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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 Lontar Komputer: Jurnal Ilmiah Teknologi 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

Author-ID : 221631

Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Spatial Condition in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs Wawan Gunawan; Agus Zainal Arifin; Undang Rosidin; Nina Kadaritna
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.48699

Dental panoramic radiographs heavily depend on the performance of the segmentation method due to the presence of unevenly illumination and low contrast of the images. Conditional Spatial Fuzzy C-mean (csFCM) Clustering have been proposed to achieve through the incorporation of the component and added in the FCM to cluster grouping. This algorithm directs with consideration conditioning variables that consider membership value. However, csFCM does not consider Intuitionistic Fuzzy Set to take final membership and final non-membership value into account, the effect does not wipe off the deviation by illumination and low contrast of the images completely for improvement to skip some scope. In this current paper, we introduced a new image segmentation method namely Conditional Spatial in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs. Our proposed method adds hesitation function aiming to settle the indication of the knowledge lack that belongs to the final membership function to get a better segmentation result. The experiment result shows this method achieves better segmentation performance with misclassification error (ME) and relative foreground area error (RAE) values are 4.77 and 4.27 respectively.

OTOMATISASI PERBANDINGAN PRODUK BERDASARKAN BOBOT FITUR PADA TEKS OPINI Yufis Azhar; Agus Zainal Arifin; Diana Purwitasari
Jurnal Ilmu Komputer Vol 6 No 2: September 2013
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (507.538 KB)

Proses otomatisasi perbandingan produk berdasarkan teks opini dapat dilakukan dengan caramengekstrak fitur yang dimiliki produk tersebut. Fitur-fitur inilah yang umumnya dinilai kemudian digunakanuntuk membandingkan suatu produk dengan produk yang lain. Banyak peneliti yang menggunakan kamus kataopini untuk mengekstrak fitur tersebut. Akan tetapi hal tersebut tidak efektif karena sangat bergantung padakelengkapan kamus kata yang digunakan. Oleh karena itu, dalam penelitian ini diusulkan suatu metode untukmembandingkan produk berdasarkan bobot fitur produk tanpa harus menggunakan kamus kata opini yanglengkap. Caranya adalah dengan menjumlahkan bobot dari fitur-fitur unggul yang dimiliki oleh suatu produkuntuk mendapatkan skor tiap produk. Hasil yang didapat menunjukkan bahwa penerapan metode tersebut dapatmeningkatkan akurasi dari proses perbandingan dua buah produk sebesar 81% dari pada metode sebelumnyayang hanya 71%.

PENDEKATAN POSITIONAL TEXT GRAPH UNTUK PEMILIHAN KALIMAT REPRESENTATIF CLUSTER PADA PERINGKASAN MULTI-DOKUMEN I Putu Gede Hendra Suputra; Agus Zainal Arifin; Anny Yuniarti
Jurnal Ilmu Komputer Vol 6 No 2: September 2013
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (643.965 KB)

Coverage and saliency are major problems in Automatic Text Summarization. Sentence clusteringapproaches are methods able to provide good coverage on all topics, but the point to be considered is theselection of important sentence that can represent the cluster’s topic. The salient sentences selected asconstituent to the final summary should have information density so that can convey important informationcontained in the cluster. Information density from the sentence can be mined by extracting the sentenceinformation density (SID) feature that built from positional text graph approach of every sentence in the cluster.This paper proposed a cluster representative sentence selection strategy that used the positional text graphapproach in multi-document summarization. There are three concepts that used in this paper: (1) sentenceclustering based on similarity based histogram clustering, (2) cluster ordering based on cluster importance and(3) representative sentence selection based on sentence information density feature score. The candidatesummary sentence is a sentence that has greatest sentence information density feature score of a cluster. Trialsconducted on task 2 DUC 2004 dataset. ROUGE-1 measurement was used as performance metric to comparethe use of SID feature with other method namely Local Importance and Global Importance (LIGI). Test resultshowed that the use of SID feature was successfully outperform LIGI method based on ROUGE-1 values wherethe greatest average value of ROUGE-1 that achieved by SID features is 0.3915.

