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All Journal Jurnal Ilmu Komputer Teknika Jurnal Informatika Sinergi Telematika Jurnal Informatika dan Teknik Elektro Terapan InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT Information Management For Educators And Professionals (IMBI) Informatika Pertanian Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Komtika (Komputasi dan Informatika) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Inotera Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Sains Riset Jurnal Sosial Humaniora Sigli JTIK (Jurnal Teknik Informatika Kaputama) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Jurnal Sistem Komputer dan Informatika (JSON) Medika Teknika : Jurnal Teknik Elektromedik Indonesia Idealis : Indonesia Journal Information System International Journal of Engineering, Science and Information Technology JUTECH : Journal Education and Technology Conten : Computer and Network Technology Brilliance: Research of Artificial Intelligence Jurnal Teknik Informatika dan Elektro (JURTIE) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Energi Elektrik Jurnal Bangun Abdimas IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Komtika (Komputasi dan Informatika) Jurnal Informatika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Nurdin Nurdin
Universitas Malikussaleh
Religion 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Classification Analysis of Single Tuition Fees Using the Random Forest Method with K-Fold Cross Validation Khaidar, Al; Nurdin, Nurdin; Fajriana, Fajriana; Taufiq, Taufiq; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11798

Abstract

Classification is the process of grouping data into specific categories based on their characteristics or features, which plays a crucial role in the analysis, decision-making, and prediction of new data. In academic settings, classification is used to determine the Single Tuition Fee to place students according to their economic ability. Lhokseumawe State Polytechnic has implemented the UKT system since 2020 with eight categories, but some students are still placed in UKT groups that do not match the results of the manual process, which has limited accuracy. This study uses the Random Forest method as a technology-based solution to improve the accuracy and objectivity of UKT classification. The dataset used consists of 10,000 student data with 10 variables, covering economic and social information. The research process includes data preprocessing, Random Forest model training, performance evaluation using accuracy, precision, recall, and F1-score, and model stability testing through 10-fold K-Fold Cross Validation. The results show that Random Forest is able to classify most UKT classes well, especially classes 0–5 and 7. Class 6 has lower performance with a recall of 0.39 and an F1-score of 0.56 due to the limited number of samples. The overall accuracy of the model reaches 96%, while K-Fold Cross Validation produces an average accuracy of 95.50% with a standard deviation of 0.66%, indicating the model is stable and able to generalize to new data. This study proves that Random Forest is effective in UKT classification, producing an objective, fair, and efficient system. This implementation model supports data-driven decision-making in higher education and increases transparency in UKT determination.
Analisis Perbandingan Kinerja Algoritma You Only Look Once (YOLOv8) Dan Single Shot Detector (SSD) dalam Pengenalan Nominal Uang Kertas Ulfah, Julia; Ula, Munirul; Fajriana, Fajriana; Nurdin, Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.7471

Abstract

The advancement of technology in the field of image recognition has significantly facilitated and improved the effectiveness of object detection in computer-based banknote recognition systems. This study aims to automatically identify banknotes based on their denominations, with the objective of minimizing human errors—such as lack of concentration, fatigue, and other factors—and enabling its application in ATMs and automated payment systems. This research compares the accuracy levels and detection success rates between the YOLO and SSD algorithms in recognizing the denominations of banknotes. The YOLO model operates by dividing the image into grids and predicting bounding boxes along with object classes in a single step, resulting in fast and consistent detection. In contrast, the SSD model employs a multi-scale approach by utilizing feature maps from multiple levels to generate predictions. The parameters used in this study include 7 classes of Indonesian banknotes: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. A total of 353 images were used in the dataset, and three images from each class were selected for testing purposes. The results of the study indicate a significant performance difference. The YOLO algorithm achieved a 100% accuracy rate under both normal and low-light conditions, while the SSD algorithm achieved an accuracy rate of 87.2% under normal lighting and 91.4% under low-light conditions.
Implementation of Triple Exponential Smoothing in Predicting Blood Stock Inventory Afif Diapari Ma'aruf Lubis Afif Diapari; Nurdin; Kurniawati
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Blood availability is an important component for the Indonesian Red Cross (PMI) Blood Donor Unit (UDD) in maintaining blood supplies so that blood is not wasted and there is no shortage. This study aims to test the effectiveness of using the Triple Exponential Smoothing (TES) method in predicting blood stock inventory at UDD PMI. Triple Exponential Smoothing is a forecasting method that considers seasonal patterns in data, which is relevant in predicting blood demand based on historical data. This study began by collecting historical blood stock data from January 2019 to December 2023. Next, the data was analyzed to identify seasonal patterns and trends. This method is applied to the four main blood types (A, B, AB, and O) by calculating the accuracy value using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results show that the TES method can accurately predict blood availability and demand, with a low MAPE value of 2.15% for blood type A. For blood type B, the MAPE value is 1.38%, blood type O is 1.03%, and blood type AB is 2.42%. This research is expected to significantly contribute to more effective and efficient bloodstock management at PMI and become an academic reference for future blood stock forecasting studies.
Analisis Efektivitas Metode Naïve Bayes terhadap Sentimen Opini Publik di X Mengenai Relokasi Ibu Kota Indonesia (IKN) Anas, Mukhtar; Nurdin, Nurdin; Taufiq, Taufiq
Jurnal Teknik Informatika dan Elektro Vol 8 No 1 (2026): Jurnal Teknik Elektro dan Informatika
Publisher : Universitas Gajah Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55542/jurtie.v8i1.1874

