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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) IJISTECH Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology jeti JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Teknik Informatika dan Elektro (JURTIE) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Journal of Advanced Computer Knowledge and Algorithms Applied Engineering, Innovation, and Technology Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Munirul Ula
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry 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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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.
Comparison of the Results of Double Exponential Smoothing Method with Triple Exponential Smoothing for Predicting Chili Prices Nadia Saphira; Munirul Ula; Sujacka Retno
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

Double Exponential Smoothing (DES) is a forecasting method that combines two components level and trend, used for data with a trend pattern that tends to increase or decrease over time. In contrast, Triple Exponential Smoothing (TES) incorporates three components: level, trend, and seasonality, making it suitable for data with trend and seasonal patterns. This study uses historical chili price data from 2020 to 2023, obtained from the Bank Indonesia website, managed by the National Strategic Food Price Information Center (PIHPS), to compare the effectiveness of DES and TES in predicting chili prices in Medan City. Prediction accuracy was evaluated using MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error). The study results show MAPE values for DES as follows: Large Red Chili 1.25%, Curly Red Chili 1.39%, Green Bird’s Eye Chili 1.14%, and Red Bird’s Eye Chili 1.13%. TES produced slightly lower MAPE values: Large Red Chili 1.25%, Curly Red Chili 1.38%, Green Bird’s Eye Chili 1.12%, and Red Bird’s Eye Chili 1.10%. The MAE values for DES are as follows: Large Red Chili 447.9, Curly Red Chili 494.83, Green Bird’s Eye Chili 430.92, and Red Bird’s Eye Chili 423.36. TES showed better accuracy with MAE values of Large Red Chili at 447, Curly Red Chili at 493.02, Green Bird’s Eye Chili at 416.2, and Red Bird’s Eye Chili at 409.36. The results conclude that Triple Exponential Smoothing performs better than Double Exponential Smoothing in predicting chili prices.
Identification of Environmental Security in Relation to Crime Rates in Simeulue Regency Using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Method Yopy Anfelia; Munirul Ula; Sujacka Retno
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

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Abstract

Criminal offenses are acts that violate criminal law and are punishable by the state, either through imprisonment, fines, or other sanctions. These offenses cause significant distress and harm to the general public, individuals, and the state. In Simeulue Regency, the number of criminal cases has been increasing annually, driven by social, economic, environmental, cultural, legal, technological, and psychological factors. This study aims to analyze the relationship between environmental security and the level of criminal cases in Simeulue Regency using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The data used includes criminal cases from 2019 to 2023 across 10 districts, along with environmental information such as population density, public facilities, and socioeconomic indicators. The research methodology involves data collection and cleaning, Euclidean distance calculation, parameter selection for DBSCAN, and the application of validation formulas to determine the vulnerability to criminal offenses in Simeulue Regency. The analysis results, using an epsilon parameter of 5 and MinPts of 3, yielded clusters 0, -1, and 1. Cluster 0 includes Salang and Teluk Dalam districts; cluster -1 includes Alafan, Simeulue Tengah, Simeulue Timur, Simeulue Barat, Teupah Barat, and Teupah Selatan districts; and cluster 1 includes Simeulue Cut and Teupah Tengah districts. The validation formula indicates that the highly vulnerable area is in Simeulue Timur district, while the at-risk areas are Teupah Tengah, Teluk Dalam, and Teupah Barat districts. The areas classified as not at risk are Alafan, Salang, Simeulue Tengah, Simeulue Cut, Simeulue Barat, and Teupah Selatan districts. This study provides insights into areas that require increased attention in efforts to address and prevent criminal offenses. Keywords: environmental security, criminal offenses, DBSCAN, clustering, Simeulue Regency
Penerapan Hybrid Data Mining Menggunakan K-Means Clutering Dan Decision Tree Untuk Klasifikasi Kasus Perceraian Kabupaten Aceh Tengah Fahruddin, Fahruddin; Ula, Munirul; Muthalib, Muchlis Abd
Jurnal Teknik Informatika dan Elektro Vol 7 No 1 (2025): Jurnal Teknik Elektro dan Informatika
Publisher : Universitas Gajah Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55542/jurtie.v7i1.1879

