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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan EKSAKTA: Journal of Sciences and Data Analysis Jurnal Ilmiah Informatika Komputer Prosiding SNATIF Jurnal Informatika dan Teknik Elektro Terapan Journal of Information System Sistem : Jurnal Ilmu-Ilmu Teknik INTEGER: Journal of Information Technology JIKO (Jurnal Informatika dan Komputer) JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) JURNAL ILMIAH INFORMATIKA Jurnal Infomedia JURNAL PENDIDIKAN TAMBUSAI Jurnal Teknik Elektro dan Komputer TRIAC JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Elektronik bit-Tech JE-Unisla ILKOMNIKA: Journal of Computer Science and Applied Informatics Generation Journal JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara Nusantara Science and Technology Proceedings Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Ilmiah Teknologi Informasi dan Robotika HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal of Information System and Technology (JOINT) Jurnal Teknologi dan Manajemen TIERS Information Technology Journal Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Decode: Jurnal Pendidikan Teknologi Informasi International Journal Of Computer, Network Security and Information System (IJCONSIST) ALINIER: Journal of Artificial Intelligence & Applications Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) SinarFe7 Jurnal Informatika Software dan Network (JISN) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer VARIANSI: Journal of Statistics and Its Application on Teaching and Research STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Journal of Informatics and Electronics Engineering J-Icon : Jurnal Komputer dan Informatika TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Informatika Polinema (JIP) VISA: Journal of Vision and Ideas Journal of Innovative and Creativity Journal of Technology and System Information Journal of Software Engineering and Multimedia (JASMED) Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Himpunan: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Brilliant International Journal of Management and Tourism Jurnal Informatika Dan Tekonologi Komputer Jurnal Nasional Teknologi Informasi dan Aplikasinya
Anggraini Puspita Sari
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Comparison of Batch Size Values in MobileNetV2 for Stroke Classification Using CT Scan Images Ajeng Listya Devani; Anggraini Puspita Sari; Afina Lina Nurlaili; Nurul Hidajati
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3301

Abstract

Stroke is still one of the world's leading causes of death and permanent disability, necessitating a quick and precise diagnosis in order to choose the best course of treatment.  The purpose of this study is to examine how different batch size configurations affect the MobileNetV2 architecture's ability to classify stroke types from CT-scan brain pictures. The dataset comprises three categories Normal, Ischemic, and Bleeding sourced from Kaggle and RSUD Haji, East Java Province. The strategy to transfer learning was used utilizing pretrained ImageNet weights, with the network fine-tuned for stroke classification tasks. Experimental testing was conducted using three batch size configurations: 16, 32, and 64, while maintaining consistent hyperparameters for other training components. Among the assessment measures were accuracy, macro F1-score, and AUC (macro) to measure performance comprehensively. The results revealed that a batch size of 16 achieved the highest overall performance, with an accuracy of 96.14%, a macro F1-score of 96.15%, and an AUC of 99.62%, outperforming larger batch configurations. These findings indicate that smaller batch sizes enhance model generalisation and improve gradient update dynamics, enabling the CNN to better capture subtle patterns within CT-scan images. Thus, our study finds that the best trade-off between convergence speed and batch size is 16., model generalisation, and diagnostic accuracy, demonstrating the effectiveness of the MobileNetV2 architecture for automated stroke detection based on CT-scan imaging
Application of Transfer Learning for Breast Tumor Classification Adinda Putri Budi Saraswati; Anggraini Puspita Sari; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3343

