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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Ilmiah Informatika Komputer Prosiding SNATIF Jurnal Informatika dan Teknik Elektro Terapan 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 Jurnal Riset 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 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 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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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Bitcoin Mining Hardware Profitability Prediction Using Categorical Boosting and Extreme Gradient Boosting Algorithms Dimas Satria Prayoga; Puspita Sari, Anggraini; Junaidi, Achmad
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/9xb2dz14

Abstract

Cryptocurrencies, especially Bitcoin, have gained global recognition, with mining being one of its most interesting aspects. This is especially important in the context where only a few types of bitcoin mining rigs are expected to operate profitably. On the other hand, in the field of machine learning, there are widely used algorithms, namely Extreme Gradient Boosting (XGBoost), which is known for its effectiveness, and Categorical Boosting (CatBoost), which excels in handling categorical data. This study aims to combine the performance of CatBoost and XGBoost using the Ridge Regression technique in predicting a case study that is not often encountered, namely predicting the profitability of Bitcoin mining hardware. The main steps include collecting data from reliable sources, preprocessing the data to ensure compatibility, feature selection to select the most relevant features, building a prediction model using the preprocessed data set, and then training and testing both models to evaluate their predictive accuracy. The evaluation metrics on the test data reveal the performance of CatBoost, XGBoost, and the CatBoost-XGBoost. CatBoost demonstrates a training time of 3.35 seconds with a MAPE of 15.67% and an RMSE of 0.1733. In comparison, XGBoost has a longer training time of 5.27 seconds but achieves a significantly lower MAPE of 6.49% and an RMSE of 0.1737. Meanwhile, the CatBoost-XGBoost, with the longest training time of 6.84 seconds, delivers a competitive MAPE of 6.57% and the lowest RMSE of 0.1696 among the three approaches. These results highlight that while XGBoost and CatBoost meta model outperform CatBoost in terms of accuracy, the Ridge meta model provides slightly better overall predictive performance based on RMSE.
Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports Prasetya, Dwi Arman; Sari, Anggraini Puspita; Idhom, Mohammad; Lisanthoni, Angela
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/skzqbd57

Abstract

Agriculture is a key contributor to Indonesia's economic growth, with tubers representing the second most important food crop. Despite their significance, the export value of Indonesia’s tuber crops has not yet reached its full potential given the decline in the value of tuber exports since 2021. One of the contributing factors is the restricted range of export market options. This study aims to analyze export trade patterns to identify the most high-potential markets for Indonesian tuber commodities.  Clustering analysis is used as a key method to identify market locations by grouping countries based on similar trade characteristics. Clustering was conducted using the Gaussian Mixture Model (GMM), which enhanced by Particle Swarm Optimization (PSO) and evaluated by silhouette score and DBI. The dataset is collected from Indonesia’s Central Bureau of Statistics from 2019 to 2023, focusing on 5 kinds of tuber exports with total of 455 entries and 8 columns. Using the AIC/BIC method, the optimal number of clusters obtained is 2 which are low market opportunities (cluster 0) and high market oppurtunities (cluster 1). Results showed that the GMM model without optimization has silhouette score of 0.7602 and DBI of 0.8398, while the GMM+PSO model achieved an improved silhouette score of 0.8884 and DBI of 0.5584. Both score are categorized as strong structure but, GMM+PSO has higher silhouette score and lower DBI score, demonstrating the effectiveness of PSO in enhancing the clustering model’s performance. The key potential markets for Indonesian tuber exports are primarily concentrated in Asia, including countries such as China, Malaysia, Thailand, Vietnam, Hong Kong, and United States.
ANALISIS SENTIMEN PUBLIK PADA TAGAR #BTSCOMEBACK DI PLATFORM X MENGGUNAKAN INDOBERTWEET Damayanti, Natasya Meryl; Ariningtyas, Imelda Dwi; Icham, Maulana Izuddin Audadi; Sari, Anggraini Puspita
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.7176

