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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Jurnal Teknik ITS Majalah Kedokteran Bandung IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Jurnal technoscientia Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications REKAYASA JPM17: Jurnal Pengabdian Masyarakat POROS TEKNIK Annual Research Seminar Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Jurnal Inspiration Dental Journal (Majalah Kedokteran Gigi) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal ULTIMATICS Journal of Computer Science and Informatics Engineering (J-Cosine) Systemic: Information System and Informatics Journal Specta Journal of Technology EPI International Journal of Engineering ILKOMNIKA: Journal of Computer Science and Applied Informatics Indonesian Journal of Electrical Engineering and Computer Science JURNAL TEKNOLOGI TECHNOSCIENTIA Makara Journal of Technology Sewagati Nusantara Journal of Computers and its Applications Jurnal INFOTEL
Agus Zainal Arifin
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
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Performance Analysis of Specification Computer and Mobile with Implementation Tawaf Virtual Reality using A* Algorithm and RVO System Zikky, Moh.; Arifin, M. Jainal; Fathoni, Kholid; Arifin, Agus Zainal
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.902 KB) | DOI: 10.24003/emitter.v7i1.321

Abstract

High-Performance Computer (HPC) is computer systems that are built to be able to solve computational loads. HPC can provide a high-performance technology and short the computing processes timing. This technology was often used in large-scale industries and several activities that require high-level computing, such as rendering virtual reality technology. In this research, we provide Tawaf’s Virtual Reality with 1000 of Pilgrims and realistic surroundings of Masjidil-Haram as the interactive and immersive simulation technology by imitating them with 3D models. Thus, the main purpose of this study is to calculate and to understand the processing time of its Virtual Reality with the implementation of tawaf activities using various platforms; such as computer and Android smartphone. The results showed that the outer-line or outer rotation of Kaa’bah mostly consumes minimum times although he must pass the longer distance than the closer one.  It happened because the agent with the closer area to Kaabah is facing the crowded peoples. It means an obstacle has the more impact than the distances in this case.
Pengembangan Metode Klasterisasi Data Berbasis Hybrid Improved Artificial Bee Colony (IABC) dan K – Harmonic Means Musa, Saiful Bahri; Humaira, Fitrah Maharani; Widiartha, I Made; Herumurti, Darlis; Arifin, Agus Zainal; Fiqar, Tegar Palyus
Specta Journal Vol 2 No 3 (2018): SPECTA Journal of Technology
Publisher : Specta Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.517 KB) | DOI: 10.0610/specta.v2i3.3

Abstract

One of data grouping process method is k-harmonic clustering method (KHM) which has a relatively short and simple process. However, it has a weakness at cluster center point. Randomly formed cluster center point causes difficulty to converge solutions. One way to solve the problem at the cluster center point requires a method which has a global solution for KHM. The method is Improved artificial bee colony (IABC), improvement of artificial bee colony (ABC) method based on behavior patterns of honey bee colony in food searching process. Advantage of the IABC method is able to have more optimum global solution. This research proposes a new method of clustering using improved artificial bee colony and K-Harmonic means (IABC-KHM) to optimize the center point in clusters that lead to global solution. In this study, the IABC is functioned for finding the most optimum cluster center point for the data clustering process using KHM. Furthermore, the performance test of the IABC-KHM clustering method is compared with ABC and ABC-KHM methods on three different datasets. The result of mean value of best function of IABC-KHM method of Iris dataset is 152,87, Contraceptive Method Choice dataset is 918,54, and Wine dataset is 31,01. Moreover, the result of the average value of the best F-Measure method IABC-KHM Iris dataset is 0.90, the Contraceptive Method Choice dataset is 0.41, the Wine dataset is 0.95. To conclude, IABC-KHM method has successfully optimized the position of cluster center point that directs the cluster result which has global solution.
Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag Priyatno, Arif Mudi; Muttaqi, Muhammad Mirza; Syuhada, Fahmi; Arifin, Agus Zainal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January-June
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1346.279 KB) | DOI: 10.26594/register.v5i1.1382

Abstract

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer.  Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.
Query Expansion menggunakan Word Embedding dan Pseudo Relevance Feedback Tanuwijaya, Evan; Adam, Safri; Anggris, Mohammad Fatoni; Arifin, Agus Zainal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January-June
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1248.276 KB) | DOI: 10.26594/register.v5i1.1385

