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All Journal Teknika Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Vocatech : Vocational Education and Technology Journal Jurnal Vokasi 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 Algoritme Jurnal Mahasiswa Teknik Informatika TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Terapan and Sains 4.0 MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Pengabdian Masyarakat Ilmu Komputer Journal Serambi Engineering (JSE) Bulletin of Engineering Science, Technology and Industry JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
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
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.
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.
Edukasi K3 Bidang Kelistrikan Bagi Anak-Anak di Desa Cot Mee, Kecamatan Nisam, Kabupaten Aceh Utara Nurfebruary, Nanda Sitti; Nisa, Fidyatun; Ikhwani, Muhammad; Dian Putri, Yohana; Maimunah, Siti; Rosnita, Lidya
Jurnal Malikussaleh Mengabdi Vol. 3 No. 1 (2024): Jurnal Malikussaleh Mengabdi, April 2024
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v3i1.16200

Abstract

Pengetahuan mengenai Kesehatan dan Keselamat Kerja (K3) bidang kelistrikan sangat penting dalam kehidupan sekarang ini. Ditambah lagi dengan semakin banyaknya penggunaan peralatan elektronik. Ketergantungan pada listrik tidak hanya terbatas pada aktivitas rumah tangga, tetapi juga berdampak pada anak-anak yang tentunya bisa membahayakan bagi anak-anak tersebut jika tidak dibekali dengan pemahaman yang baik mengenai penggunaan peralatan elektronik. Desa Cot Mee merupakan salah satu desa di Kecamatan Nisam, Kabupaten Aceh Utara, Provinsi Aceh memiliki kurang lebih 50 anak-anak usia sekolah dalam rentang usia 5-14 tahun. Kegiatan sehari-hari banyak dihabiskan dengan menggunakan peralatan elektronik dan juga kegiatan bermain di luar ruangan yang dekat area aliran listrik seperti gardu listrik. Apabila anak-anak tidak diberikan pengetahuan mengenai penggunaan peralatan elektronik yang benar, maka akan sangat rentan terhadap kecelakaan. Oleh karena itu, dilakukan kegiatan pengabdian kepada masyarakat untuk mengedukasi K3 kelistrikan bagi anak-anak di Desa Cot Mee. Diharapkan melalui kegiatan edukasi ini, anak-anak di Desa Cot Mee menjadi lebih paham tentang K3 kelistrikan dan selalu waspada terhadap pengunaan peralatan elektronik dalam kehidupan sehari-hari.
Grouping Sales Levels Smartphone Of Offline Store Using BIRCH Clustering Algorithm Rahmadani Sari, Putri Dwi; Qamal, Mukti; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.558

Abstract

From 2020 to 2024, TM_Store and Jaya Com exhibited different sales patterns based on cluster analysis using the BIRCH algorithm. The background of this research is to provide strategic insights to both stores for improving their sales performance through data analysis. The sales data used includes brand, type, month, year, stock quantity, quantity sold, unit price, and total sales. The BIRCH method was chosen for its effectiveness in handling large datasets and providing accurate clustering results. The clustering results indicate a significant increase in the "Moderate" category, from 12 sales in 2020 to 354 sales in 2023. Meanwhile, the "Very High" category also saw an increase from 5 sales in 2020 to 97 sales in 2023, with sales in the "Very Low" category remaining high at 70 sales in 2023. On the other hand, Jaya Com was dominated by the "Very High" category, with a sharp increase from 25 sales in 2020 to 597 sales in 2023. The "High" category also showed significant growth, from 6 sales in 2020 to 98 sales in 2023. This data indicates that Jaya Com focuses on high-performance products, while TM_Store shows a more balanced distribution across various sales categories. Based on the analysis, Jaya Com had 1988 data points with 1984 cluster points, whereas TM_Store had 2012 data points with 1811 cluster points. Overall, the study concludes that the BIRCH algorithm can identify significant sales patterns in both stores, aiding in the development of more effective and efficient promotional strategies tailored to each sales category's performance.
SOSIALISASI UU ITE BAGI SISWA SMA NEGERI 4 LHOKSEUMAWE "CERDAS MENANGKAL HOAX DALAM MENGGUNAKAN INTERNET" Fidyatun Nisa; Nanda Sitti Nurfebruary; Muhammad Ikhwani; Zalfie Ardian; Lidya Rosnita; Habib Muharry Yusdartono
Jurnal Pengabdian Masyarakat Ilmu Komputer Vol. 1 No. 2 (2024): Mei
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpmik.v1i2.714