Abstract

Breast tumor classification from mammogram images plays an essential role in supporting clinical decision-making, particularly because manual interpretation is often challenged by variations in breast tissue density and suboptimal image quality. This study develops a three-class classification model for normal, benign and malignant categories using the ResNet50 architecture with a transfer learning strategy on the mini-MIAS dataset, which contains 322 images with an imbalanced class distribution. Three optimizers are compared, namely Adam, RMSProp and SGD. Adam represents an adaptive moment-based optimization approach. RMSProp emphasizes stable updates under fluctuating gradients. SGD with momentum serves as a conventional baseline relying on direct gradient updates. The model is trained using a 60 percent training and 40 percent validation split with class weighting and evaluated through accuracy, AUC and F1-score metrics. Experimental results show that Adam achieves the highest performance with 68.27 percent accuracy, 88.58 percent AUC and an F1-score of 0.68. RMSProp attains 58.63 percent accuracy, 76.05 percent AUC and an F1-score of 0.59. SGD yields the lowest performance with 44.18 percent accuracy, 61.33 percent AUC and an F1-score of 0.44. Confusion matrix analysis for the Adam configuration indicates reasonably consistent recognition across all classes, although misclassification remains present. The findings demonstrate that adaptive optimizers are more effective for training ResNet50 on small and imbalanced mammogram datasets. This study provides a foundation for developing more reliable computer-aided diagnostic systems for early breast cancer detection.
Application of SARIMA and XGBoost Models in Forecasting International Tourist Arrivals at Ngurah Rai Maisie Yunita Malva; Anggraini Puspita Sari; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3352

Abstract

The tourism sector constitutes a vital component of Indonesia's economic growth, especially in Bali Province, where Ngurah Rai International Airport functions as the principal entry point for international travelers. Precise prediction of tourist arrivals is critical for strategic planning, resource distribution, and infrastructure development. Nevertheless, conventional statistical techniques often struggle to adequately capture the intricate patterns in tourism data, which exhibit both periodic regularities and non-linear characteristics shaped by external influences, including global economic fluctuations, travel regulations, and the COVID-19 pandemic. This research proposes a hybrid SARIMA-XGBoost framework that combines traditional statistical modeling with machine learning techniques to simultaneously capture linear temporal dependencies and non-linear residual patterns—an integration not previously explored for Bali's tourism forecasting. The study employs 204 monthly records of international tourist arrivals spanning January 2008 to December 2024, integrating seasonal indicators and the COVID-19 pandemic period as external covariates. The SARIMA component extracts linear temporal trends and seasonal structures, whereas XGBoost captures non-linear dynamics embedded in the residuals. The hybrid model achieves substantially higher forecasting precision with MAPE of 3.22%, MAE of 0.0492, and RMSE of 0.0597, outperforming standalone SARIMA (MAPE 25.02%, MAE 0.4305, RMSE 0.5035) and XGBoost (MAPE 7.36%, MAE 0.0736, RMSE 0.0995). These results validate that integrating statistical and machine learning methodologies significantly enhances predictive accuracy. The proposed model offers airport management, tourism boards, and policymakers a robust forecasting instrument for capacity planning and strategic decision-making, facilitating sustainable tourism development and enhancing Bali's competitiveness as an international destination.
PENGENALAN CITRA TULISAN TANGAN HURUF HIRAGANA MENGGUNAKAN SUPPORT VECTOR MACHINE Mustofa, Tsabita Safana; Sari, Anggraini Puspita; Maulana, Hendra
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7841

Abstract

Pengenalan huruf Hiragana merupakan tantangan tersendiri bagi pembelajar bahasa Jepang, terutama karena bentuk karakter yang kompleks dan mirip satu sama lain. Penelitian ini bertujuan untuk mengembangkan sistem pengenalan citra tulisan tangan huruf Hiragana menggunakan metode Support Vector Machine (SVM) dan teknik ekstraksi fitur Histogram of Oriented Gradients (HOG). Dataset yang digunakan terdiri dari 230 citra tulisan tangan huruf Hiragana dasar sebanyak 46 karakter, yang diperoleh dari hasil pemindaian tulisan tangan di atas kertas. Tahapan penelitian meliputi pre-processing (konversi ke grayscale dan resize citra), ekstraksi fitur menggunakan HOG, serta klasifikasi menggunakan SVM dengan berbagai kernel yang berbeda. Evaluasi model dilakukan dengan menggunakan confusion matrix yang menghasilkan metrik evaluasi seperti akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kernel RBF dengan parameter C=100 dan Gamma=0,001 memberikan hasil terbaik dengan akurasi sebesar 97,10%, precision sebesar 97,35%, recall sebesar 97,10%, dan F1-score sebesar 97,11% pada skenario pembagian data 70:30. Temuan ini menunjukkan bahwa kombinasi metode HOG dan SVM mampu mengenali huruf Hiragana secara efektif. Sistem ini dapat menjadi alat bantu edukatif yang bermanfaat dalam proses pembelajaran bahasa Jepang serta memiliki potensi untuk dikembangkan lebih lanjut dalam aplikasi pembelajaran interaktif.
PENERAPAN ALGORITMA SVM DENGAN FITUR WARNA HSV PADA KLASIFIKASI IKAN ARWANA Sutrisni, Erica Aprilia; Sari, Anggraini Puspita; Maulana, Hendra
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7842

