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All Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika JISTech (Journal of Islamic Science and Technology) JURNAL TEKNOLOGI DAN OPEN SOURCE JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Technologia: Jurnal Ilmiah Jurnal Ilmu Komputer dan Bisnis Health Information : Jurnal Penelitian Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) INFOKUM Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) El-Qist : Journal of Islamic Economics and Business (JIEB) Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Walisongo Journal of Information Technology Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Instal : Jurnal Komputer Jurnal Teknisi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Sains dan Teknologi JOMLAI: Journal of Machine Learning and Artificial Intelligence Data Sciences Indonesia (DSI) Internet of Things and Artificial Intelligence Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Ilmu Komputer dan Sistem Informasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Nasional Komputasi dan Teknologi Informasi
Mhd Furqan
Universitas Islam Negeri Sumatera Utara
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Implementasi Data Mining dengan K-Means Clustering untuk Memprediksi Pengadaan Obat Pane, Putri Pratiwi; Ramadhan Nasution, Yusuf; Furqan, Mhd.
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4920

Abstract

Community Health Center is one of the institutions that provides healthcare services. To ensure the provision of quality healthcare services, the Community Health Center management must be able to effectively manage medicine inventory to avoid the risks of shortages or excess stock. Therefore, the purpose of this research is to observe and perform clustering of medicine demands at Puskesmas Mandala using the K-Means Clustering technique. The data used includes medicine demand data from January to December 2023 at the health center. In its implementation, the RapidMiner application or software is utilized to perform clustering using the K-Means Clustering algorithm. The available medicine data will be grouped into 3 clusters: cluster 0 for high medicine demands, cluster 1 for moderate medicine demands, and cluster 2 for low medicine demands. Out of the 28 test data used, the results show the first cluster consisting of 24 items, the second cluster consisting of 3 items, and the third cluster consisting of 1 item with a Davies Bouldin Index value of 0.276. From this research, the Puskesmas can continue to procure medicine for the types classified under high-demand clusters to ensure that the medicine needs are consistently met.
Classification of Scholarships for Students in Schools Using the Naïve Bayes Method Rizki Siregar, Awal; Furqan, Mhd.
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5417

Abstract

This research addresses the challenge faced by educational institutions in selecting scholarship recipients by implementing the Naïve Bayes algorithm. The objective of this study is to simplify and improve the accuracy of the scholarship selection process at MTs As-Syarif Kuala Beringin, using data from 50 students. The background highlights the importance of scholarships in providing equal educational opportunities, particularly for students with financial challenges. The research method involves the use of Naïve Bayes to calculate the probability of eligibility based on academic performance, economic background, and student activity. The results show that seven students met the scholarship criteria, demonstrating the efficiency and objectivity of the algorithm. The practical implications include the development of a user-friendly application that facilitates data input, scholarship criteria determination, and clear evaluation results. This system enhances transparency and reliability in decision-making. In conclusion, the Naïve Bayes algorithm proves to be an effective and efficient tool for scholarship selection, enabling a more equitable opportunity for students. Further research could focus on integrating additional data points or comparing the algorithm's performance with other classification methods to enhance system reliability.
Penerapan Data Mining dalam Pengelompokan Kualitas Produk Kelapa Sawit Menggunakan Algoritma K-Means Clustering Putra, Suan Ekie Nanda; Furqan, Mhd.
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.61682

Abstract

Minyak kelapa sawit banyak digunakan dalam berbagai produk, termasuk makanan, kosmetik, dan biodiesel. Untuk menjaga kualitas produk, diperlukan pemantauan serta analisis data secara terperinci sangat penting. Pada PT. Sri Ulina Ersada Karina, proses produksi Crude Palm Oil saat ini hanya mengikuti standar nasional tanpa analisis lebih lanjut tentang kualitas produk. Dengan analisis yang lebih mendalam, perusahaan dapat meningkatkan efisiensi dan mutu produk. Penelitian ini bertujuan untuk menerapkan teknik data mining, khususnya algoritma K-Means Clustering, untuk mengelompokkan kualitas produk kelapa sawit yang diolah menggunakan tools Jupyter Notebook. Hasil dari penelitian ini menghasilkan 3 cluster yaitu cluster 0 kategori baik dengan jumlah data sebanyak 89 sampel, Cluster 1 kategori kurang baik dengan jumlah data sebanyak 72 sampel, dan Cluster 2 kategori sangat baik dengan jumlah data sebanyak 132 sampel.
Penerapan Algoritma C4.5 Pada Klasifikasi Status Gizi Balita Ramadhan Nasution, Yusuf; Armansyah; Furqan, Mhd; Matondang, Toibatur Rahma
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6941

