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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Proceeding of the Electrical Engineering Computer Science and Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research SISFOTENIKA JURNAL INTEGRASI IT JOURNAL RESEARCH AND DEVELOPMENT JURNAL REKAYASA TEKNOLOGI INFORMASI JURNAL PENGABDI ILKOM Jurnal Ilmiah METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi MIND (Multimedia Artificial Intelligent Networking Database) Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) EXPLORE METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi JUKI : Jurnal Komputer dan Informatika Indonesian Journal of Data and Science Infotek : Jurnal Informatika dan Teknologi PLAKAT : Jurnal Pelayanan Kepada Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Jurnal Kesehatan Saintika Meditory JUSTIN (Jurnal Sistem dan Teknologi Informasi) Transformasi Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer EXPLORE Indonesian Journal of Informatic Research and Software Engineering Journal of Technology Research in Information System and Engineering Data Sciences Indonesia (DSI) Adopsi Teknologi dan Sistem Informasi Jurnal Masyarakat Madani Indonesia Jurnal Pengabdian Pada Masyarakat Journal of Computer Science and Information Technology Inovasi Teknologi Masyarakat KETIK : Jurnal Informatika
Wati, Masna
Program Studi Informatika, Fakultas Teknik, Universitas Mulawarman
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Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet Simanungkalit, Julius Rinaldi; Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Puspitasari, Novianti; Wati, Masna
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.521.32-38

Abstract

Rubber plantation sector is one of the leading commodities in East Kalimantan Province contributing greatly to non-oil and gas exports. Currently, the price of rubber in the world is increasingly competitive. The aim of this research is to predict the rubber prices as a reference for the government and companies in making policies and preparing work plans. Data of 60 months during the period of 2014-2018 taken from Plantation office of East Kalimantan Province has been analyzed using Backpropagation Neural Network (BPNN) algorithm in predicting rubber prices. Based on the testing results, parameters of the BPNN algorithm with ratio of 4: 1, architectural models 5-10-10-10-1, trainlm learning function, learning rate of 0.5, error tolerance of 0.01, and epoch of 1000 have gained good accuracy with a mean square error (MSE) of 0.00015464. The results showed that the BPNN algorithm can be used as an alternative method in forecasting.
IDENTIFIKASI POLA KECELAKAAN LALU LINTAS DENGAN K-MEANS CLUSTERING Bandhaso, Victor; Wati, Masna; uddin, Havil
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 14, No 1 (2026): Jurnal Tikomsin, Vol 14, No.1, April 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v14i1.1037

Abstract

Traffic accidents represent a complex issue with significant social and economic impacts. This study aims to identify temporal patterns of traffic accidents based on temporal and demographic attributes using the K-Means Clustering algorithm applied to 9,659 accident records in Central Java Province in 2024. Time attributes were converted to decimal format, while occupational data for the involved parties were transformed into numerical codes to enable clustering analysis. The K-Means Clustering algorithm was then employed to generate cluster models. Cluster 0 is characterized by an afternoon peak in incident time around 18.10, with the closest encoded occupational category corresponding to TNI–POLRI personnel. Cluster 1 consists of an average incident occurring at 06.26, predominantly involving homemakers. Cluster 2 is dominated by homemakers, with incidents generally occurring around 17.03. Cluster 3 shows the dominance of TNI–POLRI personnel, with incidents most frequently occurring at 07.19. These findings indicate that the most frequently involved occupational groups are military/police personnel and homemakers, both of which exhibit high mobility during peak hours and also threaten officers who are supposed to maintain traffic order.
Generative Artificial Intelligence as an Adaptive Medium to Optimize Interactive Learning for Teachers at Public Special Schools Kheyene Molekandella Boer; Nurliah Nurliah; Masna Wati; Muhammad Aidil Ilham; Latifa Latifa
Plakat : Jurnal Pelayanan Kepada Masyarakat Vol 8, No 1 (2026): Plakat: Jurnal Pelayanan Kepada Masyarakat
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/plakat.v8i1.26200