Abstract

Identifikasi jenis ikan arwana secara manual kerap menimbulkan kesalahan akibat kemiripan visual antar jenis, terutama bagi kolektor pemula. Kesalahan dalam mengenali jenis ikan dapat berujung pada keputusan pembelian yang keliru. Penelitian ini bertujuan untuk membangun sistem klasifikasi otomatis jenis ikan arwana berbasis citra digital menggunakan metode Support Vector Machine (SVM) dan ekstraksi fitur warna HSV (Hue, Saturation, Value). Proses penelitian mencakup akuisisi citra dari enam jenis arwana, pra-pemrosesan (resize, segmentasi menggunakan model U²-Net, dan augmentasi data), konversi citra ke ruang warna HSV, serta ekstraksi fitur berupa rata-rata dan standar deviasi dari komponen H, S, dan V. Dataset yang digunakan terdiri dari 1.260 citra setelah augmentasi. Pengujian model dilakukan dengan variasi kernel dan parameter pada algoritma SVM. Hasil terbaik diperoleh dengan kernel linear dan nilai parameter C=10, yang menghasilkan akurasi klasifikasi sebesar 89%. Evaluasi metrik klasifikasi menunjukkan nilai precision, recall, dan F1-score rata-rata sebesar 0,89, dengan distribusi prediksi yang cukup baik untuk semua kelas. Sistem ini menunjukkan performa yang andal dalam membedakan jenis ikan arwana berbasis fitur warna, dan dapat menjadi solusi yang efektif dan objektif dalam membantu identifikasi ikan hias di kalangan masyarakat umum.
KLASIFIKASI DAUN HERBAL BERTULANG MENYIRIP MENGGUNAKAN K-NEAREST NEIGHBOR Putri Wardhani, Lintang Sari; Sari, Anggraini Puspita; Maulana, Hendra
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7840

Abstract

Indonesia memiliki kekayaan keanekaragaman hayati, termasuk berbagai jenis tanaman herbal yang dimanfaatkan untuk pengobatan tradisional. Salah satu bagian tanaman yang sering dimanfaatkan ada-lah daun, karena kandungan senyawa bioaktifnya dan kemudahan da-lam pengolahan. Namun, proses identifikasi daun herbal sering men-galami kendala karena kemiripan bentuk antar jenis daun. Untuk mengatasi tantangan tersebut, penelitian ini mengembangkan sistem klasifikasi daun herbal bertulang menyirip menggunakan algoritma K-NN yang dikombinasikan dengan ekstraksi fitur berbasis deep learning menggunakan arsitektur ResNet-50. Penelitian dilakukan dengan mengumpulkan 600 citra daun dari empat jenis tanaman herbal. Citra mengalami tahapan praproses, ekstraksi fitur menggunakan ResNet-50, vektor fitur yang dihasilkan diklasifikasikan menggunakan K-NN dengan berbagai parameter. Evaluasi dilakukan menggunakan confu-sion matrix, akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa skenario terbaik diperoleh pada kombinasi metrik Euclidean dengan nilai k = 5, pembobotan uniform, dan proporsi data latih-uji 70:30, menghasilkan akurasi sebesar 98,61%. Temuan ini menunjuk-kan bahwa kombinasi metode K-NN dan ekstraksi fitur ResNet-50 dapat mengklasifikasi daun herbal dengan akurasi tinggi, serta mem-iliki potensi untuk diterapkan secara luas dalam sistem identifikasi tanaman berbasis citra digital.
COMPARISON OF NEWTON RAPHSON AND SECANT METHODS TO DETERMINE THE OPTIMAL POINT OF TIKTOK APPLICATION Pratama, Fabio Arayya; Muhammad Shaquille Syafiq; Muhammad Rudmardiansyah Pratama Putra; Anggraini Puspita Sari; Sischa Wahyuning Tyas
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm499