Abstract

Kata kunci merupakan hal terpenting dalam mencari sebuah informasi. Penggunaan kata kunci yang tepat menghasilkan informasi yang relevan. Saat penggunaannya sebagai query, pengguna menggunakan bahasa yang alami, sehingga terdapat kata di luar dokumen jawaban yang telah disiapkan oleh sistem. Sistem tidak dapat memproses bahasa alami secara langsung yang dimasukkan oleh pengguna, sehingga diperlukan proses untuk mengolah kata-kata tersebut dengan mengekspansi setiap kata yang dimasukkan pengguna yang dikenal dengan Query Expansion (QE). Metode QE pada penelitian ini menggunakan Word Embedding karena hasil dari Word Embedding dapat memberikan kata-kata yang sering muncul bersama dengan kata-kata dalam query. Hasil dari word embedding dipakai sebagai masukan pada pseudo relevance feedback untuk diperkaya berdasarkan dokumen jawaban yang telah ada. Metode QE diterapkan dan diuji coba pada aplikasi chatbot. Hasil dari uji coba metode QE yang diterapkan pada chatbot didapatkan nilai recall, precision, dan F-measure masing-masing 100%; 70% dan 82,35 %. Hasil tersebut meningkat 1,49% daripada chatbot tanpa menggunakan QE yang pernah dilakukan sebelumnya yang hanya meraih akurasi sebesar 68,51%. Berdasarkan hasil pengukuran tersebut, QE menggunakan word embedding dan pseudo relevance feedback pada chatbot dapat mengatasi query masukan dari pengguna yang ambigu dan alami, sehingga dapat memberikan jawaban yang relevan kepada pengguna.  Keywords are the most important words and phrases used to obtain relevant information on content. Although users make use of natural languages, keywords are processed as queries by the system due to its inability to process. The language directly entered by the user is known as query expansion (QE). The proposed QE in this research uses word embedding owing to its ability to provide words that often appear along with those in the query. The results are used as inputs to the pseudo relevance feedback to be enriched based on the existing documents. This method is also applied to the chatbot application and precision, and F-measure values of the results obtained were 100%, 70%, 82.35% respectively. The results are 1.49% better than chatbot without using QE with 68.51% accuracy. Based on the results of these measurements, QE using word embedding and pseudo which gave relevance feedback in chatbots can resolve ambiguous and natural user?s input queries thereby enabling the system retrieve relevant answers.
Ambiguitas Machine Translation pada Cross Language Chatbot Bea Cukai Al Haromainy, Muhammad Muharrom; Setyawan, Dimas Ari; Waluya, Onny Kartika; Arifin, Agus Zainal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January-June
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1284.702 KB) | DOI: 10.26594/register.v5i1.1387

Abstract

Sistem Information Retrieval (IR) maupun chatbot semakin banyak dikembangkan. Salah satu bagian yang banyak diteliti adalah cross language. Masalah pada pengembangan cross language yaitu terjadinya kesalahan pada hasil terjemahan mesin translasi yang memberikan arti tidak sesuai dengan bahasa natural, sehingga pengguna tidak mendapatkan jawaban yang semestinya, bahkan tidak jarang pula pengguna tidak menemukan jawaban. Penelitian ini mengusulkan skema baru mesin translasi yang bertujuan meningkatkan performa dalam masalah ambiguitas. Mesin translasi bekerja dengan cek kebenaran kata kunci, kemudian melakukan Part-of-Speech (POS) Tagging pada kata benda (noun). Kemudian, setiap kata benda yang terdeteksi akan dicari sinonimnya. Lalu, sinonim yang didapatkan akan ditambahkan dan menjadi alternatif kueri baru. Kueri yang mempunyai nilai confident tertinggi diasumsikan sebagai kueri yang paling sesuai. Pada hasil yang didapatkan setelah dilakukan uji coba, melalui penambahan metode yang kami usulkan pada machine translation, dapat meningkatkan akurasi chatbot dibandingkan tanpa menggunakan skema yang diusulkan. Hasil akurasi bertambah 5%, dari yang semula 73% menjadi 77%.  Information retrieval and chatbot systems are increasingly being developed with its language part mostly studied. However, the problem associated with its development is the occurrence of errors in the translation machine resulting in inaccurate answers not in accordance with the natural language, thereby providing users with wrong answers. This study proposes a new translation machine scheme that aims to improve performance while translating ambiguous terms. Translation machines functions by checking the correctness of keywords, and carrying out Part-of-Speech (POS) Tagging on nouns (noun). The synonyms of any detected noun are searched for and obtained added to become alternative new queries. Those with the highest confident value are assumed to be the most appropriate. The results obtained after testing, through the addition of the method proposed in machine translation, can improve the accuracy of the chatbot compared to not using the proposed scheme. The results of the accuracy increased from the original 73% to 77%.
Pengembangan Metode Klasterisasi Data Berbasis Hybrid Improved Artificial Bee Colony (IABC) dan K – Harmonic Means Fiqar, Tegar Palyus; Musa, Saiful Bahri; Humaira, Fitrah Maharani; Widiartha, I Made; Herumurti, Darlis; Arifin, Agus Zainal
SPECTA Journal of Technology Vol 2 No 3 (2018): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.517 KB) | DOI: 10.35718/specta.v2i3.3

