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All Journal Teknika Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Building of Informatics, Technology and Science Vocatech : Vocational Education and Technology Journal JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology ARRUS Journal of Engineering and Technology Instal : Jurnal Komputer Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Ilmu Komputer dan Informatika Brilliance: Research of Artificial Intelligence TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Terapan and Sains 4.0 Electronic Journal of Education, Social Economics and Technology MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Pengabdian Masyarakat Ilmu Komputer Serambi Engineering JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Lidya Rosnita
Universitas Malikussaleh
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SISTEM PENDUKUNG KEPUTUSAN MEMPREDIKSI KELULUSAN MAHASISWA INFORMATIKA MENGGUNAKAN METODE SAW Risawandi Risawandi; Lidya Rosnita; Rian Kelana Putra
JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Vol 9, No 1 (2023): April 2023
Publisher : Ubudiyah Indonesia University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33143/jics.v9i1.2944

Abstract

Abstrak— Kelulusan mahasiswa merupakan tanda berakhirnya mahasiswa dalam menyelesaikan pendidikan pada jenjang sarjana. Kelulusan juga merupakan hasil akhir pencapaian yang membanggakan dalam menempuh suatu pendidikan pada jenjang tertentu. Untuk memenuhi standar kopetensi lulusan bagi mahasiswa program sarjana (S1) beban wajib yang harus ditempuh adalah paling sedikit 144 SKS dengan masa studi waktu maksimal 14 semester. Tetapi penulis melihat di lapangan terdapat beberapa mahasiswa yang tidak bisa lulus tepat waktu. Dalam kasus ini, penulis melakukan penelitian di prodi Teknik Informatika. Penulis melihat ada beberapa mahasiswa yang tidak dapat menyelesaikan masa perkuliahannya dengan tepat waktu. Untuk itu, penulis membuat sebuah aplikasi prediksi kelulusan mahasiswa untuk melihat apakah para mahasiswa dapat lulus tepat waktu atau tidak.Dalam penelitian ini, penulis menggunakan metode SAW (Simple Additive Weight) untuk melakukan proses prediksi kelulusan dengan perhitungan kriteria seperti nilai IPK, IP, semester berjalan, dan juga kecukupan SKS. Penelitian ini menguji setidaknya 25 mahasiswa dengan kriteria nilai berupa IPK, 2 nilai IPS terakhir, Semester berjalan dan banyaknya SKS yang diambil. Hasil dari sistem yaitu, V23 dengan nilai 1, mendapatkan peringkat 1, memiliki kemungkinan tinggi untuk bisa menyelesaikan perkuliahan tepat waktu.Kata kunci: Kelulusan, Informatika, SAW, IPK, SKSAbstract— Student graduation is a sign of the end of students in completing education at the undergraduate level. Graduation is also the final result of a proud achievement in pursuing an education at a certain level. To meet graduate competency standards for undergraduate students (S1) the mandatory load that must be taken is at least 144 credits with a maximum study period of 14 semesters. However, the author sees that in the field there are several students who cannot graduate on time. In this case, the authors conducted research in the Informatics Engineering study program. The author sees that there are some students who cannot complete their studies on time. For this reason, the authors created a student graduation prediction application to see whether students could graduate on time or not. In this study, the authors used the SAW (Simple Additive Weight) method to carry out the graduation prediction process by calculating criteria such as GPA, GPA, semester running, and also the adequacy of credits. This study tested at least 25 students with grade criteria in the form of GPA, the last 2 IPS scores, the current semester and the number of credits taken. The results of the system, namely, V23 with a value of 1, get a rank of 1, have a high probability of being able to complete lectures on time.Keywords: Keywords : Graduation, Informatics, SAW, GPA, Credit
ANALISIS SENTIMEN KEPUASAN CUSTOMER TERHADAP EKSPEDISI TIKI, SICEPAT EXPRESS DAN NINJA EXPRESS MENGGUNAKAN ALGORITMA NAIVE BAYES Nurhaliza Bin Aras; Risawandi Risawandi; Lidya Rosnita
JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Vol 9, No 1 (2023): April 2023
Publisher : Ubudiyah Indonesia University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33143/jics.v9i1.2943

