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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Elektron Jurnal Ilmiah Jurnal Sains dan Teknologi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Rang Teknik Journal ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech International Journal of Informatics and Computation Dinasti International Journal of Education Management and Social Science Systematics Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Digital Business Management Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Jurnal Manajemen Sains Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Soft Computing Exploration
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method Ahmad Zamsuri; Sarjon Defit; Gunadi Widi Nurcahyo
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v4i2.1964

Abstract

Emotions are expressions manifested by individuals in response to what they see or experience. In this study, emotions were examined through individuals' tweets regarding the election issues in Indonesia in 2024. The collected tweets were then labeled based on emotions using the emotion wheel, which consisted of six categories: joy, love, surprise, anger, fear, and sadness. After the labeling process, the next step involved weighting using TF-IDF (Term Frequency-Inverse Document Frequency) and Bag-of-Words (BoW) techniques. Subsequently, the model was evaluated using the K-Nearest Neighbor (KNN) algorithm with three different data splitting ratios: 80:20, 70:30, and 60:40. From the six labels used in the modeling process, the accuracy was then calculated, and the labels were subsequently merged into positive and negative categories. Then the modeling was conducted using the same process with the six labels. The results of this study revealed that the utilization of TF-IDF outperformed BoW. The highest accuracy was achieved with the 80:20 data splitting ratio, attaining 58% accuracy for the six-label classification and 79% accuracy for the two-label classification
Algoritma K-Means Clustering Penggunaan Bandwidth Internet (Studi Kasus di Pemerintah Daerah Kabupaten Padang Pariaman) Rizki Mubarak; Sarjon Defit; Gunadi Widi Nurcahyo
Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Vol 14, No 1 (2023): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jsit.v14i1.3037

Abstract

Untuk menunjang kegiatan di Pemerintahan dibutuhkan koneksi jaringan yang yang cepat dan tepat. Sehingga memerlukan jaringan bandwith yang lebar. Manajemen Bandwidth perlu dilakukan agar kecepatan jaringan tetap stabil. Penelitian ini bertujuan untuk melihat pola penggunaan bandwidth di Pemerintah Daerah Kabupaten Padang Pariaman menggunakan K-Means Clustering. Data diambil dari aplikasi Cacti sebuah software open-source, pemantauan jaringan berbasis web. Total datasets hasil ekstraksi yang digunakan adalah sebanyak 32 data OPD (Organisasi Perangkat Daerah) yang ada di Pemerintah Daerah Kabupaten Padang Pariaman tahun 2022.. Data-data yang tersedia selanjutnya diolah untuk mendapatkan target cluster dengan memanfaatkan konsep data mining menggunakan metode K-Mean Clustering. Pengelompokan data pengunaan bandwidth di Kabupaten Padang Pariaman  menggunakan metode Clustering dengan algoritma K-Means dengan atribut Nama OPD, Inbound Average, Inbound Maksimum, Outbound  Average, Outbound Maximum yang digunakan dalam proses perhitungan dan pembagian data ke dalam 3 cluster dengan kategori penggunaan bandwidth tinggi, rendah, dan sedang. Perhitungan dilakukan secara manual dan kemudian dilakukan pengujian dengan software RapidMiner. Hasil dari perhitungan manual  diperoleh  jumlah anggota cluster yang sama dengan perhitungan dengan software RapidMiner.
Pengembangan Sistem Keamanan Jaringan Komputer Melalui Perumusan Aturan (Rule) Snort untuk Mencegah Serangan Synflood Nori Sahrun; Rusdianto Roestam; Sarjon Defit
SATIN - Sains dan Teknologi Informasi Vol 1 No 2 (2015): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (843.257 KB) | DOI: 10.33372/stn.v1i2.23