Peringkasan Dokumen Berbahasa Inggris Menggunakan Sebaran Local Sentence Aminul Wahib; Agus Zainal Arifin; Diana Purwitasari
Jurnal Buana Informatika Vol. 7 No. 1 (2016): Jurnal Buana Informatika Volume 7 Nomor 1 Januari 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v7i1.482

Abstract. The number of digital documents grows very rapidly causing time waste in searching and reading the information. To overcome these problems, many document summary methods are developed to find important or key sentences from the source document. This study proposes a new strategy in summarizing English document by using local sentence distribution method to find and dig up hidden important sentence from the source document in an effort to improve quality of the summaries. Experiments are conducted on dataset DUC 2004 task 2. Measurement ROUGE-1 and ROUGE-2 are employed as a performance evaluation of the proposed method with sentence information density and sentence cluster keyword (SIDeKiCK). The experiment shows that the proposed method has better performance with an average achievement ROUGE-1 0.398, an increase of 1.5% compared to SIDeKiCK method and ROUGE-2 0.12, an increase 13% compared to SIDeKiCK method.Keywords: Summarize Document, Important Sentences, Distribution of Local Sentence, ROUGE. Abstrak. Jumlah dokumen digital yang berkembang sangat pesat menyebabkan banyaknya waktu terbuang dalam mencari dan membaca informasi. Untuk mengatasi permasalahan tersebut banyak dikembangkan metode peringkasan dokumen yang diharapkan mampu menemukan kalimat-kalimat penting dari dokumen sumber. Penelitian ini mengajukan strategi baru peringkasan dokumen berbahasa inggris menggunakan metode sebaran local sentence untuk mencari dan menggali kalimat penting yang tersembunyi dalam dokumen sumber sebagai upaya untuk meningkatkan kualitas hasil ringkasan. Uji coba dilakukan terhadap dataset task 2 DUC 2004. Pengukuran ROUGE-1 dan ROUGE-2 digunakan sebagai evaluasi performa metode yang diusulkan dengan metode lain yaitu metode sentence information density dan kata kunci cluster kalimat (SIDeKiCK). Hasil ujicoba didapatkan bahwa metode yang diusulkan memiliki performa lebih baik dengan capaian rata-rata ROUGE-1 0,398, meningkat 1,5% dibanding metode SIDeKiCK dan ROUGE-2 0,12 meningkat 13% dibanding metode SIDeKiCK.Kata Kunci: Peringkasan Dokumen, Kalimat Penting, Sebaran Local Sentence, ROUGE.

Segmentasi Citra Ikan Tuna Dengan Otomatisasi Parameter Dbscan Menggunakan Jumlah Titik Puncak Pada Histogram Wanvy Arifha Saputra; Didih Rizki Chandranegara; Agus Zainal Arifin
POROS TEKNIK Vol. 10 No. 1 (2018)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/porosteknik.v10i1.658

Segmentasi pada citra ikan tuna menggunakan Density-Based Spatial Clustering of Application (DBSCAN) membutuhkan dua parameter utama, yaitu Eps dan MinPts. Parameter tersebut dapat melakukan segmentasi citra tanpa mengetahui jumlah kluster. Setiap citra memiliki nilai parameter yang berbeda untuk mendapatkan hasil segmentasi yang terbaik. Input nilai parameter dengan metode manual memiliki kelemahan dalam mendapatkan nilai yang optimal dan secara subjektif dalam menentukan nilai parameter tersebut. Kelemahan dalam mendapatkan nilai parameter yang optimal dapat menyebabkan nilai parameter yang salah dan akan berpengaruh pada hasil segmentasi dari setiap citra. Kami mengajukan metode baru yaitu segmentasi citra ikan tuna dengan otomatisasi parameter DBSCAN menggunakan jumlah titik puncak pada histogram, sehingga mendapatkan nilai parameter yang optimal untuk segmentasi dari setiap citra. Untuk mendukung hal tersebut, kami menggunakan Eps Spatial, Eps Color dan MinPts di algoritma DBSCAN. Parameter tersebut mengambil nilai dari jumlah titik puncak pada histogram dalam ruang warna yang berbeda. Hasil dari metode ini dapat melakukan segmentasi citra ikan tuna dibuktikan dengan 30 citra yang telah digunakan dan mendapatkan akurasi diatas 90&. Jadi ini dapat melakukan segmentasi tanpa mengetahui nilai parameter tersebut.

Trabecular Bone Segmentation Based On Segment Profile Characteristics Using Extreme Learning Machine On Dental Panoramic Radiographs Rizqi Okta Ekoputris; Agus Zainal Arifin; Arya Yudhi Wijaya; Dini Adni Navastara
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Dental panoramic radiograph contains a lot of Information which one of them can be identified from trabecular bone structure. This research proposes segmentation of trabecular bone area on dental panoramic radiograph based on segment profile characteristics using Extreme Learning Machine as classification method. The input of this method is dental panoramic radiograph. The selection of region of interest (ROI) is performed on the lower jawbone of the trabecular bone area in which there are teeth and cortical bone. The ROI is subdivided into two where the upper ROI contains the teeth and the lower ROI contains cortical bone. After that, the result of the ROI deduction is done by preprocessing using mean and median filters for upper ROI and motion blur filter for lower ROI. The separate images are extracted each pixel into four features consisting of image intensity, 2D Gaussian filter with two different sigma, and Log Gabor filter for upper ROI. For lower ROI, five feature extractions are image intensity, Gaussian 2D filter with two different sigma, phase congruency, and Laplacian of Gaussian. Then used some sample pixels as training data to create Extreme Learning Machine model. The output of this classifier is the segmentation area of trabecular bone. On the upper ROI, the average of sensitivity, specificity, and accuracy were 82.31%, 93.67%, and 90.33%, respectively. While on the lower ROI obtained the average of sensitivity, specificity, and accuracy of 95.01%, 96.50%, and 95.29%, respectively.