Abstract

One of data grouping process method is k-harmonic clustering method (KHM) which has a relatively short and simple process. However, it has a weakness at cluster center point. Randomly formed cluster center point causes difficulty to converge solutions. One way to solve the problem at the cluster center point requires a method which has a global solution for KHM. The method is Improved artificial bee colony (IABC), improvement of artificial bee colony (ABC) method based on behavior patterns of honey bee colony in food searching process. Advantage of the IABC method is able to have more optimum global solution. This research proposes a new method of clustering using improved artificial bee colony and K-Harmonic means (IABC-KHM) to optimize the center point in clusters that lead to global solution. In this study, the IABC is functioned for finding the most optimum cluster center point for the data clustering process using KHM. Furthermore, the performance test of the IABC-KHM clustering method is compared with ABC and ABC-KHM methods on three different datasets. The result of mean value of best function of IABC-KHM method of Iris dataset is 152,87, Contraceptive Method Choice dataset is 918,54, and Wine dataset is 31,01. Moreover, the result of the average value of the best F-Measure method IABC-KHM Iris dataset is 0.90, the Contraceptive Method Choice dataset is 0.41, the Wine dataset is 0.95. To conclude, IABC-KHM method has successfully optimized the position of cluster center point that directs the cluster result which has global solution.
Klasifikasi Berita Berbahasa Indonesia Mengggunakan Seleksi Fitur Dua Tahap Dan Naïve Bayes Fauzi, M Ali; Gosario, Sony; Arifin, Agus Zainal
Systemic: Information System and Informatics Journal Vol 3 No 2 (2017): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v3i2.240

Abstract

Jumlah dokumen digital telah meningkat secara pesat, sehingga klasifikasi dokumen secara otomatis menjadi sangat penting. Pemilihan fitur diperlukan dalam klasifikasi dokumen otomatis. Salah satu metode seleksi fitur yang terbukti handal adalah Maximal Marginal Relevance for Feature Selection (MMR-FS), namun metode ini memiliki kompleksitas yang tinggi. Dalam penelitian ini, diusulkan sebuah metode baru dalam pemilihan fitur untuk klasifikasi dokumen. Metode yang diusulkan terdiri dari dua tahap, yang pertama adalah Information Gain dan yang kedua adalah MMR-FS . Pada proses klasifikasinya digunakan metode Naïve Bayes. Dalam percobaan yang dilakukan, metode yang diusulkan bisa mencapai akurasi 86%. Metode baru ini dapat menurunkan kompleksitas MMR-FS namun tetap mempertahankan keakuratannya.
Optimasi Pembobotan pada Query Expansion dengan Term Relatedness to Query-Entropy based (TRQE) Ludviani, Resti; Hayati, Khadijah F.; Arifin, Agus Zainal; Purwitasari, Diana
Jurnal Buana Informatika Vol 6, No 3 (2015): Jurnal Buana Informatika Volume 6 Nomor 3 Juli 2015
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (297.228 KB) | DOI: 10.24002/jbi.v6i3.433