Abstract

Abstrak– perceraian adalah pengakhiran suatu perkawinan karena sesuatu sebab dengan keputusan hakim atas tuntutan dari salah satu pihak atau kedua belah pihak dalam perkawinan. Islam sendiri telah memberikan penjelasan dan definisi bahwa perceraian menurut ahli fikih disebut talak atau furqoh. Untuk saat ini angka kasus perceraian di Kabupaten Aceh Tengah mengalami peningkatan yang sangat signifikan pada tahun 2019 sampai dengan pertengahan tahun 2022, bahkan dari 23 Kabupaten di Provinsi Aceh yaitu Kabupaten Aceh Tengah adalah kasus perceraian tertinggi hingga mencapai 1273 kasus pada pertengahan 2022. Dari 1273 jumlah kasus tersebut perlu adanya penerapan algoritma kombinasi atau yang di sebut dengan Hybrid Data Mining menggunakan metode K-Means Clustering dan Decision Tree di mana metode ini berfungsi untuk mengolah data kasus perceraian sebagai tujuan mengklasifikasikan data kasus perceraian di kabupaten Aceh Tengah. Pengujian klaster di lakukan dengan 3 model klaster yaitu k=2,k=3 dan k4. Untuk mendapatkan data dari hasil klaster maka di lakukan pengujian kinerja davies bouldin maka menghasilkan nilai kinerja klaster dengan k=2 adalah -2,127, untuk nilai davies bouldin kinerja klaster dengan k=3 adalah -1,794, sedangkan nilai davies bouldin kinerja klaster dengan k=3 adalah -1,854. Berdasarkan simpulan diatas maka pada model 2 dengan jumlah k=3 dapat ditentukan klaster yang akan direduksi yaitu klaster dengan keanggotaan terkecil yaitu cluster 2 dengan jumlah data yang direduksi yaitu 59 data, sehingga jumlah dataset hasil reduksi yaitu 1.214 data. Dengan data hasil reduksi maka di uji menggunakan algoritma decision tree dengan komposisi split data 90:10’80:20 dan 70:10. Dengan demikian maka menghasilkan nilai akurasi data sebelum di reduksi dengan data setelah di reduksi dengan demikian nilai rata-rata akurasi untuk klasisfikasi tanpa reduksi adalah 85,96%, presisi 84,71% dan recall 79,36% dan untuk akurasi setelah direduksi adalah 87,90%, presisi 87,22%, dan recall 82,72%. Sehingga dapat disimpulkan bahwa akurasi klasifikasi dataset setelah direduksi lebih tinggi dari akurasi klasifikasi tanpa reduksi. Kata Kunci: data perceraian, hybrid, k-means clustering, Decision Tree. Abstract– Divorce is the termination of a marriage for any reason by a judge's decision based on the demands of one or both parties in the marriage. Islam itself has provided an explanation and definition that according to fiqh experts, divorce is called talak or furqoh. Currently, the number of divorce cases in Central Aceh Regency has increased very significantly from 2019 to mid-2022, In fact, of the 23 districts in Aceh Province, Central Aceh District has the highest number of divorce cases, reaching 1273 cases in mid-2022. Of the 1273 cases, it is necessary to apply a combination algorithm or what is called Hybrid Data Mining using the K-Means Clustering and Decision Tree method, where this method functions to process divorce case data for the purpose of classifying divorce case data in Central Aceh district. Cluster testing was carried out with 3 cluster models, namely k=2, k=3 and k4, To get data from the cluster results, the Davies Bouldin performance test was carried out, resulting in a cluster performance value with k=2 which was -2.127, for the Davies Bouldin value of cluster performance with k=3 is -1.794, while the Davies Bouldin value of cluster performance with k=3 is -1.854. Based on the conclusions above, in model 2 with the number k=3, the cluster that will be reduced can be determined, namely the cluster with the smallest membership, namely cluster 2 with the amount of data reduced, namely 59 data, so that the total dataset resulting from the reduction is 1,214 data. With the reduced data, it was tested using a decision tree algorithm with a data split composition of 90:10'80:20 and 70:10. In this way, the accuracy value of the data before reduction is produced with the data after reduction, so the average value of accuracy for classification without reduction is 85.96%, precision is 84.71% and recall is 79.36% and for accuracy after reduction is 87. .90%, precision 87.22%, and recall 82.72%. So it can be concluded that the classification accuracy of the dataset after reduction is higher than the classification accuracy without reduction. Keywords: divorce data, hybrid, k-means clustering, Decision Tree.
Analisis Performa Voice Recognition Pada Smart Speaker Menggunakan Metode Random Forest Yani, Muhammad; Fikry, Muhammad; Hasibuan, Arnawan; Nurdin; Munirul Ula; Husaini
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID654

