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All Journal Publikasi Pendidikan JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal Ilmiah Kursor 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 Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Indonesian Journal of Data and Science Nusantara Science and Technology Proceedings SINTA Journal (Science, Technology, and Agricultural) Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Manajemen Informatika Jayakarta Jurnal Teknologi dan Manajemen International Journal Of Computer, Network Security and Information System (IJCONSIST) Algoritme Jurnal Mahasiswa Teknik Informatika Literasi Nusantara Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) 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
Department Of Informatics, Universitas Pembangunan Nasional Veteran Jawa Timur, Jl. Raya Rungkut Madya, Surabaya, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Social Sciences Veterinary Other

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PENERAPAN LIGHTGBM MENGGUNAKAN EKSTRAKSI FITUR RUANG WARNA HSV UNTUK KLASIFIKASI REMPAH RIMPANG Fahmi Al Hafidz, Achmad; Yulia Puspaningrum, Eva; Lina Nurlaili, Afina
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.14306

Abstract

Rempah rimpang memiliki peran penting dalam dunia kuliner dan industri pangan, namun identifikasi jenis rempah secara manual seringkali menemui hambatan karena kemiripan visual antar jenis. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis yang akurat dan efisien menggunakan metode Light Gradient Boosting Machine (LGBM) dengan fitur utama berupa representasi warna HSV (Hue, Saturation, Value). Dataset yang digunakan terdiri dari 2.250 citra rempah yang telah melalui proses preprocessing seperti resizing ke ukuran 512x512 piksel, penghapusan latar belakang, normalisasi, dan augmentasi. Hasil evaluasi menunjukkan bahwa penggunaan fitur HSV dengan rasio pembagian data 70:30 memberikan performa yang cukup optimal, dengan akurasi mencapai 82,67%. Temuan ini menegaskan bahwa fitur HSV bisa diterapkan dalam membedakan jenis rempah rimpang dan dapat menjadi pendekatan yang andal untuk membangun sistem klasifikasi otomatis, terutama dalam mendukung proses identifikasi cepat di industri pangan dan sektor UMKM
KLASIFIKASI BUNGA HERBAL MENGGUNAKAN MODEL INCEPTIONV3 DAN VISION TRANSFORMER Prisheila Dharmawan, Diaz; Yulia Puspaningrum, Eva; Lina Nurlaili, Afina
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.14308

Abstract

Bunga merupakan bagian penting dari tumbuhan yang memiliki berbagai manfaat medis. Namun, beberapa jenis bunga memiliki kemiripan dalam bentuk dan warna, sehingga sulit diklasifikasikan secara manual. Kesulitan dalam membedakan spesies yang mirip menjadi tantangan utama dalam klasifikasi otomatis, sehingga diperlukan model yang dapat menangkap informasi visual lebih kompleks. Penelitian ini mengusulkan pendekatan InceptionV3 dan Vision Transformer untuk ekstraksi fitur mendalam, dengan tujuan mengembangkan model klasifikasi yang lebih akurat dalam membedakan bunga herbal. Metode penelitian terdiri dari enam tahap utama, yaitu akuisisi data, pengubahan ukuran citra, ekstraksi fitur menggunakan InceptionV3 dan Vision Transformer, pembagian data, klasifikasi dengan MLP, dan evaluasi model. Model diuji dengan berbagai konfigurasi parameter, termasuk data split 90:10, learning rate 0.0001, batch size 64, serta arsitektur hidden layer 1024–512–256. Hasil eksperimen menunjukkan bahwa konfigurasi terbaik menghasilkan akurasi 78.29% dengan loss 0.6162. Evaluasi lebih lanjut menunjukkan bahwa model memiliki precision, recall, dan F1-score sebesar 78.4%, dengan prediksi terbaik pada kelas Common Daisy dan Black-Eyed Susan, di mana tingkat prediksi benar mencapai lebih dari 84%. Penelitian ini berkontribusi dalam pengembangan metode klasifikasi bunga herbal dengan memanfaatkan model InceptionV3 dan Vision Transformer untuk ekstraksi fitur mendalam. Hasil yang diperoleh menunjukkan efektivitas pendekatan ini dalam meningkatkan akurasi klasifikasi bunga yang memiliki karakteristik visual serupa
KLASIFIKASI VARIETAS ANGGUR BERDASARKAN CITRA DAUN MENGGUNAKAN VGG16-XGBOOST Elzandy, Imeldha; Yulia Puspaningrum, Eva; Lina Nurlaili, Afina
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.14309