Abstract

Abstrak—Perkembangan ilmu pengetahuan dan teknologi informasi pada masa ini sangat pesat. Adanya pemasaran produk secara global tersebut menjadikan perkembangan ekspedisi barang juga mengalami kemajuan yang signifikan. Kebutuhan penggunaan jasa ekspedisi barang yang dipergunakan masyarakat untuk memenuhi berbagai kebutuhannya sangat meningkat pesat. Hadirnya berbagai jasa ekspedisi barang tidak hanya mempermudah masyarakat namun juga para pengusaha atau seller. Penelitian ini bertujuan untuk menganalisis sentimen terhadap kepuassan customer ekspedisi yaitu tiki, sicepat express dan ninja express pada twitter dengan menggunakan metode Algoritma Naïve Bayes. Beberapa proses dalam melakukan klasifikasi sentimen, yang pertama melakukan koleksi data di twitter menggunakan scraping setalah itu pemberian labelling, kemudian dilakukan text pre-processing pada data yang meliputi cleansing data, case folding, tokenizing, stopword removal, dan stemming. Selanjutnya dilakukan proses klasifikasi pada data. Data yang digunakan dalam penelitian ini berjumlah 3000, setiap objeknya dengan jumlah 1000 data kemudian dibagi menjadi 3 kelas yaitu positif, negatif dan netral. Dari 3000 data dibagi menjadi 2 bagian yaitu 70% data training dan 30% data testing. Berdasarkan hasil evaluasi klasifikasi dengan algoritma Naïve Bayes menghasilkan akurasi yang sangat tinggi. Akurasi Sicepat Express sebesar 89,73%, presisi sebesar 58,81%, recall sebesar 40,1% dan f1-score sebesar 42,6%. Akurasi Ninja Express sebesar 80,66%, presisi sebesar 49,4%, recall sebesar 40,8% dan f1-score sebesar 41,5%. Akurasi Tiki sebesar 74,48%, presisi sebesar 65,42%, recall sebesar 57,14% dan f1-score sebesar 56,81%.Kata kunci: Ekspedisi, Sentimen, Data, Naïve BayesAbstract— The development of science and information technology at this time is very rapid. The existence of global product marketing has made the development of freight forwarding also experience significant progress. The need for the use of freight forwarding services that are used by the community to meet their various needs is increasing rapidly. The presence of various freight forwarding services not only makes it easier for the community but also entrepreneurs or sellers. This study aims to analyze sentiment on customer satisfaction on expeditions, namely tiki, sicepat express and ninja express on twitter using the Naïve Bayes algorithm. There are several processes in classifying sentiments, the first is to collect data on twitter using scraping after that labeling, then text pre-processing is carried out on the data which includes data cleansing, case folding, tokenizing, stopword removal, and stemming. Furthermore, the classification process is carried out on the data. The data used in this study amounted to 3000, each object with a total of 1000 data was then divided into 3 classes, namely positive, negative and neutral. Of the 3000 data is divided into 2 parts, namely 70% training data and 30% testing data. Based on the results of the classification evaluation with the Naïve Bayes algorithm, it produces a very high accuracy. The accuracy of Sicepat Express is 89.73%, precision is 53,5%, recall is 40,1% and f1-score is 42,6%. Ninja Express accuracy is 80.66%, precision is 49,4%, recall is 40,8% and f1-score is 41,5%. Tiki's accuracy is 74.48%, precision is 65,42%, recall is 57,14% and f1-score is 56,81%.Keywords: Ekspedition, Sentiment, Data, Naïve bayes
Sistem Informasi Pelayanan Cuti Berbasis Web Pada PT Pupuk Iskandar Muda Menggunakan PHP dan MySQL Sujacka Retno; Lidya Rosnita; Said Fadlan Anshari
TECHSI - Jurnal Teknik Informatika Vol 14, No 1 (2023)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v14i1.12076

