eko Hari Rachmawanto; Pulung Nurtantio Andono
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 11 No. 3 (2022)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v11i3.50144

Salah satu jenis huruf dasar yang digunakan dalam Bahasa Jepang ialah Hiragana. Dalam penulisan Hiragana memiliki aturan guratan dasar atau berbentuk garis – garis dan coretan melengkung (kyokusenteki), penulisan dari garis atas ke bawah atau dari kiri ke kanan. Aturan tersebut harus diikuti dan diperhatikan. Diusulkan penggunaan Metode Convolutional Neural Network (CNN) untuk melakukan pengenalan terhadap tulisan tangan karakter Hiragana. Pada tahap preprocessing, terdapat proses segmentasi yang menggunakan metode thresholding, setelah itu dilakukan proses menghilangkan noise, mengubah ukuran, dan proses normalisasi dengan cara memotong gambar dataset. Pada tahap pengujian, digunakan Metode Adam Optimizer sebagai alat untuk menguji metode yang digunakan dimana akan menghasilkan nilai akurasi. Dengan menggunakan 1000 dataset gambar yang terdiri dari 50 karakter, masing-masing karakter memiliki 20 sampel gambar. Dari 1000 data tersebut, telah dilakukan variasi data dengan melakukan split dataset. Dalam penelitian ini dilakukan 2 kali percobaan dengan variasi data 70:30 dan 60:40. Akurasi yang didapat yaitu 86,5% dan 83%.. Akurasi yang di dapat menggunakan split dataset 70:30 ternyata menghasilkan prosentase lebih tinggi di banding menggunakan split dataset 60:40.

The evaluation of convolutional neural network and genetic algorithm performance based on the number of hyperparameters for English handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Moch Arief Soeleman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1250-1259

Convolutional neural network (CNN) has been widely applied to image recognition, especially handwritten English recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Mochammad Arief Soeleman
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.881

Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.

Co-Authors Abdussalam Abdussalam, Abdussalam Achmad Ridwan Affandy Agus Winarno, Agus Al zami, Farrikh Al-Fatih, Gilang Fajar Alzami, Farrikh Aria Hendrawan, Aria Arry Maulana Syarif, Arry Maulana Asih Rohmani Asih Rohmani, Asih Bastiaans, Jessica Carmelita Budi Harjo Cahaya Jatmoko Candhy Fadhila Arsyad Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Christy Atika Sari D, Ishak Bintang Dalimarta, Fahmy Ferdian Danang Bagus Chandra Prasetiyo Darmawan, Aditya Aqil Denny Senata Dito, Aliffia Putri Doheir, Mohamed Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Dwiza Riana Edi Noersasongko Edi Noersasongko Edi Noersasongko Egia Rosi Subhiyakto, Egia Rosi Ekaprana Wijaya Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erna Zuni Astuti Fajrian Nur Adnan Fauzi Adi Rafrastara Firman Wahyudi, Firman Fitri Yakub Guruh Fajar Shidik Hamir, Mun Hanny Haryanto Hartojo, James Harun Al Azies Heru Lestiawan Hidayat, Sholeh Hisyam Syarif Husain Husain I Ketut Eddy Purnama Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Irwan, Rhedy Islam, Hussain Md Mehedul Ivan Maulana Jumanto Jumanto, Jumanto Junta Zeniarja Karis Widyatmoko Khafiizh Hastuti Kiat, Ng Poh Kunio Kondo L. Budi Handoko M Arief Soeleman M. Arief Soeleman M. Arif Soeleman Maria Goretti Catur Yuantari Megantara, Rama Aria Mila Sartika, Mila Minghat, Asnul Dahar Bin Moch Arief Soeleman Moch Arief Soeleman Moch Arief Soeleman, Moch Arief Mochamad Hariadi Mochammad Arief Soeleman Muhammad Munsarif Muhammad Naufal, Muhammad Muljono Muljono Nanna Suryana Herman Ningrum, Novita Kurnia Nita Merlina Noor Ageng Setiyanto, Noor Ageng Nur Azise Ocky Saputra, Filmada Panca Hutama Caniago Paramita, Cinantya Pergiwati, Dewi Pramitasari, Ratih Prasetyoningrum, Devi Puji Purwatiningsih, Aris Pujiono Pujiono Purwanto Purwanto Putra, Angga Permana Raden Arief Nugroho Rafsanjani, Muhammad Ivan Rahmatullah, Muhammad Rifqi Fadhlan Ramadhan Rakhmat Sani ramayanti, ismarita Ricardus Anggi P Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Ruri Suko Basuki Saputra, Filmada Ocky Saputri, Pungky Nabella Saputro, Wicaksono Agung Saraswati, Galuh Wilujeng Sari Ayu Wulandari Sarker, Md. Kamruzzaman Satriyawibawa, Muhammad Yiko Savicevic, Anamarija Jurcev Senata, Denny Sendi Novianto Shafa, Raihanaldy Ash Shier Nee Saw Sinaga, Daurat Sindhu Rakasiwi Siti Hadiati Nugraini Soeleman, M Arief Soeleman, M. Arief Soeleman, Moch. Arief Soong, Lim Way Sri Winarno Sri Winarno Steven, Alvin Sudibyo, Usman Sukmawati Anggraeni Putri, Sukmawati Anggraeni Sukmono, Indriyo K. Supriyono Asfawi Susanto Susanto Tendi Tri Wiyanto, Tendi Tri Tengku Riza Zarzani N Thifaal, Nisrina Salwa Torhino, Rizal Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yusianto Rindra Zahrotul Umami, Zahrotul Zainal Arifin Hasibuan

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