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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ELKHA : Jurnal Teknik Elektro Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) DoubleClick : Journal of Computer and Information Technology Informatik : Jurnal Ilmu Komputer Kurawal - Jurnal Teknologi, Informasi dan Industri JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan dan Teknologi Indonesia Jumat Informatika: Jurnal Pengabdian Masyarakat Bulletin of Computer Science Research Jurnal Abdi Masyarakat Indonesia Jurnal Pengabdian Masyarakat IPTEK Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Nasional Teknologi Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Informatika Progres The Indonesian Journal of Computer Science Research Mestaka: Jurnal Pengabdian Kepada Masyarakat Innovative: Journal Of Social Science Research MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Software Engineering and Computational Intelligence Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Applied Information Technology and Computer Science (AICOMS) Welfare: Jurnal Pengabdian Masyarakat Jurnal Nasional Teknologi Informasi dan Aplikasinya Jurnal Nasional Komputasi dan Teknologi Informasi
Muhammad Rizky Pribadi
Universitas Multi Data Palembang
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Analisis Sentimen Fenomena “Brewek” Kartu Pokémon Pada Platform Reddit Menggunakan Arsitektur RoBERTa Siti Fatimah Az Zahrah; Klaudius Audie Irsansaputra; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/t3a91d20

Abstract

Social media platforms such as Reddit have long served as major discussion forums for various communities. This study aims to analyze public sentiment in order to understand community trends and perceptions toward Pokémon TCG. The research applies the RoBERTa (Robustly Optimized BERT Approach) Deep Learning architecture using the pre-trained model “cardiffnlp/twitter-roberta-base-sentiment” to perform sentiment analysis. The text data were cleaned, tokenized with a maximum limit of 512 tokens, and classified into positive, neutral, and negative sentiments, followed by word length distribution analysis and Top-N Words extraction. The model successfully classified sentiments objectively. The visualization results reveal the characteristics of word distribution after outlier handling and identify the top ten keywords representing the main discussion focus within each sentiment label. The findings indicate that the community sentiment is predominantly negative, providing a clear overview of the opinion dynamics within the Pokémon community on Reddit.
Analisis Sentimen Publik terhadap Isu Pembuatan CBDC di Indonesia Menggunakan IndoBERT Muhammad Radja Juang Jamemiko; Joseph Eduard Uly Loni; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/srtytf27

Abstract

Perkembangan teknologi finansial mendorong munculnya inovasi sistem pembayaran digital, salah satunya melalui pengembangan Central Bank Digital Currency (CBDC) atau Rupiah Digital oleh Bank Indonesia. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap isu pembuatan CBDC di Indonesia berdasarkan opini masyarakat pada platform media sosial X. Penelitian menerapkan pendekatan Natural Language Processing menggunakan model Deep Learning berbasis Transformer, yaitu IndoBERT, untuk melakukan klasifikasi sentimen secara otomatis. Data tweet yang telah dikumpulkan melalui proses crawling kemudian melalui tahapan pre-processing, tokenisasi, serta klasifikasi ke dalam tiga kategori sentimen, yaitu positif, netral, dan negatif. Selain itu, penelitian juga melakukan visualisasi distribusi sentimen dan pemetaan kata dominan menggunakan wordcloud untuk mengidentifikasi fokus pembahasan masyarakat terkait CBDC ataupun Rupiah Digital. Hasil penelitian menunjukkan bahwa sentimen netral mendominasi diskusi publik sebanyak 61,01%, diikuti oleh sentimen negatif 29,11% dan positif 9,87%. Temuan ini mengindikasikan bahwa masyarakat masih berada pada tahap pengamatan dan diskusi terhadap implementasi CBDC, namun tetap terdapat kekhawatiran terkait aspek keamanan, privasi, dan kontrol sistem keuangan digital.
Analisis Sentimen Komentar Youtube terhadap Kondisi Bursa Saham Indonesia akibat Isu Pengunduran Serempak Dewan BEI Menggunakan IndoBERT Daffa Yudha Musyaffa; Felix Gunawan; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/q27ea163

