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Raissa Amanda Putri

Universitas Islam Negeri Sumatera Utara

Author-ID : 2532773

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Analisis Sentimen Terhadap Isu Kandungan Produk PinkFlash Menggunakan Algoritma Support Vector Machine Harahap, Nurhaliza; Raissa Amanda Putri
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 03 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i03.1624

Tujuan penulisan studi ini yaitu untuk menerapkan algoritma support vector machine terhadap pelanggan Eyeshadow pallete pada produk pink flash serta menghitung kurasi konsumen eyeshadow pallete pink flash. Data yang peneliti pakai didapati dari sumber-sumber seperti skripsi, buku, jurnal, literatur sejenis serta website yang berkaitan dengan teori yang dikaji. Studi ini menggunakan sebanyak 500 data ulasan pelanggan yang dikumpulkan dari toko Pinkflash di platform e-commerce Tokopedia. Data tersebut dipilih karena dianggap mewakili opini konsumen secara nyata terhadap produk yang ditawarkan. Ulasan-ulasan ini mencakup berbagai pengalaman pengguna, mulai dari kepuasan terhadap kualitas produk hingga keluhan terkait pengemasan maupun pengiriman. Hasilnya menampilkan jika model SVM bisa menguraikan ulasan pelanggan dengan keakurasian yang tinggi. sehingga memberikan indikasi bahwa metode ini efektif dalam menganalisis sentimen kepuasan konsumen terhadap isu kandungan produk pinkflash. Selain itu, hasil akurasi tinggi ini juga menunjukkan seberapa baik ia dapat membedakan antara sentimen positif dan negatif, yang dapat membantu perusahaan memahami pendapat pelanggan lebih dalam dan membuat strategi pemasaran yang lebih terarah.

Implementasi K-Means Clustering Pada Pemilihan Santri dan Santriwati Berprestasi Nasution, Maimanah Salsabila; Putri, Raissa Amanda
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i2.3150

Managing large amounts of data on male and female students poses a challenge for Pesantren Darul Arafah Raya in assessing the academic performance of its students. The K-Means algorithm is one of the most widely used methods in data mining for clustering large amounts of data. This study aims to cluster students into two categories: high-performing and low-performing, using the K-Means Clustering method. The variables analyzed include academic grades, attendance, attitude, involvement in organizations/extracurricular activities, and academic history. Data from 1,010 students were processed, and the algorithm stopped at the 35th iteration with significant centroid centers. The clustering results showed that 490 students were classified as high-achieving and 520 as low-achieving. Performance validation using a Silhouette Coefficient of 0.63 indicates that the clustering quality is in the good category, so the grouping results can be considered optimal. Integrating K-Means into a web-based information system facilitates strategic decision-making for the boarding school and supports the development of specialized training programs to cultivate students' potential in a more targeted manner.Keywords: Data Mining; K-Means Clustering; Outstanding Santri; Education; Website AbstrakPengelolaan data santri/wati dalam jumlah besar menjadi tantangan bagi Pesantren Darul Arafah Raya dalam menilai prestasi akademik santri. Algoritma K-Means merupakan salah satu metode dalam data mining yang banyak digunakan untuk mengelompokkan data dalam jumlah besar melalui pendekatan clustering. Penelitian ini bertujuan untuk mengelompokkan santri dan santriwati ke dalam dua kategori, yaitu berprestasi dan tidak berprestasi, dengan memanfaatkan metode K-Means Clustering. Variabel yang dianalisis meliputi nilai akademik, absensi, sikap, keterlibatan berorganisasi/ekstrakurikuler, dan riwayat prestasi. Data sebanyak 1.010 santri dan santriwati diproses, dan algoritma berhenti pada iterasi ke-35 dengan pusat centroid yang signifikan. Hasil klasterisasi menunjukkan 490 santri dan santriwati tergolong berprestasi dan 520 lainnya tidak berprestasi. Validasi performa menggunakan Silhouette Coefficient sebesar 0,63 menunjukkan kualitas klasterisasi berada pada kategori baik, sehingga hasil pengelompokan dapat dianggap optimal. Integrasi K-Means ke dalam sistem informasi berbasis web memudahkan pihak pesantren dalam pengambilan keputusan strategis, serta mendukung penyusunan program pembinaan khusus untuk mengembangkan potensi santri secara lebih terarah.