PEMISAHAN GIGI PADA DENTAL PANORAMIC RADIOGRAPH DENGAN MENGGUNAKAN INTEGRAL PROJECTION YANG DIMODIFIKASI Bilqis Amaliah; Anny Yuniarti; Anindita Sigit Nugroho; Agus Zainal Arifin
Jurnal Ilmiah Kursor Vol 6 No 2 (2011)
Publisher : Universitas Trunojoyo Madura

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Tidak mudah untuk mengetahui identitas seorang korban, jika sebagian besar tubuhnya sudah tak berbentuk lagi. Terdapat banyak cara untuk mengidentifikasi korban yang meninggal dunia, antara lain dengan DNA, sidik jari dan citra gigi. Gigi merupakan bagian dari tubuh yang biasanya masih utuh, karena struktur gigi yang padat. Sehingga peneliti mengajukan penelitian tentang identifikasi korban dengan menggunakan citra gigi. Terdapat beberapa tahap untuk identifikasi korban menggunakan citra gigi. Tahapan awal dan sangat menentukan adalah tahap pemisahan citra gigi. Dengan semakin akuratnya hasil dari pemisahan citra gigi, maka akan semakin akurat pula hasil identifikasi korban menggunakan citra gigi. Pemisahan citra gigi yang dilakukan adalah menggunakan metode Integral Projection yang dimodifikasi. Metode Integral Projection yang dimodifikasi ini digunakan untuk memberi garis pemisah antara satu gigi dengan gigi lainnya. Citra gigi yang digunakan adalah dental panoramic radiograph. Keberhasilan Integral Projection biasa dalam memisahkan antara gigi adalah 88,23 %, sedangkan dengan menggunakan Integral Projection yang dimodifikasi meningkat menjadi 93,47 %. Kata Kunci: Dental Panoramic Radiograph, Segmentasi, Integral Projection. Abstract It’s not easy to find out the identity of a victim, if most of his body was not shaped anymore. There are some ways to identify a victims, for example are using DNA matching, fingerprints and dental image. Teeth are part of the body that usually remains intact, because the solid tooth structure. Because of that, identify victim using dental image are purposed. There are several stages for victim identification using dental images. The first stage and the important one is teeth separation. The more accurate the results of the teeth separation, the more accurate the identification victim using dental images. Teeth separation is using modified integral projection method. The modified integral projection method is to make a line between the teeth so that the result is more accurate than the ordinary integral projection. In this research, dental panoramic radiographs are used. Accuration of ordinary integral projection is 88,23 %, and modified projection integral is 93,47 %.

ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS I Made Widiartha; Agus Zainal Arifin; Anny Yuniarti
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