Abstract

Pada era-globalisasi ini teknologi informasi menjadi alat dasar yang dibutuhkan oleh setiap perusahaan yang ada. Dengan menggunakan teknologi informasi keakuratan dan kecepatan akses data akan lebih mudah di jalankan. PT Pupuk Iskandar Muda merupakan pabrik pupuk urea ke-11 di Indonesia dan pabrik ke-2 di Provinsi Aceh. Proses pengelolaan cuti pada PT Pupuk Iskandar Muda saat ini masih dilakukan secara manual. Proses pengelolaan cuti tersebut memiliki beberapa kelemahan. Karyawan tidak bisa mengetahui sisa hak cuti pribadi dan pengambilan cuti oleh rekan kerja secara langsung, sehingga karyawan tidak bisa melakukan manajemen cuti dengan baik.Pimpinan juga belum dapat mengambil keputusan cuti berdasarkan prinsip pemerataan hak cuti karyawan. Kelemahan yang lain adalah proses pengurusan cuti karyawan kurang efektif dan efesien. Dalam menyelesaikan masalah tersebut, penulis merancang sebuah sistem dengan menggunakan pemodelan ERD dan DFD, Personal Home Page (PHP) dan menggunakan basis data MySQL. Dengan adanya sistem pelayanan cuti di PT Pupuk Iskandar Muda ini, karyawan akan bisa lebih mudah untuk mengakses masalah percutian.
Implementasi Data Mining Dalam Menentukan Pola Pembelian Obat Menggunakan Metode Apriori Lidya Rosnita; Zara Yunizar; Elma Fitria Ananda
Jurnal Serambi Engineering Vol. 9 No. 3 (2024): Juli 2024
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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

Abstract

The fierce competition in the pharmacy industry requires sellers to continue to improve their sales strategies to increase sales of medicines. The availability of different types of medicines that consumers need is one step in overcoming this. This research uses an a priori algorithm to determine drug purchasing patterns. By using a priori algorithms in pharmacies, a system can be created to determine drug purchasing patterns, which is useful in determining drug purchasing targets well and can improve sales strategies. The data studied are one year's retail and wholesale transaction data. The pattern of drug purchasing associations obtained with a minimum support of 5% and a minimum confidence of 60% produces 8 association rules.The association rule with the highest confidence of 96.1% is that if consumers buy pseudoephedrine 30 mg and amoxicillin trihydrate 500 mg, they will also buy paracetamol 500 mg. Drug types that meet the minimum support and minimum confidence are Pseudoephedrine 30mg, Amoxicillin Trihydrate 500mg, Mefenamic Acid 500mg, Prednisone Triman 5mg pot, Cetirizine Hcl 10mg, Cefadroxil Monohydrate 500mg and Paracetamol 500mg.
Peningkatan Efisiensi Kecepatan dan Akurasi Rekapitulasi Faktur Pajak Dengan Optical Character Recognition Di Orbit Future Academy Rosnita, Lidya; Adek, Rizal Tjut
TECHSI - Jurnal Teknik Informatika Vol. 14 No. 2 (2023)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v14i2.14861

