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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 Neuroscience Physics Public Health Social Sciences Other

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Sistem Informasi Keuangan dengan Prediksi Pendapatan Menggunakan Regresi Linier Malika, Sela; Putri, Raissa Amanda
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10037

Financial management and forecasting are critical aspects in supporting decision-making within an organization, particularly amid the increasing demand for fast and accurate data analysis. In general, many companies in Indonesia still face challenges in utilizing historical financial data to optimally predict revenue. This issue is also encountered by a company that continues to rely on manual record-keeping using spreadsheet-based systems, which makes it difficult to conduct analysis and forecast future financial conditions. This study aims to implement a linear regression method to predict revenue based on historical financial transaction data. The methodology employed follows the CRISP-ML(Q) framework, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The modeling process is carried out by developing a linear regression model using independent and dependent variables. The results indicate that the constructed linear regression model is capable of generating revenue predictions with a relatively low error rate, thereby effectively representing patterns within the historical data. Model evaluation using error metrics demonstrates that the model performs adequately within the context of the dataset used. In conclusion, the linear regression method is effective for revenue prediction and can support data-driven decision-making processes. Future research is recommended to enhance the model by incorporating more complex variables and applying alternative prediction methods to improve accuracy.

Prediksi Hasil Panen Karet di Gunung Tua Menggunakan Support Vector Machine Siregar, Siti Khairunnisa; Putri, Raissa Amanda; Furqan, Muhammad
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10040

Penelitian ini bertujuan untuk memprediksi hasil panen karet di wilayah Gunung Tua, Kabupaten Padang Lawas Utara, dengan menggunakan algoritma Support Vector Machine (SVM). Produksi karet dipengaruhi oleh berbagai faktor musiman dan kondisi lingkungan yang menyebabkan fluktuasi hasil panen, sehingga menyulitkan perencanaan bagi petani maupun instansi terkait. Penelitian ini menerapkan pendekatan supervised learning dengan metode Support Vector Regression (SVR) untuk memodelkan prediksi hasil panen karet berdasarkan data produksi historis yang diperoleh dari instansi pertanian setempat. Tahapan penelitian meliputi pengumpulan data, prapemrosesan, normalisasi data, pelatihan model, dan pengujian. Evaluasi kinerja model dilakukan menggunakan Root Mean Square Error (RMSE) sebagai indikator tingkat kesalahan prediksi. Hasil penelitian menunjukkan bahwa model SVM mampu memprediksi hasil panen karet dengan nilai RMSE sebesar 191 dan tingkat akurasi sebesar 96,2%, yang menunjukkan bahwa model memiliki performa yang baik dalam menangkap pola data produksi. Dengan demikian, algoritma Support Vector Machine dapat dimanfaatkan sebagai alat pendukung pengambilan keputusan dalam perencanaan dan pengelolaan produksi pertanian karet

Analisis Emosi Komentar Pengguna TikTok terhadap Film Jumbo Menggunakan Metode Naive Bayes Panggabean, Trisatin; Putri, Raissa Amanda
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10036

TikTok has become a widely used social media platform where users actively express opinions through comment features. This study aims to classify the emotions contained in TikTok user comments on the Indonesian animated film Jumbo using the Naive Bayes Classifier method. The dataset consisted of 1,341 comments collected from the official Visinema Pictures account using the Apify Web Scraper. The collected data were processed through several preprocessing stages, including case folding, tokenization, normalization, stopword removal, and stemming using the Sastrawi library. Emotion labeling was performed based on the Indonesian NRC EmoLex lexicon by categorizing comments into three emotional classes: angry, happy, and sad. Feature extraction was conducted using the TF-IDF weighting method to generate relevant text representations and identify dominant terms in each emotional category. The dataset was divided into 80% training data and 20% testing data to evaluate the model performance. The experimental results show that the Naive Bayes model achieved an accuracy of 78.81%. The emotion distribution indicates that anger was the most dominant class with 904 comments, followed by happy with 415 comments, and sad with 22 comments. The model demonstrated the best performance in the anger class, achieving 100% recall, 75% precision, and an F1-score of 85.71%. However, the classification performance for minority classes, particularly happy and sad, still requires improvement. This research contributes to the development of text mining-based emotion analysis and provides insights into audience emotional responses that may support film evaluation and marketing strategies.