Abstract

Rule  snort  merupakan  database  yang  berisi  polapola  serangan  signature  jenis  serangan  yang  disusun sesuai dengan perintah-perintah snort. Rule snort ini, harus di update secara rutin supaya ketika ada sesuatu teknik  serangan  yang  baru  maka  serangan  tersebut dapat  terdeteksi,  dan  program  dalam  penelitian  ini yang  akan  mengupdate  rule  snort  tersebut  dalam mencegah serangan SYNflood. Dalam penulisan rule snort terdapat aturan-aturan yang harus di ikuti yaitu pertama  rule  snort  harus  ditulis  dalam  satu  baris  ( single line), dan yang  kedua snort terbagi menjadi dua bagian yaitu rule header dan rule option. Rule header berisi  tentang  rule  action,  protocol,  source  dan destination IP address,netmask,  source dan destination port.  Rule  option  berisi  alert  message  dan  berbagai dan  berbagai  informasi  dimana  seharusnya  paket tersebut  diletakkan.  Dalam  pengembangan  keamanan jaringan sangat penting untuk di rumuskan  seranganserangan  yang  akan  mengakibatkan  system  down dapat diatasi oleh rule terbaru
SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP Susandri Susandri; Sarjon Defit; Muhammad Tajuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4201

Abstract

The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from "Alumni94" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree models
Analisa Dini Gangguan Disleksia Anak Sekolah dengan Metode Backpropagation Novi Yanti; Adil Setiawan; Sarjon Defit
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 2 (2023): Volume 9 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i2.64588

Abstract

Disleksia sering disalah artikan sebagai kebodohan atau kemalasan pada anak. Gejala disleksia dikenal dengan gangguan belajar yang meliputi mengenal huruf, mengeja, membaca, dan menulis. Meskipun gejala disleksia tidak terlihat dengan jelas, kondisi ini dapat berdampak pada perkembangan pola belajar anak. Tujuan penelitian adalah untuk mengidentifikasi gejala disleksia sedini mungkin agar tidak mengganggu perkembangan belajar pada anak. Selain itu, penelitian juga bertujuan untuk mengevaluasi keakuratan teknik yang digunakan. Analisa menggunakan metode jaringan syaraf tiruan dengan teknik backpropagation dengan memberikan nilai bobot, sehingga dapat memberikan nilai input dengan benar. Penelitian menggunakan 150 dataset, 40 variabel input dan 40 lapisan tersembunyi. Keluaran yang diharapkan mencakup disleksia atau non-disleksia. Hasil implementasi dan pengujian untuk data latih dan data uji terbaik adalah 90:10. Dengan nilai epoch maksimum 5000 dan nilai error target 0,001. Metode backpropagation dapat memberikan hasil akurasi terbaik 100% pada learning rate 0,5. Sehingga metode backpropagation dapat dengan baik mendeteksi gangguan disleksia pada anak sejak dini.
Standardscaler's Potential in Enhancing Breast Cancer Accuracy Using Machine Learning Febri Aldi; Febri Hadi; Nadya Alinda Rahmi; Sarjon Defit
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.3080

Abstract

The major consequence of breast cancer is death. It has been proven in many studies that machine learning techniques are more efficient in diagnosing breast cancer. These algorithms have also been used to estimate a person's likelihood of surviving breast cancer. In this study, we employed machine learning algorithms to predict breast cancer. A total of 569 breast cancer datasets were obtained from kaggle sites. Some of the machine learning algorithms that we use are K-Nearest Neighbor (KNN), besides Random Forest (RF), there is also Gradient Boosting (GB), then Gaussian Naive Bayes (GNB), Vector Support Machine (SVM), and then Logistic Regression (LR). Before algorithms were used to train and test breast cancer datasets, StandardScaler was leveraged to transform training datasets and test datasets for improved algorithm performance. As a result of this utilization, the performance measurement carried out succeeded in producing high accuracy. The highest results were obtained from the Logistic Regression algorithm with an accuracy value of 99%. The value of precison is 99% benign, and 100% malignant. The recall results are 100% benign, and 98% malignant. The F1-Score results show 99% benign, and 99% malignant. It is hoped that this research can help the medical party to determine the next step in dealing with breast cancer.
Sistem Pakar Menggunakan Metode Forward Chaining Untuk Mendeteksi Kerusakan Jaringan Internet (Studi Kasus : Di Layanan Internet Diskominfotik Sumatera Barat) Ahmad Zaki; Sarjon Defit; Sumijan Sumijan; Rahmi Fauzana
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9, No 3 (2023): Desember 2023
Publisher : Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.227-236