Abstract

Social media platforms such as YouTube have long served as a primary discussion space for retail investor communities in Indonesia. This study aims to analyze public sentiment in order to understand perception trends and the digital psychology of capital market participants regarding the issue of the simultaneous resignation of the Indonesia Stock Exchange (IDX) board members. The research applies the IndoBERT (Bidirectional Encoder Representations from Transformers for the Indonesian language) deep learning architecture through a fine-tuning process on a dataset of YouTube comments. The textual corpus was cleaned from noise, normalized from stock market slang vocabulary, tokenized, and automatically classified into three sentiment polarities: positive, neutral, and negative. The analysis stage was further continued with dominant keyword extraction using Word Cloud visualization and word frequency trend mapping to identify psychological variables driving market opinions. The model successfully classified the semantic complexity of informal language objectively. Visualization results indicate that communication dynamics were overwhelmingly dominated by negative sentiment (57.5%), reflecting widespread public concern and declining confidence in capital market stability due to the structural crisis. This study demonstrates the effectiveness of local transformer models as instruments for extracting digital market psychology to support real-time automated investment decision-making.
Analisis Sentimen Masyarakat terhadap Kenaikan Harga BBM Non-Subsidi Akibat Penutupan Selat Hormuz Menggunakan IndoBERT Jaysen Stephanus; Jonathan Tanujaya; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/kx4xgz78

Abstract

Public discussions regarding the potential increase in non-subsidized fuel prices resulting from the closure of the Strait of Hormuz on the X platform between January 1, 2026, and May 17, 2026, were highly intensive and generated diverse public responses to the global economic impacts triggered by the geopolitical conflict between Iran and Israel. The primary issue addressed in this study is the growing public concern over the possibility of rising non-subsidized fuel prices, which may affect transportation costs, logistics distribution, and daily living expenses. This study aims to analyze public sentiment toward this issue using the IndoBERT deep learning model to obtain a more accurate understanding of public opinion trends. Data were collected through a scraping process on the X platform using keywords related to non-subsidized fuel and the Strait of Hormuz. The collected data were then processed through several preprocessing stages, including case folding, noise removal, tokenization, stopword removal, and stemming, before being classified into positive, neutral, and negative sentiment categories. Out of 412 analyzed tweets, negative sentiment emerged as the dominant category at 49.8%, followed by neutral sentiment at 48.5%, while positive sentiment accounted for only 1.7%. The findings indicate that the majority of the public expressed concern regarding the potential increase in non-subsidized fuel prices and its impact on economic conditions and household expenditures.
Analisis Sentimen Pengguna X dan YouTube Terhadap Carmen Hearts2Hearts Menggunakan Metode IndoBERT Fellycia Caroline; Syalsabilla Valentisyesa; Muhammad Rizky Pribadi
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2026.v04.i03.p23

Abstract

The rapid growth of social media has increased the amount of public opinion expressed online, particularly on platforms such as X and YouTube, where users actively share their views regarding public figures and entertainment topics. This study aims to analyze public sentiment toward Carmen, a member of the K-pop group Hearts2Hearts, using the IndoBERT model for sentiment classification. Data were collected from X and YouTube comments through web scraping techniques and combined into a single dataset to obtain more diverse opinions. The research process involved several stages, including text preprocessing, manual sentiment labeling, dataset splitting, model training, and evaluation. The preprocessing stage consisted of duplicate data removal, case folding, noise removal, tokenization, stopword removal, and stemming to improve data quality before classification. The dataset was categorized into three sentiment classes: positive, neutral, and negative, then divided into training and testing data using an 80:20 ratio. The IndoBERT model was trained using transformer-based deep learning to understand the context of Indonesian-language text more effectively. Evaluation results showed that the model achieved an accuracy of 72.41%, precision of 75.82%, recall of 72.41%, and F1-score of 71.15%, indicating that IndoBERT performs effectively in classifying sentiment on Indonesian social media data despite challenges such as informal language and ambiguous expressions.
Klasifikasi Penyakit Tanaman Jeruk Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network Arsitektur EfficientNetV2-S Christian Richie Wijaya; Muhammad Rizky Pribadi
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17003