Abstract

Kemajuan ilmu pengetahuan terutama dalam ranah Artificial Intelligence (AI), telah membawa perubahan signifikan bagi kehidupan manusia. Di Indonesia, Orbit Future Academy (OFA) hadir sebagai lembaga pelatihan terbesar dalam bidang AI. Program AI 4 Jobs bertujuan untuk mempersiapkan individu dalam memasuki dunia kerja yang didominasi oleh teknologi AI. Program didesain untuk mengenalkan teknologi AI kepada pelajar guna menginspirasi pengembangan produk AI yang berdampak sosial. Berdasarkan pengetahuan tentang kemampuan AI, penulis menemukan suatu tantangan dalam kantor konsultan pajak yaitu pengolahan dokumen faktur pajak yang masih dilakukan secara manual, dimana hal tersebut dapat diatasi dengan kehadiran AI yang mampu mengolah data berulang dengan efisiensi tinggi. Untuk menyelesaikan tugas tersebut, sebuah website AI dibangun dengan memanfaatkan domain AI Computer Vision dan menggunakan model Optical Character Recognition (OCR) dengan library deep learning EasyOCR, Pytesseract, dan PDF Plumber. Tahapan pada pembuatan AI ini terdiri dari problem scoping, data acquisition, data exploration, modeling, evaluation, dan deployment. Pengujian dilakukan menggunakan dua jenis file faktur pajak (PDF dan JPG) yang masing-masing terdiri dari lima sampel faktur pajak, diujikan langsung pada website dengan tiga library yang berbeda. Hasil pengujian menunjukkan tingkat accuracy, recall, precision, dan f-score deteksi faktur pajak sebesar 100%. Pengujian PDF Faktur Pajak dengan PDF Plumber memiliki tingkat accuracy, recall, precision, dan f-score sebesar 100%. Pengujian gambar faktur pajak dengan Tesseract OCR memiliki tingkat accuracy sebesar 60%, recall 100%, precision 60% dan f-score 75%. Pengujian gambar faktur pajak dengan EasyOCR memiliki accuracy, recall, precision, dan f-score 100%.
Penerapan Sistem Deteksi Pengisian Ruang Parkir Kendaraan Roda 4 Menggunakan Metode Computer Vision Di Orbit Future Academy Rosnita, Lidya; Retno, Sujacka
TECHSI - Jurnal Teknik Informatika Vol. 15 No. 1 (2024)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v15i1.16142

Abstract

Keberadaan kecerdasan buatan (AI) telah mengubah lanskap teknologi dan membawa perubahan signifikan bagi kehidupan manusia. Di Indonesia terdapat perusahaan Orbit Future Academy (OFA) yang berfokus pada program Artificial Intelligence 4Jobs (AI 4JOBS). AI 4JOBS bertujuan untuk memperkuat kompetensi individudalam kecerdasan buatan (AI) sebagai persiapan untuk terjun ke dunia kerja yangterus berkembang. Program AI 4JOBS di OFA dirancang dengan beragam modulyang mencakup pemahamankonsep AI, keterampilan teknis, aspek etika profesi,dan kesiapan berkarir. Dalam penelitian ini berfokus pada" Penerapan Sistem Deteksi Pengisian Ruang Parkir Kendaraan Roda Menggunakan Metode Computer Vision".Untuk menyelesaikan tugas tersebut, sebuah website AI dibangun dengan memanfaatkan domain AI Computer Vision  dengan ruang warna HSV (Hue, Saturation, Value) dan library OpenCV adalah pendekatan yang umum digunakan dalam pengolahan citrauntuk membedakan kendaraan dari latar belakanguntukmengidentifikasitata dalam pengaturan parkir kendaraan roda 4.Melaluiprogram AI 4JOBS di OFA, peneliti berhasil memperoleh pengetahuan yang luastentangAIdanmengasahketerampilanteknisyangsangatdibutuhkandalammenghadapiperkembanganteknologiAI. Selainitu,programinijugamemberiwawasantentangetikaprofesidankesiapanberkarirdieraAI.
Review AHP dalam Fenomena Gelumbung Ekonomi Asrianda, Asrianda; Aidilof, Hafizh Al Kautsar; Rosnita, Lidya; Zulfadli, Zulfadli
TECHSI - Jurnal Teknik Informatika Vol 14, No 1 (2023)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v14i1.12588