Abstract

The rapid advancement of Generative Artificial Intelligence (GenAI) has transformed the global educational landscape; however, its implementation in Public Special Schools remains limited due to low digital competence and technological access gaps. This condition contributes to the continued dominance of conventional, teacher-centered instruction that lacks adaptability to diverse student needs. This community service research aimed to enhance teachers’ competence in utilizing GenAI as an adaptive medium to optimize interactive learning. The program was conducted through a two-day workshop at SLBN Pembina, East Kalimantan Province, involving 46 teachers from elementary, junior high, and senior high special education levels. The activities included the development of two instructional modules (“Smarter Teaching with ChatGPT” and “Creative Teaching with Canva”), pre- and post-tests, hands-on practice, individual assignments, and post-activity monitoring. The findings revealed a 28.20% increase in teachers’ understanding during the ChatGPT session and a 48.96% improvement during the Canva session. Classroom monitoring further indicated enhanced student engagement and enthusiasm when AI-based materials were implemented. The implications highlight that structured and contextual training can effectively bridge the digital divide in special education settings, strengthen teacher self-efficacy, and promote more personalized, interactive, and inclusive AI-enhanced learning practicesPerkembangan pesat Generative Artificial Intelligence (GenAI) telah mentransformasi lanskap pendidikan global, namun implementasinya di Sekolah Luar Biasa Negeri (SLBN) masih terbatas akibat rendahnya kompetensi digital dan kesenjangan akses teknologi. Kondisi ini berdampak pada masih dominannya metode pembelajaran konvensional yang kurang adaptif terhadap kebutuhan peserta didik berkebutuhan khusus. Penelitian pengabdian ini bertujuan untuk meningkatkan kompetensi guru dalam memanfaatkan GenAI sebagai media adaptif guna mengoptimalkan interactive learning. Metode pelaksanaan dilakukan melalui pendekatan pelatihan berbasis workshop selama dua hari di SLBN Pembina Provinsi Kalimantan Timur dengan melibatkan 46 guru dari jenjang SDLB, SMPLB, dan SMALB. Kegiatan meliputi penyusunan dua modul (“Mengajar Lebih Cerdas dengan ChatGPT” dan “Kreatif Mengajar dengan Canva”), pre-test dan post-test, praktik langsung, penugasan individu, serta monitoring pascakegiatan. Hasil penelitian menunjukkan peningkatan pemahaman guru sebesar 28,20% pada sesi ChatGPT dan 48,96% pada sesi Canva. Monitoring lapangan juga menunjukkan meningkatnya keterlibatan dan antusiasme siswa saat materi berbasis AI diterapkan di kelas. Implikasi kegiatan ini menegaskan bahwa pelatihan terstruktur dan kontekstual mampu menjembatani kesenjangan digital di sekolah khusus, memperkuat kepercayaan diri guru, serta mendorong implementasi pembelajaran yang lebih personal, interaktif, dan inklusif berbasis teknologi AI.
Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price Masna Wati; Haviluddin Haviluddin; Akhmad Masyudi; Anindita Septiarini; Heliza Rahmania Hatta
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.22286

Abstract

Crude oil is the main commodity of the global economy because oil is used as an ingredient for many industries globally and is the price base used in the state budget. Indonesian Crude Price (ICP) fluctuates following developments in world crude oil prices. A significant increase in crude oil prices will certainly disrupt the economy. Thus, the movement or fluctuation of ICP is essential for business players in the energy market, especially domestically. Therefore, crude oil price forecasting is needed to assist business people in making decisions related to the energy market. This study aims to find a suitable forecasting model for Indonesian crude oil prices using the Autoregressive Integrated Moving Average (ARIMA) method. The forecasting process used ICP time-series data per month for 50 types of crude oil within five years or 63 months. Based on the experimental results, it was found that the most fit ARIMA models were (0,1,1), (1,1,0), (0,1,0), and (1,2,1). The test results for April to September 2020 have a good and proper interpretation, except the type of BRC oil indicates inaccurate forecasts. The ARIMA error rate is very dependent on the value of the data before it is predicted and external factors, the more unstable the data value every month, the higher the error rate.
Resin Code Classification on Plastic Packaging Using Few-Shot Learning Alyani Noor Septalia; Anindita Septiarini; Masna Wati
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.406