Abstract

The classification of citrus leaf diseases still largely relies on traditional assessment by farmers, which may lead to errors in identifying disease types. Previous studies have widely applied Convolutional Neural Networks (CNNs) for plant disease classification; however, most have utilized first-generation EfficientNet architectures, while the application of EfficientNetV2-S for citrus leaf disease classification remains relatively limited. Furthermore, the implementation of a progressive fine-tuning strategy on the EfficientNetV2-S architecture for this task has not been extensively investigated. Therefore, this study aims to implement the EfficientNetV2-S architecture for citrus leaf disease classification. The dataset used was the Citrus Leaves Prepared dataset from Kaggle, consisting of 596 images categorized into four classes: blackspot, canker, greening, and healthy. The data underwent preprocessing and image augmentation, including flipping, rotation, and zooming, before being divided into training, validation, and testing sets with a ratio of 70:10:20. The model was developed using a transfer learning approach combined with progressive fine-tuning. Experimental results demonstrated that the proposed model achieved a testing accuracy of 93.33% under the 100-epoch training scenario. With this level of accuracy, the model shows strong potential for implementation as an early detection system for citrus leaf diseases, assisting farmers in making timely and appropriate decisions to prevent crop failure.
Analisis Topik Komentar Youtube pada Lagu Tema FIFA World Cup 2026 Menggunakan LDA M. Dhafa Adjie Saputra; Fadhel Muhammad; Muhammad Rizky Pribadi
Jurnal Riset Informatika dan Inovasi Vol 4 No 1 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Komentar yang ditinggalkan pengguna pada platform YouTube dapat dimanfaatkan untuk memahami berbagai respons publik terhadap suatu konten digital. Penelitian ini berfokus pada identifikasi pola pembahasan yang muncul pada komentar video musik Lighter yang digunakan sebagai lagu resmi FIFA World Cup 2026. Data penelitian berupa 398 komentar berbahasa Inggris diperoleh melalui proses web scraping menggunakan platform Apify. Sebelum dianalisis, data melalui serangkaian tahapan preprocessing yang mencakup pembersihan teks, tokenisasi, penghapusan stopword, pembentukan bigram, dan lemmatization. Proses ekstraksi topik dilakukan menggunakan metode Latent Dirichlet Allocation (LDA) untuk menemukan kelompok pembahasan yang dominan dalam kumpulan komentar. Hasil pemodelan menunjukkan tiga tema utama yang berkaitan dengan penilaian terhadap kualitas musik, tanggapan mengenai kesesuaian lagu dengan atmosfer sepak bola, dan diskusi umum seputar video musik FIFA. Evaluasi menggunakan coherence score menghasilkan nilai 0,466 yang mengindikasikan bahwa topik yang terbentuk memiliki tingkat konsistensi yang cukup baik untuk diinterpretasikan. Temuan penelitian menunjukkan bahwa pendekatan LDA mampu digunakan sebagai metode yang efektif dalam mengidentifikasi kecenderungan pembahasan dan opini pengguna pada komentar YouTube berbasis teks pendek.
Analisis Sentimen Terhadap Ulasan Pengguna Aplikasi Notion pada Google Play Store Menggunakan IndoBERT Serenity Devina Suryanto; Albert Cahayadi; Muhammad Rizky Pribadi
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/1r3xcx08

