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All Journal Techno.Com: Jurnal Teknologi Informasi JURNAL PENGABDIAN KEPADA MASYARAKAT Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika MODELING: Jurnal Program Studi PGMI IT JOURNAL RESEARCH AND DEVELOPMENT PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science Zero : Jurnal Sains, Matematika, dan Terapan ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JISTech (Journal of Islamic Science and Technology) AXIOM : Jurnal Pendidikan dan Matematika Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Kurawal - Jurnal Teknologi, Informasi dan Industri Jurnal Riset Informatika AL-ULUM: JURNAL SAINS DAN TEKNOLOGI Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Jurnal Review Pendidikan dan Pengajaran (JRPP) Progresif: Jurnal Ilmiah Komputer Jurnal Informatika dan Rekayasa Elektronik JSR : Jaringan Sistem Informasi Robotik Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Teknik Informatika C.I.T. Medicom G-Tech : Jurnal Teknologi Terapan Science Midwifery JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) INFOKUM Community Development Journal: Jurnal Pengabdian Masyarakat U-NET Jurnal Teknik Informatika Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) MEANS (Media Informasi Analisa dan Sistem) Journal of Computer Networks, Architecture and High Performance Computing JiTEKH (Jurnal Ilmiah Teknologi Harapan) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Journal La Multiapp Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Mandiri IT Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Armada Informatika Journal of Information Systems and Technology Research Jurnal Sains dan Teknologi JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Innovative: Journal Of Social Science Research Jurnal Komputer Antartika Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Pengabdian Masyarakat Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Ilmiah Nusantara Modem : Jurnal Informatika dan Sains Teknologi Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Teknologi : Jurnal Ilmiah Sistem Informasi

Abdul Halim Hasugian

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Author-ID : 5765076

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities 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 Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering 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 Nursing Physics Public Health Social Sciences Veterinary Other

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Penentuan Kelas Unggulan Berbasis Decision Support System Dengan Metode Simple Additive Weighting (Saw) Zufria, Ilka; Hasugian, Abdul Halim; Simanjuntak, Salmah
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

MTs 2 state Medan Jl. Administrator No. 3 Medan Estate Complex is one of the school governments engaged in education. With the rapid development of technology that develops with the development of science and technology, we must prepare human resources who are able to compete excellently. In order for the formation of quality students, the superior class is a gap in efforts to improve the quality of education in Indonesia. To get quality students will not be separated from good religious knowledge, because if students do not have religious knowledge it will cause competent children to do things that are deviant, whereas in the hadith it has been stated that morals are higher in the placement of knowledge, then a student who does not there are morals then their knowledge will be in vain. This study is to see that to enter the superior class with several specified criteria, namely: Al-Quran test, IQ test, medical history, parent's income, Sem-Odd Value, Sem-Even Value. If these criteria are met, a student will be selected through the results of the ranking of each criterion. Because it takes too much time and the limited ability to see all aspects at one time that has been determined accurately, it causes several errors in decision making. Therefore, a decision support system for selecting a superior class is needed with the criteria determined by the MTs state 2 Medan school, by using the simple additive weighting method (SAW) which can help the school to determine superior class students quickly and accurately. By using the SAW method formula that is connected with each existing criterion to produce a decision in selecting the superior class student.Â Keywords: superior class, decision support system, SAW

Aplikasi e-Directory Berkas Tridharma Kinerja Dosen Limbong, Tonni; Hasugian, Abdul Halim
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 1 No. 2 Tahun 2016
Publisher : LPPM UNIKA Santo Thomas

Pengurusan jabatan fungsional seorang dosen sesuai dengan peraturan pemerintah melalui dirjen dikti akan dilakukan minimal 1 (satu) kali dalam 3 (tiga) tahun, ini sangat memungkinkan berkas-berkas dari seorang dosen akan tercecer atau rusak bahkan hilang, dan menyebabkan kekurangan berkas padahal seorang dosen tersebut sudah melaksanakan tugasnya tapi berkasnya hilang ataupun rusak. Demikian juga bagi dosen yang sudah lulus sertifikasi memiliki kewajiban melaporkan pekerjaan nya setiap semester yakni pelaksanaan Tri Dharma Perguruan Tinggi untuk mendapatkan haknya yakni tunjangan sertifikasi dari negara. Aplikasi e-Directory berkas tridharma kinerja dosen dengan memanfaatkan fasilitas yang ada (Server) serta pengaruhnya terhadap kualitas layanan pemberkasan dosen dalam bentuk digital merupakan langkah penting yang harus dilakukan, sehingga dosen maupun pihak manajemen mendapat data / informasi yang cepat, tepat dan akurat.