Abstract

Sistem informasi yang interaktif dapat membantu kinerja pegawai dalam mendukung program SPBE (Sistem Pemerintah Berbasis Elektronik. Dinas Kominfotik Sumatera Barat berperan penting dalam memberikan layanan internet kepada OPD-OPD di bawah lingkup Pemerintahan Provinsi Sumatera Barat. Pembangunan sistem jaringan internet yang sudah baik tidak dapat dijamin bahwa jaringan tersebut terbebas dari gangguan dan kerusakan. Gangguan terhadap akses internet akan berdampak terhadap produktifitas bekerja pegawai dan pelayanan kepada masyarakat. Kurangnya pemahaman PIC OPD dan pengguna dalam menangani permasalahan gangguan jaringan internet, maka dibutuhkan keahlian pakar dalam melakukan identifikasi kerusakan pada jaringan internet berdasarkan gejala-gejala yang terjadi, serta diberikan solusi perbaikan pada gangguan yang ada. Pengumpulan data dilakukan melalui wawancara dan observasi lapangan. Metode yang digunakan untuk pengolahan data pada Sistem Pakar ini yaitu metode forward chaining. Forward Chaining adalah sebuah strategi pencarian dalam system pakar yang dimulai dari sekumpulan data atau fakta, dari data-data tersebut, system akan mencari suatu kesimpulan yang menjadi solusi dari permasalahan yang dihadapi. Berdasarkan hasil pengujian Sistem Pakar menggunakan metode forward chaining untuk mendeteksi gangguan jaringan internet menghasilkan tingkat akurasi sebesar 100 % menggunakan 29 data uji. Berdasarkan hasil yang didapatkan dari Sistem Pakar dengan metode forward chaining, system tersebut dapat digunakan untuk mendeteksi kerusakan jaringan internet di Layanan Internet Diskominfotik Sumatera Barat.
Framework LTSA untuk Analisis dan Pengembangan Learning Management System Dalam Mendukung Peningkatan Proses Pembelajaran Nur Aini; Sarjon Defit; S Sumijan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.366

Abstract

Learning Management System is a software for the need to manage learning activities such as searching for materials, reporting learning matters, providing materials for learning matters carried out online and connected to an internet connection. The benefits that can be obtained Form the use of e-learning are the existence of facilities for e-moderating where teachers can carry out learning activities without being constrained by distance, teachers and students can also use teaching materials via the internet, students can review learning materials online, if students require additional materials for learning so students can access the internet, changes in the role of students and teachers become more active and learning is relatively more efficient and effective. This research aims to apply the LTSA framework to the design of a Learning Management System. The method used in this research is the LTSA framework. This method explains that the LTSA framework consists of five architectural layers, each layer describes a system at a different level. The dataset processed in this research comes Form SMK N 1 Ranah Batahan. The dataset consists of students majoring in TKJ class XI in Indonesian, English, mathematics and vocational subjects. The results of research using the LTSA framework make learning data more structured in managing learning activities. This research can be a reference in developing a Learning Management System using other methods
Integrasi Knowledge Management System Dan Teknik Knowledge Discovery In Database Dalam Sharing Culture Pada Proses Pembelajaran Berbasis Blended Learning Iswandi Saputra; Sarjon Defit; Gunadi Widi Nurcahyo
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.385

Abstract

Education is rapidly changing in the digital age, especially with blended learning, which mixes online and in-person classes. This approach is popular because it offers a well-rounded learning experience. However, getting students and teachers to share knowledge remains a challenge. This study looks at how combining Knowledge Management Systems (KMS) and Knowledge Discovery in Databases (KDD) can help improve knowledge sharing in blended learning at universities. By analyzing data from the E-Learning section of UPI YPTK Padang, involving 120 students, the research aims to create more effective learning systems that encourage sharing. It's a step towards better education in the digital era, promoting collaboration and knowledge exchange among students and educators.
Implementasi K-Means Clustering Dalam Analisa Soal Ujian CBT Universitas Baiturrahmah Rico Anggara; Sarjon Defit; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.367