Abstract

Introduction: Accurate sorting of plastic waste using Resin Identification Codes (RICs) is essential for improving recycling quality. However, conventional deep learning approaches generally require large labeled datasets, which are difficult and costly to collect for small RIC symbols on plastic packaging. Method: This study employed a Few-Shot Learning approach based on Prototypical Networks using a 7-way 5-shot episodic training configuration. A self-collected dataset of 350 images, comprising 50 images for each of seven RIC categories—PETE, HDPE, PVC, LDPE, PP, PS, and OTHER—was used. Three backbone architectures, ConvNet4, ResNet-18, and EfficientNet-B2, were compared. An ablation study evaluated support-set augmentation, followed by supervised fine-tuning of the selected model. Results and Discussion: EfficientNet-B2 achieved the highest episodic accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. EfficientNet-B2 with light augmentation attained 85.71% accuracy on the fixed 42-image test set, with perfect recall for PVC, LDPE, and PP. Most errors involved visually similar HDPE and PP symbols. Fine-tuning corrected five of six misclassifications, increasing test accuracy to 90.48% and the F1-score from 0.857 to 0.903. Conclusion: Prototypical Networks with an EfficientNet-B2 backbone and cosine distance provide an effective solution for RIC classification under limited-data conditions and offer a practical foundation for automated plastic-waste sorting systems.
Analisis Kinerja K-Means dan DBSCAN dalam Pengelompokan Kepadatan Kendaraan Bermotor Tingkat Provinsi Farizi, Syafiq Hafizh; Rasyid, Muhammad; Wati, Masna; Widians, Joan Angelina
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 6 No 3 (2026): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v6i3.1900

Abstract

Pertumbuhan kendaraan bermotor di Indonesia meningkat signifikan dengan distribusi antarprovinsi yang sangat timpang. Data BPS (2024) mencatat ketimpangan ekstrem: Jawa Barat memiliki 27.104.924 unit, sedangkan Kalimantan Utara hanya 305.187 unit. Hal ini menuntut pendekatan analisis untuk mengelompokkan kepadatan kendaraan secara sistematis. Penelitian ini bertujuan membandingkan kinerja algoritma K-Means dan Density-Based Spatial Clustering of Applications with Noise (DBSCAN) dalam mengelompokkan kepadatan kendaraan pada 34 provinsi di Indonesia. Tahapan penelitian meliputi pengumpulan data sekunder, preprocessing (pembersihan, transformasi logaritmik, dan standardisasi), serta penentuan parameter optimal melalui metode Elbow dan Silhouette (K-Means) dan k-distance plot (DBSCAN). Evaluasi performa menggunakan Silhouette Coefficient, Davies-Bouldin Index, dan Calinski-Harabasz Index. Hasilnya, K-Means (K=4) menghasilkan empat tingkat kepadatan yang interpretable dengan nilai Silhouette 0,537, Davies-Bouldin 0,560, dan Calinski-Harabasz 98,00. Sebaliknya, DBSCAN (?=0,8; MinPts=5) hanya membentuk 2 cluster dengan 5 titik noise dan Calinski-Harabasz 42,67. Kesimpulannya, K-Means terbukti lebih unggul dalam menghasilkan separasi cluster yang granular dan informatif untuk pengelompokan tingkat kepadatan kendaraan di Indonesia.
Teknologi AI Untuk Meningkatkan Proses Belajar Mengajar Di SMP Patra Dharma 1 Balikpapan Masna Wati; Anindita Septiarini; Novianti Puspitasari; Ummul Hairah; Raudhya Azzahra; Maya Agustina
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 1 (2026): Februari
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/4k6zav55