K-Means clustering analysis of public satisfaction with 50% electricity tariff reduction Harahap, Muhammad Fitrah Affandi; Hasugian, Abdul Halim
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.418

At the beginning of 2025, the Indonesian government implemented a policy to reduce electricity tariffs by 50% for household customers with power capacities of up to 2,200 VA. This policy aims to boost public purchasing power and stimulate economic growth, particularly among lower-middle-income groups. However, public responses to the policy have been varied and widely expressed on social media, especially on platform X (formerly known as Twitter). This study aims to evaluate public satisfaction with the electricity tariff reduction policy through sentiment analysis on social media X using the K-Means Clustering method. Data were collected through a crawling process using specific relevant keywords, followed by preprocessing steps such as cleansing, case folding, tokenizing, stemming, and conversion into numerical form using TF-IDF. The clustering results show that Cluster 1 dominates with 662 tweets (68.74%), predominantly reflecting positive sentiment, indicating that the majority of the public responded favorably to the 50% electricity tariff reduction policy. Cluster 2 consists of 165 tweets (17.13%) expressing negative sentiment, suggesting that some members of the public voiced concerns or dissatisfaction with the policy. Meanwhile, Cluster 0 includes 136 tweets (14.12%) containing neutral sentiment, representing moderate responses without a strong stance. These findings indicate that, overall, the policy received a generally positive reception from the public, although there are still critical and neutral perspectives.

Analisis Sentimen Ulasan Pelanggan Aplikasi Hokben Di Google Playstore Menggunakan Metode Support Vector Machine Ryo Vikri Alif; Hasugian, Abdul Halim
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 02 (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.v7i02.1501

Perkembangan teknologi informasi mendorong berbagai perusahaan untuk memberikan layanan digital yang praktis dan efisien. Salah satu bentuk layanan tersebut adalah aplikasi pemesanan makanan berbasis seluler seperti HokBen. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi HokBen di Google Playstore menggunakan metode text mining dan algoritma klasifikasi Support Vector Machine (SVM). Data diperoleh melalui teknik web scraping dengan memanfaatkan perpustakaan google-play-scraper untuk mengumpulkan 1.500 komentar pengguna. Komentar yang dikumpulkan diproses melalui tahap prapemrosesan teks, yang mencakup pembersihan teks, normalisasi, tokenisasi, penghapusan kata henti, dan stemming. Setelah itu, data diberi label menggunakan metode VADER Lexicon menjadi dua kategori: positif dan negatif. Hasil pelabelan menunjukkan bahwa 835 komentar positif dan 651 negatif. Data kemudian diubah menjadi representasi numerik menggunakan metode TF-IDF (Term Frequency-Inverse Document Frequency) sebelum dimasukkan ke dalam model klasifikasi SVM. Proses evaluasi dilakukan dengan matriks kebingungan dan menghasilkan akurasi 76,85%, presisi 76,72%, recall 67,94%, dan skor F1 72,09%. Hasil ini menunjukkan bahwa model SVM cukup efektif dalam mengklasifikasikan opini pengguna tentang aplikasi HokBen. Penelitian ini dapat dijadikan dasar untuk mengevaluasi kinerja aplikasi berdasarkan ulasan pengguna dan membantu pengambilan keputusan dalam pengembangan layanan digital di masa mendatang.

Deteksi Warna Dasar Menggunakan Metode Thresholding HSV dengan OpenCV Zidanul Akbar; Asrul Suwondo; Rizky Ramadhan; Abdul Halim Hasugian
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.1020