Abstract

Undang Undang Nomor 11 tahun 2008 tentang Informasi Transaksi Elektronik atau UU ITE merupakan undang-undang atau peraturan yang sering kali bersentuhan langsung dengan berbagai orang, terutama pada era teknologi yang sedang berkembang saat ini. Secara umum, UU ITE masih belum tersosialisasikan dengan baik ke semua kalangan, termasuk pada kalangan siswa/siswi Sekolah Menengah Atas. Oleh karena itu, SMA Negeri 4 Lhokseumawe menjadi sasaran tim Pengabdian kepada Masyarakat Universitas Malikussaleh sebagai sarana untuk mensosialisasikan UU ITE. Hal ini dianggap penting karena pada saat ini kalangan muda (terutama siswa/siswi SMA) tidak terpisahkan dari teknologi internet. Sehingga perlu disampaikan bahwa ada sanksi atau hukuman yang berlaku apabila tidak waspada dalam memakai internet. Kegiatan sosialisasi ini diharapkan menjadi pembelajaran bagi siswa/siswi maupun guru-guru di SMA Negeri 4 Lhokseumawe agar lebih cerdas dan bijak dalam memakai internet, terutama untuk mencegah penyebaran berita hoax melalui social media maupun aplikasi chatting. Dari hasil sosialisasi yang dilaksanakan, dapat disimpulkan bahwa kesadaran dan pengetahuan siswa/siswi dan guru SMA Negeri 4 Lhokseumawe mengenai UU ITE masih rendah, sehingga sosialisasi ini diharapkan menjadi pembekalan agar mereka dapat waspada dalam menggunakan internet, terutama untuk menangkal berita hoax.
Applying TF-IDF and K-NN for Clickbait Detection in Indonesian Online News Headlines Afif, Muhammad Athallah; Ula, Munirul; Rosnita, Lidya; Rizal, Rizal
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 2 (2024): Journal of Advanced Computer Knowledge and Algorithms - April 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i2.15810

Abstract

This research explores the application of TF-IDF (Term Frequency-Inverse Document Frequency) and K-Nearest Neighbor (K-NN) in constructing a clickbait detection system for Indonesian online news headlines. The TF-IDF method is employed to ascertain the significance of words in news headlines, utilizing a tokenization process to generate numeric representations. The TF-IDF matrix serves as features in the K-NN classification model, with k=1 determining the most similar class. Model evaluation yields outstanding results, achieving accuracy, precision, recall, and F1-Score all reaching 1.0. The confusion matrix unveils no misclassifications, affirming the model's adeptness in correctly classifying all samples.
Decision Support System for Selecting the Best Facial Wash Brand for Acne-Prone Skin Using the Fuzzy Analytical Hierarchy Process (F-AHP) Method Armaya, Devira Yuda; Rosnita, Lidya; Asrianda, Asrianda; Rachman, Aulia; Azhari, Muhammad
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.19542

Abstract

Acne is a problem that is often experienced by women. The factors that trigger acne skin problems are due to the pores on the facial skin that are clogged with oil, and the presence of bacteria. This research was conducted to provide a decision support system in recommending brands facial washthe best for acne prone skin types through the value of the intensity of importance of criteria such as price, packaging form, packaging size, active ingredient content, and packaging design. The final result of the calculation process using the method fuzzy AHP produces the lowest to the highest weight value for each brandfacial wash. And the final ranking data shows that there are 5 brand recommendations facial wash with the highest value of the other alternatives. That is there is an alternative code A04 which has the highest value asfacial wash the best for acne prone skin types, namely the brand is The Body Shop Tea Tree Skin Clearing Facial Wash with a total value of 7.663, and followed by alternative code A13 namely is Some By Mi AHA BHA PHA with a total value of 7.663, alternative code A07 is Miracle Cleansing with a total value of 7.337, the alternative code A15 is Ponds Anti Bacterial Facal Foam with a total value of 7.326, and the last alternative code A14 is Emina MS Pimple Acne Solutonwith a total score of 6.663.
Pelatihan Pembuatan Sabun Cuci Piring Serta Pemasaran Online Sebagai Peningkatan Peluang Wirausaha Masyarakat di Desa Bale, Kecamatan Syamtalira Bayu, Kabupaten Aceh Utara Rosnita, Lidya; Nisa, Fidyatun; Nurfebruary, Nanda Sitti; Ikhwani, Muhammad; Rachman, Aulia; Azhari, Muhammad
Jurnal Malikussaleh Mengabdi Vol. 3 No. 2 (2024): Jurnal Malikussaleh Mengabdi, Oktober 2024
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v3i2.19100