Abstract

The growth of digital application users generally follows a non-linear pattern that can be modeled using the logistics growth function, which has the characteristic of an inflection point, which is a condition when the growth rate reaches the maximum value. Optimal point determination involves solving non-linear equations that cannot always be solved directly, so a numerical approach is required. This study aims to determine the optimal growth point of TikTok application users and compare the performance of the Newton–Raphson and Secant methods in solving non-linear equations in the logistics model. User growth data was obtained from the Google Play Store and simulated using logistics growth parameters that represent the characteristics of applications with a high level of virality, with analytics solutions as an evaluation reference. The calculation results show that the optimal point of growth of TikTok users is around the 6th week. The Secant method yielded an optimal point estimate of 5.972 with an RMSE value of 0.0150 and a relative error of 0.25%, while the Newton–Raphson method yielded an estimate of 5.773 with an RMSE value of 0.2140 and a relative error of 3.57%. The difference in error rate and convergence stability shows that the Secant method provides a more effective approach in determining the optimal growth point of digital application users based on the logistics model.
Inflation Convergence Modeling Using Binary Logistic Regression With SGD-Newton Raphson Optimization Methods in Indonesia Fatma Novalia Kussumarani; Istiqomah, Nerissabila Uswatun; Siva Ifin Azzahra; Anggraini Puspita Sari; Sischa Wahyuning Tyas
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 1, April 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss1.art9

Abstract

Global economic changes have necessitated the development of inflation models that can accurately describe Indonesia's economic dynamics. This study aims to compare two optimization methods, Newton Raphson and Stochastic Gradient Descent (SGD), in binary logistic regression modeling to analyze the effectiveness of monetary policy. This study contributes to evaluating the performance of both methods in terms of convergence speed and accuracy of inflation model parameter estimation. The results of the analysis show that the Newton Raphson method is more efficient in achieving convergence with an iteration value of 0.2933 compared to SGD, while both methods produce equivalent model quality based on the Akaike Information Criterion (AIC) values of 34.4008 and 34.4254. These findings emphasize the importance of selecting the right optimization method to support more efficient monetary policy analysis.
Klasifikasi Penyakit Mata Menggunakan ResNet-50 Berdasarkan Citra Fundus Kurniawan, Muh. Irsyad Dwi; Sari, Anggraini Puspita; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3306

Abstract

Visual impairment from diabetic retinopathy, glaucoma, and cataracts remains a critical global health issue, emphasizing the need for early and accurate diagnosis to prevent permanent vision loss. This research presents an automated detection system utilizing ResNet-50, a deep learning architecture, to classify fundus images into multiple retinal disease categories. Unlike conventional convolutional neural networks used in prior studies, this approach leverages ResNet-50's residual learning mechanism to better identify complex retinal patterns. The study employed 4,184 fundus photographs from Kaggle, divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. Images were preprocessed through resizing to 224×224 pixels, normalized with ImageNet parameters, and augmented using random rotation and flipping techniques to enhance model generalization. Dataset splitting followed stratified sampling with an 80-20 train-test ratio, maintaining balanced class representation. Model training spanned 20 epochs using the Adam optimizer across three learning rates: 0.1, 0.01, and 0.001. The 0.001 learning rate produced optimal results with 90.35% accuracy, 90.28% precision, 90.18% recall, and 90.21% F1-score. The confusion matrix indicated strong performance in detecting diabetic retinopathy (219 correct predictions) and normal cases (189 correct predictions), though minor misclassifications occurred between glaucoma and normal categories. These findings validate ResNet-50's residual architecture as an effective tool for extracting discriminative retinal features, offering a computationally efficient solution for automated eye disease screening. Future work should incorporate explainability methods like Grad-CAM to enhance clinical interpretability and build trust among healthcare professionals in AI-assisted diagnostic systems.
MODEL ESTIMASI WAKTU TEMPUH MENGGUNAKAN PENDEKATAN PEMODELAN MATEMATIS DAN OPTIMASI: TRAVEL TIME ESTIMATION MODEL USING MATHEMATICAL MODELING AND OPTIMIZATION APPROACHES Hamid, Aisyah Amalia; Shafara, Anindya Restu; Rachmawati, Siti Naia Hesti; Sari, Anggraini Puspita; Tyas, Sischa Wahyuning
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p140-154