Abstract

The study aims to classify the nutritional status of the child using the C4.5 algorithm. The secondary data used is derived from the assessment of the nutrition status of a child in Puskesmas Promji and Puksesmas Suka Makmur. A classification model is constructed using the C4.5 algorithm based on a number of predictor factors that have been determined. The research methodology includes data collection, data preprocessing, model development with C4.5 algorithms, model evaluation, and results analysis. Model evaluation is done using measurements such as accuracy. In addition, the significance of predictor variables in affecting the nutritional status of infants was also evaluated through data analysis. This research contributed to the development of a method of classifying the nutritional status of infants using the C4.5 algorithm approach. The implication of this study is that the classification model developed can be used as a tool to support early identification and intervention against nutritional problems in infants. Furthermore, based on testing using the confusion matrix technique with the 80:20 data division of a total of 502 datasets, consisting of 402 training data and 100 testing data, an accuracy rate of 80 percent was obtained.
PENINGKATAN KUALITAS TENAGA PENDIDIK MELALUI PUBLIKASI KARYA ILMIAH BEREPUTASI INTERNASIONAL Hasugian, Abdul Halim; Furqan, Mhd.
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 (2024): Vol. 5 No. 5 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i5.40408

Abstract

Penelitian ini mengkaji tantangan dan strategi dalam meningkatkan kualitas pendidik melalui publikasi ilmiah bereputasi internasional. Dengan menggunakan pendekatan kualitatif, data dikumpulkan melalui wawancara komprehensif, observasi langsung, dan analisis dokumen di beberapa perguruan tinggi terpilih. Penelitian ini mengungkapkan adanya hambatan yang signifikan termasuk kemampuan bahasa Inggris yang terbatas, keterbatasan waktu, dan kurangnya keterampilan menulis penelitian di antara para pendidik. Melalui program intervensi yang ditargetkan termasuk lokakarya khusus dan sesi pendampingan, para peserta menunjukkan peningkatan yang nyata dalam kemampuan publikasi mereka. Studi ini menunjukkan bahwa pendekatan pelatihan sistematis yang dikombinasikan dengan dukungan kelembagaan dapat secara efektif meningkatkan kapasitas pendidik untuk menghasilkan publikasi ilmiah yang diakui secara internasional. Rekomendasi yang diberikan termasuk membuat program pengembangan penulisan yang berkelanjutan, menciptakan jaringan penelitian kolaboratif, dan menerapkan sistem insentif untuk publikasi internasional.
Implementasi Gangguan Psikologi Anak Selama Belajar Daring Akibat Pandemi COVID-19 Menggunakan Metode C5.0 Nasution, Romaito; Furqan, Mhd; Santoso, Heri
Jurnal Ilmu Komputer dan Bisnis Vol. 15 No. 2 (2024): Vol. 15 No. 2 (2024)
Publisher : STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/jikb.v15i2.826

Abstract

Pada tahun 2020 terjadi wabah virus covid 19 diseluruh didunia, dimana seluruh aspek belajar mengejar dilakukan melalui daring. Dengan meluasnya penggunaan kemajuan teknologi yang semakin canggih, seperti Google Classroom, WhatsApp, Telegram, Google Meet, e-learning, dan aplikasi Zoom, pembelajaran online dapat berfungsi secara efektif. Dengan adanya wabah virus ini ada beberapa anak yang mengalami gangguan psikologi . Dalam penelitian ini, peneliti mencoba untuk menganalisis poin utama dari masalah yang ada dan tekad oleh temuan memperkuat kasus ini bahwa data dan hasil keputusan menggunakan data mining dengan metode algoritma C5.0 Pohon keputusan dapat menemukan hubungan tersembunyi antara sejumlah variabel input dengan sebuah variabel target dari data. .Penelitian ini menghasilkan pohon keputusan dari kasus yang. Akan ditampilkan daftar nilai gain dari tiap atribut dengan atribut tertinggi ialah Tidak Menderita dengan nilai entropy 0,92552578 dan atribut nilai gain terendeh ialah atribut Mood Swing Berat dengan nilai entropy 0,063067808 dengan akurasi dengan nilai 96,66%.
Classification of Dates Based on Texture Using Local Binary Pattern Algorithm and Support Vector Machine Mhd Furqan; Sriani Sriani; Suci Syahputri
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9358

