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All Journal Techno.Com: Jurnal Teknologi Informasi Pixel : Jurnal Ilmiah Komputer Grafis SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputer Terapan CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) CSRID (Computer Science Research and Its Development Journal) JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika dan Rekayasa Elektronik Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal J-PEMAS Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence JAIA - Journal of Artificial Intelligence and Applications Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi VISA: Journal of Vision and Ideas Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Rahmaddeni Rahmaddeni
STMIK Amik Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media Bunga Nanti Pikir; M. Khairul Anam; Hadi Asnal; Rahmaddeni; Triyani Arita Fitri; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 1 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (623.792 KB) | DOI: 10.33372/jaia.v2i1.795

Abstract

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the Naïve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%.
Komparasi Algoritma Machine Learning Untuk Memprediksi Penyakit Alzheimer Firman Akbar; Rahmaddeni
Jurnal Komputer Terapan Vol. 8 No. 2 (2022): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.371 KB) | DOI: 10.35143/jkt.v8i2.5713

Abstract

Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. It is characterized by deterioration of memory, language, problem-solving, and other cognitive skills that affect a person's ability to perform everyday activities. This decrease occurs because nerve cells (neurons) in parts of the brain involved in cognitive function are damaged and stop working properly. One way to detect Alzheimer’s is to use models of machine learning algorithms. In this study, the authors' team aimed to compare models of machine learning algorithms to find the one that gives better results in prediction Alzheimer's disease. Machine learning models algorithms in this study were built using Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machines, and Naive Bayes. The author's team then tested his 373 Alzheimer's disease patient data from Kaggle Open Datasets and showed that the Logistic Regression algorithm model can achieve better with 85,71% accuracy rate.
Design of library noise detection tools based on voice pressure parameters Yuda Irawan; Refni Wahyuni; Hasnor Khotimah; Herianto -; Bambang Kurniawan; Haris Tri Saputra; Yulisman Yulisman; Abdi Muhaimin; Reno Renaldi; Rahmaddeni Rahmaddeni
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1191.237-244

Abstract

A library visitor would want a quiet atmosphere without noise when in the library so that he can concentrate when reading a book. However, not all visitors come to the library to read books; some want to chat and use free Wi-Fi or other, so it disturbs the concentration of other visitors who read books. Therefore, it is necessary to have a tool to detect sound pressure or sound based on the sound level and the sound produced in a library based on the noise level limit in the library, namely 45-55 dB (desible). This tool is designed based on a microcontroller where the definition of a microcontroller is a complete microprocessor system contained in a microcontroller chip which is different from the multi-purpose microprocessor used in a PC because a microcontroller generally already includes the minimum system supporting components of a microprocessor, namely memory, and programming. This tool can help officers monitor the library room for noise that can interfere with the concentration and comfort of library visitors. Based on the results of testing, the overall system is as desired, including the noise detection tool can work in an integrated system, where when the sound sensor detects a noise that exceeds the sound limit, the buzzer will sound, the red led light turns on, the sound module issues a voice message pre-recorded and also the device can be controlled or monitored from the web application.
Sistem Pengendalian Persediaan Perlengkapan Perorangan Lapangan Menggunakan Metode Economic Order Quantity dan Reorder Point Yesaya Twin Situmorang; Wirta Agustin; M. Khairul Anam; Rini Yanti; Mardainis Mardainis; Rahmaddeni Rahmaddeni
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5133