Digital image processing is a rapidly developing branch of computer science and has many applications in everyday life. One of the fields that most often utilizes this technique is object detection and color identification in images and videos. This study specifically aims to implement the thresholding method in the HSV (Hue, Saturation, Value) color space to detect three basic colors, namely red, green, and blue, in digital images. The research process begins with uploading images using the Google Colab platform, a cloud-based computing environment that makes it easy for users to run Python programs without requiring additional software installation. After the image is uploaded, the next step is to convert it from the RGB (Red, Green, Blue) color space to the HSV color space. This conversion is important because the HSV color space is more suitable for use in the color segmentation process. The Hue value represents the type of color, Saturation shows the level of saturation, while Value describes the level of brightness. Once the image is in the HSV color space, the next step is to determine the HSV value range for each basic color. This range is determined based on experimental results and references from related literature. Using this range, masking is performed to extract the appropriate pixels so that only the red, green, or blue portions of the image are visible, while the other colors are reduced. The results show that the thresholding method in the HSV color space is capable of detecting primary colors with a good level of visual accuracy, especially in simple images with contrasting backgrounds. The implementation of this program is relatively lightweight, easy to run directly in Google Colab, and does not require high-spec hardware. Therefore, this method is very suitable for use as basic learning material for digital image processing, both for students and novice researchers.

Implementation of Deep Learning Method Using BERT Model in Career Choice Analysis of Gen Z Ramadhani, Silvia; Hasugian, Abdul Halim
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8917

The development of digital technology has significantly influenced how individuals, particularly Generation Z (born between 1997 and 2012), make career decisions. Faced with an abundance of digital information, many individuals in this cohort experience difficulties in selecting career paths that align with their interests, abilities, and labor market demands. This study analyzes the career preferences of Generation Z using a deep learning approach through the Bidirectional Encoder Representations from Transformers (BERT) model, specifically the IndoBERT variant, which is pre-trained on Indonesian-language data. The research data were collected from textual responses to Google Form questionnaires, focusing on four digital career paths: Software Engineer, Content Creator, Digital Marketing, and Entrepreneur. From 601 data samples, sentiment analysis revealed that 57.85% of the responses were positive, while 42.15% were negative. Classification results indicated that Content Creator was the most preferred career, followed by Entrepreneur, Digital Marketer, and Software Engineer. Model evaluation showed a test accuracy of 51.24%, with better performance in categories that had larger data volumes. These findings demonstrate that IndoBERT is effective in capturing opinions and career tendencies from unstructured text and provides a scientific basis for educational institutions, industries, and policymakers to design more relevant career development strategies in the digital era.

Evaluasi Kepuasan Penggemar Sepak Bola Terhadap Pemilihan Pelatih Timnas Indonesia Di Media Sosial X Dengan Metode K-Means Clustering Harahap, Nasywa Al Afif; Hasugian, Abdul Halim
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13503

Tingginya antusiasme publik terhadap pemilihan pelatih timnas Indonesia seringkali memunculkan beragam opini di media sosial, khususnya platform X. Opini tersebut tersebar dalam bentuk komentar yang tidak terstruktur, sehingga menyulitkan evaluasi kepuasan publik secara objektif. Penelitian ini merumuskan permasalahan: bagaimana mengelompokkan opini publik terhadap pemilihan pelatih timnas Indonesia secara sistematis untuk mengevaluasi tingkat kepuasan penggemar. Tujuan penelitian ini adalah menerapkan algoritma K-Means Clustering dalam proses analisis sentimen berbasis teks untuk mengetahui persepsi publik secara terukur. Penelitian ini menggunakan pendekatan kuantitatif dengan tahapan utama berupa crawling data tweet, text preprocessing, pembobotan TF-IDF, serta klasterisasi menggunakan metode K-Means. Penentuan jumlah klaster optimal dilakukan dengan Elbow Method dan validasi menggunakan Silhouette Score. Hasil analisis terhadap 947 data menunjukkan distribusi sentimen positif sebanyak 649 tweet (68,46%), netral 185 tweet (19,51%), dan negatif 114 tweet (12,03%). Evaluasi performa menghasilkan akurasi model sebesar 53,59%, dengan performa terbaik pada klaster sentimen positif. Penelitian menyimpulkan bahwa metode K-Means Clustering dapat menjadi pendekatan awal dalam menganalisis opini publik di media sosial, meskipun akurasinya masih terbatas untuk data dengan distribusi tidak seimbang. Penelitian ini bermanfaat dalam memberikan rekomendasi berbasis data bagi federasi sepak bola Indonesia untuk memahami suara publik sebagai bahan evaluasi dalam pengambilan keputusan strategis. Kata kunci - Analisis Sentimen, K-Means Clustering, Machine Learning, TF-IDF, Confusion Matrix

