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All Journal Publikasi Pendidikan JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem Informasi dan Bisnis Cerdas Format : Jurnal Imiah Teknik Informatika Sistemasi: Jurnal Sistem Informasi InComTech: Jurnal Telekomunikasi dan Komputer J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Indonesian Journal of Data and Science Nusantara Science and Technology Proceedings Jurnal Pengabdian Masyarakat Indonesia Jurnal Manajemen Informatika Jayakarta International Journal Of Computer, Network Security and Information System (IJCONSIST) Algoritme Jurnal Mahasiswa Teknik Informatika Literasi Nusantara Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Kohesi: Jurnal Sains dan Teknologi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Router : Jurnal Teknik Informatika dan Terapan Modem : Jurnal Informatika dan Sains Teknologi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Router : Jurnal Teknik Informatika dan Terapan
Eva Yulia Puspaningrum
Universitas Pembangunan Nasional “Veteran” Jawa Timur
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Optimization of CNN Activation Functions using Xception for South Sulawesi Batik Classification Aswan, Aswan; Puspaningrum, Eva Yulia; Asrul, Billy Eden William
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5281

Abstract

Batik motifs from South Sulawesi such as the Pinisi boat, Lontara script, Tongkonan house and Toraja combinations embody rich cultural narratives but are difficult to identify automatically. Automatic classification supports cultural preservation and education and empowers tourism and digital heritage applications. This study improves the performance of convolutional neural networks for South Sulawesi batik classification by optimizing activation functions within the Xception architecture which exploits depthwise separable convolutions for efficient and detailed feature extraction. A balanced dataset of 1400 labeled images in four classes was divided into eighty percent for training, ten percent for validation and ten percent for testing. Images were resized to 224 by 224 pixels, converted to grayscale and augmented through zoom, flip and rotation. With identical hyperparameters including a learning rate of 0.001, a batch size of 64 and training for 100 epochs using the Adam optimizer, ReLU, ELU, Leaky ReLU and Swish activation functions were compared. Evaluation metrics comprised accuracy, precision, recall, F1 score and cross entropy loss. ELU achieved the highest test accuracy of 98.57 percent, precision of 0.9864, recall of 0.9857 and F1 score of 0.9857, outperforming ReLU and Leaky ReLU with 97.86 percent accuracy and Swish with 97.14 percent accuracy. The results demonstrate that selecting an optimal activation function substantially enhances convolutional neural network classification of complex batik patterns. The findings offer practical guidance for development of resource aware batik identification systems in support of cultural digitization and education initiatives.
MRI image enhancement of the brain using U-NET Etniko Siagian, Pangestu Sandya; Puspaningrum, Eva Yulia; Wan Awang, Wan Suryani; Mas Diyasa, I Gede Susrama
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29775

Abstract

The quality of Magnetic Resonance Imaging (MRI) images is often compromised by various types of noise, such as salt, pepper, salt-and-pepper, and speckle noise, caused by technical or environmental disturbances. This study aims to develop a brain MRI image denoising model based on the U-Net architecture, capable of effectively removing different types of noise. The methodology includes collecting normal brain MRI datasets, applying data augmentation to increase variability, and introducing artificial noise to simulate possible noise conditions. The U-Net model is trained and evaluated using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The novelty of this study lies in its combination of augmentation techniques, multi-intensity artificial noise variations, and its exclusive focus on normal brain MRI images. The results demonstrate that the U-Net model achieves optimal performance on salt-and-pepper noise at an intensity of 0.1, marked by the highest PSNR value of 37.2047 dB and the lowest MSE value of 0.000207. Conversely, the model shows the lowest performance on high-intensity speckle noise, indicating greater challenges in addressing multiplicative noise. This study contributes a systematic and empirically tested approach to improving the quality of brain MRI images with high efficiency, supporting the development of image-based diagnostic systems in the medical field.Keywords: Deep Learning, Denoising, Image Enhancement, Noise, U-Net.
Improving Classification Accuracy of Breast Ultrasound Images Using Wasserstein GAN for Synthetic Data Augmentation Mas Diyasa, I Gede Susrama; Humairah, Sayyidah; Puspaningrum, Eva Yulia; Durry, Fara Disa; Lestari, Wahyu Dwi; Caesarendra, Wahyu; Dewi, Deshinta Arrova; Aryananda, Rangga Laksana
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.25075

