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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics MUSTEK ANIM HA Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Wikrama Parahita : Jurnal Pengabdian Masyarakat IT JOURNAL RESEARCH AND DEVELOPMENT JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi MIND (Multimedia Artificial Intelligent Networking Database) Journal KOMPUTIKA - Jurnal Sistem Komputer TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Elektronik Scientific Journal of Informatics Journal of Innovation Information Technology and Application (JINITA) Indonesian Journal of Data and Science Infotek : Jurnal Informatika dan Teknologi Jurnal Teknologi Informatika dan Komputer SKANIKA: Sistem Komputer dan Teknik Informatika Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Sains Teknologi dan Sistem Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Transformasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer JOMPA ABDI: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Data Sciences Indonesia (DSI) Jurnal Masyarakat Madani Indonesia Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Journal of Computer Science Contributions (Jucosco) Journal of Computer Science and Information Technology Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi International Journal of Applied Mathematics and Computing.
Anindita Septiarini, Anindita
Mulawarman University
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Perbandingan Metode Naïve Bayes dan Support Vector Machine dalam Analisis Sentimen Citra Polri berdasarkan Opini pada Platform Twitter/X Sophy Awaliah; Arindra Nurshadrina Ramadini; Najwa Felira Zetti; Anindita Septiarini; Novi Puspitasari
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.4537

Abstract

The Indonesian National Police (Polri) is a law enforcement agency responsible for maintaining security and order in Indonesia. In the digital era, Polri’s image has increasingly been highlighted on social media platforms such as Twitter/X, which serve as a major channel for the public to express opinions and criticism. This study aims to compare the performance of the Naive Bayes method and Support Vector Machine (SVM) in sentiment analysis of public opinion toward Polri. Naive Bayes, known for its probabilistic approach, is compared with SVM, a robust machine learning algorithm capable of classifying data with clear margins between classes. The dataset was divided into 80% training data and 20% testing data with stratification to ensure balanced sentiment proportions. Performance evaluation was conducted using accuracy, precision, recall, and F1-score through a confusion matrix. Results show that SVM achieved the highest accuracy of 90%, while Naive Bayes obtained 83%. In terms of F1-score, SVM reached a macro average of 0.90 with its best performance in the positive category (0.97), while Naive Bayes reached 0.83 with its best in the positive category (0.90). Overall, SVM outperformed Naive Bayes, particularly in classifying neutral sentiment. This study provides insights into the effectiveness of SVM for analyzing informal tweets and can serve as a reference for future research and public opinion monitoring system development.
PERBANDINGAN METODE TRANSFER LEARNING DALAM KLASIFIKASI PENYAKIT DAUN PADI Aldi Daffa Arisyi; Muhammad Aidil Saputra; Muhammad Rafif Hanif; Anindita Septiarini; Akhmad Irsyad
Jurnal Ilmiah Informatika Vol. 11 No. 1 (2026): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/.v11i1.9611

Abstract

This study compares four transfer learning-based CNN models, namely VGG19, ResNet152, MobileNetV2, and DenseNet121, for the classification of 10 classes of rice leaf diseases. Evaluation results on the test dataset show that ResNet152 achieves the best performance with an accuracy of 0.9553, precision of 0.9589, recall of 0.9553, and F1-score of 0.9558, followed by DenseNet121 (accuracy 0.9433), MobileNetV2 (0.9353), and VGG19 (0.9247). ResNet152 excels in recognizing complex features through its skip connection mechanism, while DenseNet121 is more efficient with the lowest validation loss. MobileNetV2 is the lightest and fastest model, making it suitable for resource-limited devices. Based on the confusion matrix analysis, all models are able to classify the neck blast class perfectly; however, misclassifications still occur among visually similar classes such as brown spot, narrow brown spot, and leaf blast. Overall, transfer learning is proven effective for rice leaf disease classification, with ResNet152 and DenseNet121 being the most recommended models.
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.
Automated ergonomic sitting postures detection for office workstation using XGBoost method Theresia Amelia Pawitra; Farida Djumiati Sitania; Anindita Septiarini; Hamdani Hamdani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp506-514

Abstract

Sedentary office work increases musculoskeletal risk, underscoring the need for non-intrusive, real-time posture monitoring. This study presents a computer vision approach that classifies ergonomic versus non-ergonomic sitting postures using upper body key points extracted by MoveNet thunder. Images from 30 participants were captured from frontal and side views, and labeled according to SNI 9011:2021 criteria. Seventeen key points were detected, with head-to-hip landmarks retained, then normalized and centered. Three classifiers—adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and a multi-layer perceptron (MLP)—were trained and evaluated with 10-fold stratified cross-validation. XGBoost achieved the best performance, with accuracy 93.0%±1.9%, precision 94.6%, recall 91.4%, F1-score 92.9%, and area under the receiver operating characteristic curve (ROC-AUC) 0.974±0.010, outperforming MLP and AdaBoost. The method supports privacy-preserving, on-device inference and is suitable for integration into smart office systems to reduce exposure to high-risk postures. Limitations include controlled capture conditions and an upper body focus; future work will expand posture taxonomy and real-world deployment.
Resin Code Classification on Plastic Packaging Using Few-Shot Learning Anindita Septiarini; Alyani Noor Septalia; 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