Abstract

[1]     A. Junaidi and C. Sumirat, “Aplikasi Persediaan Barang PT. CAD Solusindo Menggunakan Metode Waterfall,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 7, no. 1, p. 28, 2018, doi: 10.32736/sisfokom.v7i1.280.[2]     N. Apriyani and A. Muhsin, “Analisis Pengendalian Persediaan Bahan Baku Dengan Metode Economic Order Quantity Dan Kanban Pada Pt Adyawinsa Stamping Industries,” Opsi, vol. 10, no. 2, p. 128, 2017, doi: 10.31315/opsi.v10i2.2108.[3]     M. Sukamto, “ANALISIS PENGENDALIAN PERSEDIAAN BAHAN BAKU DENGAN METODE FIXED ORDER INTERVAL (FOI) TERHADAP BIAYA TOTAL PERSEDIAAN DAN LABA OPERASI PADA RESTORAN BENEDICT,” Jurnal Mozaik, vol. 9, no. 1, pp. 81–93, 2017.[4]     Haslindah, A. S. Iriani, M. Ardi, and Zulkifli, “PENERAPAN MANAJEMEN PERSEDIAAN DALAM MENGANTISPASI KERUGIAN BARANG DAGANGAN DI TOKO MEGA JILBAB,” Banco: Jurnal Manajemen dan Perbankan Syariah, vol. 2, no. 2, pp. 58–68, 2020, doi: 10.35905/banco.v2i2.1811.[5]     P. C. P. Dewi, N. T. Herawati, and M. A. Wahyuni, “Analisis Pengendalian Persediaan Dengan Metode (EOQ) Economic Order Quantity Guna Optimalisasi Persediaan Bahan Baku Pengemas Air Mineral,” Jurnal Akuntansi Profesi, vol. 10, no. 2, pp. 54–65, 2019.[6]     R. Jappi and D. F. Koan, “PENERAPAN INVENTORY MANAGEMENT DALAM MENINGKATKAN PROFITABILITAS DI TOKO X KUPANG,” Calyptra: Jurnal Ilmiah Mahasiswa Universitas Surabaya , vol. 3, no. 1, pp. 1–16, 2014.[7]     T. Lukmana and D. Trivena, “Penerapan Metode EOQ dan ROP (Studi Kasus: PD. BARU),” Jurnal Teknik Informatika dan Sistem Informasi, vol. 1, no. 3, pp. 271–279, 2015.[8]     N. Hartih Aeni, Satibi, and G. Pamudji Widodo, “Penerapam Metode Economic Order Quantity Dan Reorder Point Dalam Meningkatkan Efisiensi Persediaan Obat,” Jurnal Manajemen dan Pelayanan Farmasi, vol. 3, no. 4, pp. 249–254, 2013.[9]     T. Rafliana and B. R. Suteja, “Penerapan Metode EOQ dan ROP untuk Pengembangan Sistem Informasi Inventory Bengkel MJM berbasis Web,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 4, no. 2, pp. 345–354, 2018.[10]   D. Misbachul Umami, M. Fuad Fauzul Mu, and R. Rakhmawati, “ANALISIS EFISIENSI BIAYA PERSEDIAAN MENGGUNAKAN METODE EOQ (ECONOMIC ORDER QUANTITY) PADA PT. XYZ Analysis of Cost Efficiency on Inventory System Using EOQ (Economic Order Quantity) Method in The PT. XYZ,” Jurnal Agroteknologi, vol. 12, no. 1, pp. 64–70, 2018.[11]   M. K. Anam, T. Nasution, S. Erlinda, L. Efrizoni, and Susanti, “The Analysis and Optimization of Business Processes for Students in Higher Education Based on Togaf 9 . 2,” Scientific Journal of Informatics, vol. 8, no. 2, pp. 230–243, 2021, doi: 10.15294/sji.v8i1.29952.[12]   I. P. C. P. Dewi, I. N. T. Herawati, and I. made A. Wahyuni, “ANALISIS PENGENDALIAN PERSEDIAAN DENGAN METODE (EOQ) ECONOMIC ORDER QUANTITYGUNA OPTIMALISASI PERSEDIAAN BAHAN BAKU PENGEMAS AIR MINERAL,” Jurnal Akuntansi Profesi, vol. 10, no. 2, pp. 55–65, 2019.[13]   R. Cahya Pratiwi, C. Iswahyudi, and R. Yuliana Rachmawati, “SISTEM MANAJEMEN PERSEDIAAN BARANG DAGANG MENGGUNAKAN METODE SAFETY STOCK DAN REORDER POINT BERBASIS WEB (STUDI KASUS: ART KEA CENTRO PLAZA AMBARRUKMO YOGYAKARTA),” Jurnal SCRIPT, vol. 7, no. 2, pp. 213–222, 2019.[14]   M. Jamaris, H. Saputra, M. K. Anam, K. Andesa, and Rahmaddeni, “Sistem Marketplace Pencarian Lapangan Futsal Menggunakan Metode Haversine Berbasis Android,” JURNAL ILMIAH KOMPUTER GRAFIS, vol. 15, no. 1, pp. 53–65, 2022, doi: 10.51903/pixel.v15i1.712.[15]   H. Asnal et al., “Sistem Monitoring Persediaan Stok Onderdil Menggunakan Metode Reorder Point Pada Sani Computer,” 2022.[16]   M. K. Anam and R. Anwar, “Penerapan Aplikasi Pendukung Touring Pada Komunitas Motor Berbasis Android,” Edumatic: Jurnal Pendidikan Informatika, vol. 4, no. 1, pp. 1–10, 2020, doi: 10.29408/edumatic.v4i1.1980.[17]   M. K. Anam and H. Ulayya, “Implementasi dan Analisa SARDrive Sebagai Media Penyimpanan Cloud,” JUITA: Jurnal Informatika, vol. 8, no. 1, pp. 83–90, 2020, doi: 10.30595/juita.v8i1.5748.
ANALISA PERFORMA ALGORITMA MACHINE LEARNING DALAM PREDIKSI PENYAKIT LIVER Mahdiawan Nurkholifah; Jasmarizal; Yusran Umar; Rahmaddeni
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.149