Bitcoin Price Forecasting Using Random Forest and On‑Chain Data Samsudin, Samsudin; Irawan, Muhammad Dedi; Nasution, Muhammad Irwan Padli; Putri, Raissa Amanda
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46690

Bitcoin’s extreme price volatility has long posed challenges for both investors and researchers seeking reliable forecasting models. Conventional financial approaches often fail to capture the highly complex, nonlinear, and fast-moving nature of cryptocurrency markets. To address this gap, this study develops a Bitcoin price prediction model using Random Forest Regression based on on-chain market data. The dataset was obtained from publicly available historical Bitcoin daily trading records spanning more than five years. Key features include opening price, daily high and low ranges, trading volume, and percentage change. The research was carried out in several stages. First, data preprocessing was conducted through normalization, handling of missing values, and feature engineering. Second, model training was performed with Random Forest, including parameter tuning to optimize predictive accuracy. Third, model evaluation employed R² and Mean Absolute Percentage Error (MAPE) as primary performance indicators. Fourth, visualization was implemented using interactive charts to allow users to observe short-term price fluctuations and long-term market patterns. The system development followed an iterative methodology inspired by the Streamlit Framework, which is an open-source Python library that simplifies building interactive web applications for data science and machine learning. This approach provides flexibility, enabling rapid experimentation and adaptation to evolving market conditions. The results show that the proposed model achieves near-perfect R² values (approaching 1.0) with consistently low MAPE, highlighting its reliability. Beyond predictive performance, the framework is designed to be scalable, supporting future integration with deep learning methods such as LSTM and external macroeconomic indicators, thus offering both practical utility for investors and academic contributions to decentralized finance research.

SISTEM INFORMASI MANAJEMEN PERSEDIAAN PADA CV AZRAEL GRAFIKA DENGAN METODE AVERAGE Rambe, Risti; Putri, Raissa Amanda; Nasution, Adnan Buyung
Jurnal Sistem Informasi Bisnis (JUNSIBI) Vol. 6 No. 1 (2025): Jurnal Sistem Informasi Bisnis (JUNSIBI)
Publisher : Program Studi Sistem Informasi Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55122/junsibi.v6i1.1562

Saat ini, di era digital, setiap orang mempunyai akses mudah terhadap berbagai informasi, dimana pun dan kapan pun. Teknologi sistem informasi membantu dalam memecahkan berbagai masalah, seperti pengelolaan data, administrasi, keuangan, pengendalian inventaris, dan lain-lain. Sistem informasi manajemen adalah perangkat dalam suatu organisasi yang dimaksudkan untuk menangani transaksi sehari-hari, mendukung operasi, tugas administratif, dan perencanaan strategis. Selama ini, persediaan stok mengacu pada daftar barang yang disimpan untuk mengantisipasi penjualan di masa depan. Persediaan ini diklasifikasikan menjadi dua kategori, stok awal dan stok akhir. Dalam menyediakan persediaan ini dibutuhkan manajemen agar barang dapat dikelola dengan mudah. Saat ini CV Azrael Grafika melakukan pencatatan barang terjual yang ditulis di buku, dan barang yang stoknya kosong dapat dilihat ketika ada pelanggan yang memesan barang tersebut dan akan dikelola di bagian administrasi. Untuk mengatasi masalah yang terjadi Oleh karena itu diperlukan suatu sistem informasi untuk mengelola data komoditas agar persediaan barang dapat dikelola dengan lebih baik. Sistem ini dirancang dengan menggunakan metode average yang bertujuan untuk mencatat, memantau dan mengolah data persediaan. Hasil pengolahan data nantinya dapat disajikan dalam bentuk laporan. Penelitian ini menghasilkan sistem informasi manajemen persediaan barang berbasis website yang diterapkan pada CV Azrael Grafika.