Poorly managed plastic waste poses a serious environmental threat. Accurate sorting based on the Resin Identification Code (RIC) can improve recycling quality; however, conventional deep learning methods generally require large labeled datasets that are costly and difficult to obtain, particularly for small RIC symbols printed on packaging. This study applies Few-Shot Learning using Prototypical Networks with a 7-way 5-shot episodic training scheme. A self-collected dataset containing 350 images was used, consisting of 50 images for each of seven RIC classes: PETE, HDPE, PVC, LDPE, PP, PS, and OTHER. Three feature-extraction backbones—ConvNet4, ResNet-18, and EfficientNet-B2—were evaluated, while an ablation experiment examined the contribution of light augmentation applied to the support set. Performance was assessed using episodic training accuracy and accuracy and F1-score on an independent test set of 42 images. EfficientNet-B2 achieved the highest episodic training accuracy of 93.36%, outperforming ResNet-18 at 88.71% and ConvNet4 at 54.14%. With light augmentation, EfficientNet-B2 obtained 85.71% test accuracy and correctly classified all PVC, LDPE, and PP samples. Most errors involved visually similar categories, particularly HDPE samples misclassified as PP. Subsequent fine-tuning corrected five of six initial errors, increasing test accuracy to 90.48% and macro F1-score from 0.857 to 0.903. These findings demonstrate that cosine-distance Prototypical Networks are effective for RIC classification under limited-data conditions and provide a promising foundation for automated plastic-waste sorting systems. Training code and experimental configurations are available upon request for research purposes.
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
Comparison of ResNet50, ResNet101, and ResNet152 Architectures in Image-Based Rice Leaf Disease Classification Ardi Setyiawan; Anindita Septiarini; Andi Tejawati
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3289

Abstract

Rice leaf diseases are one of the main threat that can reduce rice crop productivity especially if they are not detected at an early stage. Conventional disease identification still has limitations because it relies on visual observation and the experience of farmers. Therefore, this study proposes a rice leaf disease classification approach based on digital images using deep learning methods. This study aims to compare the performance of three Residual Network architectures, namely ResNet50, ResNet101, and ResNet152. The dataset used was collected from three public Kaggle datasets, consisting 7.322 images divided into four classes (healthy, hispa, sheath blight, and brown spot). The dataset was split into training, validation, and testing sets with a ratio of 70:20:10 and processed through image preprocessing and data augmentation. All models were trained using a transfer learning approach with the same training configuration to ensure a fair comparison. Model performance was evaluated with the test sets using loss, accuracy, and confusion matrix analysis. The experimental results show that ResNet101 achieved the best performance with a loss value of 0,0146 and an accuracy of 0,9973. Followed by ResNet50 with an accuracy of 0,9918, and ResNet152 with an accuracy of 0,9837. These results indicate that ResNet101 provides the best balance between network depth and classification performance.
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.
Oil Palm Stem Disease Detection Based on Color Moments and GLCM Texture Features Using Artificial Neural Networks Hamdani, Hamdani; Septiarini, Anindita; Akhmad Syaifudin, Encik; Tejawati, Andi; Zulfariansyah, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5554