Abstract

Estimasi waktu tempuh yang akurat sangat krusial bagi efisiensi sistem transportasi dan layanan logistik di perkotaan. Penelitian ini mengembangkan model estimasi waktu tempuh untuk wilayah Surabaya dengan mengintegrasikan metode interpolasi, regresi linear, dan teknik optimasi Newton-Raphson. Data yang digunakan bersumber dari rute OpenStreetMap serta variabel cuaca (curah hujan dan suhu) dari BMKG. Hasil analisis menunjukkan bahwa jarak tempuh, intensitas hujan, dan kondisi jam sibuk secara signifikan memengaruhi durasi perjalanan. Model ini memiliki tingkat akurasi yang tinggi dengan koefisien determinasi RSquare (???? 2 ) sebesar 0,92. Adapun tingkat kesalahan model diukur melalui Mean Absolute Error (MAE) sebesar 201,05 detik (sekitar 3,3 menit) dan Root Mean Squared Error (RMSE) sebesar 267,53 detik. Melalui simulasi optimasi rute, model ini mampu memberikan saran perjalanan yang 8–15% lebih cepat dibandingkan strategi pemilihan rute konvensional berbasis jarak terpendek. Dengan hasil tersebut, model ini dapat diimplementasikan pada sistem navigasi adaptif dan responsif terhadap perubahan kondisi lingkungan dan lalu lintas.   Accurate travel time estimates are crucial for the efficiency of transportation systems and logistics services in urban areas. This study developed a travel time estimation model for the Surabaya area by integrating interpolation, linear regression, and Newton-Raphson optimization techniques. The data used was sourced from OpenStreetMap routes and weather variables (rainfall and temperature) from the BMKG. The results of the analysis show that travel distance, rainfall intensity, and rush hour conditions significantly affect travel duration. This model has a high level of accuracy with a coefficient of determination R Square (R2 ) of 0.92. The model's error rate is measured by the Mean Absolute Error (MAE) of 201.05 seconds (approximately 3.3 minutes) and the Root Mean Squared Error (RMSE) of 267.53 seconds. Through route optimization simulations, this model is able to provide travel suggestions that are 8–15% faster than conventional route selection strategies based on the shortest distance. With these results, this model can be implemented in adaptive navigation and responsive systems that respond to changes in environmental and traffic conditions.
Co-Authors Abd Rabi’ Achmad Junaidi Achmad Junaidi, Achmad Achmad Yusuf Yulestiono Adhi Dwi Saputra Adiguna Yudhanto Adila, Mar’atul Adinda Putri Budi Saraswati Aditya, Wigananda Firdaus Putra Adiyatma, Hesel Faza Afandi, Rizki Baehtiar Afina Lina Nurlaili Afina Lina Nurlaili Afina Lina Nurlaili Agung Darmawansyah Agung Mustika Rizki, Agung Mustika Agussalim, Agussalim Agustiardani, Salsa Pramudhita Ajeng Listya Devani Aji Paringga Jati Akbar, Fawwaz Ali Akbar, M.Azriel Yaqi Al-Ayyubi, Iqbal Alam, Fajar Indra Nur Aldito Restu Wintama Alfajr, Achmad Yuneda Alfi Hendri Alhamda, Denisa Septalian Alif Bayu Ammarizky