Abstract

Tanaman anggur memiliki varietas beragam dengan karakteristik daun yang cukup mirip antar varietas, hal ini menjadi tantangan tersendiri dalam proses pengenalan varietas secara manual, terutama bagi masyarakat awam. Penelitian ini bertujuan untuk melakukan klasifikasi varietas anggur berdasarkan citra daun menggunakan kombinasi metode CNN dengan arsitektur VGG-16 sebagai ekstraksi fitur dan XGBoost sebagai klasifikasi. Adapun empat varietas anggur yang digunakan pada penelitian ini, yaitu Jumer, Ninel, Ruby88, dan Transfigurasi. Dataset yang digunakan terdiri dari 560 citra untuk pelatihan dan 60 citra untuk pengujian, yang kemudian dilakukan proses ekstraksi fitur menggunakan VGG-16 dan diklasifikasikan menggunakan XGBoost. Model dievaluasi menggunakan metrik accuracy, precision, recall, dan f1-score. Hasil pada penelitian menunjukkan bahwa kombinasi metode VGG-16 dan XGBoost mampu menghasilkan accuracy klasifikasi dengan hasil 89.58%, precision = 89.64%, nilai recall = 89.58%, nilai f1-Score = 89.55% dengan estimasi waktu training selama 832.55 detik
IMPLEMENTASI BILSTM DAN CUCKOO SEARCH OPTIMIZATION PADA APLIKASI PREDIKSI HARGA EMAS ANTAM Nurul Taukid, Mochamad; Vita Via, Yisti; Yulia Puspaningrum, Eva
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.14310

Abstract

Emas merupakan komoditas utama yang diminati investor karena dianggap sebagai safe haven yang dapat melindungi dan meningkatkan kekayaan. Namun, investasi emas memiliki risiko akibat fluktuasi harga yang tajam. Oleh karena itu, prediksi harga emas sangat penting untuk membantu investor menentukan waktu yang tepat dalam membeli dan menjual emas guna meningkatkan potensi keuntungan. Penelitian ini menggunakan metode prediksi hybrid BiLSTM-CSO, yang menggabungkan kemampuan BiLSTM dalam mengelola data deret waktu dua arah dengan algoritma CSO untuk mengoptimasi hyperparameter seperti jumlah neuron pada setiap hidden layer, batch size, dan dropout rate, sehingga dapat meningkatkan akurasi prediksi. Hasil evaluasi menunjukkan bahwa model ini mampu memberikan performa prediksi yang baik dengan nilai RMSE sebesar 0.01315 dan MAPE sebesar 0.81%. Model ini kemudian diimplementasikan ke dalam aplikasi web yang menjadi potensi sebagai alat bantu yang informatif untuk memantau dan memprediksi harga emas
KLASIFIKASI PENYAKIT DAUN MANGGA MENGGUNAKAN ANFIS Dwi Anggraeni, Shinta; Yulia Puspaningrum, Eva; Lina Nurlaili, Afina
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.14311