Abstract

Oil palm is an essential commodity for the economy; however, basal stem rot caused by Ganoderma boninense poses a significant threat to plantation productivity and long-term vitality. It highlights the importance of early detection of stem disease to facilitate timely intervention and minimize potential economic losses. This study presents an image-based approach to diagnosing oil palm stem maladies, leveraging handcrafted color and texture features within a supervised machine learning framework. The dataset contained 525 images of oil palm stems, of which 205 depicted healthy specimens, and 320 depicted diseased ones. These were captured within their natural environment. Color features were derived by analyzing color moments within the HSV color space, while texture features were extracted from the Grey-Level Co-occurrence Matrix (GLCM). The extracted features were classified employing an Artificial Neural Network (ANN) and were subsequently contrasted with classifiers including Decision Tree, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine. Model performance was evaluated using k-fold cross-validation with k = 5 and k = 10 to ensure the consistency and reliability of the assessment. The experimental results demonstrated that the highest accuracy of 97.52% was achieved when the ANN model was used to classify the integrated color and texture features. The innovative aspect of this research resides in demonstrating that handcrafted features integrated with artificial neural networks can attain high detection accuracy in scenarios with limited data, providing a viable alternative to data-intensive deep learning techniques. This method facilitates a dependable, computer vision-driven early detection system for oil palm stem diseases, thereby promoting sustainable plantation management. 
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 Abdul Razak Aliudin Achmad Solichan Adi Muhammad Syifai Adnan, Fahrizal Afifah, Dinda Nur Agus Qomaruddin Munir AHMAD ANSYORI Ahmad Nur Fauzan Ajay, Muhammad Akhmad Masyudi Akhmad Syaifudin, Encik Alameka, Faza Aldi Daffa Arisyi Alif Rifa’i Alvito Gabbriel Saputra Alyani Noor Septalia Amalia, Syaffira Rizky Ambari, Nasser Ambon, Matelda Yunanta Andi Tejawati Andri Syafrianto Anita Ahmad Kasim Annisa Putri Novalianti Anton Prafanto Antonieta Aryuka Paskalia Nggotu Ardi Setyiawan Arif Hidayat Arindra Nurshadrina Ramadini Arini Wijayanti Asmita, Rizka Aulia Rahman Awang Harsa Kridalaksana Awang Zheri Rhesvianur Az Zahrah, Rezha Nur Bandhaso, Victor Bima Prihasto Briyan Efflin Syahputra Budi Rahmani Budiman, Edy Cakra Dewandaru Chairunnisa Ardiansyah Lamasitudju Christy Maulidiah Daffa Putra Mahardika Didit Suprihanto, Didit Dwi Prasetio Dyna Marisa Khairina Edy Winarno Enny Itje Sela Ery Burhandenny, Aji Ery Burhandeny, Aji Evi Wildana Fahrozi, Muhammad Naufal Fairil Anwar Fajri, Muhamad Mushfa Hikmatal Fandi Alief Al Akbar Farida Djumiati Sitania Fathia Nuq Qamarina Fauzan, Ahmad Nur Fayza Virdana Addiza Firyal, Tasya Nadina Fornia, Daviana Dwitasari Enka Fuad, Natalie Gempar Panggih Dwi Gideon Simalango, Yanuar Gunawan, Ayu Lestari Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hariyanto Harry Tanni Pagiu Hatta, Heliza Rahmania Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Henderi . Heni Sulastri Herlawati Herlawati Heru Ismanto Hidayat, Ahmad Nur Hutagalung, Wilson Boyaron Hutapea, Vedra Dian Sierrafina Ibnu Amri Thaher Ifnu Umar Indah Fitri Astuti Indah Wulan Lestari Irfan, Aliya Irsyad, Akhmad Kalingga Dwindra Putraka Kamila, Vina Zahrotun Kiki Purwanti Laraswati, Sherina Lempas, Gidion Lili, Juniver Veronika Lukman Nadjamuddin M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Maya Agustina Mewengkang, Alfrina Muhamad Azhari Muhammad Abdillah Muhammad Abdillah Muhammad Aidil Saputra Muhammad Andas Lesmana Muhammad Bakri Muhammad Dzacky Muhammad Ifandi Muhammad Nur Ramadhan Muhammad Rafif Hanif Muhammad Sofian Sauri Mu’nisah Assisi Najwa Felira Zetti Nanda Arianto Nathaniela Aptanta Parama Nggotu, Antonieta Aryuka Paskalia Novi Puspitasari Novianti Puspitasari Nupa, Joy Disanto Nur Madia Nurcahyono, Damar Nurhidayat, Rifki Nurmadewi, Dita Olivia Octavia Padmo Azam Masa, Amin Patricia Chandra Pebianoor, Pebianoor Prafanto, Anton Pramudya, Pranata Eka Pratiwi, Sinthya Ayu Puguh Budi Prakoso Puspitasari, Novianti Puspitasari, Novitanti Putra Ramdani, Aditya Putri, Septi Aulia Rafi Ichsanul Iqbal Rahmadya Trias Handayanto Rahmat Kamara Raihanfitri Adi Kalipaksi Rajiansyah, Rajiansyah Rakhmat Purnomo Ramadhaniaty, Dinda Raudhya Azzahra Reski Harisma Dewi Barkah Reviansa Fakhruddin Aththar Ricky Anggari Risky Kurniawan Riswandi Syam Rita Diana Riyayatsyah, Riyayatsyah Rizqi Saputra Rohman, Reisa Maulidya Rondongalo Rismawati Rosmasari, Rosmasari Sadewa, Bintang Putra Saipul, Saipul Sakti, Dwi Nika Salsabila, Nur Maya Saragih, Muhammad Nabil Sarira, Brayen Tisra Satria Bagus Eka Chandra Saucha Diwandari Setiawan, Maulana Agus Sihombing, Yobel Fernanda Siti Retno Wulandari Sophy Awaliah Sugandi Sugandi Sumaini Sumaini Supriyono Supriyono Supriyono Supriyono Surya Eka Priyatna Syaffira Rizky Amalia Syifani, Sarah Taruk, Medi Tejawati, Andi Theresia Amelia Pawitra Tulili, Hadie Pratama Ummul Hairah Ummul Hairah Vicky Pranandika Wijaksana Viny Christanti M Wahyudi, Moh Ikhwan Wati, Masna Wibisono, Bramantyo Ardi Harimurti Widians, Joan Angelina Wintin, Chintia Liu Wiwien Hadikurniawati Yanuar Satria Gotama Yasmin, Annisa Yudi Sukmono Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin Zulfariansyah, Muhammad