Sistem Pendukung Keputusan Untuk Menentukan Kelayakan Penerima Bantuan Langsung Tunai Menggunakan Metode AHP-Topsis Simatupang, Aidil Akbar; Hasugian, Abdul Halim
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8833

Social inequality and inaccuracy in aid distribution are still challenges in the Direct Cash Assistance (BLT) program, especially at the village level such as Bandar Selamat Village, North Labuhan Batu Regency. The process of determining BLT recipients which is still manual and subjective poses a risk of injustice and inefficiency. This study formulates the problem: how to develop an objective and targeted decision support system (DSS) for the selection of BLT recipients. The purpose of this study is to design and implement a DSS based on the Analytical Hierarchy Process (AHP) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) which can increase the accuracy and efficiency of aid recipient selection. The method used is Research and Development (R&D), with data collection techniques through interviews and observations, as well as comprehensive system testing. The results show that from 110 household head data, the system is able to identify 69 families eligible to receive assistance with a preference value ? 0.6. Employment and home conditions are the dominant criteria in determining eligibility. The system is proven to be consistent (CR = 0.0298 <0.1) and is able to simplify the decision-making process. This research provides real benefits in improving transparency, accountability, and effectiveness of social assistance distribution at the village level through a data and technology-based approach.

Sistem Rekomendasi TV Series Berdasarkan Genre Menggunakan Algoritma KNN Deni Fahrizal; Abdul Halim Hasugian
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.6225

The problem of choice overload on TV series streaming platforms often makes it difficult for users to find content that suits their preferences. To address this challenge, this study develops a Content-Based Filtering-based recommendation system by applying the K-Nearest Neighbor (KNN) algorithm and the Jaccard Similarity metric. The designed system analyzes users' genre preferences, such as Drama, Sci-Fi, and Comedy, while integrating rating, popularity, and release year factors to generate more personalized recommendations. Evaluation of 500 TV series titles from the TMDB API shows a high level of accuracy, with Precision and Recall reaching 1.0 for specific genre preferences, as well as stable performance with an F1-Score of 0.67 for cross-genre preferences. These findings prove that the proposed model is effective in reducing choice overload and significantly improving the user experience in exploring content on streaming platforms. Furthermore, this approach has the potential to be further developed by integrating sentiment analysis and real-time audience behavior data to generate increasingly adaptive and relevant recommendations.

Sentiment Analysis on the Planned Nickel Mining Development in Raja Ampat Using the Random Forest Algorithm Rajani, Attila; Hasugian , Abdul Halim
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.v7i03.1565

The planned nickel mining development on Kawe and Manuran Islands in Raja Ampat has sparked various public reactions, especially on social media platforms. Raja Ampat is known for having one of the highest levels of marine biodiversity in the world, raising concerns about the potential ecological and social impacts of such development. This study aims to analyze public sentiment regarding the nickel mining plan in Raja Ampat by utilizing social media comments. The method used is the Random Forest algorithm, which is recognized for its high performance in classifying complex text data. A total of 2,010 comments were collected, and after the preprocessing stage, 1,658 clean data entries remained for analysis. The preprocessing steps included text cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The results show that 57.85% of the comments expressed positive sentiment, while 42.15% showed negative sentiment. The Random Forest model was able to classify the sentiments with an accuracy of 80.1%, using three decision trees as the basis for majority voting. Furthermore, n-gram analysis and word cloud visualization provided insight into the dominant words in public opinion, offering a deeper understanding of the issues being discussed. This research is expected to serve as a consideration in development policy-making that prioritizes environmental sustainability and the well-being of local communities.