Transformasi Ekonomi Pertanian Lokal Melalui Pendekatan Ekonomi Islam di Kutalimbaru Pasar X Muhammad Abdul Arip; Dicky Pratama; Raissa Amanda Putri
Jurnal Ilmiah Manajemen dan Kewirausahaan Vol. 5 No. 2 (2026): Mei: Jurnal Ilmiah Manajemen dan Kewirausahaan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jimak.v5i2.6830

The transformation of the local agricultural economy in Kutalimbaru Pasar X faces challenges due to the low added value of banana products, caused by a simple distribution pattern. Farmers sell their harvest in its raw form to middlemen without any processing or collective management. As a result, the bargaining power of farmers is weak, and profit distribution is uneven. This study aims to analyze the implementation of the Islamic economic approach in the transformation of the local agricultural economy. The method used is descriptive qualitative, involving field observations, interviews with farmers, and literature review. The research findings show that strengthening farmer institutions, implementing transparent pricing mechanisms, and post-harvest processing can increase the added value of products and improve profit distribution. The application of Islamic economic principles, such as justice, cooperation, and accountability, can establish a fairer and more sustainable village economic system. This economic transformation also impacts the increase in farmers' income and strengthens the economic resilience of the community-based society. By integrating Islamic economic principles, it is hoped that the local agricultural sector can develop more productively and provide equitable benefits for all parties involved in the production and distribution processes.

Klasifikasi Stance Opini Publik Komentar TikTok Kasus Guru Menampar Murid memakai TF-IDF dan SVM Yusra, Salsabila; Putri, Raissa Amanda
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9544

Media sosial TikTok menjadi salah satu platform utama dalam menyampaikan opini publik terhadap isu sosial yang bersifat viral, termasuk kasus guru yang menampar murid karena merokok di sekolah. Kasus ini memunculkan perbedaan pandangan yang tajam di masyarakat antara pihak yang mendukung tindakan guru sebagai bentuk pendisiplinan dan pihak yang menilai tindakan tersebut sebagai bentuk kekerasan dalam pendidikan. Penelitian ini bertujuan untuk menganalisis sentimen publik di TikTok terhadap kasus tersebut menggunakan metode Support Vector Machine (SVM). Data penelitian berupa 2.200 komentar publik dikumpulkan melalui teknik web scraping menggunakan Apify Web Scraper dan diolah menggunakan Google Colab. Tahapan penelitian meliputi preprocessing teks yang terdiri dari cleaning, case folding, tokenization, normalisasi, stopword removal, dan stemming. Selanjutnya, fitur diekstraksi menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF) dan diklasifikasikan ke dalam empat kategori sentimen, yaitu Mendukung Guru, Menyalahkan Guru, Mendukung Murid, dan Menyalahkan Murid. Hasil penelitian menunjukkan bahwa metode SVM dengan kernel linear berhasil mengklasifikasikan sentimen publik dengan akurasi 94% (precision 0.94, recall 0.94, F1-score 0.94). Distribusi sentimen menunjukkan 69.2% komentar mendukung guru, 20.8% mendukung murid, 5.5% menyalahkan guru, dan 4.5% menyalahkan murid. Hasil ini memberikan gambaran komprehensif bahwa mayoritas masyarakat cenderung membenarkan tindakan disiplin guru, meskipun terdapat kesadaran yang berkembang terhadap perlindungan anak dan penolakan kekerasan dalam dunia pendidikan.