Abstract

Selesainya Rancangan Undang-Undang (RUU) pemindahan ibu kota pada awal September 2021, pembangunan Ibu Kota Nusantara (IKN) resmi dimulai pada bulan Juli tahun 2022. Persoalan ini mengundang pro dan kontra di masyarakat, karena kepentingan dan pandangan masing-masing. perkembangan dunia digital semakin canggih, informasi lebih mudah didapatkan melalui portal dan media sosial. Media sosial merupakan salah satu tempat untuk menyampaikan opini masyarakat. Melalui text mining di X kita dapat memahami gambaran orang dalam persepsi mereka terhadap kebijakan pemerintah, baik Positif, Netral dan Negatif. Analisis sentimen ini perlu dilakukan, oleh karena itu penelitian Analisis Efektivitas Metode Naïve Bayes Pada Sentimen Opini Publik Di X Terhadap Pemindahan Ibu Kota Nusantara (IKN) dilakukan untuk melihat polarisasi opini masyarakat dan menguji efektivitas metode Naïve Bayes terhadap dataset IKN. Rancangan penelitian cross sectional karena data yang diambil dari aplikasi X sejak tanggal 18 Januari 2022 sampai dengan 3 Maret 2024. Hasil screping data didapatkan sebanyak 3775 tweet, kemudian dicleaning sehingga menghasilkan 2778 data tweet. Dalam scrapping dan analisis data tweet alat yang digunakan google colab dan bahasa pemograman phyton. Data dibagi menjadi data latih sebanyak 80% dan data uji 20%. Polarisasi sentimen tweet positif sebanyak 1153 (39.32%) Netral 991 (33.80%) dan Negatif 788 (26.88%). Tingkat akurasi untuk tiap tiap jenis naïve bayes, GaussianNB 0.5168, MultinomialNB 0.6133 dan bernoulliNB 0.6115. hasil uji akurasi menggunakan confusion matrix menggambarkan metode Naïve Bayes dengan jenis MultinomialNB mendapatkan akurasi yang lebih tinggi dalam mengklasifikasi dataset IKN
Analysis of the Efficiency and Performance Effectiveness of Srikandi Application Using the UTAUT Model and Delone & Mclean Syahputra, Wawan; Abdullah, Dahlan; Nurdin, Nurdin; Daud, Muhammad; Taufiq, Taufiq
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1806