Abstract

Currently in the world of medicine, determining liver inflammation is something that is not easy to do. But there are medical records that have kept the patient's symptoms and diagnosis of liver inflammation. The weaknesses of the manual method encourage researchers to develop a method that does not depend 100% on humans. The developed method utilizes a computer as a tool to analyze data. This kind of thing is certainly very useful for health experts. They can use existing medical records as an aid in making decisions about the diagnosis of a patient's disease. In this study, we analyzed the performance of machine learning algorithms by comparing the support vector machine, naïve Bayes and k-nearest neighbor algorithms. This study aims to determine the performance of which algorithm has the highest accuracy in liver disease data. From the research results using splinting data 80:20 it can be concluded that the Naïve Bayes algorithm model has better performance than other algorithm models when using the SMOTE technique with an accuracy value of 65.51%, whereas when not using the SMOTE technique the Support Vector Machine algorithm has the highest performance. better than other algorithm models with an accuracy value on the data not 72.41%.
Comparison of Machine Learning Algorithms in Analyzing Public Opinion Sentiments Against Fuel Price Increases Hanif Wira Saputra; Rahmaddeni Rahmaddeni; Fazri Fazri
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 1 (2023): January 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i1.41911

Abstract

Twitter is a social media platform that is quite widely used by the world community, especially people in Indonesia. Twitter is one of the social media that provides information, one of which is the increase in the price of crude oil which was recorded at 105 US dollars per barrel. The increase in fuel prices has a negative impact on society, causing pros and cons. Based on these problems, the authors aim to compare the performance of the artificial neural network and naïve Bayes algorithms to determine the best model for sentiment analysis of fuel price hikes. The data used amounted to 1000 datasets in the form of text documents with labeling using the lexicon and split data 90:10, 80:20, 70:30 and 60:40 as a comparison of precision values. The application of word vectorization utilizes TF-IDF in assigning a weight value to each word. Based on the results of the experiments that have been carried out, it is found that the best algorithm using an artificial neural network is capable of producing an accuracy value of 87% for 1000 data on public opinion sentiment on fuel price hikes. Based on the evaluation results, the model built can categorize public opinion sentiment into positive sentiment, negative sentiment, and neutral sentiment automatically and the polarity of public sentiment tends to be positive towards the issue of the fuel price increase that occurred.  
Algorithm Decission Tree C4.5 and Backpropagation Neural Network for Smarthpone Price Classification Muhammad Ridho Al Fathan; M Fadhil Arfa; Habibah Br. Lumbantobing; Rahmaddeni Rahmaddeni
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.19064