Abstract

Penerapan Artificial Intelligence (AI) meningkat pesat dalam beberapa tahun terakhir. Namun, kurangnya pemahaman terhadap teknologi AI bagi siswa menyebabkan minimnya pemanfaatan AI dalam menunjang proses pembelajaran secara optimal. Edukasi pemanfaatan AI untuk siswa penting dilakukan untuk membantu meningkatkan keterampilan belajar siswa. Metode yang digunakan pada kegiatan ini yaitu pelatihan dan simulasi IPTEKS dimana peserta diperkenalkan tools ChatGPT, QuillBot, Gamma AI dan Runway ML. Kegiatan dilaksanakan di Laboratorium Komputer SMP Patra Dharma 1 Balikpapan selama dua hari dengan peserta sebanyak 46 siswa SMP Patra Dharma 1 Balikpapan kelas VII. Hasil kegiatan menunjukkan bahwa kegiatan ini tidak hanya meningkatkan pengetahuan dan keterampilan siswa dalam penggunaan teknologi AI sebesar 40,61% meskipun dilaksanakan dalam durasi yang relatif singkat, tetapi juga memberikan dampak kualitatif berupa meningkatnya kepercayaan diri dalam menggunakan teknologi, peningkatan kreativitas digital, berkembangnya kemampuan berpikir terstruktur dan kritis. AI secara spesifik berperan sebagai alat bantu yang mempermudah siswa dalam mengembangkan ide, mengorganisasi informasi, serta menyajikan materi dan informasi dalam bentuk digital yang lebih menarik dan interaktif. Peningkatan peserta pada aspek pengetahuan teknologi AI sebesar 41,85%, sedangkan pada aspek keterampilan penggunaan teknologi AI sebesar 39,33%. Dengan pemahaman teknologi AI, siswa mampu mengenali manfaat AI sebagai pendukung proses belajar yang lebih efektif dan menarik
Komparasi Algoritma K-Means dan DBSCAN dalam Klasterisasi Indeks Pembangunan Gender Tingkat Kabupaten/Kota di Indonesia: Comparison of K-Means and DBSCAN Algorithms in Clustering Indonesia's Regional Gender Development Index Sifwah Fatin Sofwani; Muhammad Riva Fachrodhiya; Masna Wati; Joan Angelina Widians
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 6 No. 1 (2026): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v6i1.2838

Abstract

Analisis terhadap ketimpangan pencapaian pembangunan antara laki-laki dan perempuan memerlukan pendekatan pemodelan data tingkat lanjut dalam pemetaan distribusi spasial secara akurat. Penelitian ini mengeksplorasi masalah disparitas pembangunan gender di tingkat kabupaten/kota di Indonesia menggunakan data tahun 2025 yang diakses dari BPS pada Mei 2026 dengan melakukan studi perbandingan antara dua algoritma yaitu K-Means dan Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Pemecahan masalah difokuskan pada pengelompokkan 514 wilayah administratif di Indonesia menggunakan indikator pembentuk Indeks Pembangunan Manusia (IPM) berbasis gender, yang meliputi Angka Harapan Lama Sekolah, Rata-rata Lama Sekolah, Pengeluaran per Kapita yang Disesuaikan, dan Umur Harapan Hidup saat lahir. Metodologi yang digunakan dalam penelitian ini yaitu preprocessing data, kalkulasi indeks komposit menggunakan rata-rata geometrik sesuai standar Badan Pusat Statistik terbaru, serta menguji bagaimana struktur algoritma tersebut diatur dalam mengolah data. Pengujian menggunakan Silhouette Score dan Davies-Bouldin Index menunjukkan bahwa algoritma K-Means secara keseluruhan lebih unggul dalam menghasilkan klaster yang kompak dan terstruktur untuk kategorisasi umum wilayah berbasis capaian IPG. Algoritma DBSCAN berperan sebagai pelengkap yang efektif dalam mengidentifikasi 33 kabupaten/kota sebagai anomali dengan profil pembangunan gender yang menyimpang secara signifikan dari pola umum. Penelitian ini merekomendasikan K-Means sebagai algoritma utama klasterisasi wilayah IPG, dengan DBSCAN sebagai instrumen pendukung untuk deteksi daerah prioritas intervensi kebijakan gender secara khusus
Alphabet Gesture Classification of Indonesian Sign Language Using Convolutional Neural Networks Gideon Simalango, Yanuar; Septiarini, Anindita; Wati, Masna; Hamdani, Hamdani; Rajiansyah, Rajiansyah
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5240