Abstract

Keinginan memperoleh keuntungan didasarkan pada sifat kerakusan pada diri seseorang sehingga harga komoditas tidak akan turun dan semakin naik, kosekuensinya masyarakat berusaha untuk memiliki barang sebanyak mungkin sehingga harga naik dan mendapat keuntungan yang banyak. Mencari penyebab alasan menurun minat masyarakat terhadap barang akibat gembung ekonomi yang ada dipasaran. AHP menggabungkan pertimbangan dan penilaian pribadi dengan cara yang logis dan dipengaruhi imajinasi, pengalaman, dan pengetahuan untuk menyusun hierarki dari suatu masalah yang berdasarkan logika, intuisi dan juga pengalaman untuk memberikan pertimbangan. Dari hasil perhitungan menggunakan metode AHP dapat diambil kesimpulan bahwa minat tren gelumbung ekonimi dimasyarakat Aceh khususnya Lhokseumawe disebakan oleh nilai seni dari barang yang menjadi minat dimasyarakat. Dengan perhitungan yang tersebut sesuai dengan hasil penilaian responden yang telah peneliti lakukan menggunakan metode AHP.
Performance Analysis Algorithm Classification and Regression Trees and Naive Bayes Based Particle Swarm Optimization for Credit Card Transaction Fraud Detection Afridah, Rita; Ula, Munirul; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.523

Abstract

With the advancement of technology, credit cards have become a popular tool for transactions, both physically and online, due to their ease of use and seamless integration with banking systems. However, with the increasing use of credit cards, the cases of fraud have also risen, resulting in financial losses for both cardholders and banks. To address this issue, effective and efficient credit card transaction fraud detection has become a top priority. Using machine learning algorithms is one of the techniques that can be employed to detect fraud in credit card transactions. The purpose of this research is to determine the performance and find the best method of the CART algorithm, Naive Bayes, and their combination with Particle Swarm Optimization (PSO) in detecting fraud in credit card transaction histories. The data used consists of 568,630 big data entries with parameters including id, V1-V28, amount, and class. The research results obtained are as follows: the accuracy of the Naive Bayes algorithm is 93.15%, precision is 94%, recall is 93%, and AUC is 0.99. For the CART algorithm, the accuracy is 99.96%, with precision and recall at 100%, and AUC at 1.00. Additionally, the Naive Bayes algorithm combined with PSO achieved an accuracy of 98.50%, precision and recall of 98%, and AUC of 1.00. Lastly, the CART algorithm combined with PSO reached an accuracy of 99.97%, with precision and recall at 100%, and AUC at 1.00. It can be concluded that the best method resulting from the tests conducted is the Classification and Regression Trees method combined with Particle Swarm Optimization.
Mobile Learning Application Tahsin Al-Quran Using Dynamic Time Warping Method Based on Adroid Nasution, Wahidatunnisa; Ula, Munirul; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.512

Abstract

This research aims to design and build an Android-based Quran tahsin learning mobile application using the Dynamic Time Warping (DTW) method. This application offers tajweed learning features and voice exercises to find out the readings of Al-Quran readers. The DTW method is used to analyze the similarity between the user's voice pattern and the reference voice pattern in the application. The research methods used include reference collection, direct observation, and literature study. The application is designed with a user-friendly interface and equipped with an accurate ability evaluation feature, so that users can find out their weaknesses and strengths in learning Qur'an tahsin. Based on the test results, out of 42 voice data tested, 38 data were successfully recognized correctly and 4 data had errors. The average accuracy rate of this application reached 90.47%. This application is designed to overcome some of the main problems in learning Quran tahsin: lack of understanding of basic tahsin techniques, lack of appropriate learning tools, difficulty in evaluating skills, and lack of motivation to learn. With this application, users can learn Quran tahsin more easily and effectively through interactive and varied methods. Evaluation of users' ability to recite Quranic verses can also be done accurately, so that users can know their strengths and weaknesses in tahsin learning. The implementation of this application is expected to make a significant contribution in improving the quality of Quran tahsin learning among the wider community.
Analysis of Public Sentiment Towards Celebrity Endorsment On Social Media Using Support Vector Machine Syahputra, M Oriza; Bustami, Bustami; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.543

