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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 J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science 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 Journal of Applied Data Sciences 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) International Journal of Education, Social Studies, And Management (IJESSM) 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)
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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Multiclass Skin Lesion Classification Algorithm using Attention-Based Vision Transformer with Metadata Fusion Furqan, Mhd.; Katuk, Norliza; Hartama, Dedy
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1017

Abstract

Early and accurate classification of skin lesions is essential for timely diagnosis and treatment of skin cancer. This study presents a novel multiclass classification framework that integrates dermoscopic images with clinical metadata using an attention-based Vision Transformer (ViT) architecture. The proposed model incorporates a mutual-attention fusion mechanism to jointly learn from visual and tabular inputs, augmented by a class-aware metadata encoder and imbalance-sensitive loss function. Training was conducted using the HAM10000 dataset over 30 epochs with a batch size of 32, utilizing the Adam optimizer and a learning rate of 0.0001. The model demonstrated superior performance compared to a ViT Baseline, achieving 93.4% accuracy, 92.2% F1-score, 0.95 AUC, and significant reductions in MAE and RMSE. Additionally, Grad-CAM visualizations confirmed the model’s ability to focus on diagnostically relevant regions, enhancing interpretability. These findings suggest that the integration of structured clinical information with transformer-based visual analysis can significantly improve classification robustness, particularly in underrepresented lesion types. However, the model’s current performance is evaluated only on the HAM10000 dataset, and its generalizability to other clinical or non-dermoscopic image sources remains to be validated. Future studies should therefore explore multi-institutional datasets and real-world deployment scenarios to assess robustness and scalability. The proposed framework offers a practical, interpretable solution for AI-assisted skin lesion diagnosis and demonstrates strong potential for clinical deployment.
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.
Design and Implementation of a Web-Based Document Archive Application at the North Sumatra Education Quality Assurance Agency Hasibuan, Naina Nazwa; Harahap, Tiara Bela; Furqan, Mhd.
International Journal Of Education, Social Studies, And Management (IJESSM) Vol. 6 No. 1 (2026): The International Journal of Education, Social Studies, and Management (IJESSM)
Publisher : LPPPIPublishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52121/ijessm.v6i1.975

Abstract

Document archiving management is an important aspect of administrative activities in institutions. Manual archiving processes often cause problems such as delays in document retrieval, risk of data loss, and inefficiency in report preparation. This study aims to design and develop a web-based document archiving application that can manage incoming and outgoing documents in a structured and integrated manner. The research method consists of data collection, system requirements analysis, system design, application implementation, and functional testing. The application is developed using PHP as the server-side programming language, MySQL as the database management system for data storage, and the Bootstrap framework to create a responsive and user-friendly interface. The results show that the developed application is able to store, manage, and display document data properly, as well as provide document recap features based on specific periods. Therefore, this web-based document archiving application can improve the effectiveness, efficiency, and security of document management.
PENERAPAN ALGORITMA BRUTE FORCE PADA APLIKASI PENERJEMAH BAHASA INDONESIA - BAHASA MANDAILING BERBASIS MOBILE Dalimunthe, Ayu Sahriani; Furqan, Mhd.; Hasugian, Abdul Halim
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3128