Abstract

Media sosial telah menjadi ruang utama bagi publik dalam mengekspresikan opini terhadap fenomena budaya populer, termasuk comeback grup K-pop BTS yang yang sering kali menimbulkan intensitas percakapan dan partisipasi digital. Tagar #BTSComeback menjadi salah satu kanal ekspresi publik yang ramai digunakan, mencerminkan beragam respons dari pengguna internet di Indonesia, mulai dari dukungan antusias hingga bentuk kritik. Penelitian ini bertujuan untuk menganalisis sentimen publik Indonesia terhadap tagar tersebut dengan memanfaatkan model IndoBERTweet, yaitu model pralatih yang dirancang khusus untuk memahami teks berbahasa Indonesia di media sosial. Sebanyak 6.300 tweet berbahasa Indonesia dikumpulkan dari platform X dalam rentang waktu Januari hingga Juni 2025. Hasil penelitian menunjukkan bahwa IndoBERTweet mampu mengklasifikasikan sentimen dengan akurasi mencapai 95%, serta menghasilkan performa evaluasi yang konsisten tinggi pada ketiga kategori sentimen, terutama dalam mendeteksi sentimen positif. Visualisasi dalam bentuk word cloud memperlihatkan keberagaman ekspresi publik terhadap peristiwa comeback tersebut. Penelitian ini membuktikan efektivitas IndoBERTweet dalam menganalisis sentiment teks media sosial berbahasa Indonesia dan memberikan wawasan empiris tentang dinamika opini publik Indonesia terhadap fenomena budaya popular global.
OPTIMASI LOKASI PEMBANGUNAN RUMAH SAKIT DI KECAMATAN NGRAYUN KABUPATEN PONOROGO DENGAN K-MEANS Anindhyta, Erisa Dwi Xena; Paramita, Maheswari Dian; Sari, Anggraini Puspita
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 2 (2025): JIRE November 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i2.1646

Abstract

Ketersediaan layanan kesehatan yang memadai berkontribusi secara signifikan dalam mendukung perkembangan daerah dan meningkatkan kualitas hidup masyarakat. Kecamatan Ngrayun memiliki fasilitas kesehatan yang minim, sehingga diperlukan lokasi yang optimal dalam pembangunan rumah sakit baru. Metode tradisional sering kali digunakan dalam menentukan lokasi pembangunan. Namun, kini teknologi seperti algoritma pembelajaran K-Means Clustering berbasis Silhouette Score dapat digunakan untuk menemukan lokasi pembangunan rumah sakit yang optimal. Adapun faktor-faktor yang diperhatikan diantaranya jumlah penduduk, aksesibilitas, jarak ke rumah sakit terdekat, serta jumlah fasilitas kesehatan. Berdasarkan silhouette score, menunjukkan bahwa jumlah cluster yang mendapatkan score paling mendekati 1 yaitu sebanyak 2 cluster dengan score 0.7370. Desa dibagi menjadi cluster 0 dan cluster 1, dimana cluster 1 diidentifikasi sebagai lokasi utama yang sesuai untuk pembangunan rumah sakit, yang mencakup desa Baosan Kidul, Mrayan, Baosan Lor, Ngrayun, Selur, dan Cepoko. Berdasarkan dari penelitian ini, dapat diketahui bahwa metode K-Means dapat digunakan untuk menentukan lokasi pembangunan rumah sakit secara efektif.
Classification Of Cyber Attack And Anomaly In Web Server Using Transformer and Transfer Learning Prasetyo, Edi Dwi; Rahmat, Basuki; Sari, Anggraini Puspita
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.119

Abstract

Cybersecurity is a crucial aspect in maintaining the integrity and availability of information systems, especially on web servers which are vulnerable to various types of attacks and anomalies. This research aims to investigate the application of transfer learning in the classification of cyber attacks and anomalies on web servers. Transfer learning, a powerful deep learning approach, enables pre-trained models to adapt to new tasks with limited data, offering an efficient solution for detecting malicious activities and unusual patterns in web server logs. The goal is to improve detection accuracy while reducing the time and resources required to train models from scratch. This study uses a bi-layer classification approach with pre-trained Transformer models, RoBERTa and BERT, through transfer learning to detect cyber attacks and anomalies in web server log data. The process includes preprocessing the log data, extracting relevant features, and fine-tuning BERT to classify known attacks in the first layer, followed by RoBERTa in the second layer to detect unusual or unknown behaviors. Model performance is evaluated using accuracy, precision, recall, and F1-score, and results are compared with traditional deep learning methods like RoBERTa and BERT to highlight the advantages of this bi-layer transfer learning approach. The result of this proposed bi-layer classification method is improved performance in detecting cyber attacks and anomalies compared to using RoBERTa and BERT individually. By combining both models, the system is anticipated to achieve higher accuracy, better precision in identifying true threats, improved recall for detecting a wider range of attacks, and a more balanced F1-score. This layered approach leverages the strengths of both RoBERTa and BERT, enabling more robust and reliable threat detection, with reduced false positives and false negatives compared to single-model implementations. 
Face Recognition Untuk Rancangan Sistem Pintu Kamar Kos Menggunakan Algoritma Haarcascade OKTAVIAN, JAGUAR DEVA NANGGALASAKTI OKTAVIAN; Maulana, M. Zaky Pria; Sari, Anggraini Puspita
ALINIER: Journal of Artificial Intelligence & Applications Vol. 6 No. 2 (2025): ALINIER Journal of Artificial Intelligence & Applications
Publisher : Program Studi Teknik Elektro S1 ITN Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/alinier.v6i2.10046