Abstract

The development of information technology has encouraged the government to carry out digital transformation in administrative governance, one of which is through the implementation of the SRIKANDI Application (Integrated Dynamic Archive Information System). This application is designed to support the management of electronic archives and correspondence integrated across government agencies. This study aims to analyze the efficiency and effectiveness of the SRIKANDI Application in supporting government administration, focusing on service speed, documentation accuracy, and resource efficiency. The method used in this study is a mixed methods approach with a sequential explanatory design. Quantitative data were collected by distributing questionnaires to employees who used the application to assess perceptions of efficiency and effectiveness. Furthermore, qualitative data were obtained through in-depth interviews and document analysis to delve into the quantitative findings and explore contextual factors that influence application implementation. Data analysis is carried out in stages, starting with descriptive and inferential statistical analyses for quantitative data and with thematic analysis for qualitative data. This research is expected to contribute to the development of an electronic government system and serve as a reference for evaluation and policymaking related to bureaucratic digitalization. In addition, the results of this study are also expected to strengthen the literature on the effectiveness of government information systems and provide an empirical picture of the practice of implementing the SRIKANDI Application in government agencies.
Comparison of Logistic Regression and Random Forest Methods in Predicting Vehicle Tax Payment Compliance Khairul Fuadi; Taufiq Taufiq; Arnawan Hasibuan; Dahlan Abdullah; Nurdin Nurdin
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i1.11944

Abstract

Motor vehicle tax is a major source of Regional Original Income (PAD). However, the level of motor vehicle tax payment compliance in North Aceh Regency is still suboptimal, particularly related to late payments. A data-driven approach is needed to predict and understand taxpayer compliance patterns more accurately. This study aims to compare the performance of the Logistic Regression and Random Forest methods in predicting motor vehicle tax payment compliance, as well as to identify factors that influence taxpayer compliance behavior at the North Aceh Samsat (Sat). This study uses secondary data in the form of motor vehicle tax payment transactions at the North Aceh Samsat for the 2022–2024 period, totaling 100,000 observations. The response variable is the tax payment compliance status (compliant and non-compliant), while the predictor variables include vehicle age, type of ownership, vehicle type, and vehicle brand. The data is divided into 70% training data and 30% testing data. The performance evaluation model is conducted using accuracy, precision, recall, and Area Under Curve (AUC) metrics. The analysis results show that Random Forest has better predictive performance than Logistic Regression, with higher accuracy and AUC values. Vehicle age and type of ownership are the most influential variables in predicting tax payment compliance, while vehicle brand has a relatively smaller influence. Logistic Regression provides a clear interpretation of the variable relationship, but has lower discrimination ability than Random Forest. Random Forest has proven to be more effective as a prediction model for motor vehicle tax payment compliance at the North Aceh Samsat. The application of machine learning-based predictive models has the potential to support more targeted policy making in an effort to improve motor vehicle tax payment compliance, especially in reducing late payments.
Prediction Of Unemployment Rate Using The Fuzzy Time Series Chen Model Method Annisa Karima; Dahlan Abdullah; Muchlis ABD Muthalib; Nurdin Nurdin; Muhammad Daud
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7310

Abstract

Unemployment is a significant socio-economic problem in Lhokseumawe City that requires serious attention from policymakers. The unemployment rate fluctuates from year to year, making accurate forecasting an important aspect in formulating effective strategies and policies to reduce unemployment. One method that can be used to analyze and forecast time series data with uncertainty is the Fuzzy Time Series (FTS) method, which applies fuzzy logic concepts to handle vague and imprecise data patterns. In this study, the Fuzzy Time Series method is applied to predict the number of unemployed people in Lhokseumawe City. The data used are historical unemployment data over a period of 10 years, from 2013 to 2022. The research process begins with defining the universe of discourse (U), determining the number and length of interval classes, defining fuzzy sets on U, and fuzzifying the unemployment data. Furthermore, Fuzzy Logical Relationships (FLR) are identified and grouped into Fuzzy Logical Relationship Groups (FLRG). The defuzzification process is then carried out to obtain crisp values, followed by forecasting calculations.The analysis was conducted using the RStudio application. The forecasting results show that the predicted number of unemployed people in 2023 is 10,514.125, which is rounded to 10,514 people. The accuracy of the forecasting model is evaluated using Mean Absolute Percentage Error (MAPE) and Average Forecasting Error Rate (AFER), both of which yield values of 6.70%. Since the MAPE and AFER values are less than 10%, the forecasting results can be categorized as very good and reliable for decision-making purposes.
APLIKASI TEKNOLOGI INTERNET OF THING PADA ROBOT PENDETEKSI KEBOCORAN GAS AMONIA (NH3) Khairina, Jikti; Nurdin, Nurdin; Fikry, Muhammad
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7457