Abstract

Abstract: Language is a means to communicate. Knowledge of language is very important because in a conversation or conversation requires a language. In Indonesia, there are many various regional languages, including the Mandailing language. Mandailing language is one of the regional languages in South Tapanuli, North Sumatra. The use of regional languages has experienced a lot of decline in use in the language of everyday communication. Preserving regional languages is very necessary in the midst of increasingly rapid technological developments. Dictionary media can be a solution to introduce various regional languages in Indonesia. In this study, the design and development of the Indonesian-Mandailing Translator Application was carried out with the application of the Mobile-based Brute Force Algorithm. This application was built using Android Studio software using the Java programming language and XML. The database used to store data for the Batak Mandailing-Indonesian translator is a SQLite database so that the application can be used offline. Applications that are designed in a user friendly manner can perform the search function for Indonesian-Mandailing and Mandailing-Indonesian Vocabulary, making it easier for users to operate them. Keywords: Mandailing language, dictionary, Brute Force Algorithm, Android                  Application Abstrak: Bahasa merupakan sarana untuk berkomunikasi. Pengetahuan bahasa sangatlah penting karena dalam sebuah percakapan atau pembicaraan memerlukan sebuah bahasa. Di Indonesia ada banyak beragam bahasa daerah diantaranya adalah Bahasa Mandailing. Bahasa Mandailing merupakan salah satu bahasa daerah bagian Tapanuli Selatan, Sumatera Utara. Penggunaan bahasa daerah telah mengalami banyak penurunan penggunaan dalam bahasa komunikasi sehari-hari. Melestarikan bahasa daerah sangat perlu ditengah perkembangan teknologi yang semakin pesat. Media kamus dapat menjadi solusi untuk mengenalkan beragam bahasa daerah yang ada di Indonesia. Dalam penelitian ini dilakukan perancangan dan membangun Aplikasi Penerjemah Bahasa Indonesia-Mandailing dengan penerapan Algoritma Brute Force berbasis mobile. Aplikasi ini dibangun menggunakan perangkat lunak Android Studio menggunakan bahasa pemrograman Java dan XML. Database yang digunakan untuk menyimpan data penerjemah bahasa Batak Mandailing-Indonesia adalah SQLite database sehingga aplikasi dapat digunakan secara offline. Aplikasi yang dirancang secara user friendly dapat melakukan fungsi pencarian Kosa kata Bahasa Indonesia - Mandailing dan Mandailing - Indonesia sehingga memudahkan para pengguna dalam mengoperasikannya. Kata kunci: Bahasa Mandailing, Kamus, Aplikasi Android 
Comparative Analysis of K-NN and Naïve Bayes Algorithms for Early-Stage Chronic Kidney Disease Classification Rahma, Intan Dwi; Furqan, Mhd; Triandi, Budi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9392

Abstract

Chronic Kidney Disease (CKD) is a global health issue characterized by low early detection rates and high diagnostic costs. Artificial intelligence, particularly machine learning, offers a promising solution as a rapid and cost-effective decision support system. This study aims to comprehensively analyze and compare the performance of two simple and interpretable classification algorithms, K-Nearest Neighbor (K-NN) and Naïve Bayes (NB), for predicting CKD based on clinical data. The dataset was sourced from the UCI Machine Learning Repository, comprising 400 instances and 25 clinical attributes such as blood pressure and serum creatinine. The methodology included data preprocessing (median imputation for numerical features, mode imputation for categorical features), encoding, Min-Max normalization, data splitting (70:30 ratio), model training, K parameter optimization for K-NN via 5-fold cross-validation, and evaluation using accuracy, precision, recall, F1-Score, and Confusion Matrix metrics. Experimental results demonstrated that the Naïve Bayes algorithm achieved superior performance with an accuracy of 95.83%, precision of 95.95%, recall of 97.26%, and F1-Score of 96.60%. The K-NN algorithm with an optimal K=5 attained an accuracy of 91.67%. Statistical analysis using a paired t-test (α=0.05) with p-value=0.012 confirmed that this performance difference was significant. It is concluded that Naïve Bayes is more effective for this CKD dataset, likely due to its robustness in handling feature independence assumptions and varied data scales. This model holds strong potential for development into an early-stage CKD screening tool to assist healthcare professionals.
Analisis Sentimen Pengguna X terhadap Kebijakan PPN 12% Menggunakan Naive Bayes Alwi Andika Panggabean; Diah Putri Kartikasari; Rafif Risdi Aulia; Tiara Ayu Triarta Tambak; Siti Fadiyah Nabila; Mhd Furqan
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1002

Abstract

Kebijakan kenaikan Pajak Pertambahan Nilai (PPN) dari 11% menjadi 12% yang direncanakan berlaku pada tahun 2025 telah menimbulkan berbagai reaksi publik, terutama di media sosial. Penelitian ini bertujuan untuk menganalisis sentimen pengguna media sosial X (sebelumnya Twitter) terhadap kebijakan tersebut menggunakan metode Naive Bayes yang diimplementasikan dalam bahasa pemrograman R. Data diperoleh dari tweet yang relevan dengan topik PPN 12%, kemudian diproses melalui tahapan pra-pemrosesan dan pelabelan manual. Hasil analisis menunjukkan bahwa sentimen negatif mendominasi dengan proporsi 39%, diikuti sentimen netral 32%, dan sentimen positif 29%. Evaluasi performa model Naive Bayes menunjukkan akurasi sebesar 50%, dengan ketepatan klasifikasi tertinggi pada kategori negatif. Analisis lebih lanjut terhadap istilah kunci dan topik diskusi mengungkapkan bahwa kekhawatiran terhadap beban ekonomi dan dampak terhadap UMKM menjadi sumber utama sentimen negatif, sementara sentimen positif dikaitkan dengan harapan terhadap perbaikan layanan publik dan pembangunan. Penelitian ini memberikan wawasan penting bagi pembuat kebijakan untuk memahami persepsi publik terhadap kebijakan fiskal secara lebih mendalam dan berbasis data.
Analisis Data Biologis dalam Mengidentifikasi Gen atau Protein yang Memiliki Pola Ekspresi Serupa Muhammad Haikal Akmal; Dimas Pangestu; Dzilhulaifa Siregar; Khaila Mukti Harahap; Mhd. Furqan
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1008