Abstract

Abstract. An appropriate selection term for expanding a query is very important in query expansion. Therefore, term selection optimization is added to improve query expansion performance on document retrieval system. This study proposes a new approach named Term Relatedness to Query-Entropy based (TRQE) to optimize weight in query expansion by considering semantic and statistic aspects from relevance evaluation of pseudo feedback to improve document retrieval performance. The proposed method has 3 main modules, they are relevace feedback, pseudo feedback, and document retrieval. TRQE is implemented in pseudo feedback module to optimize weighting term in query expansion. The evaluation result shows that TRQE can retrieve document with the highest result at precission of 100% and recall of 22,22%. TRQE for weighting optimization of query expansion is proven to improve retrieval document.     Keywords: TRQE, query expansion, term weighting, term relatedness to query, relevance feedback Abstrak..Pemilihan term yang tepat untuk memperluas queri merupakan hal yang penting pada query expansion. Oleh karena itu, perlu dilakukan optimasi penentuan term yang sesuai sehingga mampu meningkatkan performa query expansion pada system temu kembali dokumen. Penelitian ini mengajukan metode Term Relatedness to Query-Entropy based (TRQE), sebuah metode untuk mengoptimasi pembobotan pada query expansion dengan memperhatikan aspek semantic dan statistic dari penilaian relevansi suatu pseudo feedback sehingga mampu meningkatkan performa temukembali dokumen. Metode yang diusulkan memiliki 3 modul utama yaitu relevan feedback, pseudo feedback, dan document retrieval. TRQE diimplementasikan pada modul pseudo feedback untuk optimasi pembobotan term pada ekspansi query. Evaluasi hasil uji coba menunjukkan bahwa metode TRQE dapat melakukan temukembali dokumen dengan hasil terbaik pada precision  100% dan recall sebesar 22,22%.Metode TRQE untuk optimasi pembobotan pada query expansion terbukti memberikan pengaruh untuk meningkatkan relevansi pencarian dokumen.Kata Kunci: TRQE, ekspansi query, pembobotan term, term relatedness to query, relevance feedback
Perangkingan Dokumen Berbahasa Arab Menggunakan Latent Semantic Indexing Wahib, Aminul; Pasnur, Pasnur; Santika, Putu Praba; Arifin, Agus Zainal
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 | Full PDF (417.199 KB) | DOI: 10.24002/jbi.v6i2.411

Abstract

Berbagai metode perangkingan dokumen dalam aplikasi InformationRetrieval telah dikembangkan dan diimplementasikan. Salah satu metode yangsangat populer adalah perangkingan dokumen menggunakan vector space modelberbasis pada nilai term weighting TF.IDF. Metode tersebut hanya melakukanpembobotan term berdasarkan frekuensi kemunculannya pada dokumen tanpamemperhatikan hubungan semantik antar term. Dalam kenyataannya hubungansemantik antar term memiliki peranan penting untuk meningkatkan relevansi hasilpencarian dokumen. Penelitian ini mengembangkan metode TF.IDF.ICF.IBFdengan menambahkan Latent Semantic Indexing untuk menemukan hubungansemantik antar term pada kasus perangkingan dokumen berbahasa Arab. Datasetyang digunakan diambil dari kumpulan dokumen pada perangkat lunak MaktabahSyamilah. Hasil pengujian menunjukkan bahwa metode yang diusulkanmemberikan nilai evaluasi yang lebih baik dibandingkan dengan metodeTF.IDF.ICF.IBF. Secara berurut nilai f-measure metode TF.IDF.ICF.IBF.LSIpada ambang cosine similarity 0,3, 0,4, dan 0,5 adalah 45%, 51%, dan 60%. Namun metode yang disulkan memiliki waktu komputasi rata-rata lebih tinggidibandingkan dengan metode TF.IDF.ICF.IBF sebesar 2 menit 8 detik.
Citra Radiografi Panoramik pada Tulang Mandibula untuk Deteksi Dini Osteoporosis dengan Metode Gray Level Cooccurence Matrix (GLCM) Azhari, -; Suprijanto, -; Diputra, Yudhi; Juliastuti, Endang; Arifin, Agus Zainal
Majalah Kedokteran Bandung Vol 46, No 4 (2014)
Publisher : Faculty of Medicine, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (722.539 KB)