Abstract

Indonesian Sign Language (BISINDO) serves as a communication medium for deaf individuals to engage with their environment. Alphabet gestures in BISINDO play a crucial role in the formation of words and sentences. Nonetheless, the automatic recognition of BISINDO alphabet movements remains a difficulty in the advancement of accessible technology. This research intends to categorize BISINDO alphabet gestures via the Convolutional Neural Network (CNN) model. The CNN approach was used due to its proficiency in recognizing visual patterns and images. The dataset comprises BISINDO alphabet gesture photos captured from diverse perspectives and lighting conditions. The data processing procedure encompasses pre-processing phases, including picture normalization, data augmentation, and the segmentation of the dataset into training, validation, and test subsets. The constructed CNN model has multiple convolutional and pooling layers to thoroughly extract visual characteristics. The study's results indicate that the CNN model can classify BISINDO alphabet gestures with a high accuracy of 90% on the test data. This model's deployment is anticipated to aid in the creation of automatic sign language translation programs, hence enhancing communication between the deaf community and the general populace. This study demonstrates the potential of CNN models to support the development of inclusive communication technologies for the hearing impaired in Indonesia, particularly for under-researched sign languages like BISINDO.
PENERAPAN FUZZY INFERENCE SYSTEM METODE TSUKAMOTO UNTUK PENGUKURAN TINGKAT INFLASI SUATU NEGARA Ricky Anggari; Muhammad Ifandi; Nanda Arianto; Anindita Septiarini; Masna Wati
Journal of Computer Science and Information Technology Vol. 2 No. 3 (2025): Juni
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v2i3.2311