Application of Dempster-Shafer Theory Method in Expert System for Diagnosis of Psychological Disorders in Children Azhari, M. Faishal; Putri, Raissa Amanda
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3764

The development of technology at this time has changed very rapidly in recent decades. Expert system is one of the sub-sections of artificial intelligence that aims to process and display the results of the conclusion of the previous process based on the knowledge that has been obtained. The purpose of this research is to apply the Dempster-Shafer method to the expert system for diagnosing psychological disorders in children and to design and build an expert system using the Dempster-Shafer method on a website-based expert system for diagnosing psychological disorders in children. Based on the results and discussion in this study, it can be concluded that the Dempster Shafer Method can be applied properly in an expert system for diagnosing psychological disorders in children. The Dempster Shafer method in this system gets results that are accurate enough so that it can generate a diagnosis based on symptoms accompanied by a handling solution. This expert system application for diagnosing psychological disorders in children can diagnose and determine the diagnosis results from consultations conducted by users using the Dempster Shafer method. Where in the example case, the percentage result is around 87.15%. This system can also help parents to get information about children's psychological disorders, symptoms, and solutions that can be applied.

Web-Based Wind Speed Forecasting System Using Prophet Siregar, Elvan Dito; Putri, Raissa Amanda
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1240

The research undertaken has the central purpose of creating as well as applying a digital platform accessible through the internet, which is structured specifically to anticipate variations in wind velocity by employing the Prophet algorithm as the analytical framework. The system addresses the need for accurate and accessible forecasting tools in Medan, where highly variable wind patterns affect transportation, agriculture, and disaster mitigation. The research methodology consists of several stages including data collection from BMKG Medan, preprocessing through cleaning and aggregation of daily measurements into monthly averages, forecasting using the Prophet model, system development, and evaluation. Prophet was selected due to its ability to capture trend and seasonal components effectively with minimal parameter tuning. The system was developed using Laravel, MySQL, and Chart.js, integrating Prophet through Python to generate interactive visualizations and downloadable reports. The effectiveness of the predictive framework was measured by means of the Root Mean Square Error (RMSE = 0.19) and Mean Absolute Error (MAE = 0.15), validating the suitability of the method for producing consistent monthly forecasts of wind velocity. The system provides stakeholders such as disaster management agencies, marine operators, and agricultural planners with a practical platform for accessing accurate and timely forecasts. The findings further demonstrate the novelty of integrating Prophet forecasting with a web-based information system equipped with visualization and reporting features, thereby enhancing usability, accessibility, and decision-making support for regional meteorological applications.

SISTEM INFORMASI PENENTUAN KELAYAKAN PENERIMA ALAT BANTU FISIK BAGI PENYANDANG DISABILITAS DENGAN ALGORITMA PSI Irvan, Muhammad; Putri, Raissa Amanda; Harahap, Aninda Muliani
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 8 No 1 (2024)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v8i1.4187

Dinas Sosial Kota Medan bertanggung jawab untuk meningkatkan kesejahteraan sosial dan memberikan perlindungan serta pelayanan kepada individu yang membutuhkan, termasuk melalui seksi rehabilitasi sosial penyandang disabilitas yang menangani bantuan seperti kursi roda, alat bantu dengar, tangan palsu, kaki palsu, dan tongkat. Instansi menyadari kendala dalam proses pengajuan alat bantu fisik terkait akses informasi persyaratan, proses manual yang lambat, kesulitan menilai kelayakan penerima manfaat secara efektif, dan risiko kecurangan. Penelitian ini bertujuan merancang dan membangun platform berbasis Website untuk pengajuan alat bantu fisik, dengan menerapkan algoritma Preference Selection Index (PSI) dalam menilai kelayakan penerima alat bantu fisik bagi penyandang disabilitas. Metode penelitian bersifat kualitatif, fokus pada pemahaman mendalam terhadap proses pengajuan alat bantu fisik serta evaluasi kelayakan menggunakan algoritma PSI. Implementasi sistem informasi ini diharapkan memberikan kemudahan bagi pegawai Dinas Sosial dan masyarakat, khususnya penyandang disabilitas, dalam mengakses dan mengajukan alat bantu fisik. Hasil penelitian berhasil mengembangkan Sistem Informasi Penentuan Kelayakan Penerima Alat Bantu Fisik bagi Penyandang Disabilitas dengan Algoritma PSI, dengan dampak positif dalam meningkatkan efisiensi proses pengajuan dan penilaian kelayakan, diharapkan memberikan dampak positif terhadap pelayanan sosial di Dinas Sosial Kota Medan.