Abstract

Breast cancer remains one of the most prevalent cancers in Indonesia, and early detection plays a vital role in improving patient outcomes. Ultrasound imaging is a non-invasive and accessible technique used to classify breast conditions into normal, benign, or malignant categories. The advancement of deep learning, particularly Transfer Learning with Convolutional Neural Networks (CNNs), has significantly enhanced the performance of automated image classification. However, the effectiveness of CNNs heavily relies on large, balanced datasets—resources that are often limited and imbalanced in medical domains. To address this issue, this study explores the use of Wasserstein Generative Adversarial Networks (WGAN) for synthetic data augmentation. WGAN is capable of learning the underlying distribution of real ultrasound images and generating high-quality synthetic samples. The inclusion of the Wasserstein distance stabilizes training, with convergence observed around 2500–3000 epochs out of 5000. While synthetic data improves classifier performance, there remains a potential risk of overfitting, particularly when the synthetic images closely mirror the training data. Compared to traditional augmentation techniques such as rotation, flipping, and scaling, WGAN-generated data provides more diverse and realistic representations. Among the tested models, VGG16 achieved the highest accuracy of 83.33% after WGAN augmentation. Nonetheless, computational resource limitations posed challenges in training stability and duration. Furthermore, issues related to model generalizability, as well as ethical and patient privacy considerations in using synthetic medical data, must be addressed to ensure responsible deployment in real-world clinical settings.
Image Color Correction for Color Vision Deficiency Using ResNet and CycleGAN Adyani, Adelia Putri; Tri Anggraeny, Fetty; Yulia Puspaningrum, Eva
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2506

Abstract

Color blindness is a visual impairment that limits an individual's ability to accurately perceive certain colors, particularly red, green, or blue. This condition can hinder daily tasks, especially when color identification is crucial. This study proposes a color correction system designed to enhance color perception for individuals with color vision deficiency (CVD), focusing on important visual areas within an image. The method involves converting RGB images into LMS color space, simulating types of color blindness (protanopia, deuteranopia, and tritanopia), detecting visually important regions using a saliency mask, applying color correction through a ResNet-based deep learning model, and performing a reverse transformation back to RGB using a CycleGAN. A total of 5,020 images were used for evaluation, and the proposed system achieved an average Root Mean Square (RMS) error of 0.0212. The Mean Absolute Error (MAE) ranged from 0.1541 to 0.5582 depending on the CVD type. In addition to quantitative evaluation, qualitative validation was conducted through a GUI-based user test involving 10 color blind participants. The system showed the highest effectiveness for deuteranopia with a color recognition accuracy of 71.666%, followed by tritanopia at 59.666% and protanopia at 46.500%. These results indicate that the proposed system offers significant potential in aiding individuals with CVD to better interpret color-based information, especially in visually important regions of an image. Future work may explore broader datasets and alternative deep learning architectures to further improve accuracy and adaptability.
Stacking Ensemble of XGBoost, LightGBM, and CatBoost for Green Economy Index Prediction Salsabilah, Andini Fitriyah; Rahmat, Basuki; Puspaningrum, Eva Yulia
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2530

Abstract

Indonesia faces persistent challenges in achieving sustainable development, particularly in harmonizing economic growth with environmental sustainability. The imbalance among economic, social, and environmental dimensions necessitates a comprehensive and reliable measurement tool to assess progress toward a green economy. The Green Economy Index (GEI), developed by the Ministry of National Development Planning (BAPPENAS), serves this function. However, limited data availability at the provincial level, such as in East Java, hampers accurate evaluation and informed policy formulation. This study aims to develop a machine learning-based predictive model for the GEI using a stacking ensemble approach that combines three powerful algorithms: XGBoost, LightGBM, and CatBoost. The model was built using relevant economic, social, and environmental indicators and evaluated on a holdout dataset to assess its predictive accuracy and generalizability. The results show that the stacking ensemble model achieved superior performance compared to the individual models, recording an RMSE of 0.0298, MAE of 0.0225, and the R² score of 0.9774. In comparison, CatBoost, XGBoost, and LightGBM individually performed with slightly lower accuracy. These findings confirm that the stacking ensemble approach is highly effective for predicting GEI values and offers a practical, data-driven solution for supporting sustainable development strategies at the regional level. The study concludes that such predictive tools can significantly enhance policy planning and monitoring of green economic growth, although further research is recommended to validate the model across other provinces.
Educational Game Design to Raise Awareness of Social Anxiety and Cognitive Behavioral Therapy Az-Zahro', Syaikhhanun Nabila; Atmaja, Pratama Wirya; Puspaningrum, Eva Yulia
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2547