Abstract

Abstract - Dates are a food that is widely favored by Muslims in Indonesia, especially during the month of Ramadan. The many types of dates make it difficult to distinguish the types of dates. To distinguish the types of dates can be seen from the shape, color, size or texture. In this study, dates will be distinguished based on their texture. Local Binary Pattern is one of the algorithms that can be used to extract images of dates based on their texture by comparing the center value of the pixel with the value of the surrounding pixels to facilitate the classification process. The classification used uses Support Vector Machine which works by finding the best hyperplane to determine data for each class. The combination of these two algorithms has proven to be able to classify with an accuracy level of 93%.Keywords: Classification, Local Binary Pattern, Support Vector Machine, Dates Abstrak - Kurma adalah makanan yang sangat disukai oleh umat Muslim di Indonesia, terutama selama bulan Ramadan. Beragam jenis kurma membuatnya sulit untuk membedakan jenis-jenis kurma tersebut. Untuk membedakan jenis kurma dapat dilihat dari bentuk, warna, ukuran, atau teksturnya. Dalam penelitian ini, kurma akan dibedakan berdasarkan teksturnya. Local Binary Pattern (LBP) adalah salah satu algoritma yang dapat digunakan untuk mengekstrak gambar kurma berdasarkan teksturnya dengan membandingkan nilai pusat piksel dengan nilai piksel di sekitarnya untuk memudahkan proses klasifikasi. Klasifikasi yang digunakan menggunakan Support Vector Machine (SVM) yang bekerja dengan mencari hiperplane terbaik untuk menentukan data untuk setiap kelas. Kombinasi kedua algoritma ini terbukti mampu mengklasifikasikan dengan tingkat akurasi 93%.Kata kunci: Klasifikasi, Local Binary Pattern, Support Vector Machine, Kurma
Klasifikasi Berita detik.com Terkait Teknologi Informasi Menggunakan TF-IDF dan Naive Bayes Nur Bainatun Nisa; Rivaldi Prima Nanda; Zahra Humaira Kudadiri; Bagus Ageng Alfahri; Mhd Furqan
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9171

Abstract

Abstrak – Penelitian ini membahas tentang klasifikasi berita Detik.com terkait teknologi informasi dengan menerapkan metode Term Frequency-Inverse Document Frequency (TF-IDF) sebagai ekstraksi fitur dan algoritma Naive Bayes sebagai model klasifikasi. Tujuan dari penelitian ini adalah untuk mengelompokkan berita-berita yang dimuat pada situs Detik.com ke dalam beberapa kategori utama di bidang teknologi informasi, seperti kecerdasan buatan, keamanan siber, gadget, dan aplikasi. Proses penelitian diawali dengan pengumpulan 1.050 data berita dari Detik.com menggunakan search query ‘teknologi informasi’ pada rentang Maret hingga April 2025. Data kemudian diproses melalui tahapan text preprocessing, meliputi case folding, tokenizing, stopword removal, dan stemming. Selanjutnya, fitur teks diubah menjadi representasi numerik menggunakan TF-IDF, lalu dilakukan pelatihan model klasifikasi dengan algoritma Naive Bayes. Evaluasi kinerja model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kombinasi TF-IDF dan Naive Bayes efektif dalam mengklasifikasikan berita teknologi informasi, dengan akurasi model mencapai 85%. Temuan ini menunjukkan bahwa pendekatan klasifikasi berbasis machine learning dapat membantu pengelompokan dan identifikasi topik utama secara otomatis dalam berita teknologi informasi di Detik.com.Kata Kunci: TF-IDF; Naive Bayes; Klasifikasi; Detik.com; Teknologi Informasi.Abstract – This study discusses the classification of Detik.com news related to information technology by applying the Term Frequency-Inverse Document Frequency (TF-IDF) method as a feature extraction and the Naive Bayes algorithm as a classification model. The purpose of this study is to group news published on the Detik.com site into several main categories in the field of information technology, such as artificial intelligence, cybersecurity, gadgets, and applications. The research process began with the collection of 1,050 news data from Detik.com using the search query 'information technology' in the range of March to April 2025. The data was then processed through the text preprocessing stage, including case folding, tokenizing, stopword removal, and stemming. Furthermore, text features were converted into numeric representations using TF-IDF, then training a classification model with the Naive Bayes algorithm. Model performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the combination of TF-IDF and Naive Bayes was effective in classifying information technology news, with a model accuracy reaching 85%. This finding suggests that a machine learning-based classification approach can help automatically cluster and identify key topics in information technology news on Detik.com.Keywords: TF-IDF; Naive Bayes; Classification; Detik.com; Information Technology.
Landasan Teori Metodologi Penelitian dalam Ilmu Komputer: Analisis Pendekatan Kuantitatif dan Kualitatif Farhan Amar Pramudya; M. Alfatoni Muarrip; Sigit Muslim Anggoro Pratono; Jundi Haqqoni; Radhifan Mardhi; Mhd Furqan
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.2011