Abstract

Teknologi pengenalan wajah telah menjadi solusi yang penting dalam meningkatkan keamanan, terutama dalam lingkungan rumah dan kamar kos. Penelitian ini mengusulkan pengembangan sistem keamanan berbasis Face Recognition menggunakan metode Haar Cascade untuk membuka pintu kamar kos. Tujuan penelitian ini adalah untuk mengimplementasikan teknologi pengenalan wajah dalam konteks membuka pintu kamar dan meningkatkan keamanan dengan menggunakan metode Haar Cascade. Melalui analisis literatur dan pengembangan sistem, penelitian ini menyoroti pentingnya OpenCV sebagai fondasi teknologi untuk pengenalan wajah. Metode penelitian yang digunakan mencakup studi literatur dan analisis pengembangan sistem. Sistem dikembangkan menggunakan Python dan OpenCV, dengan dataset gambar yang diambil langsung dari webcam dan diolah menggunakan algoritma Haar Cascade. Hasil pengujian menunjukkan tingkat akurasi antara 40% hingga 70%, dengan keunggulan algoritma Haar Cascade dalam respons cepat. Namun, tantangan seperti potensi pemalsuan menggunakan foto dan kondisi pencahayaan yang buruk masih perlu diatasi. Saran untuk pengembangan lebih lanjut termasuk meningkatkan akurasi sistem, perbaikan dalam penanganan kondisi pencahayaan, dan kontinuasi pengembangan untuk meningkatkan keamanan dan efisiensi akses dalam konteks kamar kos. Dengan mengatasi tantangan tersebut, penerapan teknologi ini memiliki potensi besar untuk menjadi solusi yang efektif dalam meningkatkan keamanan dan efisiensi akses.
Implementasi Modified K-Nearest Neighbor (MKNN) untuk Deteksi Penyakit Anemia Putra Dwi Wira Gardha Yuniahans; Anggraini Puspita Sari; Yisti Vita Via
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.13425

Abstract

Anemia is a condition where the hemoglobin level in the human body drops below the normal threshold. It can cause several negative effects, such as delayed psychomotor development, a higher risk of infectious diseases, and in women, the possibility of premature birth. Therefore, early detection of anemia is essential to speed up treatment and recovery. One method that can support the diagnostic process is machine learning, particularly the Modified K-Nearest Neighbor (MKNN) algorithm. MKNN is an improved of standard KNN, incorporating additional steps such as validity calculation and weighted voting, which are not present in the original version. In this study, MKNN was applied to detect anemia and achieved an accuracy of 84% using a 75:25 train-test data split and k=5. The dataset was collected from Jemursari Hospital in Surabaya, consisting of 100 patient records. These records were used to evaluate the performance of the MKNN algorithm in anemia detection.
Comparison of C4.5 Decision Tree and Naive Bayes Algorithms for Classification of Nutritional Status in Stunting Toddlers Ishak Febrianto; Anggraini Puspita Sari
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.122

Abstract

Stunting is a condition where growth and development of children under 5 years of age is impaired due to chronic malnutrition. Data mining with classification techniques on the nutritional status of stunting toddlers can be performed to help identify toddlers experiencing stunting and provide objective measurements of their nutritional status. There are several classification methods, but this research will compare the performance of the C4.5 decision tree algorithm, which is included in the decision tree approach, and naive Bayes, which uses a probability-based approach of class occurrence in classifying nutritional status of stunting toddlers, with discretization performed in the preprocessing stage. The data used in this research was obtained from Jagir Health Center, Surabaya, in the form of secondary data on toddler nutrition in 2021, totaling 2,801 records. The labeling of stunting or normal in the dataset uses the reference of child anthropometric standards in Indonesia as stated in the Republic of Indonesia Minister of Health Regulation number 2 of 2020. The best method based on the AUC (Area Under the Curve) value was the C4.5 decision tree with a value of 86% (good classification), while naive Bayes achieved 77% (fair classification) using a 70:30 training and testing data ratio.
Design and Development of a Web-Based Application for Managing Incoming and Outgoing Letters Using Agile Methodology Masyhuri, Alif Syahda Adji; Rahmat, Basuki; Sari, Anggraini Puspita
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.739