Abstract

Social Anxiety Disorder (SAD) is a common yet often misunderstood mental health condition that significantly impacts the lives of adolescents and university students. Despite its prevalence, awareness and understanding of SAD and its evidence based treatment, Cognitive Behavioral Therapy (CBT), remain limited among young adults. This study aims to design and develop Social Survival, a 2D educational game intended to raise awareness of SAD and introduce CBT techniques through an interactive and engaging medium. The game is developed using the Unity engine and employs the Interactive Digital Narrative (IDN) framework to deliver a singleplayer narrative experience. It presents scenarios simulating SAD symptoms and embeds CBT strategi such as relaxation, cognitive restructuring, and exposure into gameplay via minigames. The development process included a needs analysis, general and detailed design phases, and implementation of mechanics aligned with CBT principles. To evaluate learning effectiveness, a pre-test and post-test were administered and analyzed using the N-Gain formula. Player satisfaction was assessed using the Game User Experience Satisfaction Scale (GUESS-18), which measures dimensions such as enjoyment, engagement, and educational value. The results indicate a positive improvement in player understanding of SAD and CBT, along with favorable user experience ratings. The study concludes that serious games can serve as effective tools for mental health education, although clinical treatment should still be guided by professionals.
Analisa Komparasi Algoritma Machine Learning dan Deep Learning Dalam Klasifikasi Citra Ras Kucing Royan Fajar Sultoni; Achmad Junaidi; Eva Yulia Puspaningrum
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 2 No. 3 (2024): Agustus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v2i3.251

Abstract

Cats (Felis catus) are a type of carnivorous mammal from the Felidae family that was domesticated and has been one of the animals that has mingled with humans since time immemorial. Domestic cats are broadly divided into 2 types, namely village cats and purebred cats. Purebred cats have quite a varied number of types. Therefore, confusion often occurs in determining the type or breed of cat. Meanwhile, in practice, each race does not have the same treatment (especially in the aspect of care). In digital image processing, Machine Learning and Deep Learning are the main aspects in the process of applying technology that can overcome this problem, so research related to this problem was designed. This research was conducted to add insight for further research in a more sophisticated and effective image recognition process. In the experiments carried out in this research, the SVM, KNN, and CNN methods were tested with the Xception and EfficientNet-B1 architectures. Based on the final results obtained from this test, the CNN method with the Xception architecture is the best model. By using fine-tuning and a learning-rate of 1e-5, this method produces a micro average value of 0.974, on a cat breed image dataset of 13 classes and 7800 images. Meanwhile, the method that produces the fastest ETA Training and Testing is obtained by the KNN method, with an ETA Training time of 0.194 seconds, and an ETA Testing time of 1.782 seconds.
Analisis Ekstraksi Fitur LBP, GLCM Dan HSV Untuk Klasifikasi Kualitas Cabai Rawit Menggunakan Xgboost ZAMAZANI, ZAIN MUZADID; Puspaningrum, Eva Yulia; Via, Yisti Vita
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.13307

Abstract

Cayenne pepper (Capsicum frutescens L.) is a horticultural commodity of high economic value, so determining its quality is an important factor in determining the selling price and suitability for consumption. So far, quality assessment is still mostly done manually, but this method tends to be subjective and less efficient. To overcome this, this research evaluates the quality classification of cayenne pepper based on digital image processing using the XGBoost algorithm with three types of features, namely Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Hue, Saturation, Value (HSV). The primary dataset used consists of 1,200 images of six quality classes (raw, undercooked, cooked, dry, rotten, and anthracnose). The methodology stages include pre-processing in the form of background removal, resizing, and data augmentation. Next, LBP, GLCM, and HSV feature extraction is carried out, then classification by dividing the test training data by 80:20. The test results show that the best configuration is obtained with the HSV feature, using learning rate parameters 0.1, n_estimators 100, and max depth 12, which produces accuracy (98.92%), higher than using GLCM (88.08%) or LBP (79.17%). These findings confirm that color information is more dominant than texture in supporting automatic quality classification of cayenne peppers.
Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input Nugroho, Budi; Puspaningrum, Eva Yulia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834515