Abstract

This study discusses the theoretical foundations of research methodology in computer science by analyzing quantitative and qualitative approaches. The rapid development of computer science requires appropriate research methods to ensure the validity and reliability of findings. This study aims to examine the characteristics, advantages, limitations, and applications of quantitative and qualitative methods in computer science research. The method used is a literature review of national and international scientific publications relevant to research methodology in computer science. The results show that quantitative approaches are suitable for measurement-based, experimental, and algorithm performance studies, while qualitative approaches are more appropriate for exploratory research, user experience analysis, and system evaluation in social contexts. This study is expected to provide theoretical guidance for researchers in selecting appropriate research methodologies.
Klasifikasi Komentar Kasar pada TikTok Menggunakan TF-IDF dan Logistic Regression Anggraini, Delia; Wahyudin, Rahmat; Wicaksana, Agum; ., Zulpadli; Zulnun, M. Ridho Azmuddin; Furqan, Mhd
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3906

Abstract

The increasing intensity of user interaction on the TikTok platform makes the comment section vulnerable to the emergence of rude comments, impolite speech, and negative verbal expressions that can reduce the quality of digital communication. The characteristics of TikTok language, which is informal, concise, and rich in slang variations and non-standard spelling, present challenges in the process of automatically identifying rude comments, especially in the Indonesian context. This study aims to develop and evaluate a binary classification model capable of distinguishing rude and non-rude comments on the TikTok platform using a text-based machine learning approach. The research method began with the collection of 650 Indonesian-language public comments from TikTok, which were then manually annotated into two classes: rude and non-rude comments. The labeled data were processed through preprocessing stages including text cleaning, case folding, slang normalization, repeated character reduction, tokenization, and stopword removal. Feature representation was carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) method with a combination of unigrams and bigrams, while the classification process used the Logistic Regression algorithm. The data were divided into training data and test data with a ratio of 80:20. The analysis techniques used included evaluating model performance using accuracy, precision, recall, and F1-score metrics. The results showed that the model achieved an accuracy of 87.4%, with precision, recall, and F1-score values ​​of 0.87 each, indicating good and balanced classification performance across both classes. These findings indicate that the combination of TF-IDF and Logistic Regression is effective as a baseline in classifying abusive Indonesian comments on the TikTok platform.