Abstract

The management of incoming and outgoing correspondence in government institutions such as the Palesanggar Village Office is still carried out manually, leading to various problems such as delayed recording, difficulty in retrieving archived letters, and potential data loss. This study aims to develop a web-based Incoming and Outgoing Mail Information System to improve administrative efficiency and facilitate the archiving and reporting processes. Data were collected through direct observation, interviews with village officials, and literature review. The system development method used in this research is the Agile model, which consists of requirement analysis, system design, implementation, and testing phases. The result of this study is a web-based information system capable of recording, storing, and displaying data on incoming and outgoing letters in a structured manner and accessible to village staff. System testing shows that the application functions properly and is well received by users. The conclusion of this research is that the developed information system successfully addresses the issues of manual mail management and provides convenience and speed in archiving letter data at the Palesanggar Village Office.
Optimasi Hyperparameter Model GRU untuk Prediksi Harga Saham ANTM Subairi, Subairi; Sari, Anggraini Puspita; Mandyartha, Eka Prakarsa
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.817

Abstract

Prediksi harga saham berperan penting meminimalisir kerugian akibat fluktuasi harga saham. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga saham PT Aneka Tambang Tbk (ANTM) menggunakan model Gated Recurrent Unit (GRU) dengan optimasi hyperparameter melalui metode Grid Search. Model GRU dipilih karena mampu mengatasi permasalahan vanishing gradient dan efektif dalam mempelajari pola ketergantungan jangka panjang pada data deret waktu walaupun dengan arsitektur yang sederhana. Sementara itu, Grid Search digunakan karena memiliki keunggulan dalam menjelajahi ruang hyperparameter secara menyeluruh, sehingga setiap kombinasi parameter dapat diuji dan memungkinkan diperolehnya konfigurasi terbaik. Proses Grid Search dilakukan dengan ruang pencarian hyperparameter yang mencakup jumlah units, jumlah epoch, ukuran batch, serta variasi optimizer. Keunggulan utama penelitian ini terletak pada penerapan optimasi hyperparameter yang mampu meningkatkan efektivitas model GRU dalam menemukan konfigurasi terbaik, sehingga menghasilkan prediksi harga saham yang lebih akurat dan stabil. Evaluasi kinerja model menggunakan metrik RMSE, MAE, MAPE. Hasil penelitian menunjukkan bahwa model GRU dengan optimasi Grid Search menggunakan optimizer Adam memberikan performa yang optimal dengan nilai evaluasi RMSE sebesar 67.8805, MAE sebesar 45.6501, dan MAPE sebesar 2.2309%. Temuan ini membuktikan bahwa optimasi hyperparameter melalui Grid Search mampu meningkatkan akurasi prediksi model GRU pada data harga saham.
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 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 Binti Hasim, Norhaslinda Budiman, Daniel cahyono, wahyu eko Cinta Ramayanti Citra Firdausi, Putri Aulia Damai Arbaus, Damai Damayanti, Natasya Meryl 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 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 Fajrina, Nur Septia Farhans, Muhammad Izzudin Fatchur Rozci Fauzan, Daffa Athallah Firdaus Putra Aditya, Wigananda Firmansyah, Fahrul Firmantara, Wahyu Firza Prima Aditiawan Firzannabeel Aqila Rafid Gatot Yulisianto Gatut Yulisusianto Hafiyan Fazagi Adnanto 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 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 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 Mawadah, Divia Astrina Mayya, Kalfin Syah Kilau Millati, Fina Amru Millati 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 Muharrom Al Haromainy Muhammad Rohman Irsyadi Mulyani Satya Bhakti Mulyo, Budi Mukhamad Nabila Anggita Luna Nachrowie, Nachrowie Nadia, Prasinta Hari Nadirco, Daniel Gloryo Nafis Pratama Putra Nandana Wahyu Rizqullah Nicholas, Sandy Ninis Herawati Noor Imansyah Basoeki, Dandy Nugraini Dewi Puspitasari 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, Hendrico Edhent Surya Pratama, Moch Nasikh Andhyka Prismahardi Aji Riyantoko Putra Dwi Wira Gardha Yuniahans Putra, Chrystia Aji Putri Salsabila, Belia Putricia Hendra, Ria Amelia Shinta 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 Siahaan, Renita Enjel Siharta, Niken Febrinikmah Silitonga, Paulenta Silvania Sischa Wahyuning Tyas Siti Sri Wahyuni Subairi Subairi SUGENG HARIANTO Sugeng Harianto Sugiarto S Suherman Suherman Suryahadi, Farrel Zikri Suryangga, Nova Suryantari, Putu Anggi 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 Zulkarnaen, Fahri Izzuddin