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ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS a I Made Widiartha, b Agus Zainal Arifin, c Anny Yuniarti a Jurusan Ilmu Komputer, FMIPA, Universitas Udayana Kampus Bukit, Gedung BJ Lt.I, Jimbaran Bali, b,c Informatics Department, Faculty of Information Technology Institute of Technology Sepuluh Nopember E-Mail: a imdewidiartha@cs.unud.ac.id Abstrak Berbagai metode telah dibuat untuk dapat melakukan klasterisasi data. Salah satu metode tersebut adalah K-Harmonic Means Clustering (KHM). KHM merupakan metode klasterisasi data yang menyempurnakan K-Means Clustering (KM). Metode KHM telah mampu mengurangi permasalahan KM dalam hal sensitifitas pada inisialisasi titik pusat awal, meskipun demikian dalam KHM masih terdapat kemungkinan solusi yang dihasilkan merupakan suatu lokal optimal. Permasalahan lokal optimal ini dapat diatasi dengan memanfaatkan suatu metode yang memiliki karakteristik pencarian solusi global ke dalam metode KHM. Artificial Bee Colony (ABC) merupakan suatu metode swarm yang berbasis pada perilaku mencari makan dari koloni lebah madu yang memiliki karakteristik untuk menghindari kemungkinan konvergensi terhadap lokal optimal. Dalam penelitian ini diusulkan sebuah metode baru untuk klasterisasi data yang berbasis pada metode ABC dan KHM (ABC-KHM). Kinerja metode ABC-KHM ini telah dibandingkan dengan metode KHM dan ABC dengan memanfaatkan lima dataset. Dari hasil penelitian didapatkan hasil dimana metode ABC-KHM ini telah berhasil mengoptimalkan posisi titik pusat klaster KHM yang mengarahkan hasil klaster menuju suatu solusi global. Kata kunci: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM. Abstract Various methods have been made to cluster the data. One such method is K-Harmonic Means Clustering (KHM). KHM is a clustering method that improves K-Means Clustering (KM). KHM method was able to reduce the problem of KM in terms of sensitivity to the initialization of the initial center point nevertheless there is still a possibility that the result of KHM is a local optimum. The local optimal problem can be solved by utilizing a method that has characteristic of a global search into KHM method. Artificial Bee Colony (ABC) is a swarm method based on foraging behavior of honey bee colony that has characteristics to avoid the possibility of local optimum convergence. In this research, a new method for data clustering based on ABC and KHM (ABC-KHM) is proposed. The performance ABC-KHM method has been compared with ABC and KHM by using five datasets. The results show that ABCKHM method is able to optimize the position of the cluster center and directs the center to a global solution. Key words: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM.

EFISIENSI PHRASE SUFFIX TREE DENGAN SINGLE PASS CLUSTERING UNTUK PENGELOMPOKAN DOKUMEN WEB BERBAHASA INDONESIA Desmin Tuwohingide; Mika Parwita; Agus Zainal Arifin
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 8 No 2 Februari 2016
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

The number of indonesian documents which available on internet is growing very rapidly. Automatic documents clustering shown to improving the relevant documents search results of many found documents. Suffix tree is one of documents clustering method that developed, because it is proven to increase precision. In this paper, we propose a new method to clustering indonesian web documents based on phrase efficiency in the choice process of base cluster with the combination of documents frequency and term frequency calculation on the phrase with a single pass clustering algorithm (SPC). Every phrase that is considered as the base cluster will be vectored then calculate of the term frequency and document frequency. Furthermore, the documents will be calculate their similarity based on the tf-idf weighted using the cosine similarity and documents clustering is done by using a single pass clustering algorithm. The proposed method is tested on 6 dataset with number of different document 10, 20, 30, 40, 50 and 60 documents. The experiment result show that the proposed method succeeded clustering indonesian web documents by reducing the leaf node with no derivative and produces the F-measure an average of 0.78 while STC traditional produces the F-measure an average of 0.55.This result prove that the efficiency of phrase by phrase choice on internal nodes and leaf nodes that have derivative, and a combination of term frequency and document frequency calculation on the base cluster, gives a significant impact on the process of clustering documents.

Optimasi Naive Bayes Dengan Pemilihan Fitur Dan Pembobotan Gain Ratio I Guna Adi Socrates; Afrizal Laksita Akbar; Mohammad Sonhaji Akbar; Agus Zainal Arifin; Darlis Herumurti
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 7, No. 1 April 2016
Publisher : Institute for Research and Community Services, Udayana University

Naïve Bayes is one of data mining methods that are commonly used in text-based document classification. The advantage of this method is a simple algorithm with low computation complexity. However, there is weaknesses on Naïve Bayes methods where independence of Naïve Bayes features can’t be always implemented that would affect the accuracy of the calculation. Therefore, Naïve Bayes methods need to be optimized by assigning weights using Gain Ratio on its features. However, assigning weights on Naïve Bayes’s features cause problems in calculating the probability of each document which is caused by there are many features in the document that not represent the tested class. Therefore, the weighting Naïve Bayes is still not optimal. This paper proposes optimization of Naïve Bayes method using weighted by Gain Ratio and feature selection method in the case of text classification. Results of this study pointed-out that Naïve Bayes optimization using feature selection and weighting produces accuracy of 94%.

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 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 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, Irna Dwi 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 Kholid Fathoni Lafnidita Farosanti Laili Cahyani Lutfiani Ratna Dewi Luthfi Atikah M. Ali Fauzi M. Jainal Arifin Mamluatul Hani’ah Maulana, Hendra Maulana, Hendra Mika Parwita Moch Zawaruddin Abdullah Moh. Zikky 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 Nova Hadi Lestriandoko 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 Wiwik Dyah Septiana Kurniati Yudhi Diputra Yufis Azhar Yulia Niza Yunianto, Dika R. Zainal Abidin Zakiya Azizah Cahyaningtyas

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