Penerapan Metode Logika Fuzzy Sugeno Pada Prediksi Stok Bahan Baku Kulit Pie Yudha, Fajrul Aulia; Raissa Amanda Putri
Tech-E Vol. 8 No. 1 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i1.3193

Accurate prediction of raw material stocks is essential for cost management and effective production planning in the food industry. The Sugeno fuzzy logic method is employed to predict the stock levels of pie leather raw materials. This method aims to offer a reliable prediction system that enhances stock management, thereby minimizing the risks associated with overstocking or stock shortages. The performance of the model is evaluated using the average error percentage test, which yielded a result of 3.94%. This indicates an accuracy level of 96.06%, demonstrating a high degree of precision. The findings suggest that the Sugeno fuzzy logic method is a highly effective tool for predicting raw material requirements in the pie leather production process. The study underscores the potential of fuzzy logic methods in supply management, ensuring smooth production operations. By implementing this method, manufacturers can achieve better inventory control, leading to more efficient production planning and cost savings. The results validate the application of Sugeno fuzzy logic as a robust approach for inventory prediction, capable of significantly improving the overall management of raw material stocks in the food industry. This research highlights the practical benefits of advanced predictive models in optimizing supply chains, supporting continuous production flow, and enhancing the overall efficiency of production systems. Consequently, the use of fuzzy logic methods can play a critical role in streamlining production processes and maintaining optimal inventory levels, ultimately contributing to the success and sustainability of food manufacturing operations.

SISTEM INFORMASI MONITORING INDUSTRI KECIL MENENGAH MENGGUNAKAN ALGORITMA K-MEANS PADA DINAS PERINDUSTRIAN PERDAGANGAN PROVSU BERBASIS WEBSITE Aulia, Dea Liza; Putri, Raissa Amanda
ZONAsi: Jurnal Sistem Informasi Vol. 5 No. 3 (2023): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2023
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v5i3.15553

Peran industri kecil menengah (IKM) sangat penting dalam perekonomian Indonesia. Namun, dalam pengembangannya, IKM sering menghadapi berbagai tantangan, seperti masalah pendataan dan pengawasan produksi yang belum optimal. Dalam rangka meningkatkan pengawasan dan pengembangan industri kecil menengah di wilayah tersebut, Dinas Perindustrian dan Perdagangan Provinsi Sumatera Utara perlu mengembangkan sebuah sistem informasi monitoring yang efektif. Penelitian ini bertujuan untuk merancang sistem informasi monitoring industri kecil menengah dengan menggunakan algoritma k-means pada Dinas Perindustrian dan Perdagangan Provinsi Sumatera Utara berbasis website. Melalui sistem informasi monitoring berbasis website ini, diharapkan dapat terjadi peningkatan dalam pengumpulan data yang akurat, pemrosesan data yang efisien, serta pengambilan keputusan yang lebih tepat dalam pengembangan industri kecil menengah. Algoritma K-Means digunakan untuk mengelompokkan industri kecil menengah berdasarkan klasifikasi dan atribut tertentu. Metode penelitian yang diterapkan dalam penelitian ini adalah Research and Development (R&D), dengan metode pengumpulan data melalui wawancara, observasi, dan studi pustaka. Selain itu, metode pengembangan sistem yang digunakan adalah SDLC (Software Development Life Cycle) model Waterfall. Tahapan pengembangan sistem meliputi analisis, desain, pengkodingan, dan pengujian. Framework Laravel digunakan untuk tampilan interface website sistem informasi monitoring. PHP serta database MySQL digunakan untuk pengkodingan. Pengujian menggunakan black box dan menggunakan metode System Usability Scale (SUS).

JARINGAN SYARAF TIRUAN PREDIKSI HARGA PASAR TEH MENGGUNAKAN METODE BACKPROPAGATION Sari, Dinda Mayang; Ikhsan, Muhammad; Putri, Raissa Amanda
Jurnal Sistem Informasi Bisnis (JUNSIBI) Vol. 5 No. 1 (2024): Jurnal Sistem Informasi Bisnis (JUNSIBI)
Publisher : Program Studi Sistem Informasi Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55122/junsibi.v5i1.1221