Abstract

Deteksi penyakit daun mangga dapat dilakukan melalui analisis bentuk dan tekstur daun. Setiap jenis penyakit menyebabkan perubahan karakteristik daun, sehingga analisis bentuk dan tekstur dapat menjadi dasar klasifikasi penyakit. Metode manual seperti pengamatan visual memiliki kelemahan karena subjektif dan kurang akurat. Oleh karena itu, diperlukan pendekatan berbasis pengolahan citra digital untuk meningkatkan akurasi identifikasi penyakit. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis penyakit daun mangga menggunakan metode Gray Level Co-occurrence Matrix (GLCM) untuk ekstraksi fitur tekstur dan Adaptive Neuro-Fuzzy Inference System (ANFIS) sebagai algoritma klasifikasi. Dataset yang digunakan terdiri dari citra daun mangga berpenyakit anthracnose, bacterial canker, die back, gall midge, powdery mildew, sooty mould, serta daun sehat. Hasil pengujian menunjukkan bahwa akurasi tertinggi diperoleh pada pengujian lima membership function, dengan hasil terbaik oleh MF Gaussian. Fungsi keanggotaan Gaussian memberikan akurasi rata-rata 74,6% dan nilai loss terendah sebesar 0,346 dibandingkan fungsi keanggotaan lainnya. Dengan demikian, metode GLCM dan ANFIS terbukti cukup efektif dalam mengklasifikasikan penyakit daun mangga secara otomatis dan dapat menjadi solusi alternatif terhadap metode manual
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.
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: Neptunus: 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.
Co-Authors Abiyan Naufal Hilmi Achmad Junaidi Adelia Putri Adyani Adityawan, Firza Prima Afina Lina Nurlaili Afina Lina Nurlaili Afina Lina Nurlaili 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 Ama Maulidatul Khairah Ananda Azra Razali Andhika Ahnaf Daniswara Andini Fitriyah Salsabilah Andreas Nugroho Sihananto Andrianto, Mochammad Rifky Anggraini Puspita Sari Ani Dijah Rahajoe Ani Dijah Rahajoe Annisaa Sri Indrawanti annisaa sri indrawanti annisaa sri indrawanti Anny Yuniarti Aqsa Prima Cahya Ariani, Dian Dwi Ariyono Setiawan Aryananda, Rangga Laksana Aswan Aswan Attaqwa, Syukur Iman Awang, Mohd Khalid Azizah, Nabila Wafiqotul Bagus Sutikno Putra Basuki Rahmat Basuki Rahmat Basuki Rahmat Basuki Rahmat Masdi Siduppa Bimantara, Candra Kusuma Muhammad Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Chafid, M Putih Daniswara, Sena 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 Elzandy, Imeldha Etniko Siagian, Pangestu Sandya Fahmi Al Hafidz, Achmad Faisal Muttaqin Faishal Fernando Hutama Fara Disa Durry Faris Syaifulloh Farkhan, Farkhan Ferry Trilaksana Putra Fetty Tri Anggraeny Firyal Wishal Nabili Firyal Wishal Nabili Firza Prima Aditiawan Firza Prima Adityawan Firza Prima Adityawan Fitri Rahmawati Hadi, Surjo Hapsari Wiji Utami Hasby Bik, Ahmad Henni Endah Wahanani Humairah, Sayyidah Humam Maulana Tsubasanofa Ramadhan I Gede Susrama Mas Diyasa I Nyoman Sujana I Wayan Alston Argodi Idhana, Ilham Ainur indrawanti, annisaa sri Irsyad Rafi Naufaldi Karim, Mohammad Daniel Sulthonul Kartini Kartini Lestari, Kusmiyati Lina Nurlaili, Afina M. Syahrul Munir, M. Syahrul Mada Lazuardi Nazilly Made Hanindia Prami Swari Maisie Yunita Malva Mandyartha, Eka Prakarsa Manggala, Herwantoro Arya Marchel Adias Pradana Maulana, Hendra Merdin Risalul Abrori Moch. Hatta Mohammad Idhom Muchamad Dicky Alifiansyah Muhammad Aldi Maulana 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, Gede Ardi Prisheila Dharmawan, Diaz Putra, Chrystia Aji Putra, Riza Satria Putri, Desya Ristya Rafani Bardatus Salsabilah Retno Mumpuni Revelin Putri Syamjovanka Ridho Fajar Fahturohman Rizki, Agung Mustika Rizqi Mar'atus Sholiihah, Eka Royan Fajar Sultoni Rozi, Atiqur S J Saputra, Wahyu Safira, Dwi Putri 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 Sunarko, Victor Immanuel 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 Widiastuty, Riana Retno Wiji Utami, Hapsari yisti vita via Yisti Vita Via Yisti Vita Via Yogie Wilvren Saragih Yudha K., Dhian Satria Yudhistira Nanda Kumala YUSMI NUR AINI Zacky Yaser Malik Gumiwang Zalfa Ibtisamah Arishandy ZAMAZANI, ZAIN MUZADID Zuhriyah, Sitti