Evaluasi Smile dalam Pengelolaan Data dan Informasi : ( Studi Kasus BPJS Ketenagakerjaan Cab.Tanjung Morawa ) Nurul Ifkah Lolona Silalahi; Raissa Amanda Putri
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 3 (2024): Agustus : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i3.3794

Evaluation of SMILE in Data and Information Management (Case Study of BPJS Employment, Tanjung Morawa Branch) Using the User Compatibility Method. Effective data and information management in financial institutions such as BPJS Employment requires an application system that meets user needs. This research aims to evaluate SMILE at the Tanjung Morawa BPJS Employment Branch using the User Compatibility method, which assesses the extent to which the application meets the user's needs, abilities and expectations. This research method involves a survey of SMILE users at BPJS Employment Tanjung Morawa Branch, taking into account aspects of usability, efficiency and user satisfaction. Data was collected through questionnaires, interviews and direct observation of smile users. The evaluation results show that SMILE at BPJS Employment Tanjung Morawa Branch has a high level of compatibility with users. The research results show that all EUCS independent variables (indicators), consisting of Content (Content), Format (report), Accurancy (accuracy), Timeliness (speed of presentation), Ease of Use (easy to use), which have a significant relationship with user satisfaction SMILE. Users find this application easy to use, improves work efficiency, and meets their expectations regarding data and information management. However, several recommendations for improvement were also put forward, including increased training for users, improved integration with other systems, and improved data security. This study makes an important contribution to further understanding the extent to which SMILE meets user needs in internal environments. The evaluation results and recommendations produced can serve as guidelines for developing and improving smile in the future.

EDUKASI PENGGUNAAN MEDIA SOSIAL UNTUK MENGANTISIPASI IBU-IBU TERHADAP BERITA HOAX DI DESA AEK LOBA AFD I, KEC. AEK KUASAN, KAB. ASAHAN Raissa Amanda Putri; Liza Khairani; Nadiyah Khairiyah; Ummi Afzah Amirah; Izma Khoiruna
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 7 (2023): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v6i7.2595-2602

Kegiatan pengabdian masyarakat yang bertujuan mengedukasi penggunanaan media sosial untuk mengantisipasi ibu-ibu terhadap berita hoax di Desa Aek Loba Afd I Kecamatan Aek Kuasan, Kabupaten Asahan. Adapun Metode yang digunakan dalam KegiatanÂ Â PengabdianÂ Â KepadaÂ Â Masyarakat diÂ Desa Aek Loba ADF I Kecamatan Aek Kuasan, Kabupaten Asahan adalah Community Based Research (CBR) yang merupakan pendekatan dengan melibatkan masyarakat di berbagai level peran dan partisipasi yang akan memberi manfaat bagi komunitas atau masyarakat itu sendiri. RancanganÂ Â evaluasiÂ Â yangÂ Â dilakukanÂ Â dalamÂ Â kegiatanÂ Â pengabdianÂ Â masyarakat adalahÂ Â denganÂ Â cara melakukan pre-test dan post-test kepadaÂ pesertaÂ untukÂ melihat peningkatanÂ pengetahuanÂ danÂ pemahamanÂ peserta sebelumÂ danÂ setelahÂ mengikutiÂ edukasi Edukasi Penggunanaan Media Sosial Untuk Mengantisipasi Ibu-Ibu Terhadap Berita Hoax di Desa Aek Loba ADF I Kecamatan Aek Kuasan, Kabupaten Asahan melaluiÂ kuisionerÂ yangÂ telahÂ disusun oleh penulis. Setelah pelaksanaan kegiatan pengabdian masyarakat mendapatkan hasil bahwa Dari hasil jawaban kuesioner yang disebarkan kepada 40 orang peserta bahwa 33 orang (82,5%) menjawab sudah mengetahui tentang berita hoax, 29 orang (72,5%) menjawab sudah mengetahui ciri-ciri berita hoax, 32 orang (80%) menjawab sudah mengetahui dampak negative berita hoax, 35 orang (87,5%) menjawab sudah dapat membedakan berita yang valid atau yang palsu serta bagaimana cara membedakannya dan 30 orang (75%) menjawab sudah dapat mengetahui tentang pentingnya mengetahui berita hoax.Â

Sistem Pendukung Keputusan Dalam Pemilihan Buah Semangka yang Layak Dijual Menggunakan Metode AHP dan PROMETHEE Agil Indriyani; Raissa Amanda Putri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6743