Abstract

Smartphones are a necessity in this technological age. In fact, everyone has at least one smartphone, this is because of its role that can help daily activities. There are data smartphone prices from major companies from Kaggle. The data is divided into 2000 training data and 1000 test data, the price range of smartphones based on the features provided. The analysis needed is the relationship between the features of smartphone and the selling price. To get this information, data mining techniques can be used. This study uses the Decission Tree C4.5 algorithms and the Backpropagaition Neural Network algorithm for classification problems. The technique used will be compared to a better algorithm in carrying out the classification process. The classification method consists of predictor variables and one target variable. The software used to process the data is Rapid Miner software. The results of the study get the accuracy of the Backpropagation Neural Network algorithm 96.65% and the same data is also applied to the C4.5 algorithms with an accuracy of 83.75%. From the research results, it can be concluded that the backpropagation neural network algorithm is the best algorithm for smartphone price classification with accuracy 96.65%.
Comparison of Naïve Bayes Algorithm, Support Vector Machine and Decision Tree in Analyzing Public Opinion on COVID-19 Vaccination in Indonesia Rahmaddeni Rahmaddeni; Firman Akbar
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.19966

Abstract

The spread of COVID-19 in Indonesia has caused many negative impacts. Therefore, the government is taking vaccination measures to suppress the spread of COVID-19. Public response to vaccinations on Twitter has been mixed, with some supporting it and some not. The data for this study comes from the Twitter feed of the drone portal Emprit Academy (dea). Classification is performed using SVM, decision tree and Naive Bayes algorithm. The purpose of this study is to inform the public about whether vaccination against COVID-19 is inclined toward positive, neutral, or negative opinions. Moreover, this study compares the accuracy of the three algorithms used, namely Naive Bayes (NB), Support Vector Machine (SVM) and Decision Tree, and the validation performed using the K-Fold Cross-Validation method, AdaBoost feature selection, and the TF-IDF Transformer feature extraction test. The result obtained from this study is that the accuracy of the 90:10 data keeps improving, dividing by 82.86% on the SVM algorithm, 81.43% on the Naive Bayes and 78.57% on the decision tree.
Analisis Sentimen Terhadap Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM) Menggunakan Support Vector Machine: Sentiment Analysis of Cash Direct Assistance Distribution for Fuel Oil Using Support Vector Machine Rizky Rahman Salam; Muhammad Fajri Jamil; Yusril Ibrahim; Rahmaddeni Rahmaddeni; Soni Soni; Herianto Herianto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 1 (2023): MALCOM April 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i1.590

Abstract

Bahan bakar minyak (BBM) merupakan salah satu kebutuhan pokok masyarakat. Namun, harga BBM yang tinggi dapat menyebabkan beban ekonomi bagi masyarakat yang tidak mampu. Dalam rangka mengatasi masalah ini, pemerintah telah menerapkan program Bantuan Langsung Tunai (BLT) sebagai bentuk bantuan bagi masyarakat yang mengalami ketidakseimbangan ekonomi. Tujuan dari penelitian ini adalah untuk menganalisis sentimen masyarakat terhadap program Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM). Penelitian ini menggunakan teknik pengumpulan data scraping, yaitu mengambil data dari media sosial Instagram. Jumlah yang digunakan sebanyak 356 data. Proses klasifikasi yang digunakan berdasarkan model pembelajaran dari Support Vector Machine (SVM) dan evaluasi dengan confusion matrix. Dari hasil perhitungan, terlihat bahwa proses klasifikasi sentimen menggunakan metode SVM didapatkan tingkat accuracy 85,98%, rata-rata nilai precision 82,25%, nilai rata-rata recall 66,35%, dan nilai rata-rata f-measure 73,44%. Hasil yang diperoleh menunjukkan bahwa sentimen negatif lebih banyak daripada sentimen positif, dengan masing-masing persentase 78.61% dan 21.34%. Dari analisis sentimen yang dilakukan, ditemukan bahwa sentimen negatif adalah yang paling banyak muncul, hal ini menunjukkan bahwa masyarakat tidak puas dengan bantuan langsung tunai BBM. Sebagai respon terhadap sentimen negatif yang dominan, perlu diterapkan strategi untuk melakukan pemerataan bantuan langsung tunai dan pendata’an yang terstruktur agar tingkat kekecewaan masyarakat dapat diminimalisir.
Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru: Implementation of Decision Tree Algorithm and Support Vector Machine for Lung Cancer Classification Dhini Septhya; Kharisma Rahayu; Salsabila Rabbani; Vindi Fitria; Rahmaddeni Rahmaddeni; Yuda Irawan; Regiolina Hayami
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 1 (2023): MALCOM April 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i1.591