Abstract

Computer-based exams (CBT) are a type of exam where participants take the exam using a computer or digital device. CBT has become a common choice in exam administration. Exam question management is important for CBT success. Participants answer digital questions via a computer interface, and the results are processed automatically by the computer system. The results of this test can be used to assess student understanding and as a learning evaluation. This research aims to group exam questions based on participants' answers. The method used in this research is K-Means Clustering. This method has 5 stages, namely cluster center initialization, data grouping, calculation of new cluster centers, convergence and evaluation of results. This process repeats until the cluster center does not change any more or convergence has been achieved. Next, the K-Means Clustering algorithm is applied to group exam questions into appropriate clusters. This grouping process is carried out by considering the similarities between the exam questions based on the number of correct answers and the number of incorrect answers. Dataset source from UPT CBT, Baiturrahmah University. The question dataset consists of 100 exam questions that have been tested on students at the Faculty of Medicine, Baiturrahmah University. The results of this research can group exam questions into groups of difficult questions, medium questions and easy questions. This research can be a reference for academics in evaluating exam questions created by lecturers and can evaluate the level of understanding of students at Baiturrahmah University.
Co-Authors Abdul Azis Said Abulwafa Muhammad Adawiyah, Quratih Ade, Ade Puspita Sari Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Aflili Sari Afriosa Syawitri Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zaki Ahmad Zamsuri, Ahmad AHMADI Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Ali Ikhwan Alkhairi, Putrama Alvi Dwi Wahyuni Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andri Nofiar Angga Putra Juledi Anisya Anisya Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Billy Hendrik Bob Subhan Riza Bosker Sinaga Boy Sandy Dwi Nugraha.H Breinda, Engla Brestina Gultom Bufra, Fanny Septiani Chairun Nas Cyntia Trimulia Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Defi Pebriyanti Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devia Kartika Devita, Retno Dhena Marichy Putri Dhio Saputra Dicky Novriansyah Dila, Rahmah Dinda Permata Sukma Dinul Akhiyar Dwi Utari Iswavigra Dwiki Aulia Fakhri Dwiprihatmo, Mohammad Reza Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma Elfiswandi Elfiswandi eriwandi Fadillah, Riszki Fadlul Hamdi Faisal Roza Faizal Riza Faizal Riza Fajrul Islami Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febi Nur Salisah Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus Firdaus Firdaus, Muhammad Bambang Fitri Safnita Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Ghea Paulina Suri Gunadi W Nurcahyo Gunadi Widi N. Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Guslendra Guslendra Guslendra, Guslendra Habdi, Habdi Hadiyanto, Tegas Halifia Hendri Hamsir hamsir Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Iqbal Afriyadi Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Istianingsih, Nanik Iswandi Saputra Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Lengga S. Sandy Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Iqbal Zuqron M. Syahputra Mardayatmi, Suci Mardian, Zurni Mardison Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Menhard, Menhard Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen Muhammad Dahria Muhammad Tajuddin MUHAMMAD TAJUDDIN Muhammad, Abulwafa Muhammad, L. J. Mukhlis Santoso Mulyanda, Sandy Mutiana Pratiwi Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun Nori Sahrun, Nori Novi Yanti Nur Aini Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Pati, Muhammad Ibnu Pipin Refina Afindania Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Ramdani Bayu Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rafki, Rafnelly Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi Fauzana Rahmi, Nadya Alinda Rakhmad Pribowo Hariputra Ramadhan, Mukhlis Ramadhanu, Agung - Randy Permana Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rezti Deawinda Parinduri Rian Kurniawan Rianti, Eva Rico Anggara Rini Sovia Rini Sovia Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Ruri Hartika Zain Rusdianto Roestam Rusdianto Roestam Rustam, Camila S Sumijan S Sumijan Sabil, Muhammad Said, Abdul Azis Saiful Nurarif Sandrawira Anggraini Sani, Rafikasani Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Silfia Andin Sintia Sintia Siregar, Diffri Solihin Siregar, Fajri Marindra Siswahyudianto Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Dewi, Apriandini Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sukardi Sulastri Sulastri Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surmayanti, Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva tukino, tukino Tukino, Tukino Veri, Jhon Veza, Okta Virgo, Ismail Vitriani, Vitriani Wahyu, Fungki Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yamin, Abdul Yamin Yemi, Leonardo Yenila, Firna Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yudha Aditya Fiandra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yul Antonisfia Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulharbi Zulharbi Zulvitri, Z