Alif Ernanda Putra Alvin Rama Saputra Alvin Amelia Ananda Putri Lestari Amrullah, Ahmad Wildan Ana, Vika Rafi Ananda Ayu Puspitaningrum Andre Leto Andreas Nugroho Sihananto Andreas Nugroho Sihananto Anindhyta, Erisa Dwi Xena Aninidta, Sophia ANUGRAH PRASETYA, RAJAWALI SHAKTIKA Aprinia Salsabila Roiqoh Aqil Salim, Mas Muhammad Ar Rafi, Mohammad Hafiz Ardelia, Danika Najwa Ardiansyah, Muhammad Dafa Ardiansyah, Muhammad Naufal Arhinza, Rayhan Saneval Ariando, Aldo Pradana Aries Boedi Setiawan Arif Nur Cahyo Arif Rahman Hakim Arif Widiasan Subagio Arifani, Kahpi Baiquni Arifin, Hilda Desfianty Arini, Andhini Putri Ariningtyas, Imelda Dwi Arryanto, Bahiskara Ananda Arthansa, Radendha Muhammad Aryananda, Rangga Laksana Atiqur Rozi Awang Mohammad Ziadhasya Rizqaarrafi AZMI, ANDRA HUSNUL Azzahra Adelia Sabrina Salsabila Azzahra Asti Khairunnisa Bagus Satrio Wicaksono Basuki Rahmat Masdi Siduppa Bayu Setiawan Belva Cynara Trana Putri, Prudencia Bhaswara, Maulana Muzakki Bimantoro, Ryan Bagus Budiman, Daniel cahyono, wahyu eko Cinta Ramayanti Citra Firdausi, Putri Aulia Damai Arbaus, Damai Damayanti, Natasya Meryl Daniel Gloryo Nadirco Daniswara, Sena Danu Satrio Dea Rajwa Zahra Athaya Dela Ayu Putri Mayona Dela Puspita Lasminingrum Deswita Choirun Nisa Dewi, Shanty Kurnia Dian Maharani, Dian Dimas Satria Prayoga Dody Pintarko Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Eka Maurita Eka Prakarsa Mandyartha Ekawati, Anies Eko Kuncoro Eko Kuncoro EKO WAHYUDI Elizabeth, Caritta Endyarni, Regina Caeli Eva Salsabilla Eva Yulia Puspaningrum Fahlefi, Muhammad Reza Fahri Izzuddin Zulkarnaen Fajrina, Nur Septia Farhans, Muhammad Izzudin Fatchur Rozci Fatma Novalia Kussumarani Fauzan, Daffa Athallah Fina Amru Millati Millati Firdaus Putra Aditya, Wigananda Firmansyah, Fahrul Firmantara, Wahyu Firza Prima Aditiawan Firzannabeel Aqila Rafid Gatot Yulisianto Gatut Yulisusianto Hafiyan Fazagi Adnanto Hamid, Aisyah Amalia Hanin Fatma Soraya Hendri, Alfi Henni Endah Wahanani Hilya ‘Zada Mardhatilla Al Haadiy Hiroshi Suzuki Icham, Maulana Izuddin Audadi idhom, Mohammad Intan Ni'matul Fitri Intan Putri Mansyur Pratama Iqbal Bagus Satriawan Irsyadi, Muhamad Haidir Irsyadi, Muhammad Haidir Irsyadi, Muhammad Rohman Irwansyah, Ferry Ishak Febrianto Ismail, Jefri Abdurrozak Istiqomah, Nerissabila Uswatun Jaka Subagja Jamaludin . Jeki Saputra Jibran, Kemal Fahreza Joko Lasmono Jonathan Teguh Samuel Kaeng Julastri, Bregsi Atingsari Kahpi Baiquni Arifani Kartika Sari Kartini Kartini Kartini Kartini KEZIA, KEZIA Khairul Anwar Khairunnisa Khairunnisa Khofifah, Nada Firda krisna krisnawati wati Krisnawati Kuncoro, Eko Kurniawan, Muh. Irsyad Dwi Ledjap, Adventus Michael Bala Letkol Arh Desyderius Minggu Lina Nurlaili, Afina Lisanthoni, Angela Listanto, Evan Adwitiya Dwi M Julius St M. Rafi Ardiansyah Made Hanindia Prami Swari Maharani, Ardiana Deka MAHARDIKA, NAUFAL INDRA Mahendra, Zenryo Yudi Arnava Darva Maisie Yunita Malva