Abstract

Saat ini banyak dikembangkan proses pendeteksian pneumonia berdasarkan citra paru-paru dari hasil foto rontgen (x-ray), sebagaimana juga dilakukan pada penelitian ini. Metode yang digunakan adalah Convolutional Neural Network (CNN) dengan arsitektur yang berbeda dengan sejumlah penelitian sebelumnya. Selain itu, penelitian ini juga memodifikasi model CNN dimana metode Extreme Learning Machine (ELM) digunakan pada bagian klasifikasi, yang kemudian disebut CNN-ELM. Dataset untuk uji coba menggunakan kumpulan citra paru-paru hasil foto rontgen pada Kaggle yang terdiri atas 1.583 citra normal dan 4.237 citra pneumonia. Citra asal pada dataset kaggle ini bervariasi, tetapi hampir semua diatas ukuran 1000x1000 piksel. Ukuran citra yang besar ini dapat membuat pemrosesan klasifikasi kurang efektif, sehingga mesin CNN biasanya memodifikasi ukuran citra menjadi lebih kecil. Pada penelitian ini, pengujian dilakukan dengan variasi ukuran citra input, untuk mengetahui pengaruhnya terhadap kinerja mesin pengklasifikasi. Hasil uji coba menunjukkan bahwa ukuran citra input berpengaruh besar terhadap kinerja klasifikasi pneumonia, baik klasifikasi yang menggunakan metode CNN maupun CNN-ELM. Pada ukuran citra input 200x200, metode CNN dan CNN-ELM menunjukkan kinerja paling tinggi. Jika kinerja kedua metode itu dibandingkan, maka Metode CNN-ELM menunjukkan kinerja yang lebih baik daripada CNN pada semua skenario uji coba. Pada kondisi kinerja paling tinggi, selisih akurasi antara metode CNN-ELM dan CNN mencapai 8,81% dan selisih F1 Score mencapai 0,0729. Hasil penelitian ini memberikan informasi penting bahwa ukuran citra input memiliki pengaruh besar terhadap kinerja klasifikasi pneumonia, baik klasifikasi menggunakan metode CNN maupun CNN-ELM. Selain itu, pada semua ukuran citra input yang digunakan untuk proses klasifikasi, metode CNN-ELM menunjukkan kinerja yang lebih baik daripada metode CNN. AbstractThis research developed a pneumonia detection machine based on the lungs' images from X-rays (x-rays). The method used is the Convolutional Neural Network (CNN) with a different architecture from some previous research. Also, the CNN model is modified, where the classification process uses the Extreme Learning Machine (ELM), which is then called the CNN-ELM method. The empirical experiments dataset used a collection of lung x-ray images on Kaggle consisting of 1,583 normal images and 4,237 pneumonia images. The original image's size on the Kaggle dataset varies, but almost all of the images are more than 1000x1000 pixels. For classification processing to be more effective, CNN machines usually use reduced-size images. In this research, experiments were carried out with various input image sizes to determine the effect on the classifier's performance. The experimental results show that the input images' size has a significant effect on the classification performance of pneumonia, both the CNN and CNN-ELM classification methods. At the 200x200 input image size, the CNN and CNN-ELM methods showed the highest performance. If the two methods' performance is compared, then the CNN-ELM Method shows better performance than CNN in all test scenarios. The difference in accuracy between the CNN-ELM and CNN methods reaches 8.81% at the highest performance conditions, and the difference in F1-Score reaches 0.0729. This research provides important information that the size of the input image has a major influence on the classification performance of pneumonia, both classification using the CNN and CNN-ELM methods. Also, on all input image sizes used for the classification process, the CNN-ELM method shows better performance than the CNN method.
KLASIFIKASI SENTIMEN TENTANG PEMINDAHAN IBU KOTA NEGARA INDONESIA DENGAN CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN GLOVE DAN FASTTEXT Putri, Desya Ristya; Puspaningrum, Eva Yulia; Maulana, Hendra
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4882

