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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 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 The Indonesian Journal of Computer Science Research Innovative: Journal Of Social Science Research MDP Student Conference JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Software Engineering and Computational Intelligence Scientific Journal of 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
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Analisis Keamanan Sistem E-Voting Berbasis Blockchain Dengan Menggunakan Ganache Dan Echidna Peter Reynard Susanto; Muhammad Rizky Pribadi
JUKI : Jurnal Komputer dan Informatika Vol. 7 No. 2 (2025): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2025
Publisher : Yayasan Kita Menulis

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

Abstract

E-voting merupakan sebuah proses pemungutan suara yang dilakukan dengan bantuan media teknologi informasi yang bertujuan untuk mempercepat serta mempermudah proses pemungutan dan perhitungan suara pada kegiatan pemilihan umum. Namun, penerapan e-voting masih menghadapi berbagai tantangan, terutama terkait aspek keamanan dan integritas data pemilihan suara karena keamanan merupakan aspek yang krusial dalam proses demokratis. Sistem e-voting berbasis blockchain menawarkan transparansi dan integritas data, tetapi masih menghadapi tantangan keamanan khususnya pada smart contract yang merupakan inti dari proses pemungutan suara. Penelitian ini bertujuan untuk menguji tingkat keamanan smart contract pada e-voting berbasis blockchain Ethereum dengan metode pengujian berbasis properti menggunakan fuzzer Echidna. Pengujian dilakukan terhadap lima properti keamanan, yaitu tidak bisa double vote, total suara tidak pernah berkurang, jumlah kandidat selalu tetap, jumlah suara dan pemilih sama serta pemilih selalu memberikan satu suara. Hasil pengujian menunjukkan bahwa seluruh properti dinyatakan passing karena tidak ditemukan pelanggaran logika meskipun Echidna telah melakukan ribuan transaksi acak untuk menemukan bug.
Rice Leaf Disease Classification Using ResNet-50: A Comparative Study of Adam, SGD, and RMSProp Paula, Bebin; Pribadi, Muhammad Rizky
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.7582

Abstract

Rice plant diseases significantly affect crop productivity and require accurate and timely identification to support effective management. This study proposes a rice leaf disease classification approach using the ResNet-50 convolutional neural network and compares the performance of three optimization algorithms, namely ADAM, Stochastic Gradient Descent (SGD), and RMSProp. The model was trained and evaluated on a rice leaf image dataset consisting of four classes BrownSpot, Healthy, Hispa, and LeafBlast. The dataset contains visual variations in color, texture, and disease patterns that influence classification performance. Performance was assessed using training accuracy, loss, precision, recall, F1-score, and confusion matrix analysis. These evaluation metrics provide a comprehensive measurement of model effectiveness and class-wise prediction behavior. Experimental results show that the ADAM optimizer achieved the best performance with a training accuracy of 75.84%, followed by RMSProp at 74.60%, while SGD obtained the lowest accuracy of 71.34%. The differences in performance highlight the impact of optimization strategies on deep neural network training stability. Class-wise evaluation indicates that the model performed well in detecting BrownSpot and Healthy classes, but showed lower performance on the Hispa class across all optimizers. This limitation is influenced by the visual similarity of Hispa symptoms to other classes. These findings demonstrate that adaptive learning rate–based optimizers provide faster convergence and better classification performance for deep learning–based rice disease detection. The results support the use of optimized convolutional neural networks for image-based agricultural applications.
Perbandingan Naïve Bayes dan SVM terhadap Analisis Sentimen QRIS di Luar Negeri Pambudi, Readysna Krisna; Prasetyo, Zavier Billy; Pribadi, Muhammad Rizky
Jurnal Software Engineering and Computational Intelligence Vol 3 No 02 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i02.5424

Abstract

Penelitian ini membandingkan algoritma Naïve Bayes dab SVM (Support Vector Machine) dalam analisis sentimen terhadap komentar pengguna TikTok mengenai penggunaan QRIS di luar negeri. Data dikumpulkan dengan data scraping dari komentar TikTok, kemudian melakukan prepocessing text, transformasi TF-IDF, dan penerapan SMOTE. Setiap komentar diberi label secara manual ke dalam kategori positif, negatif, atau netral. Hasil Evaluasi menunjukkan bahwa algoritma SVM menunjukkan hasil yang lebih tinggi dibandingkan algoritma Naïve Bayes dengan accuracy sebesar 62.30%, sedangkan Naïve Bayes 57.40%. Precision SVM sebesar 63.44%, sedangkan Naïve Bayes 62.98%. Recall SVM sebesar 62.30%, sedangkan Naïve Bayes 57.40%. F1-Score SVM sebesar 59.60%, sedangkan Naïve Bayes 51.33%. Dengan demikian algoritma SVM lebih efektif digunakan dalam analisis sentimen dibandingkan algoritma Naïve Bayes.
An Efficient Two Stage Detection Segmentation Framework for Automated Road Crack Assessment Hujaya, Alvin; Pribadi, Muhammad Rizky
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33699