Watermelon and Melon Buying and Selling Twins is a business that exports watermelons and melons to various cities owned by Mr. Kliwon whose address is at Pulau Gambar Village. In the Watermelon and Melon Buying and Selling Twins, in selecting the best quality watermelons suitable for sale, problems were found, namely that usually because they were affected by high prices, farmers did not prioritize the best quality watermelons and only focused on the number of fruits to be sold and agents had difficulty selecting watermelons. The best quality is suitable for sale, especially for export outside the city. So, with this problem, the author took the initiative to solve the problem correctly and maximize the determination of watermelons that are suitable for sale by designing and building a web-based decision support system by applying the AHP and PROMETHEE methods to help agents determine the best quality of watermelon. The design of this web-based application was carried out by conducting research at the Watermelon and Melon Buying and Selling Twins by collecting data on watermelon fruit and criteria data on watermelon fruit. After the data was collected, each fruit was weighted and ranked and then entered into the application that had been built. Based on the calculation results in this research, alternative weighting using the AHP method helps weighting with a weight scale of 1 - 9 according to AHP provisions. After carrying out alternative weighting, the next ranking is using the PROMETHEE method to get the netflow value, ranking 1 is obtained by 15 with a netflow value of 3,583 and Rank 15 is obtained by fruit 5 with a netflow value of -1.833.

Sistem Pendukung Keputusan Dalam Menentukan Calon Nasabah Penerima Pinjaman Dana Menerapkan Metode TOPSIS dan AHP Sri Yuslina Siregar; Raissa Amanda Putri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6744

PT. FIFGroup is a company that has obtained permission from the Minister of Finance, where this company carries out business in the field of providing loans in the form of funds. PT. FIFGroup Cikampak is one of several branches in other cities, as a prospective customer there are 5 criteria that must be considered and have been determined, namely according to the prospective customer's income, collateral for the prospective customer, employment, needs and term of borrowing funds. However, when determining potential customers who will receive loan funds, PT.FIFGroup Cikampak still uses manual methods, such as analyzing the conditions attached when applying for funds. In order to avoid errors in customer decision making, a web-based decision support system is needed to provide information quickly and precisely regarding the criteria for prospective customers. This decision support system uses a combination method, namely Topsis (Technique for orders preference by siilatyt ideal solution) and AHP (Analytical hierarchy process), this system can automatically recommend potential loan recipient customers who comply with predetermined criteria. Prospective customers who receive loan funds in this system will produce a ranking based on Topsis and AHP calculations. Based on calculations using the AHP method from the five criteria elements, the alternative weightings use a satty scale weighting of 1-9 according to the provisions of the AHP method. Then the ranking was carried out using the topsis method, resulting in the first rank being the name of the Misno customer with a manual priority of 0.729 and a system of 0.729, the lowest value or lowest ranking of the 15 alternatives, namely Sri Irma Naibaho manual priority of 0.204 and system of 0.204. The design of the decision support system has been successfully built using the Topsis and AHP methods, based on the results of Black Box testing, the system runs very well as desired.

Clustering Pecandu Narkoba Menggunakan Algoritma K-Means Clustering Ikhlasul Amal; Raissa Amanda Putri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7009

With the rise of increasingly sophisticated technology in this time of globalization, it is exceptionally simple for the more extensive local area to manage exchanges (drugs). For this reason, the government is constantly trying to stop the spread of drugs among Indonesian people by using any media, ranging from verbal invitations, banners, posters, videos and photos displayed in schools, government and public places. The maltreatment of opiates and perilous medications (drugs) in Indonesia as of late has turned into a difficult issue and has arrived at a condition of concern with the goal that it has turned into a public issue.In order to make it easier for BNN to conduct monitoring and counseling to areas where there are many drug addicts, it is necessary to cluster data on drug addicts in Medan city. To solve the problem, it can be solved by clustering drug addicts in Medan city using the K-Means Clustering Algorithm. The data used comes from the BNN of North Sumatra Province, the data used is data on drug addicts in Medan City in 2020-2023. The purpose of clustering drug addict data in Medan city is to find out areas that are very high, high, low and very low levels of drug addicts.This study found that there are 2 sub-districts with the highest level of drug addiction, 7 sub-districts with a high level of addiction, 7 sub-districts with a low level of drug addiction, and 5 sub-districts with a lowest level of addiction.