Makarim, Irsyad Fadhil Maliq Reynanda , Revano Marsanda, Dea Ayu Eka Masyhuri, Alif Syahda Adji Maulana, Hendra Maulana, M. Zaky Pria Maurisa Arimbi Putri Mayya, Kalfin Syah Kilau Minggu, Desi Derius Minggu, Desi Derius Moh Avin Dharma Wijaya MOH MARIO SUBAGIO Moh. Misbahul Musthofah Mohammad Idhom Mohammad Quthbul Widad Mohammad, Bawazir Fadhil Muhammad Abdullah Hafizh Muhammad Hilmy Aziz Muhammad Lizamul Arsi Muhammad Muharrom Al Haromainy Muhammad Rohman Irsyadi Muhammad Rudmardiansyah Pratama Putra Muhammad Shaquille Syafiq Muhammad Wifaqul Azmi Mulyani Satya Bhakti Mulyo, Budi Mukhamad Mustofa, Tsabita Safana Nabila Anggita Luna Nachrowie, Nachrowie Nadia, Prasinta Hari Nafis Pratama Putra Nandana Wahyu Rizqullah Nicholas, Sandy Ninis Herawati Noor Imansyah Basoeki, Dandy Norhaslinda Binti Hasim Nur Rachman Nur Rachman Supadmana Muda Nurdiansyah, Titis Fajar Nurdianto, Muhammad Akbar Nurul Hidajati Oktavia Nur Khasanah OKTAVIAN, JAGUAR DEVA NANGGALASAKTI OKTAVIAN Olivia Dewi Ramadhani Suryoningsih Panggih Santri Paramita, Maheswari Dian Pintarko, Dody Prakoso, Akbar Tri Pramudyo, Leon Ddewandaru Prapatoni, Velian Prasetyo, Edi Dwi Pratama Putra, Moch Aditya Pratama, Fabio Arayya Pratama, Hendrico Edhent Surya Pratama, Moch Nasikh Andhyka Prismahardi Aji Riyantoko Putra Dwi Wira Gardha Yuniahans Putra, Chrystia Aji Putri Salsabila, Belia Putri Wardhani, Lintang Sari Putricia Hendra, Ria Amelia Shinta Rachmawati, Siti Naia Hesti Rahman, Fatan Izzatur Rahman, Muhammad Fadhillah Rahmawati, Deisya Dzakiyyah Rahmawati. S, Abel Dwi Raissa Atha Febrianti Ramadhani, Aimee Natya Ramadhani, Neo Rendra Ardika Resti Indah Paramita Sari Revano Maliq Reynanda Riandi Zahra, Muhammad Alvin Ridho Fajar Fahturohman Riky Hermawan Ririn Wanandi Rizki, Agung Mustika Rochmawati, Febriyan Putri Rofiah, Muflichatur Romadhoni, Firman Rozi, Atiqur Ryan Purnomo Sagita, Dhea Intan SALMAN ALFARIZI Samdono, Arif Sampurno Utomo, Moch Wahyu Sandy Nicholas Sanjaya, I Wayan Indra Sakti Sanjaya Santoso, Aries Satriya Yudha Saskia Rafika, Chesa Satrio Dharma Putra Satwika, I Kadek Susila Septyana, Dwitamara Setiawan, Aries Buedi Shafara, Anindya Restu Siahaan, Renita Enjel Siharta, Niken Febrinikmah Silitonga, Paulenta Silvania Sischa Wahyuning Tyas Sischa Wahyuning Tyas Siti Sri Wahyuni Siva Ifin Azzahra Subairi Subairi Sugeng Harianto SUGENG HARIANTO Sugiarto S Suherman Suherman Suryahadi, Farrel Zikri Suryangga, Nova Suryantari, Putu Anggi Sutrisni, Erica Aprilia Syahbana, Ahmad Nadhif Fikri Syahrul Amin, Akhmad Syamjovanka, Revelin Putri Takahiro Kitajima Takashi Yasuno Tatipang, Angeline Riendra Torrilynn Farrell Zuriely Tresna Maulana Fahrudin Ulummuddin, Ikhya Wardana, Nabila Sya’bani Wicaksono, Faris Hakim Widoretno, Astrini Aning Widya Indah Sujatmoko, Amanda Wisnu Murti, Hapsoro Yisti Vita Via Yogi Dwi Arsanti Yossie Triwinanda, Rizqullah Sandya Yunizar, Sri Fatmawati Zahran, Muhammad Sulthan Zidan, Ahmad Ziddan, Muhtasar