Abstract

The development of Internet of Things (IoT) and artificial intelligence technology has driven the increasing use of voice user interfaces (VUI) as a more natural form of human-computer interaction. One widely used VUI implementation is voice recognition-based smart speakers. Despite its widespread adoption, voice recognition performance on smart speakers is not necessarily optimal when used in real-world conditions, particularly in far-field scenarios that are influenced by user distance, environmental noise, and system response time. This study aims to analyze and compare the voice recognition performance of Amazon Alexa smart speakers and the Interactive Speaker System as a non-vendor comparison system. Testing was conducted at varying user distances in a non-soundproof room to represent real-world operational conditions.The obtained performance data was analyzed using the Random Forest method as a classification tool due to its ability to handle multivariate data and nonlinear relationships between variables. The results showed that variations in user distance significantly affected the voice recognition performance of both systems, with a tendency for performance to decrease as distance increased. In addition, differences in system architecture characteristics also influenced the level of resilience to environmental conditions. The application of the Random Forest method also enabled the identification of dominant factors that influence the success of voice recognition. This research is expected to provide theoretical contributions in the study of voice recognition performance in far-field scenarios, as well as practical contributions as a basis for consideration in the selection and development of more reliable voice-based interaction systems in real environments.
SURVEI LITERATUR INFORMATION SECURITY INTELLIGENT UNTUK MEMPREDIKSI POTENSI KERUSUHAN MELALUI ANALISA JARINGAN MEDIA SOSIAL Munirul ula
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 3 No. 1 (2019): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v3i1.6302

Abstract

Kajian ini bertujuan untuk manganalisa literature terdahulu yang berkaitan dengan penerapan Information Security Intelligent (ISI) untuk memprediksi potensi kerusuhan melalui media sosial. Kajian literatur penelitian terdahulu menunjukkan bahwa berbagai platform media sosial di Internet seperti Twitter, Tumblr, Facebook, YouTube, Blog dan forum diskusi disalahgunakan oleh kelompok-kelompok ekstremis untuk menyebarkan kepercayaan dan ideologi mereka. Situs web microblogging populer seperti Twitter digunakan sebagai platform real time untuk berbagi informasi dan komunikasi selama perencanaan dan mobilisasi massa. Penerapan analisa jaringan media sosial untuk memprediksi kejadian kerusuhan adalah area yang telah menarik perhatian beberapa peneliti selama beberapa tahun terakhir. Ada berbagai macam metode yang telah digunakan dalam literatur terkait prediksi kejadian kerusuhan. Dalam jurnal ini, penulis melakukan kajian literatur mengenai semua metode yang ada dan melakukan analisis yang komprehensif untuk memahami situasi, tren dan kesenjangan penelitian. Analisa kajian ini menghasilkan karakterisasi, klasifikasi, dan meta-anlaysis dari puluhan  jurnal untuk mendapatkan pemahaman yang lebih baik tentang literatur tentang potensi kejadian kerusuhan dengan menggunakan metode sosial media intelligent.
KAJIAN LITERATUR PENERAPAN SOCIAL MEDIA NETWORK DAN INFORMATION SECURITY INTELLIGENT UNTUK MENGIDENTIFIKASI POTENSI RADIKALISASI ONLINE Munirul ula
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 3 No. 2 (2019): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v3i2.6336