Abstract

Media sosial kini menjadi tempat untuk berkomunikasi jarak jauh yang marak digunakan. Di dalam media sosial, terdapat berbagai macam opini pengguna yang sering kali terjadi kesalahan penafsiran oleh pembaca. Kadang kala, informasi yang tersebar juga merupakan hoaks sehingga dapat mempersulit pemahaman aktual sentimen yang sesungguhnya ingin disampaikan. Beberapa waktu terakhir, topik pembicaraan mengenai pemindahan ibu kota negara Indonesia sangat banyak tersebar di media sosial. Dilakukannya penelitian ini memiliki tujuan untuk mendapatkan perbandingan hasil antara dua metode ekstraksi fitur yang digunakan. Penelitian ini menerapkan ekstraksi fitur GloVe dan FastText dengan besaran nilai ukuran vektor sebesar 100. Klasifikasi dalam penelitian ini dilakukan dengan algoritma Convolutional Neural Network yang menerapkan beberapa variasi skenario uji, yaitu dengan mengubah nilai batch size dan epoch. Penelitian dilakukan dengan 44957 data komentar YouTube yang besar perbandingannya adalah 70:30 untuk data pelatihan dan data pengujian. Hasil dari dilakukannya percobaan menunjukkan bahwa metode ekstraksi fitur GloVe menghasilkan akurasi yang lebih baik dibandingkan dengan FastText. Hasil akhir didapatkan bahwa implementasi Convolutional Neural Network dengan GloVe menghasilkan rata-rata nilai precision sebesar 74.3%, recall sebesar 73.6%, f1-score sebesar 73.6%, serta accuracy sebesar 76.1%.
Co-Authors Abiyan Naufal Hilmi Achmad Junaidi Adityawan, Firza Prima Adyani, Adelia Putri Agung Mujiono, Alfinas Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Ahmad Fahry Hamidy Ahmad Hilman Dani Akbar, Fawwaz Ali Al Danny Rian Wibisono Ali Muhhamad Saleh Baaboud Andhika Ahnaf Daniswara Andreas Nugroho Sihananto Annisaa Sri Indrawanti Anny Yuniarti Aqsa Prima Cahya Ariani, Dian Dwi Ariyono Setiawan Aryananda, Rangga Laksana Aswan Aswan Attaqwa, Syukur Iman Az-Zahro', Syaikhhanun Nabila Azizah, Nabila Wafiqotul Bagus Sutikno Putra Basuki Rahmat Basuki Rahmat Basuki Rahmat Masdi Siduppa Bimantara, Candra Kusuma Muhammad Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Chafid, M Putih Devan Cakra Mudra Wijaya Dewi, Deshinta Arrova Dhian Satria Yudha K. Dimas Saputra Diyasa, I Gede Susrama Mas Dwi Anggraeni, Shinta Dwiki Aditama Supangkat Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha, Eka Elzandy, Imeldha Etniko Siagian, Pangestu Sandya Fahmi Al Hafidz, Achmad Fara Disa Durry Faris Syaifulloh Farkhan, Farkhan Fetty Tri Anggraeny Firza Prima Adityawan Fitri Rahmawati Hapsari Wiji Utami Hasby Bik, Ahmad Henni Endah Wahanani Humairah, Sayyidah Humam Maulana Tsubasanofa Ramadhan I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Nyoman Sujana I Wayan Alston Argodi Idhana, Ilham Ainur indrawanti, annisaa sri Karim, Mohammad Daniel Sulthonul Kartini Kartini Lestari, Kusmiyati Lina Nurlaili, Afina M. Syahrul Munir, M. Syahrul Mada Lazuardi Nazilly Made Hanindia Prami Swari Manggala, Herwantoro Arya Marchel Adias Pradana Mas Diyasa, I Gede Susrama Mas Diyasa, I Gede Susrama Susrama Maulana, Hendra Merdin Risalul Abrori Moch. Hatta Mohammad Idhom Muhammad Asyraf Muhammad Fernanda Naufal Fathoni Muhammad Misbachuddin Muhammad Muharrom Al Haromainy Muhammad Syafril Hidayat Nabilah, Qonitah Jihan Nanik Suciati Noor Fitria Azzahra Nugroho, Budi Nugroho, Budi Nugroho, Budi Nurcahyo, Syai'in Bayu Nurul Taukid, Mochamad Pallawabonang, Mahabintang Pratama Wirya Atmaja Pratama, Gede Ardi Prisheila Dharmawan, Diaz Putra, Chrystia Aji Putra, Riza Satria Putri, Desya Ristya Retno Mumpuni Rizqi Mar'atus Sholiihah, Eka Royan Fajar Sultoni S J Saputra, Wahyu Safira, Dwi Putri Salsabilah, Andini Fitriyah Samuel Krispama Lumbantoruan Saputra, Raka Aji Saputra, Wahyu S J Saputra, Wahyu S J Saputra, Wahyu S. J. Saputra, Wahyu S.J. Satria Yudha Kartika , Dhian Shawn Hafizh Adefrid Pietersz Shofiya Syidada Sukendah, Sukendah Surjohadi, Surjohadi Susrama Mas Diyasa, I Gede Syahrul Hidayat Syaifullah JS, Wahyu Taruna Ardianto Tataq Distasianto Utami, Hapsari Wiji Vita Via, Yisti Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wahyu Syaifullah Jauharis Saputra Wan Awang, Wan Suryani Wan Suryani Wan Awang Wiji Utami, Hapsari Yisti Vita Via Yisti Vita Via Yogie Wilvren Saragih Yudha K., Dhian Satria Yudhistira Nanda Kumala YUSMI NUR AINI Zacky Yaser Malik Gumiwang ZAMAZANI, ZAIN MUZADID Zuhriyah, Sitti