Teh, minuman yang berasal dari daun Camellia Sinensis Asia Timur, memiliki beragam cita rasa, kandungan nutrisi, dan manfaat kesehatan, termasuk menurunkan risiko penyakit jantung, kanker, dan diabetes, serta meningkatkan kinerja fisik dan mental. Peranannya dalam ekonomi nasional sangat signifikan, mencakup kontribusi terhadap pendapatan nasional, lapangan kerja, ekspor, dan penerimaan pajak. Namun, pasar teh sering kali tidak stabil karena dipengaruhi oleh tingkat permintaan pembeli yang cenderung menurun, merugikan perusahaan. Untuk mengatasi masalah ini, diterapkan Jaringan Saraf Tiruan untuk memprediksi harga pasar teh berdasarkan data historis harga dari tahun 2018 hingga 2022. Hasil penelitian menunjukkan bahwa arsitektur terbaik adalah 16-5-16, dengan akurasi mencapai 99% dan MSE training sebesar 1.1187. Ini menegaskan bahwa metode Backpropagation efektif dalam prediksi harga pasar teh. Harapannya, penelitian ini dapat memberikan panduan kepada PTPN IV Unit Teh untuk mengoptimalkan keuntungan dan mengurangi kerugian penjualan teh di masa depan.

Co-Authors Abdillah, Muhammad Oemar Abdul Aziz Khusen Abdul Halim Hasugian ABdul Karim Batubara, ABdul Karim Abu Dardaq Putra Adlani, Farid Adnan Buyung Nasution Afifah Balqis Pasaribu Agil Indriyani Agung Setiawan Hasibuan Agung Wijaya, Agung Agusni Firi Hasian Dalimunthe Aini Sachira, Rheana aji wardana Alasi, Galih Aldy Alfiansyah Aldyansah Arrahman Rianto Ali Ikhwan Ali Ikhwan Ali Ikhwan Alsyah Harahap, Dymas Fatthur Rohim Alwi Perdana Aritonang Amanda, Retno Tri Ananda, Bella Andini Nur Bahri Andini, Novita Rizky Andriani, Mega Andriyani Dwi Astuti Anggi Dessisiliya Anggreini Anggun Monica Dewi Aninda Muliani Anisa Yasmin Annisa Shafira Zuhri Apipah, Nur Aprilsyah, Muhammad Arbi, Haris Andika Armansyah Armansyah Aryo Pratama Asti, Dini Audy Andini Lubis Aulia Kartika Dewi Aulia, Dea Liza Aulia, Diva Azhari, M. Faishal Azzahro Simatupang , Siti Fatimah Bagus Setiawan Baridah, Lailam Batara Wardana Yuswar Batubara, Abdul Karim Batubara, Muhammad Zulpan Bela Damanik Bella Ananda Budi Askhori Sirait Budiarti, Dinda Dalimunthe, Rizna Fitriana Dalimunthe, Roma Gabe Damayanti, Alvina Daulay, Ikhsan Agus Martua Daulay, Wan Akbar Arramadhan Decfina, Fauziah Delvira Salsabila Diah Indah Sari Dicky Pratama Dinary Dwihatami Dini Anggraini Disa Pratama Diva Aulia Donas Putra Dwi Himala Kasih Dwi Nenda Putri Dwi Silviana Elang, Nusa Erano, Bhirawa Atha Bassni Erlina, Fazira Fadhlan Hussaini Srg Fadilah, Ayu Fadilah, Ulfa Fadillah, Muhammad Taufik Fahimah, Nurul Fakhri Alauddin Tarihoran Fakhriza, Fakhriza Fakhriza, M. Fara Difa Aulya Farahdiba, Dhika Farhan Rizky Wahyudi Fathiya Hasyifah S FATHIYA HASYIFAH SIBARANI Fathiyah Hasyifah Sibarani Fauziah Lubis Fazril, Fazril Febiyaula, Siti Septia Feby Hasanah Ritonga Fiddarain, Syaidah Fikri Hakiki Siregar Fiqri Fakhrizal Fitrah Al Mubaroq Fitria Tilawatil Aulia Simarmata Garnish Ayu Andini Wijaya Gina Sonia Hadi, Firman Harahap, Ahdi Alfein Harahap, Faisal Harahap, Nurhaliza Hary Isdianto Hasibuan, Muhammad Imbalo Zaki Hasibuan, Novrisyah Hasibuan, Nurhabibah Febrianty Hasyifah Sibarani, Fathiya Heri Santoso Heri Santoso Heri Santoso