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem pengukuran tingkat inflasi suatu negara menggunakan pendekatan Fuzzy Inference System (FIS) metode Tsukamoto. Pendekatan ini dipilih karena kemampuannya dalam menangani ketidakpastian dan ketidaklinieran data ekonomi makro. Sistem dirancang berdasarkan tiga parameter utama: nilai tukar mata uang, Produk Domestik Bruto (GDP), dan suku bunga, dengan data diperoleh dari World DataBank tahun 2022. Fungsi keanggotaan berbentuk segitiga dan bahu digunakan untuk merepresentasikan input linguistik, dan aturan fuzzy berbasis IF-THEN dikembangkan untuk proses inferensi. Implementasi dilakukan menggunakan bahasa pemrograman Python. Hasil pengujian menunjukkan bahwa mayoritas negara (90,41%) berada dalam kategori inflasi "Sedang", sementara sebagian kecil dikategorikan "Rendah" dan "Tinggi". Sistem ini terbukti fleksibel dalam menghadapi variasi data ekonomi, namun masih memiliki keterbatasan dalam menangani data yang tidak lengkap. Penelitian ini menyimpulkan bahwa metode Tsukamoto efektif untuk klasifikasi inflasi negara dan berpotensi dikembangkan lebih lanjut melalui integrasi dengan metode hibrida guna meningkatkan akurasi dan ketepatan hasil.
Co-Authors -, Haviluddin Abdul Hadi Ade Chrisvitandy Adelowys Sinaga AHMAD ANSYORI Ahmad Nur Fauzan Aiman, Ahmad Zuhair Nur Ajay, Muhammad Akhmad Masyudi Alameka, Faza Alfajriani Alfajriani Ali Sholihin Alifah, Nur Juzieatul Alqarani, Hudzaifah Alyani Noor Septalia Ambon, Matelda Yunanta Andi Maulana Andi Maulana, Andi Anggari, Ricky Anindita Septiarini, Anindita Anton Prafanto Arabi, Muhammad Amin Quthbi Asmita, Rizka Awang Harsa Kridalaksana Awang Zheri Rhesvianur Ayu Rusnawati A’yuni, Qurrata Bahtiar , Andi Alfian Bambang Cahyono Bambang Cahyono Bandhaso, Victor Bramantyo, Dimas Ari Brins Leonard Pailan Budiman, Edy Burhandenny, Aji Ery Cahyani, Oktari Indi Davina Putri Ananta Delvina Dwiani Samjar Didit Suprihanto, Didit Dwi Kinasih Widiyati Engla Despahari Eny Maria Ervan, Muhamad Gusti Keyandi Evi Wildana Fadli Suandi Farisha Rizky Amalia Farizi, Syafiq Hafizh Fauzan, Ammar Nabil Faza Alameka Faza Alameka Fenny Indar Firdaus, Ardhifa Firdaus, Muhammad Firdaus, Muhammad Bambang Gading, Fazri Rahmad Nor Geni, Siti Putri Lenggo Gideon Simalango, Yanuar Hairah, Ummul Hairah, Ummul Hamdani Hamdani Hamdani Hamdani Hariati Hariati Hatta, Heliza Rahmania Haviluddin , Haviluddin Haviluddin Haviluddin Heliza Rahmania Hatta, Heliza Rahmania Hendi Herman Santoso Pakpahan Hidayat, Irfan Arman Hijratul Aini Hutagalung, Wilson Boyaron Hutapea, Vedra Dian Sierrafina Ifandi, Muhammad Iin Nurkarima Islamiyah Islamiyah Joan Angelina Widians, Joan Angelina Kesuma, Muhammad Afrizal Kheyene Molekandella Boer Latifa Latifa Lili, Juniver Veronika Lubis, Ferry Miechel Manik, Filipus Adriel Maya Agustina Medi Taruk Mega Yoalifa Merry, Felisitas Mewengkang, Alfrina Mochammad Taufiq As'arie Muhammad Abdillah Muhammad Aidil Ilham Muhammad Bambang Firdaus Muhammad Firdaus Muhammad Ifandi Muhammad Rafif Hanif Muhammad Rasyid, Muhammad Muhammad Riva Fachrodhiya Mu’nisah Assisi Nadifa Salsabila Purnomo Nanda Arianto Nggotu, Antonieta Aryuka Paskalia Novianti Puspitasari Nugraha, Cellia Auzia Nupa, Joy Disanto Nur Madia Nurkarima, Iin Nurliah Nurmadewi, Dita Nuzulan, Alan Olivia Octavia Pangestu, Jovan Bagas Pebianoor, Pebianoor Prano Pebri Ansari Pratama, Arief Ardi Puspitasari, Novianti Putri, Septi Aulia Rachmat Ragil Iskandar Rahman, Muhammad Ridwanansyah Rajiansyah, Rajiansyah Rasid, Khairul Raudhya Azzahra Raudhya Azzahra Rayner Alfred Razan, Muhammad Arya Fayyadh Reviansa Fakhruddin Aththar Ricky Anggari Rizqi Saputra Rosmasari, Rosmasari Sabina Nurlatifah Aurelia Sadewa, Bintang Putra Safitri, Hersa Salsabila, Nur Maya Saragih, Muhammad Nabil Sari, Lili Kurnia Sembiring, Wahyu Harry Saputra Septiani, Afra Amelia Setiawan, Maulana Agus Setyadi, Hario Jati Shiva Mutia Maffirotin Sifwah Fatin Sofwani Simanungkalit, Julius Rinaldi Simbolon, R.H. Kimebmen Sitompul, Tua Delima soleha, leha Syahputra, Andra Syarah, May Siti Syifani, Sarah Taruk, Medi Tejawati, Andi Tjikoa, Ade Fiqri uddin, Havil Ummul Hairah Vicky Pranandika Wijaksana Vina Zahrotun Kamila Viny Christanti M Wandi, Faizul Anwar Widians, Joan Angelina Wijaya, M Rangga Yaqub Wiji Astuti Yudi Kurniawan Yunus, Marlina Yusran, Sartiah Zahra Ayu Qalbina Zainal Arifin