Co-Authors ., Zulpadli Abdul Halim Hasugian Adha, Rifki Mahsyaf Agpina, Pipi Agung Nugroho Ahmad Fakhri Ab. Nasir Ahmad Fauzi Aidil Halim Lubis Aisyah Nurrahmah Siregar Akmal, Muhammad Haikal Andita Utami Anggraini, Delia Anwar, Mufti Husain Apriansyah, Yuda Ardyanti, Tiwy Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah, A Aulia, Atiqah Aulia, Muhammad Arief Aulia, Muhammad Fathir Aulia, Rafif Risdi Badria, Lailatul Bagus Ageng Alfahri Basyir, Muhammad Khalidin Bintang Kurniawan Herman Bob Subhan Riza, Bob Subhan Br Rambe, Indri Gusmita Cahyadi, Bhagaskara Dalimunthe, Ayu Sahriani Daulay, Ikhsan Agus Martua Dea Alya Dewi Aulia Tanjung Diah Putri Kartikasari Dodyk Fahlome Elce, Furkan Fadil, Ulfi Muzayyanah Fadillah, Rini Fadlan, Aulia Fahrul Azis Nasution Faiza, Nayla fandi, Fandi Ahmad Farhan Amar Pramudya Farhan Sadli Siregar Farhan Sadly Siregar Fikri Haikal FIKRI HAIKAL Fredy Kusuma Ramadhani Gunawan, Irwan Hapisfatly Sir Harahap, Khaila Mukti Harahap, Raihan Rizieq Harahap, Rosa Linda Hasrul Hasibuan, Mhd Fikri Heri Santoso Hervilla Amanda R. Siregar Himawan Hasibuan, Riswanda Ichsan HP, Kiki Iranda Hsb, Dinda Umami Hsb, Munawir Siddik Hutagalung, Muhammad Wandisyah R Ilham Fuadi Nasution Imam Zaki Husein Nst Iskandar, Rozai Ismail Pulungan Januar, Bagus Jundi Haqqoni K Khairunnisa Khairi, Nouval Khairunnisa Khairunnisa Khairunnisa, K Kurniawan, Riski Askia Laila Nurzannah Lailatul Badria Lely Sahrani Lubis, Akbar Maulana M. Alfatoni Muarrip M. Fakhriza Mahendra, Rifandi Manza, Yuke Matondang, Toibatur Rahma Maulana Ihsan, Maulana Mey Hendra Putra Sirait Mhd Fadil Ramadhana Mhd Fikri Hasrul Hasibuan Mhd Galih Khairi Mhd Ikhsan Rifki Mhd Reza Alfani Miftahul Rizky Pulungan Muhammad Akbar Ramadhan Tanjung Muhammad Fadil Ramadhana Muhammad Farhan Muhammad Fathir Aulia Muhammad Ikhsan Muhammad Irfan Gurning Muhammad Luthfi Muhammad Naufal Shidqi Muhammad Ridzki Hasibuan Muhammad Rizki Munadi Munadi Nabawy, Putri Nabila, Siti Fadiyah Naina Nazwa Hasibuan Nasution, Afri Yunda Nasution, Irma Yunita Nasution, Romaito Nasution, Zulia Lestari Nayla Faiza Nazwa Aliya Muthmainnah Hasibuan Ningsih, Siti Alus Nur Bainatun Nisa Nur Shafwa Aulia Sitorus Nurhasanah Nurhasanah Nurul Hadi Muliani Hariadi Saputra Nurzannah, Laila Pane, Putri Pratiwi Pangestu, Dimas Panggabean, Alwi Andika Pratama, Haris Prayoga Elfanda Fachmi Hasibuan Putra, Suan Ekie Nanda Putri Salsa Nabila Putri, Alma Irawanti Radhifan Mardhi Raissa Amanda Putri Rakhmat Kurniawan R Ramadani, Wily Supi Ramadhan Nasution, Yusuf Ramadhani, Fredy Kusuma Razzaq H. Nur Wijaya Reza Muhammad Rifnandy, Muhammad Fauzan Rika Rosnelly, Rika Riswanda Ichsan Himawan Hasibuan Rivaldi Prima Nanda Rizka Rizki Ananda Rizki Siregar, Awal Rizqi Hidayat Tanjung RR. Ella Evrita Hestiandari Said Arrahman Salsabila Mahfuza Saparuddin Siregar Sembiring, Yogasurya Pranantha Shafa, Dafa Ikhwanu Sigit Muslim Anggoro Pratono Sinaga, Meri Siregar, Dzilhulaifa Siregar, Hervilla Amanda R. Siregar, Kalfida Eka Wati Sitepu, Anggi Jelita Siti Saniah Siti Sarah Harahap Siti Sumita Harahap Sitorus, Nur Shafwa Aulia Solly Aryza Sri Rahmadani Sri Wahyuni Sriani Sriani Sriani Sriani Sriani, S Suci Syahputri Suci Wulandari Suhardi, S Suhardi, Suhardi Susan Mayang Sari Syamia, Nanda Tambak, Tiara Ayu Triarta Tanjung, Tegar Haryahya Tiara Bela Harahap Tria Elisa Wahyudin, Rahmat Wan Fadilla Rischa Wati, Putri Kurni Wicaksana, Agum Widiya Yuli Kartika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zabni, Nur Hera Zahra Humaira Kudadiri Zaki Musyaffa Ziqra Addilah Zulnun, M. Ridho Azmuddin