Co-Authors Abdillah, Muhammad Oemar Abdul Aziz Khusen Abdul Halim Hasugian ABdul Karim Batubara, ABdul Karim Abu Dardaq Putra Achmad Ramadhan Nst Adlani, Farid Adnan Buyung Nasution Agil Indriyani Agung Setiawan Hasibuan Agung Wijaya, Agung Agusni Firi Hasian Dalimunthe Aini Sachira, Rheana aji wardana Alasi, Galih Aldinata, Riko Aldy Alfiansyah 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 Annisa Annisa Shafira Zuhri Apipah, Nur Aprilsyah, Muhammad Arbi, Haris Andika Armansyah Armansyah Aryo Pratama Asti, Dini Audy Andini Lubis Aulia, Dea Liza Aulia, Diva Azhari, M. Faishal Azzahro Simatupang , Siti Fatimah Bagus Setiawan Bahuraksa, Sigit Balqis Pasaribu , Afifah 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, Dini Dino Farid Pratama Disa Pratama 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 Fajrul Aulia Yudha Fakhri Alauddin Tarihoran Fakhriza, M. Fakhrizal, Fiqri Fara Difa Aulya Farahdiba, Dhika Fathiya Hasyifah S FATHIYA HASYIFAH SIBARANI Fathiyah Hasyifah Sibarani Fauziah Lubis Fazril, Fazril Febiyaula, Siti Septia Fiddarain, Syaidah Fikri Hakiki Siregar 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 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 M irsyan antony manday, Irsyanmanday M Taufiq Rachman Siregar M. Fakhriza Maharani, Windi 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 Furqan, Muhammad 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 Nabila, Najwa Nabilah Aliya Tasya Nadiyah Khairiyah Nafis, Ayu Nasution, Adnan Buyung Nasution, Maimanah Salsabila Nasution, Muhammad Irwan Padli Nasution, Rizki Ansyari Nataryda Lubis, Muara Novrisyah Hasibuan Nurhabibah Febrianty Hasibuan Nurhasanah, Dhea Aulia Nurhayati Nurhayati Nurul Ifkah Lolona Silalahi Nurul Mawaddah Padang Nurul Zuriandini Panggabean, Trisatin Paranindra Ardhana Biroe Aurori Pasaribu, Haryati Pertiwi, Elsa 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 Rianto, Aldyansah Arrahman Rinaldy, Fahdly Ritonga, Siti Marlina RR. Ella Evrita Hestiandari Sabri, Muhammad Sahbandi Sahbandi 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 Silvia Kartika Sinaga, Adhe Syari Alfatah Sinaga, Imam Adlin Sinta Dewi Siregar, Ela Khairani Siregar, Elvan Dito Siregar, Fairuz Azzaria Siregar, Putri Aprilia Siregar, Sahnas Wulandari Siregar, Siti Khairunnisa Siswahyudianto Siti Zahra Situmeang, Risky Akbar Sri Yuslina Siregar Sriani Sriani Suendri Suendri, Suendri Suendri, Suendri Syah Zanul Husna Syahputra, Adam Tania Yulindra Taqiya Zahrowaini Teuku Alif Baihaqy Thamrin, Alwi Aryusya Thisara, Siti Kania Triase Triase Triase Triase Triase Triase Triase Triase Triase Triase, Triase Tua, Anri Hafiz Ulfayani Mayasari Ummi Afzah Amirah Wahyu Herlambang Wanda, Wanda Sari Wardana, Aji Wardhani, Ade Ratu Wibowo, Muhammad Rizky Widodo, Mhd. Aria Agung Willy Andri Malau Winny Wiyandari Wiradito, Ade Wulandari, Sahnas 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

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