Co-Authors Abdillah, Ibnu Faiz Adam Damiri Manurung Adi Hartono Aditya Maulana Azanzi Girsang Afandi Sahputra Afiksih, Mufliha Afriani, Dina Aidil Halim Lubis Aidil Halim Lubis Ajeng Dwi Pratiwi Alfarizi, Muhammad Alhabib, Muhammad Farhan Ali Darta Ali Ikhwan Alwy Azyari Harahap Amalia Daulay, Rizki Amelia Anggraini, Arizka Anggraini, Sindi Annisa Shafira Zuhri Apriani, Puja Arif, Mhd. Fakhrozi Armansyah Armansyah Armansyah Aruan, Nur Jamilah Asrul Suwondo AULIA, RIZKA Auliani, Wirna Rizka Azhar, Joehari Azhari, Wahyu Bandaharo, Bandaharo Bermiko Kasah Padang Bunga Nurul Manisa Dalimunthe, Ayu Sahriani Dea Amallia Deni Fahrizal Dewi Afrianti Dharma, Fahri Dinda Zukhoiriyah Eferoni Ndururu Elsa Azila Rahman Fakhriza, M. Farah Zaida Gema Ramadhan Gilang Armawan Saka Ginting, Masitha Putri Ardhana Girsang, Aditya Maulana Azanzi Gunawan, Gunawan Gunawan, Helmi Hanny Puput Eliyarista Saragih Harahap, Muhammad Fitrah Affandi Harahap, Nasywa Al Afif Hasibuan, Ardina Khoirunnisa Hendra Cipta Heni Pujiastuti Heri Santoso Heri Santoso Heri Santoso HERI SUSANTO Hidayah, Adinda Fita Hidayati, Risma Hsb, Munawir Siddik Ibnu Rusydi Ikhsan, Muhammad Ilham Ilham Ilka Zufria Imam Zaki Husein Nst Irawan, Muhammad Arief Irene Sri Morina Januar, Bagus K Khairunnisa Khaidir Hanafi Khairuna Khairuna Khairunnisa, K Kusuma, Sintiawati Lubis, Akbar Maulana Lubis, Desy Ramadhani Lubis, Indah Alfitri M Mahyudi M. Fakhriza M. Khalil Gibran M. RIZKY RAMADHAN M.Alif Fahrezy Mahara, Elvida Futri Maimunah Rahmadani Marpaung, Rizq Alwi Marwah, Khoirul Wijak Alfaizh Maulida, Dzikra Maya Khairani Mhd Furqan Mhd Ikhsan Rifki Mhd Rafly Syah Pahlevi Miftahul Jannah Muhammad Ezar Raditya Muhammad Ikhsan Muhammad Ikhsan Muhammad Ridzki Hasibuan Muhammad Sayuthi Muhammad Siddik Hasibuan Muhammad Suhery Mulya Alfan Simatupang Murdani Nadyah Almirah Simanjuntak Nasution, Yurika Nst, Fakhrurrozi Nurmaiyah Nurmaiyah Ong, Russell Pazri Prasetio, Muhammad Aditya Prayoga, M. Irsan Pristiwanto, Pristiwanto Putra, Donny Dwi Putri Hanifah Putri, Cindy Ananda Putri, Pebriani Rahadian Fatta Batubara Rahmad Prayogi Harahap Rahmawati Rahmawati Raissa Amanda Putri Rajani, Attila Rakhmat Kurniawan R Ramadhani, Muthia Ramadhani, Silvia Rano, Rano Irawan Reza Muhammad Rijal, Mhd. Nanda Khairul Rina Anggraini, Rina Rina Widyasari Rizki Amalia Rizky Pratama Putra Rizky Ramadhan Rizqi Hidayat Tanjung RR. Ella Evrita Hestiandari Ryo Vikri Alif S, Amri Yuda Sabuki, Robi Saefuddin, Anan Saka, Gilang Armawan Sela, Dhea Shania Oktawijaya Sheila Safira Siahaan, Ahmad Taufik Al Afkari Simanjuntak, Salmah Simatupang, Aidil Akbar Siregar, Muhammad Faisal Siregar, Nora Arianti Siti Hayatul Fauziah Ritonga Siti Juhroini Ritonga Siti Nurhaliza Sofyan Siti Sumita Harahap Sitorus, Ridha Saryani Situmorang, Rantouli Solifiah Batubara, Febi Sri Wulan, Sri Sriani Sriani Sriani Sriani, S Suandi Padang Suendri Suendri, Suendri Suhardi Suhardi Suhardi, Suhardi Sulindawaty T. Raihan Yudisthira TONNI LIMBONG Tria Elisa Ulfah, Auliana Wahyudi, Zul Attoriq Farhan Wina Fadia Ardianti Windary, Wanda Yani, Sri Suci Yazid Hulaini Habbani Nasution Yusuf Karim Rambe Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zaidan, Muhammad Zidanul Akbar Ziqra Addilah

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