Abstract

Abstrak - Perkembangan teknologi terus mendukung produktivitas masyarakat, salah satunya melalui beralihnya kebiasaan mencatat manual ke aplikasi manajemen tugas seperti Notion. Namun, tingginya volume ulasan pengguna di Google Play Store menghasilkan sentimen yang sangat beragam, sehingga menyulitkan pengembang untuk mengidentifikasi aspek yang perlu dioptimasi secara cepat. Penelitian ini bertujuan untuk menganalisis sentimen pengguna aplikasi Notion pada Google Play Store guna mengklasifikasikan ulasan ke dalam dua kategori, yaitu sentimen positive dan negative. Penelitian ini menerapkan model IndoBERT menggunakan 718 data ulasan yang diperoleh dari Google Play Store, kemudian dipilih 456 data dengan kategori sentimen positive dan negative untuk analisis sentimen melalui tahap pengumpulan data, pelabelan data secara manual dengan label sentimen positive dan negative, preprocessing dengan cleaning data dan data transformation (case folding dan stopword removal), analisis sentimen dengan model IndoBERT, visualisasi Word Cloud dan evaluasi mode dengan confusion matriks dan metrik evaluasi akurasi, precision, recall dan F1-Score. Hasil evaluasi menunjukkan bahwa model mampu mengklasifikasikan kategori ulasan dengan tingkat akurasi sebesar 95%, precision pada data sentimen negative dan positive sebesar 92% dan 97% , recall untuk sentimen negative dan positive sebesar 89% dan 97%, dan F1-Score pada sentimen negative dan positive sebesar 90% dan 97%. Dengan demikian, IndoBERT disimpulkan dapat menjadi metode yang efektif dalam analisis sentimen ulasan aplikasi digital. Hasil ini juga dapat menjadi acuan bagi tim pengembang dalam melakukan optimasi aplikasi. Kata kunci: Analisis Sentimen; IndoBERT; Google Play Store; Notion;   Abstract - Technological advancements continue to support public productivity, one of which is demonstrated by the shift from manual note-taking habits to task management applications like Notion. However, the high volume of user reviews on the Google Play Store generates highly diverse sentiments, making it challenging for developers to quickly identify areas that require optimization. This study aims to analyze user sentiment toward the Notion application on the Google Play Store to classify reviews into two categories: positive and negative sentiments. This study implements the IndoBERT model using 718 review data points obtained from the Google Play Store. From this dataset, 456 reviews categorized under positive and negative sentiments were selected for sentiment analysis. The methodology involves data collection, manual data labeling into positive and negative sentiment categories, preprocessing (including data cleaning and data transformation through case folding and stopword removal), sentiment analysis using the IndoBERT model, Word Cloud visualization, and model evaluation utilizing a confusion matrix alongside evaluation metrics such as accuracy, precision, recall, and F1-Score. The evaluation results demonstrate that the model is capable of classifying review categories with an accuracy rate of 95%. The precision for negative and positive sentiments is 92% and 97%, respectively; the recall for negative and positive sentiments is 89% and 97%, respectively; and the F1-Score for negative and positive sentiments is 90% and 97%, respectively. Consequently, it is concluded that IndoBERT can serve as an effective method for sentiment analysis of digital application reviews. These findings can also serve as a reference for development teams in optimizing the application. Keywords: sentiment analysis; IndoBERT; Google Play Store; Notion;
Analisis Sentimen Komentar Trailer Youtube Film Pelangi di Mars Menggunakan IndoBERT Siska Amelia; Migel Orvin Febryan; Muhammad Rizky Pribadi
LogicLink Vol. 3 No. 1, June 2026
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v3i1.02

Abstract

Pertumbuhan industri perfilman Indonesia meningkatkan jumlah opini penonton pada platform digital, khususnya YouTube. Kolom komentar pada trailer film dapat dimanfaatkan untuk mengetahui respons dan persepsi penonton terhadap sebuah film. Namun, volume komentar yang besar serta penggunaan bahasa tidak baku pada media sosial menyebabkan analisis manual menjadi kurang efektif. Penelitian ini bertujuan untuk menganalisis sentimen komentar pengguna pada trailer film Pelangi di Mars di YouTube menggunakan model pre-trained IndoBERT. Data penelitian diperoleh melalui proses web scraping komentar YouTube pada periode 24 November 2025 hingga 5 Mei 2026 dan menghasilkan 2656 komentar. Setelah proses seleksi data, diperoleh 1898 komentar yang digunakan dalam penelitian. Tahap preprocessing meliputi cleaning text, case folding, tokenizing, normalisasi kata tidak baku, stopword removal, dan stemming. Proses klasifikasi sentimen dilakukan menggunakan model Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis pada Hugging Face Transformers tanpa proses fine-tuning. Hasil klasifikasi menunjukkan terdapat 839 komentar positif, 546 komentar negatif, dan 513 komentar netral. Evaluasi model menghasilkan nilai accuracy sebesar 72,23%, precision sebesar 80,86%, recall sebesar 72,23%, dan F1-score sebesar 74,11%. Hasil penelitian menunjukkan bahwa mayoritas pengguna memberikan respons positif terhadap trailer film Pelangi di Mars, serta model IndoBERT memiliki kemampuan yang cukup baik dalam melakukan klasifikasi sentimen komentar berbahasa Indonesia pada media sosial.
Improving Oil Palm Fruit Detection under Class Imbalance Using Class-Balanced Focal Loss on YOLOv11 Adrian Suparto; Muhammad Rizky Pribadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 2 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2568