Hidayat, Julkarnain Hidayati, Lily Hutasuhut, Fazira Syafitri Ibnu Faisal Ikhlasul Amal Ilka Zufria Imam Adlin Sinaga Imam Adlin Sinaga Imam Zarkasih Harahap Irsandi, Mahmul Izma Khoiruna Jihan Fadhilah Taher Khairi, Ananda Salsabila Khotnai Shinta Khotnai Shinta Koto, Muhammad Hendrik Laily, Dwi Yanti Laylan Syafina Lestari, Rika Dinda Liza Khairani Lubis, Fitra Hidayat M irsyan antony manday, Irsyanmanday M Taufiq Rachman Siregar M. Fakhriza malika, Sela Maulana, Mhd.Rizki Maulina Tria Audina Gultom Maurico Liang Maya Khairani Mega Andriani Mega Andriani Mhd Furqan Miftah Siregar Mikraj, Ziyad Habibul Mohammad Badri Mohammad Badri Mu'arif, Risdani Mubaraq, Aras Maulana Much Nur Syams Simaja Muhamad Alda Muhamad Rizky Abdilah Muhammad Abdul Arip Muhammad Aprilsyah Muhammad Dedi Irawan Muhammad Ikhsan Muhammad Irvan Muhammad Naufal Al Hazmi Muhammad Ray Pratama Sembiring Muhammad Rivaldi Muhammad Setiawan Muliani Harahap, Aninda Muliani, Aninda Mulya Alfan Simatupang Nabila Bidawi, Hilwa Faza Nabila, Andini Nabilah Aliya Tasya Nadiyah Khairiyah Nafis, Ayu Najwa Nabila Nasution, Adnan Buyung Nasution, Imam Fadli Nasution, Maimanah Salsabila Nasution, Muhammad Irwan Padli Nasution, Rizki Ansyari Nataryda Lubis, Muara Novrisyah Hasibuan Nst, Achmad Ramadhan Nugroho, Agung Nurhabibah Febrianty Hasibuan Nurhasanah, Dhea Aulia Nurhayati Nurhayati Nurul Ifkah Lolona Silalahi Nurul Mawaddah Padang Nurul Zuriandini Paranindra Ardhana Biroe Aurori Pasaribu, Haryati Pertiwi, Elsa Pratama, Dino Farid Prayuda, Wahyu Putra Purwaningtyas, Franindya Puspa, Yulia Putra, Dafa Fahreza Putrawan, Putrawan Putri Agustina Putri Lubis, Dina Amalia Rafli Khalis Nugraha Rahma Yuni Rahma, Anisa Sri Rahma, Baqiyatur Rahma, Najahaura Rambe, Risti Reni Lestari Reni Yunita Riko Aldinata Rinaldy, Fahdly Ritonga, Siti Marlina RR. Ella Evrita Hestiandari Sabri, Muhammad Sahbandi Sahbandi Sahnas Wulandari Sahnas Wulandari Siregar Sait, M Ibnu Salsabila, Salsabila Salsalina Br Sembiring Samsudin Samsudin, Samsudin Santoso, Adinda Afriliya Saprida Saprida Saprida, Saprida Saputri, Indah Sardiyana Br Karo Sari, Dinda Mayang Shinta Permata Sari Siagian, Andika Fadillah Sigit Bahuraksa Silvia Kartika Sinaga, Adhe Syari Alfatah Sinaga, Imam Adlin Sinta Dewi Siregar, Ela Khairani Siregar, Elvan Dito Siregar, Fairuz Azzaria Siregar, Putri Aprilia Siswahyudianto Siti Kania Thisara Siti Zahra Situmeang, Risky Akbar Sri Yuslina Siregar Sriani Sriani Sriani Sriani Suendri Suendri, Suendri Suendri, Suendri Syah Zanul Husna Syahputra, Adam Tania Yulindra Teuku Alif Baihaqy Thamrin, Alwi Aryusya Triase Triase Triase Triase Triase Triase Triase Triase Triase Triase, Triase Tua, Anri Hafiz Ulfayani Mayasari Ummi Afzah Amirah Wahyu Herlambang Wanda Sari Wanda Wardana, Aji Wardhani, Ade Ratu Wibowo, Muhammad Rizky Widodo, Mhd. Aria Agung Willy Andri Malau Windi Maharani Winny Wiyandari Wiradito, Ade Yahfizham Yahfizham Yardha, La Saufa Yudha, Fajrul Aulia Yudi Lizardi Mahna Siregar Yulianda Tasya Yusniah Yusniah Yusra, Salsabila Yustria Handika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zahidah, RA. Ghina Zahrowaini, Taqiya

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