Abstract

Kajian literatur penelitian terdahulu menunjukkan bahwa berbagai platform media sosial di Internet seperti Twitter, Tumblr, Facebook, YouTube, Blog dan forum diskusi disalahgunakan oleh kelompok-kelompok ekstremis untuk menyebarkan kepercayaan dan ideologi mereka, mempromosikan radikalisasi, merekrut anggota dan menciptakan komunitas virtual online. Selama lebih dari 10 tahun terakhir penggunaan analisa jaringan media sosial untuk memprediksi dan mengidentifikasi radikalisasi online adalah area yang telah menarik perhatian beberapa peneliti selama 10 tahun terakhir. Ada beberapa algoritma, teknik, dan alat yang telah diusulkan dalam literatur yang ada untuk melawan dan memerangi cyber-ekstrimis. Dalam jurnal ini, penulis melakukan tinjauan literatur dari semua teknik yang ada dan melakukan analisis yang komprehensif untuk memahami keadaan, tren dan kesenjangan penelitian. Dalam jurnal ini dilakukan karakterisasi, klasifikasi, dan meta-anlaysis dari puluhan  jurnal untuk mendapatkan pemahaman yang lebih baik tentang literatur tentang pendeteksian ektrimis melalui sosial media intelligent .
A Comparative Study of Temporal Convolutional Network and Gated Recurrent Unit for Predicting Ethereum Prices Saiful Kiram; Munirul Ula; Kurniawati Kurniawati
Applied Engineering, Innovation, and Technology Vol. 2 No. 1 (2025)
Publisher : MSD Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62777/aeit.v2i1.55

Abstract

This study compares the performance of the Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models in predicting the price of Ethereum, which is important to support cryptocurrency investment strategies. With the high volatility of the cryptocurrency market, an accurate and reliable prediction model is needed. In this study, Ethereum's daily closing price data over four years was analyzed using TCN and GRU models to evaluate its predictive capabilities. Model accuracy is measured using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The results showed that the TCN model excelled in average accuracy with lower MAE and MAPE values, while the GRU model showed excellence in reducing the impact of large errors with smaller MSE values. This reflects TCN's superiority in capturing the overall pattern of price movements, while the GRU is more responsive to short-term price fluctuations. These findings demonstrate the potential of both models in cryptocurrency price forecasting, with their respective advantages. This research provides valuable information for investors and researchers in developing predictive strategies in dynamic financial markets. A combination of TCN and GRU models can also be explored to improve prediction performance in the future.
Firewall Analytics in DNS and SYN Flood Protection on Mikrotik CCR in the North Aceh District Government Nanda Imanda; Dahlan Abdullah; Fajriana Fajriana; Nurdin Nurdin; Munirul Ula
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1791

Abstract

This study investigates the implementation of an analytical firewall on the Mikrotik Cloud Core Router (CCR) device for network protection against Domain Name System (DNS) and Synchronise Flood (SYN Flood attacks in the information technology infrastructure of the North Aceh Regency Government. DNS-based attacks and SYN Flood have demonstrated a significant disruptive capacity for the continuity of electronic public services, illustrating the urgency of robust security protocols on government infrastructure. The study implemented a quantitative-experimental approach, with methodological triangulation in empirical data acquisition through controlled attack simulations, firewall log analysis, and semi-structured interviews with technical personnel. Experiments are designed with variations in attack intensity to evaluate system resilience thresholds, while firewall log analysis facilitates the identification of anomalous patterns through detection algorithms. The analytics process applies parametric evaluation to temporal mitigation metrics, packet processing capacity, and operational implications on network performance, complemented by descriptive statistical analysis that explores data distribution and temporal trends. The results indicate the differential effectiveness of the specific firewall configuration against a specific attack typology, with an empirical determination of optimisation parameters for real-time mitigation. This research contributes to the corpus of knowledge regarding the security of government networks through the derivation of protective models that are adaptive to the operational characteristics of public infrastructure. The findings have substantive implications for cybersecurity policy formulation in the administrative context of local governments, with extensive significance for the implementation of network architectures that are resilient to volumetric attacks and protocol exploitation.
APLIKASI TES MINAT BAKAT SERTA REKOMENDASI JURUSAN UNTUK SISWA SMA MENGGUNAKAN METODE FORWARD CHAINING Nurul Husna; Munirul Ula; Yesy Aflillia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6490