Abstract

Amonia adalah senyawa kimia dengan rumus NH3 Senyawa ini didapati berupa gas dengan bau tajam yang sangat khas, inilah yang disebut dengan bau amonia. Amonia memiliki sumbangan penting bagi keberadaan nutrisi di bumi, tetapi amonia sendiri adalah senyawa yang dapat merusak kesehatan. Jika terjadi kontak dengan gas amonia berkonsentrasi tinggi dapat menyebabkan kerusakan pada paru-paru bahkan sampai kematian. Amonia digolongkan sebagai bahan beracun jika terhirup langsung, pengangkutan amonia yang berjumlah lebih besar dari 3.500 galon (13,248 L) harus disertai dengan surat izin. Amonia umumnya bersifat basa (pKb=4.75), tetapi dapat juga bersifat sebagai asam yang amat lemah (pKa=9.25), amonia dapat terbentuk secara alami maupun sintetis. Amonia yang berada di alam merupakan hasil dekomposisi bahan organik. Di industri banyak yang menggunakan amonia sebagai salah satu bahan baku, contohnya seperti dalam penggunaan campuran bahan baku pembuatan pupuk. Gas amonia terkadang beresiko terjadi kebocoran pada pipa gas, jika terjadi kebocoran pada pipa maka dibutuhkan teknisi yang harus segera dikirimkan ke lokasi untuk mencari sumber kebocoran atau titik kebocoran pada pipa. Hal itu membuat teknisi membutuhkan tabung oksigen dan hal ini beresiko sangat tinggi dikarenakan daya tahan tabung oksigen hanya bertahan selama ±15 menit. Maka dibuatkanlah robot untuk dikirimkan ke lokasi yang berfungsi agar mengetahui informasi tentang lokasi kebocoran pipa gas dan informasi kadar gas langsung dapat diketahui melalui Android. Dalam kasus ini, rancang bangun robot menggunakan sensor MQ135. Oleh karena itu dibuatkanlah robot pendeteksi kebocoran gas dan mengetahui kadar dari gas amonia agar mempermudah teknisi dalam menemukan lokasi dan kadar gas amonia.
Traffic Accident Prediction Using Machine Learning Based on PT Jasa Raharja Data Utomo, Muhammad Fikri; Fikry, Muhammad; Hamdhana, Defry; Abdullah, Dahlan; Nurdin, Nurdin
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.25392

Abstract

Traffic accidents represent a critical issue that significantly affects public safety and generates substantial social and economic impacts, particularly within the operational area of PT. Jasa Raharja Lhokseumawe Branch. The lack of predictive information regarding accident occurrences often results in reactive policy making. This study aims to develop a machine learning–based forecasting model for traffic accident rates using a combination of K-Means Clustering and Recurrent Neural Network (RNN). The dataset consists of historical traffic accident records from 2022 to 2024, which were preprocessed and aggregated on a weekly basis at the district level. K-Means Clustering was employed to group districts according to weekly accident patterns, resulting in two optimal clusters based on silhouette score evaluation. Subsequently, separate RNN models were developed for each cluster to forecast weekly accident occurrences. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the RNN model achieved higher prediction accuracy for clusters with more stable accident patterns compared to clusters exhibiting higher fluctuation. Overall, the proposed combination of clustering and RNN demonstrates strong potential in producing accurate traffic accident forecasts
Sistem Pakar Berbasis Web untuk Diagnosis Stunting pada Balita Menggunakan Metode Naïve Bayes Cesilia, Yolinda; Nurdin, Nurdin; Cut Agusniar
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9130