Abstract

Osteoporosis  salah satu penyakit degeneratif yang berkaitan dengan proses penuaan yang ditunjukkan perubahan struktur trabekula dan penurunan bone mineral density (BMD). Tujuan penelitian  adalah mendapatkan metode kuantifikasi citra panoramik  pada region of interest (ROI) di mandibula untuk menentukan BMD. Penelitian ini menggunakan  ROI (80x80 pixel) pada  kondilus mandibula untuk kuantifikasi citra dilakukan di Bagian Radiologi  Fakultas Kedokteran Gigi Universitas Padjadjaran bulan  Oktober sampai Desember 2013. Pendekatan analisis tekstur menggunakan prinsip gray level co-occurence matrix (GLCM).  Desain dari kuantifikasi citra terdiri atas tahapan pelatihan dan pengujian.  Tahapan pelatihan melalui  9 data latih terhadap subjek wanita berusia 52–73 tahun pascamenopause.  Data  BMD vertebra lumbar dari DEXA digunakan sebagai referensi pada tahap klasifikasi dengan support vector machine (SVM) dengan fungsi kernel multilayer perceptron. Pengujian digunakan 14 data uji dari subjek selain yang digunakan untuk data latih. Pengujian untuk klasifikasi kelas normal dan osteoporosis menggunakan SVM memberikan akurasi  85,71%; sensitivitas (tingkat benar positif) 90,91%; dan spesifisitas (tingkat benar negatif) 66,67%. Pengenalan fitur paling baik didapatkan menggunakan kombinasi fitur contrast, correlation, energy, dan homogeneity sebagai input bagi klasifikasi SVM. Simpulan, analisis tekstur trabekula menggunakan metode gray level co-occurence matrix (GLCM) citra panoramik gigi dapat digunakan untuk deteksi dini osteoporosis. Kata kunci: Grey level co-occorance matrix (GLCM), panoramik, osteoporosis Panoramic Radiograph Image using Cooccurence Gray Level Matrix Method (GLCM) for Early Detection of Osteoporosis in Mandibular Bone  Abstract Osteoporosis is one of the degenerative diseases associated with aging, which is apparent from changes in trabecular structure and decreased bone mineral density (BMD) The  aim of this study  was to obtain a panoramic image quantification method on a region of interest (ROI) to determine the BMD. This study used an ROI (80x80 pixels) of the mandibular condyle for image quantification. The study was performed at the Department of Radiology, Faculty of Dentistry, Padjadjaran University during the period of October to December 2013. A texture analysis approach was applied using the principles of gray level co-occurence matrix (GLCM). The design of image quantification consisted of training and testing stages. The training stage was performed through 9 training data on the subjects of post-menopausal women between 52–73 years old . Data from the lumbar vertebrae BMD DEXA was used as a reference in the classification stage using a support vector machine (SVM) with kernel function multilayer perceptron. The testing used 14 test data from subjects which were not used for training data. The results showed that for the normal and osteoporotic class classification using SVM the accuracy was 85.71%, sensitivity (true positive rate) was 90.91%, and specificity (true negative rate) was 66.67%.  The best feature recognition was obtained using a combination of feature contrast, correlation, energy, and homogeneity as inputs for SVM classification. In conclusion, analysis of the trabecular texture using dental panoramic image produced by gray level co-occurance matrix (GLCM) method can be useful for early detection of osteoporosis.Key words: Grey level co-occorance matrix (GLCM), panoramic, osteoporosis DOI: 10.15395/mkb.v46n4.338