Abstract

In the Industrial Revolution 4.0 era, human resources play a crucial role in workplace competitiveness, requiring individuals to develop their skills and knowledge through education. High school graduates who wish to pursue higher education face difficulties in choosing the right major. This situation is influenced by external pressures, causing many students to make choices that are not in line with their abilities. This study aims to develop an Interest and Talent Test Application to facilitate high school students in providing explanations about their interests and talents as well as recommendations for appropriate majors. This application is built on a website using the Multiple Intelligences theory and the Forward Chaining method. In this process, data samples will be used to test the methods used in this study. The samples used came from 60 students of SMA Negeri 1 Jeumpa who had completed the trial. The results of this test showed that the average type of intelligence of students in the high school was Verbal (Linguistic) intelligence for 16 students with majors that correspond to their type of intelligence, namely Communication Science, Language and Literature, International Relations, Law, and Political Science. This system has achieved an average user satisfaction rating of 81.91%, indicating that users are satisfied with consulting using this aptitude and interest testing application.
Co-Authors Abdullah, ⁠Dahlan Affan Syafiq Azzikri Afif, Muhammad Athallah Afridah, Rita Agustriya, Manda Al-Ghiyats, Said Ananda Faridhatul Ulva Andreansyah, Sabda Ar Razi Arnawan Hasibuan Azzikri, Affan Syafiq ⁠Dahlan Abdullah Bustami Bustami Bustami Bustami Cut Agusniar Dahlan Abdullah Dara Farhiyah Dhani, Saniah Dinda, Dinda Fadillah, Rizky Fahruddin Fahruddin Fajriana, F Fajriana, Fajriana Fasdarsyah Fasdarsyah Fidyatun Nisa Fikhri, Aditya Aziz Fitri, Anisa Amelia Fuddin, Mudhya Hamdhana, Defry Hasan Dalimunthe, Amir Husaini Jessika Jessika Kamaruzzaman, Hilda Zulfira KURNIAWATI - Kurniawati Kurniawati Lailatul Husna Lidya Rosnita Lubis, Syahrul Andika M David Khalid Mey Suci Br Pardosi Muhammad Daud Muhammad Fauzan Muhammad Fikry Muhammad Ikhwanus Muhammad Muhammad Muhammad Yani, Muhammad Mutammimul Ula Muthalib, Muchlis Abd Nadia Saphira Nanda Imanda Nasution, Wahidatunnisa Nurdin Nurdin Nurdin Nurdin Nurdin Nurul Aula Nurul Husna Putri Agustina Dewi Putri, Nazirah Allisya Rahman, Ashri Nurhajizah Ridha, Ridha Rini Meiyanti Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek Rizki Suwanda Rizky Putra Fhonna Rizky, Rahmat Rozzi Kesuma Dinata Rusadi, Athirah Said Fadlan Anshari Saiful Kiram Salimuddin, Salimuddin Sayed Fachrurrazi Sayed Fachrurrazi Sayuti, Muhammad Siagian, Tania Annisa Sinambela, Reza Syahputra Siska Amelia Melani Siti Aminah Sudarti, Atrida Sujacka Retno Susanti Susanti Syarifah Muliana Taufiq Taufiq Taufiq Taufiq Tiara Oktavia Ulfah, Julia Veri Ilhadi Yasin, Fijri Ahmad Yessi Apprilia Yesy Aflillia Yesy Afrillia Yopy Anfelia Yulisda, Desvina Yuni SariBr Sitepu Zailani Mohamed Sidek Zara Yunizar