Abstract

Kanker paru merupakan satu dari banyaknya penyebab kematian di dunia dengan persentase 11.6%, dengan tingkat kematian hingga 18,4%. Kanker paru merupakan salah satu penyakit yang mematikan karena kanker ini sulit dideteksi sebelum berubah menjadi penyakit yang serius dan saat ini belum ada metode skrining yang efektif untuk deteksi dini kanker paru. Pada penelitian ini dilakukan teknik klasifikasi yang merupakan suatu metode pengelompokkan data yang memiliki karakter yang sama ke dalam beberapa kelompok. Teknik klasifikasi yang diteliti membandingkan 2 algoritma yaitu, algoritma Decision Tree dan Support Vector Machine (SVM) untuk mengetahui algoritma yang memberikan hasil terbaik. Dalam penelitian ini akan dilakukan seleksi fitur menggunakan forward selection yang bertujuan untuk menaikkan nilai akurasi. Berdasarkan penelitian yang telah dilakukan dapatkan hasil dari algoritma SVM menggunakan feature selection mempunyai nilai akurasi yang lebih unggul yaitu 62,3% menggunakan splitting data 80:20.
Co-Authors -, Dedek Ispandi A, M. Nakhlah Farid Adhitya Karel Maulaya Adinda Dwi Putri Afiatuddin, Nurfadlan Agung Pratama Agustin Agustin Agustin -, Agustin Agustin Agustin Agustin, Endy Wulan Agustriono Agustriono, Agustriono Ahmad Rivaldi Aisy, Alaysha Rihadatul Aisyah Nurul Putri Akbar, Vitto Rezky Alaysha Rihadatul Aisy Aldino Evel Alfianda, Baginda Anam, M Khairul Ananta, Nita Anderson, Ranap Andi Kurnianto Andri Setiawan Anugraha, Yoga Safitra Aprilia, Fanesa Aprillian Kartino Arifin, Muhammad Amirul Aulia Putri Azfar Huzaifah Siregar Baginda Alfianda Br.Situmorang, Elisabet Sinta Romaito Bunga Nanti Pikir Cahyo, M Rizky Dwi Chandra, Deni Cikita, Putri Cindy Syaficha Hardiana Dadynata, Eric Daulay, Suandi De Pani, Raihan Dea Safitri Dedek Ispandi - Deni Chandra Denok Wulandari Devi Efriadi Devi Puspita Sari, Devi Puspita Dhini Septhya Didik Sazali Diki Daryanto Dini, Ema Djamalilleil, Said Azka Fauzan Edwar Ali Efrizoni, Lusiana Eka, Wisnu Elma Novfuja Elwinda, Masyitah Erlin Ermy Pily, Annisa Khoirala Fadila, Rahmasari Fahreza, Rino Farhan Pratama Farida Try Puspa Siregar Fazri Fazri Febrianda Putra Febrio Waleska, Rangga Firman Akbar Firman, Muhammad Aditya fitri pratiwi, fitri Fitriana Sholekhah Fransiskus Zoromi Fransiskus Zoromi Ginting, Lusiana Ginting, Steven Gusmansyah, Rafly H A Supahri Habibah Br. Lumbantobing Hadi Asnal, Hadi Hafsah Fulaila Tahiyat Hamdani Hamdani - Hanif