Abstract

Analysis of public sentiment towards celebrity endorsements on social media is very important to understand the public's response to promotional campaigns involving celebrities. In this study, we combine the VADER labeling method with the Support Vector Machine (SVM) method to analyze public sentiment toward celebrity endorsements on social media. Data is taken from various social media sources such as Twitter, Instagram, and Facebook. The data is pre-processed to ensure data accuracy and relevance and then labeled with the VADER method to determine the positive, negative, or neutral sentiment of the text. The labeled data is then extracted for features and used to train the SVM model. The trained SVM model is then validated using test data to measure its accuracy and performance. The results of the analysis provide useful insight into public sentiment towards celebrity endorsements on social media and can provide recommendations for stakeholders regarding this matter. Overall, combining the VADER labeling method with SVM in analyzing public sentiment towards celebrity endorsements on social media shows more accurate results and can provide practical benefits in marketing and promotional strategies. The results shown using the Support Vector Machine method with a ratio of 80:20 can provide average precision results of 77%, recall of 100%, f1-score of 87%, and accuracy of 76.92%. Twitter application user sentiment shows that 77% (338 data) of Twitter user reviews provide positive sentiment and 23% (119 data) provide negative sentiment reviews from a total of 517 data. Suggestions from researchers are that in future research they can add more data to make modeling easier to provide higher accuracy values. Using other classification and performance evaluation methods, such as Naive Bayes, Decision Tree, Fuzzy, or Deep Learning. Use other data processing tools, such as RapidMiner, Jupyter Notebook, RStudio, or others.
Co-Authors Afif, Muhammad Athallah Afridah, Rita Aidilof, Hafizh Al Kausar Aidilof, Hafizh Al Kautsar Al Kautsar Aidilof, Hafizh Amelia, Ulva Amir Fauzi Ansyari, Taufik Habib Armaya, Devira Yuda Asrianda Asrianda Azzahra Iskandar, Farah Bancin, Udurta Bustami Bustami Bustami Dahlan Abdullah Deassy Siska Dela, Monisa Dian Putri, Yohana Diana, Mhd. Arief Efendi, Syahril Efendi, Syahril Elma Fitria Ananda Eva Darnila Eva Darnila Fachry Abda El Rahman Fadlisyah Fadlisyah Fasdarsyah Fasdarsyah Fidyatun Nisa Fuadi, Wahyu Furqan, Hafizul Habib Muharry Yusdartono Hafidh Rafif, Teuku Muhammad Hamsi, Widia Harahap, Ilham Taruna Harahap, Lina Mardiana Ikramina ikramina ikramina, Ikramina Jange, Beno Khairul Amna, Khairul Kurniawati Kurniawati Lina Mardiana Harahap Mara Wahyu Alamsyah Pane Micola Azwir, Andrea Muhammad Azhari Muhammad Fajri Muhammad Fajri Muhammad Fikry Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Muhammad Muhammad Zarlis Muhammad Zarlis, Muhammad Muharry Yusdartono, Habib Mukti Qamal Mulyadi, Rizki Munirul Ula Muzaffar Rigayatsyah Nanda Sitti Nurfebruary Nasution, Wahidatunnisa Naturizal, Rayhan Naza Amarianda Nurfebruary, Nanda Sitti Nurhaliza Bin Aras Nurqamarina Nurul Aula Nurwijayanti Pasaribu, Hafni Maya Sari Pratiwi, Dinda Pulungan, Fauzi Irham Putri, Sri Raihan Rachman, Aulia Rachmat Triandi Tjahjanto Rahmadani Sari, Putri Dwi Rahmat Triandi Rangkuti, Haris Yunanda Rian Kelana Putra Rini Meiyanti Risawandi, Risawandi Rizal Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Safriana Safriana Safwandi Safwandi, Safwandi Said Fadlan Anshari salamah salamah Samosir, Dini Kairiyah Saputri, Rifa Andriani Siti Maimunah Sujacka Retno Syahputra, M Oriza Ulva Ilyatin Wahyu Fuadi Yesy Afrillia Yunanda Rangkuti, Haris Zalfie Ardian Zara Yunizar Zulfadli Zulfadli