Abstract

Accurate detection of oil palm fruit maturity levels plays a crucial role in improving harvesting efficiency and maintaining the quality of palm oil production. In practice, this task remains challenging due to the presence of severe class imbalance in real-world field datasets, where certain classes have far fewer samples than others, often leading to biased model learning and reduced detection accuracy. This study investigates the performance of several Class-Balanced Loss Function variants integrated into the YOLOv11-nano framework using a publicly available oil-palm fruit dataset for harvest estimation, which presents a significantly imbalanced class ratio. Four training configurations were evaluated: the baseline Binary Cross-Entropy (BCE), Class-Balanced Focal Loss (CB-Focal), Class-Balanced Sigmoid Loss (CB-Sigmoid), and Class-Balanced Softmax Loss (CB-Softmax). The experimental results indicate that CB-Focal achieved the highest performance with an mAP@50 of 0.783, approximately 0.5 percent higher than the BCE baseline (0.778) and 4 to 5 percent greater than YOLOv8-n and YOLOv8-s models trained on the same dataset. CB-Focal also demonstrated smoother convergence and more balanced per-class performance compared to the other loss functions. These findings suggest that integrating CB-Focal into the YOLOv11-nano framework not only improves accuracy for minority classes but also holds strong potential for supporting more accurate, efficient, and scalable automated harvest monitoring systems in real plantation environments.
Co-Authors -, Felicia Adi Saputra Aditya Al Assad Aditya Ali Kusuma Adrian Chen Adrian Suparto Adrian Suparto Ahmad Dumyati Ahmad Zaky Nadimsyah Albert Cahayadi Alvin Hujaya Alwin Marcellino Amarullah, Rendy Ampu Syura Ananda Wijaya Andreas Andreas Andreas Danny Agus W Andreas Saputra Andrian Wijaya Angel Kelly Asyraq, Cerwyn Bakti Ananda Fernando Bautista, Christian Bebin Paula Bella Jenni Ourelia Boy Putra Brilliant Chandra Pratama Calvin Bertnas Valentino Calvin Saputra Carissa Maharani Chandra Chandra Saputra Christian Richie Wijaya Clara Meyhazlinda Putri Clement, Michael Joy Daffa Yudha Musyaffa Daniel Daniel Daniel Johan Daniel Udjulawa Daniel Wijaya Darwin Saputra David Sebastian Dedy Hermanto Desta Rahman Theja Desy Iba Ricoida Dicky Ryanto Fernandes Dina Lestari Putri Diva Putri Kynta Dwi Apriyanti Sastika Dwi Cahyadi, Ambrosius Effendi pratama, Samuel Egi Fransisco Saputra Eka Puji Widiyanto Evangs Mailoa Evi Maria Fadhel Muhammad Fadhil Sa'adat Farisi, Ahmad Farisi, Ahmad Fathimah Azzahra Felicia Felicia Felix Gunawan Fellyca Effendi Fellycia Caroline Femmy Johan Feriyanto Feriyanto Ferliansyah, Fernando Fernandi Indi Nizar G Fernando Feliansyah Fernando Fernando Fernando Namas Fionna Caroline Florence Renaldo Frans Bachtiar