Abstract

Ekspresi protein dalam data biologis umumnya memiliki kompleksitas tinggi dan dimensi besar, sehingga menyulitkan pengenalan pola secara langsung. Studi ini memanfaatkan algoritma Spectral Clustering untuk mengeksplorasi struktur tersembunyi dalam kumpulan data ekspresi protein. Langkah awal mencakup pembersihan data dengan imputasi nilai hilang menggunakan metode rata-rata serta normalisasi fitur numerik menggunakan StandardScaler. Dataset terdiri dari 1.080 observasi dan 77 atribut numerik hasil percobaan pada tikus. Proses pengelompokan dilakukan dengan pendekatan berbasis graf, menggunakan parameter empat klaster dan afinitas nearest neighbors. Selanjutnya, dilakukan reduksi dimensi melalui teknik Principal Component Analysis (PCA) untuk menghasilkan representasi dua dimensi yang mudah divisualisasikan. Hasil pengelompokan memperlihatkan pemisahan yang mencerminkan perbedaan biologis antar sampel. Hal ini menunjukkan bahwa metode tak terawasi seperti Spectral Clustering efektif dalam mengungkap struktur laten pada data ekspresi protein dan dapat menjadi dasar bagi analisis klasifikasi berbasis karakteristik biologis.
Perancangan Sistem Kontrol Pendingin Udara Otomatis Berbasis Suhu Ruangan Menggunakan Arduino Mhd Galih Khairi; Muhammad Irfan Gurning; Mhd. Furqan
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 1 (2024): Januari 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i1.96

Abstract

Air conditioning has become essential in the modern era, with nearly everyone worldwide owning an air conditioner in their homes. Its presence is crucial given the increasingly hot weather conditions due to global warming. However, we often forget to turn off the air conditioner, resulting in energy waste and potential damage to the device. Therefore, this writing aims to address these issues through the development of an automatic system using Arduino Nano, programmed in the C language, to regulate the operation of the air conditioner based on room temperature. This way, users do not need to bother manually turning the air conditioner on or off using a remote control or buttons.
Klasifikasi Algoritma K-Nearest Neighbor (KNN) untuk Pengenalan Angka Tulisan Tangan Berbasis Citra Digital Utami, Andita; Tanjung, Dewi Aulia; Ramadhana, Muhammad Fadil; Siregar, Farhan Sadli; Sir, Hapisfatly; Furqan, Mhd
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10366