Abstract

Peningkatan kesejahteraan sosial dapat dilakukan melalui kegiatan pemberdayaaan masyarakat. Dalam kaitannya dengan upaya mengembangkan kemampuan serta potensi masyarakat, dilakukan berbagai alternatif kegiatan seperti pada bidang wirausaha. Salah satu contoh kegiatan wirausaha adalah pembuatan serta penjualan suatu produk. Namun, terdapat permasalahan yang mungkin timbul dari kegiatan tersebut seperti terbatasnya pengetahuan dan pelatihan untuk menunjang proses pembuatan serta pemasaran produk itu sendiri. Oleh karena itu, dilakukan kegiatan Pengabdian kepada masyarakat yang bertujuan untuk berbagi pengetahuan melalui pelatihan pembuatan dan pemasaran online sabun cuci piring dengan memanfaatkan media internet dan berbagai sosial media, serta marketplace seperti Shopee dan TikTok Shop. Kegiatan ini dilaksanakan di Desa Bale, Kecamatan Syamtalira Bayu, Kabupaten Aceh Utara. Pelatihan berjalan dengan lancar, semua peserta aktif dalam diskusi dan tanya jawab baik saat pemberian materi maupun saat praktek pembuatan sabun cuci piring. Hasil yang diperoleh dari kegiatan pengabdian kepada masyarakat ini adalah meningkatnya pengetahuan masyarakat tentang teknik pembuatan sabun cuci piring serta bagaimana cara melakukan pemasaran online terhadap suatu produk
Co-Authors Afif, Muhammad Athallah Aidilof, Hafizh Al Kausar Aidilof, Hafizh Al Kautsar Al Kautsar Aidilof, Hafizh Amelia, Ulva Amir Fauzi Armaya, Devira Yuda Asrianda Asrianda Asrianda Asrianda Aulia Rachman Aulia Rachman Azwir, Andrea Micola Azzahra Iskandar, Farah Bustami Bustami Dahlan Abdullah Dara Fazila Deassy Siska Defry Hamdhana Dela, Monisa Dian Putri, Yohana Efendi, Syahril Efendi, Syahril Elma Fitria Ananda Eva Darnila Eva Darnila Fachry Abda El Rahman Fadlisyah Fadlisyah Fajar Satria Fasdarsyah Fasdarsyah Fauzi Irham Pulungan Fidyatun Nisa Fuadi, Wahyu Furqan, Hafizul Habib Muharry Yusdartono Hafidh Rafif, Teuku Muhammad Hafizh Al Kautsar Aidilof Harahap, Ilham Taruna Harahap, Lina Mardiana Haris Yunanda Rangkuti Ikramina ikramina ikramina, Ikramina Jange, Beno Kurniawati Kurniawati Lina Mardiana Harahap Mara Wahyu Alamsyah Pane Micola Azwir, Andrea Muhammad Azhari Muhammad Azhari Muhammad Daud Muhammad Fajri Muhammad Fikry Muhammad Ikhwani Muhammad Muhammad Muhammad Reza Muhammad Zarlis Muhammad Zarlis, Muhammad Muharry Yusdartono, Habib Mukti Qamal Mulizar Mulizar Mulyadi, Rizki Mundirawati, Cut Munirul Ula Muzaffar Rigayatsyah Nanda Sitti Nurfebruary Naturizal, Rayhan Naza Amarianda Nur Ismiza Nurdin Nurfebruary, Nanda Sitti Nurhaliza Bin Aras Nurqamarina Nurul Aula Nurwijayanti Pasaribu, Hafni Maya Sari Pratiwi, Dinda Putri, Sri Raihan Rachmat Triandi Tjahjanto Rahma Fitria, Rahma Rahmadani Sari, Putri Dwi Rahmat Triandi Rangkuti, Haris Yunanda Rayhan Naturizal Rian Kelana Putra Rini Meiyanti Risawandi, Risawandi Rita Afridah Rizal Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek Rizky Putra Fhonna Safriana Safriana Safwandi Safwandi Said Fadlan Anshari Salamah Salamah Samosir, Dini Kairiyah Saputri, Rifa Andriani Sasmita Sasmita Siti Maimunah Sujacka Retno Syahputra, M Oriza Taufik Habib Ansyari Udurta Bancin Ulva Ilyatin Wahidatunnisa Nasution Wahyu Fuadi Widia Hamsi Yesy Afrillia Yunanda Rangkuti, Haris Zahlul Fasya Zahratul Fitri Zalfie Ardian Zara Yunizar Zulfadli Zulfadli Zulfadli Zulfadli