Fransiskus Daniel Chandra Frisky Wijaya Genisshanda Nabila Matari Geraldo Wilson Gerry Christian Pilipus Gunawan, Michael Hafidz Irsyad Hafiz Irsyad Hafizh Pebrian Hansen Hansen Hendrawan, Malvin Hendry Hindriyanto Dwi Purnomo Ilham Indra Hidayat Imelia Dwinora Cahyati Indi Nizar G, Fernandi Ivan Luthfi Laksono Jackie Wijaya Jasen Jonathan Jaysen Stephanus Ja`Far Ja`Far Jelvin Krisna Putra Jennifer Verty Jerin, Nathaniel Jesen Ong Jonathan Jason Constantine Jonathan Tanujaya Jonathan Wijaya Joseph Eduard Uly Loni Jovansa Putra Laksana Kasanova, Sinyo Kelvin Dwi Wahyudi Kevin agustria zahri Kevin Andreas KGS M Ammar Yazid Klaudius Audie Irsansaputra Kurniawan, Ricky Arie Laksono, Ivan Luthfi Laurentius Ricardo Wijaya Leo Chandra Leonardo Yahya Liem, Steven Lin, Valen Julyo Armando Davincy Lipi Amanda Putra Lucretia, Jolyn M Lazuardi Ferdillian M. Dhafa Adjie Saputra Marcelino Marcelino Mario Rivaldo Michael Michael Joy Clement michael Wijaya Migel Orvin Febryan Millenia Mudita Chandra Muhammad Abdul Azizul Hakim Muhammad Alfa Rizi Muhammad Azril Fahrezi Muhammad Dafhi Mayrizkiy Muhammad Dody Muhammad Fadli Muhammad Fajar Ariansyah Muhammad Hamdandi Muhammad Naufal Anugrah Muhammad Radja Juang Jamemiko Muhammad Redho Saputra Muhammad Reyza Nirwana Muhammad Robi, Muhammad Muhammad Tri Setianto Nabila Syiva Altarisa Nabilah Dayanah Nathacia Lais Naufal Akbar Neilsen Nicholas Komah Nicolas Jacky Pratama Hasan Nova Ariansyah Opita Purwasih Pambudi, Readysna Krisna Peter Reynard Susanto Pibriana, Desi Prasetyo, Zavier Billy Pratama, Brilliant Chandra Putra Laksana, Jovansa Putri, Agnes Anastasia Raphael Lee Regian batistuta, Putra Reza Satria Rika Maulina Riki Chandra Rio Ferdynand Riska Fajriati Rivaldo Therino Elevan Rivaldo, Mario Riza Umami Rizky Kurniawan Rizvi Roshan, Muhamad Roby Julian Romi Laxi Ronaldo Putra Rusbandi rusbandi rusbandi, rusbandi Safeti Intan Pratiwi Salwa Fakhira Imletta San Gabriel Vanness Kenrick Erwi Sanila Maharani Santoso, Fian Julio Saputra Edika, Nelson Sardika, Ricky Putra Se, Abd Rosyiid Serenity Devina Suryanto Setiawan, Thomas Shela, Shela Sherdian Djunaidi Sinshevan Viswanatan Kravizt Erwi Siska Amelia Siti Fatimah Az Zahrah Sonia Sonia Sri Yulianto Joko Prasetyo Steffanie Angelica Stephanie Stephanie Stephen Setyawan Steven Tribethran Suparto, Adrian Suryasatria Trihadaru Sutarto Wijono Syahrani Nur Hakim Syalsabilla Valentisyesa Syifa Wahyuni Tad Gonsalves Tangguh Prana Welas Sukma Vannes Wijaya Vanness Bee Victoria Valensita Robert Vincent Vincent Virgiansyah, Muhammad Rifqi Wijang Widhiarso Wijang Widhiarso Wijaya, Ananda Wilcent, Wilcent William Wijaya Yennica Valentine Hagunawan Yohanes Andika Dharma Yohanes Fransisco Mardi Chandra Yohannes, Yohannes Yoko Saputra Dewa Yosefa Camilia Moniung Yunarto Yunarto, Yunarto `Adelia Anjelina