Co-Authors - Azhari AA Sudharmawan, AA Adenuar Purnomo Adhi Nurilham Adi Guna, I Gusti Agung Socrates Afrizal Laksita Akbar Ahmad Afiif Naufal Ahmad Reza Musthafa, Ahmad Reza Ahmad Syauqi Aida Muflichah Aidila Fitri Fitri Heddyanna Akira Asano Akira Taguchi Akwila Feliciano Alhaji Sheku Sankoh, Alhaji Sheku Alif Akbar Fitrawan, Alif Akbar Alifia Puspaningrum Alqis Rausanfita Amelia Devi Putri Ariyanto Aminul Wahib Aminul Wahib Aminul Wahib Ana Tsalitsatun Ni'mah Andi Baso Kaswar Andi Baso Kaswar Anindhita Sigit Nugroho Anindita Sigit Nugroho Anny Yunairti Anny Yuniarti Anto Satriyo Nugroho Arif Fadllullah Arif Mudi Priyatno Arifin, M. Jainal Arifin, M. Jainal Arifzan Razak Arini Rosyadi Arrie Kurniawardhani Arya Widyadhana Arya Yudhi Wijaya Bagus Satria Wiguna Bagus Setya Rintyarna Baskoro Nugroho Bilqis Amaliah Chandranegara, Didih Rizki Chastine Fatichah Christian Sri kusuma Aditya, Christian Sri kusuma Cinthia Vairra Hudiyanti Cornelius Bagus Purnama Putra Daniel Sugianto Daniel Swanjaya Darlis Herumurti Dasrit Debora Kamudi Desepta Isna Ulumi Desmin Tuwohingide Dhian Kartika Diana Purwitasari Didih Rizki Chandranegara Dika Rizky Yunianto Dimas Fanny Hebrasianto Permadi Dini Adni Navastara, Dini Adni Dinial Utami Nurul Qomariah Dwi Ari Suryaningrum Dyah S. Rahayu Eha Renwi Astuti Endang Juliastuti Erliyah Nurul Jannah, Erliyah Nurul Ery Permana Yudha Eva Firdayanti Bisono Evan Tanuwijaya Evelyn Sierra Fahmi Syuhada Fahmi Syuhada Fandy Kuncoro Adianto Fathoni, Kholid Fathoni, Kholid Fiqey Indriati Eka Sari Gosario, Sony Gulpi Qorik Oktagalu Pratamasunu Gus Nanang Syaifuddiin Handayani Tjandrasa Hanif Affandi Hartanto Hudan Studiawan Humaira, Fitrah Maharani Humaira, Fitrah Maharani I Guna Adi Socrates I Gusti Agung Socrates Adi Guna I Made Widiartha I Putu Gede Hendra Suputra Indra Lukmana Irna Dwi Anggraeni Ismail Eko Prayitno Rozi Januar Adi Putra Kevin Christian Hadinata Khadijah F. Hayati Khairiyyah Nur Aisyah Khairiyyah Nur Aisyah, Khairiyyah Nur Khalid Khalid Khoirul Umam Lafnidita Farosanti Laili Cahyani Lutfiani Ratna Dewi Luthfi Atikah M. Ali Fauzi Mamluatul Hani’ah Maulana, Hendra Maulana, Hendra Mika Parwita Moch Zawaruddin Abdullah Moh. Zikky, Moh. Mohammad Fatoni Anggris, Mohammad Fatoni Mohammad Sonhaji Akbar Muhamad Nasir Muhammad Bahrul Subkhi Muhammad Fikri Sunandar Muhammad Imron Rosadi Muhammad Imron Rosadi Muhammad Machmud Muhammad Mirza Muttaqi Muhammad Muharrom Al Haromainy Munjiah Nur Saadah Muttaqi, Muhammad Mirza Nahya Nur Nanang Fakhrur Rozi Nanik Suciati Nina Kadaritna Novi Nur Putriwijaya Novrindah Alvi Hasanah Nur, Nahya Nuraisa Novia Hidayati Nursanti Novi Arisa Nursuci Putri Husain Ozzy Secio Riza Pangestu Widodo, Pangestu Pasnur Pasnur Pasnur Pasnur Puji Budi Setia Asih Putri Damayanti Putri Nur Rahayu Putu Praba Santika Rangga Kusuma Dinata Rarasmaya Indraswari Ratri Enggar Pawening Renest Danardono Resti Ludviani Rigga Widar Atmagi Riyanarto Sarno Riza, Ozzy Secio Rizka Sholikah Rizka Wakhidatus Sholikah Rizqa Raaiqa Bintana Rizqi Okta Ekoputris Rosyadi, Ahmad Wahyu Ryfial Azhar, Ryfial Safhira Maharani Safri Adam Saiful Bahri Musa Salim Bin Usman Saputra, Wahyu Syaifullah Jauharis Satrio Verdianto Satrio Verdianto Setyawan, Dimas Ari Sherly Rosa Anggraeni Siprianus Septian Manek Sonny Christiano Gosaria Sugiyanto, Sugiyanto Suprijanto Suprijanto Suwanto Afiadi Syadza Anggraini Syuhada, Fahmi Takashi Nakamoto Tegar Palyus Fiqar Tesa Eranti Putri Tio Darmawan Umi Salamah Undang Rosidin Verdianto, Satrio Waluya, Onny Kartika Wanvy Arifha Saputra Wardhana, Septiyawan R. Wawan Gunawan Wawan Gunawan Wawan Gunawan Wawan Gunawan Wijayanti Nurul Khotimah Yudhi Diputra Yufis Azhar Yulia Niza Yunianto, Dika R. Zainal Abidin Zakiya Azizah Cahyaningtyas