Abstract

Stunting is a health problem caused by chronic malnutrition that affects children's physical growth and cognitive development. This condition has become a serious concern because it impacts the quality of human resources in the future. This study aims to develop an expert system for diagnosing Stunting using the Naïve Bayes method to assist healthcare workers in the early detection of at-risk toddlers. The research data were obtained from Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 170 training data and 30 testing data. The system was developed using the Python programming language with the Flask framework and SQLite database. The input variables consisted of seven symptoms (G01–G07), including age, weight, height, gender, and other supporting factors. The testing results showed that the Naïve Bayes method achieved an accuracy of 86.66%, with 26 out of 30 test data correctly classified according to expert diagnoses. This system can be used as a decision-support tool for healthcare workers to accelerate diagnosis and improve the effectiveness of Stunting management, particularly in areas with limited healthcare resources. 
Co-Authors - Miranda ., Muthmainah Adi Prasetyo Adzuha Desmi Afif Diapari Ma'aruf Lubis Afif Diapari Aflizar Aflizar Afrilia, Yesy Aidilof, Hafizh Al Kautsar Al Khaidar Alaiya, Azna Alqhifari, Azka Ama Zanati Amalia, Nova Amin Munthoha Aminsyah, Ansharulhaq Ananda Faridhatul Ulva Anas, Mukhtar Andri Alfitra Anggara, Aji Annisa Karima Arnawan Hasibuan Aynun, Aynun Aynun, Nur Azzanna, Maghriza bhakti wan khaledy Bustami Bustami Bustami Bustami Cesilia, Yolinda Chaeroen Niesa Chicha Rizka Gunawan Cut Agusniar Dadang Priyanto Dahlan Abdullah Dahlan Abdullah Darmansyah, Arif Desky, Muhammad Aulia Dewi Astika Erni Susanti Eva Darnila Fadlisyah Fadlisyah Fadlisyah Fahrozi, Fazar Fajriana Fajriana Fajriana, Fajriana Fasdarsyah Fasdarsyah fatimah Fatimah Fikhri, Aditya Aziz Fikran, Rifzan Fikri Fikri Gavinda, Virza Ginting, Andriyan gunawan, chicha rizka Gunawan, Chichi Rizka Hafizh Al Kautsar Aidilof Hafizh Al-Kautsar Aidilof Hamdhana, Defry Herman Fithra Hermansyah Hermansyah I Made Ari Nrartha Ilyana, Anis Imanda, Nanda Intan Nuriani Ira Wati Irwansyahputra Irwansyahputra Isa, Muzamir Ismun Naufal Iza Rifna Jessika, Jessika Jikti Khairina Julia Ulfah Khaidar, Al Khairina, Jikti Khairul Fuadi Khairul Khairul, Khairul Khairuni Khairuni Kurnia, Sri Kurniawati M Farhan Aulia Barus M Rizwan M Suhendri M. Ali, Rahmadi Marleni Marleni Maryana Maryana Maryana Maryana Maryana Maryana Maryana, Maryana Maulita, Maya Maya Juwita Dewi Maysura Meriatna Meriatna Muchlis ABD Muthalib Muchlis Abdul Muthalib Muhammad Daud Muhammad Faisal Muhammad fauzan Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Hutomi Muhammad Iqbal Muhammad Johan Setiawan Muhammad Nasir Muhammad Riansyah Muhammad Ridha Mukti Qamal Muliana, Syarifah Munirul Ula Munirul Ula Mutammimul Ula Muzakir Nur Nadilla Baimal Puteri Nanda Imanda NELI SUSANTI, NELI Nunsina, Nunsina Nur, Muzakir Nurdin Nurdin Nurhabsah Nurhabsah Pradita, Cindy Cika Rahma Jihan Ananta Rahmad Rahmad Rahmad Rahmat Rahmat Raihan Putri Rasyada, Reza Dian Reza, Restu Rini Meiyanti Risawandi, Risawandi Riza Mirza Rizal S.Si., M.IT, Rizal Rizki Setiawan Rizki Suwanda Rizky Putra Fhonna Rizkya, Ghinni Robi Kurniawan Rusadi, Athirah Said Fadlan Anshari salamah salamah Salimuddin, Salimuddin Salsabila, Thifal Samudera, Brucel Duta Sapitri, Anggri Sari, Cut Jora Sayuti, Muhammad Siagian, Tania Annisa Siregar, Widyana Verawaty Siti Hajar Sri Kurnia Sri Kurnia Suci Fitriani, Suci Suhaili Sahibul Muna Sujacka Retno Sultan, Kana Suryana, Fitra Syahputra, Wawan Syandriani Harahap Taufik Taufik Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Uci Mutiara Putri Nasution Ulfah, Julia Ulva Fitriani Utomo, Muhammad Fikri Wahdana, Aldi Wan Dinulaqli Wan, Syahputra Wawan Wawan Yani, Muhamamd Yeni Yeni Yesy Afrilia Yesy Afrillia Yulisda, Desvina Zahrah, Violita Aditya Zahratul Fitri Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar Zuraida Zuraida