Abstract

Abstrak - Klasifikasi angka tulisan tangan merupakan salah satu topik penting dalam analisis citra digital dan membutuhkan metode yang akurat serta efisien. Penelitian ini menerapkan algoritma K-Nearest Neighbor (KNN) untuk mengenali angka tulisan tangan pada dataset MNIST, yang dipadukan dengan teknik ekstraksi fitur zoning untuk memperoleh 16 fitur utama dari setiap citra. Beberapa nilai k diuji untuk menentukan konfigurasi yang paling optimal. Hasil pengujian menunjukkan bahwa nilai k = 7 memberikan akurasi terbaik sebesar 83.54%. Evaluasi menggunakan confusion matrix dan classification report mengonfirmasi bahwa model mampu mengklasifikasikan sebagian besar digit dengan baik, meskipun masih terdapat sebagian kecil kesalahan pada digit yang memiliki bentuk serupa. Temuan ini menunjukkan bahwa kombinasi metode zoning dan algoritma KNN tetap efektif digunakan untuk pengenalan angka tulisan tangan pada sistem yang membutuhkan komputasi ringan dan cepat.Kata kunci: Algoritma; Citra; K-Nearest Neighbors; MNIST; Abstract - Handwritten digit classification is an important topic in digital image analysis and requires accurate and efficient methods. This study applies the K-Nearest Neighbor (KNN) algorithm to recognize handwritten digits in the MNIST dataset, combined with the zoning feature extraction technique to obtain 16 main features from each image. Several values of k were tested to determine the most optimal configuration. The test results show that k = 7 provides the best accuracy of 83.54%. Evaluation using a confusion matrix and classification report confirms that the model is able to classify most digits well, although there are still some errors with digits that have similar shapes. These findings indicate that the combination of the zoning method and the KNN algorithm remains effective for handwritten digit recognition in systems that require light and fast computation.Keywords: Algorithm; Image; K-Nearest Neighbors; MNIST;
Co-Authors ., Zulpadli Abdul Aziz Abdul Halim Hasugian Adha, Rifki Mahsyaf Agpina, Pipi Ahmad Fakhri Ab. Nasir Ahmad Fauzi Aidil Halim Lubis Aisyah Nurrahmah Siregar Alwi Andika Panggabean 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 Badria, Lailatul Bagus Ageng Alfahri Basyir, Muhammad Khalidin Br Rambe, Indri Gusmita Budi Triandi, Budi Cahyadi, Bhagaskara Dalimunthe, Ayu Sahriani Daulay, Ikhsan Agus Martua Diah Putri Kartikasari Dimas Pangestu Dzilhulaifa Siregar Elce, Furkan Fadil, Ulfi Muzayyanah Fadillah, Rini Fadlan, Aulia Fahrul Azis Nasution Faiza, Nayla Fakhriza, M. fandi, Fandi Ahmad Farhan Amar Pramudya FIKRI HAIKAL Gunawan, Irwan Harahap, Raihan Rizieq Harahap, Rosa Linda Harahap, Tiara Bela Hartama, Dedy Hasibuan, Naina Nazwa Hasrul Hasibuan, Mhd Fikri Heri Santoso 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 juwita sari K Khairunnisa Katuk, Norliza Khaila Mukti Harahap Khairi, Nouval Khairunnisa Khairunnisa Khairunnisa, K Kurniawan, Riski Askia Lely Sahrani Lubis, Akbar Maulana M. Alfatoni Muarrip M. Fakhriza Mahendra, Rifandi Matondang, Toibatur Rahma Maulana Ihsan, Maulana Mey Hendra Putra Sirait Mhd Galih Khairi Mhd Ikhsan Rifki Mhd Reza Alfani Muhammad Akbar Ramadhan Tanjung Muhammad Farhan Muhammad Haikal Akmal Muhammad Ikhsan Muhammad Irfan Gurning Muhammad Luthfi Muhammad Naufal Shidqi Muhammad Ridzki Hasibuan Muhammad Rizki Munadi Munadi Nabawy, Putri Nasution, Afri Yunda Nasution, Irma Yunita Nasution, Romaito Nasution, Zulia Lestari Ningsih, Siti Alus Novrianty, Amanda Nugroho, Agung Nur Bainatun Nisa Nurhasanah Nurhasanah Nurhidayati Nurhidayati Nurul Hadi Muliani Hariadi Saputra Nurzannah, Laila Pane, Putri Pratiwi Pratama, Haris Prayoga Elfanda Fachmi Hasibuan Prayogi, Ahmad Pulungan, Miftahul Rizky Putra, Suan Ekie Nanda Putri, Alma Irawanti Radhifan Mardhi Rafif Risdi Aulia Rahma, Intan Dwi Raissa Amanda Putri Rakhmat Kurniawan R Ramadani, Wily Supi Ramadhan Nasution, Yusuf Ramadhana, Muhammad Fadil Ramadhani, Fredy Kusuma Razzaq H. Nur Wijaya Reza Muhammad Rifnandy, Muhammad Fauzan Rivaldi Prima Nanda Rizka Rizki Ananda Rizki Siregar, Awal Rizqi Hidayat Tanjung RR. Ella Evrita Hestiandari Saparuddin Siregar Saputri Nasution, Intan Widya Sembiring, Yogasurya Pranantha Shafa, Dafa Ikhwanu Sigit Muslim Anggoro Pratono Sinaga, Meri Sir, Hapisfatly Siregar, Farhan Sadli Siregar, Hervilla Amanda R. Siregar, Kalfida Eka Wati Sitepu, Anggi Jelita Siti Fadiyah Nabila 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 Tanjung, Dewi Aulia Tanjung, Tegar Haryahya Tiara Ayu Triarta Tambak Tria Elisa Utami, Andita Wahyudin, Rahmat Wan Fadilla Rischa Wati, Putri Kurni Wicaksana, Agum Widiya Yuli Kartika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zabni, Nur Hera Zahra Humaira Kudadiri Ziqra Addilah Zulnun, M. Ridho Azmuddin