Wira Saputra Hasnor Khotimah Hayami, Regiolina Hendra Saputra Hendrawan, Heri Herianto - Herianto Herianto Herisnan, Diva Nabila Huda, Isra Bil Ibrahim, Sang Adji Iftar Ramadhan Ihsan, Raja Muhammad Irawan, Sandra Septi Jamaris, Muhamad Jasmarizal Jasmarizal Junadhi Junadhi Jundi, Muhamad Kharisma Rahayu Khusaeri Andesa Koko Harianto Koko Harianto Koko Harianto, Koko Kurniawan, Bambang Kurniawan, Fadly Lili Marlia Luasiana Efrizoni Lusiana Efrizoni M Fadhil Arfa M. Arifin M. Azzuhri Dinata M. Irpan Mahdiawan Nurkholifah Mardainis Mardainis Mardainis Marhadi, Nanda Maryani, Lily Maulana, Fitra Michal Dennis Muhaimin, Abdi Muhammad Adji Purnama Muhammad afrizal Muhammad Bambang Firdaus Muhammad Fajri Jamil Muhammad Fikri Hidayat Muhammad Ihza Mahendra Muhammad Ridho Al Fathan nanda, afri Nanda, Annisa Nasution , Zikri Hardyan Nita Ananta Nova Indriyani Nurjayadi Nurjayadi Nurkholifah, Mahdiawan Oktavianda Pratama , Nanda Rizki Pratiwi, Elsa Eka Prianto, Robi Purnama, Muhammad Adji Putra, Febrianda Putri Utami, Putri Putri, Adinda Dwi Putri, Daffina Zahro R Ismanizan Rahmi Rahmi Raja Muhammad Ihsan Ramadhani, Jilang Rapindra Septia Rashid, Rashid Ratna Andini Husen Refni Wahyuni Renaldi, Reno Rinaldi Rinaldi Rini Yanti Rino Fahreza Risky Harahap Risman Risman Rizki Astuti Rizky Rahman Salam Rohana Yola Parastika Hutasoit Rohid Rometdo Muzawi, Rometdo Safitri, Dea Sahelvi, Elza Salman, Muhammad Dzaki Salsabila Rabbani Sapina, Nur Sapitri, Riska Mela Saputra, Haris Tri Saputra, Ilham Saputra, Juliandri Saputra, Pingki Ans Satria, Riyan Sazali, Didik Septhya, Dhini Septia, Rapindra Setiawan , Andri Setiawan, Ahmad Agung Sholekhah, Fitriana Sinaga, Leonardo Singgih - Widiantoro Siregar, Azfar Huzaifah Soni Suandi Daulay Suhada, Khairus Sukri Adrianto Supian, Acuan Susandri, Susandri SUSANTI Susanti, Susanti Sutisna Sutisna Syahrul Imardi Syarifuddin Elmi T. Sy. Eiva Fatdha Tahiyat, Hafsah Fulaila Taupik Hidayat, Taupik Tri Revaldo, Bagus Triyani Arita Fitri Ulfa, Arvan Izzatul Ulfah, Aniq Noviciate Umar, Yusran Unang Rio Uthami, Kurnia Vindi Fitria wahyu, haditya Wahyudi, Gustri Romi Wicaksono, M Teguh Wicaksono, M. Teguh Widia Ningsih Widia Ningsih, Widia Wirta Agustin Wirta Agustin Wulandari, Denok Yansyah Saputra Wijaya Yesaya Twin Situmorang Yoyon Efendi Yuda Irawan Yulia Fatma Yusran Umar Yusril Ibrahim zairi saputra Zalianti, Fenisya Zega, Wilman Zikri Hadryan nst Zuriatul Khairi Zuriatul Khairi