Abstract

Road cracks significantly degrade infrastructure quality and pose a threat to traffic safety. To minimize manual inspection inefficiencies, this study investigates a segmentation model integrating MobileNetV3-Small as a backbone for the U-Net architecture to reduce processing time. The performance of the proposed MobileNetV3-Small-U-Net is benchmarked against a standard U-Net using three public datasets: DeepCrack (537 images), CFD (118 images), and Crack500 (3368 images) sourced from GitHub and Kaggle. This research explores the influence of optimization algorithms on evaluation results across these diverse datasets. Specifically, the study evaluates Adam, RMSprop, and SGD optimizers at an image resolution of 224 x 224 pixels, with a 0.001 learning rate and 0.9 momentum. On-the-fly augmentation techniques, including horizontal flips and brightness adjustments (0.8 to 1.2), were implemented during training. Experimental results demonstrate that MobileNetV3-Small-U-Net enhances computational efficiency by achieving a 9 ms inference time, which is 2 ms faster than the standard U-Net. These findings confirm that a MobileNetV3-Small backbone accelerates inference, despite a slight trade-off in evaluation metrics. Additionally, results reveal that the SGD optimizer is unsuitable for these segmentation tasks due to high error rates and the lack of an adaptive learning rate.
Analisis Sentimen Opini Publik terhadap Dedi Mulyadi di Twitter Menggunakan Ekstraksi Fitur TF-IDF dan Klasifikasi Naive Bayes Pebrian, Hafizh; Kusuma, Aditya Ali; Pribadi, Muhammad Rizky
Innovative: Journal Of Social Science Research Vol. 6 No. 2 (2026): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

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Abstract

Pertumbuhan pesat media sosial, khususnya Twitter, telah membuka ruang yang luas bagi masyarakat untuk mengekspresikan pandangan mereka secara terbuka terhadap tokoh publik dan isu-isu politik. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap opini masyarakat mengenai Dedi Mulyadi, seorang tokoh politik di Indonesia, dengan memanfaatkan data yang diperoleh dari Twitter. Metodologi yang digunakan meliputi tahapan pengumpulan data tweet, praproses teks, ekstraksi fitur menggunakan pendekatan Term Frekuensi-Inverse Document Frekuensi (TF-IDF), serta proses klasifikasi sentimen melalui algoritma Naive Bayes. Sentimen yang diklasifikasikan terdiri dari tiga kategori, yaitu positif, negatif, dan netral. Hasil evaluasi menunjukkan bahwa kombinasi antara metode TF-IDF dan Naive Bayes mampu mengidentifikasi sentimen publik secara cukup efektif, dengan akurasi mencapai 68,0%. Temuan ini diharapkan dapat memberikan kontribusi dalam bidang analisis media sosial dan pemetaan opini masyarakat terhadap figur politik.
Klasterisasi Kategori Judul Buku Pada Perpustakaan Dengan Menggunakan Metode HDBSCAN Laksono, Ivan Luthfi; Pribadi, Muhammad Rizky
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || 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.v6i2.14011

Abstract

To assist in library collection management, this study aims to create a system that can automatically classify book titles. Previous studies have mostly used K-Means and DBSCAN because they have limitations in determining the number of clusters and are less responsive to varying densities of text data. Furthermore, HDBSCAN is still limited to clustering Indonesian-language book titles. The dataset consists of 1044 book titles that were processed through text preprocessing, TF-IDF weighting, and dimension reduction using Singular Value Decomposition (SVD). When HDBSCAN was used for clustering and compared with DBSCAN, the results showed that the combination of SVD and HDBSCAN had better cluster quality with a Silhouette value of 0.158 and a lower noise level. This study scientifically demonstrates that improving the stability of cluster structures in large book title data can be achieved through the integration of dimension reduction and density-based clustering.
Improving Oil Palm Fruit Detection under Class Imbalance Using Class-Balanced Focal Loss on YOLOv11 Suparto, Adrian; Pribadi, Muhammad Rizky
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (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.
Implementasi Least Squares Support Vector Machine dan SMOTE untuk Klasifikasi Kesehatan Mental Marcelino Marcelino; Muhammad Rizky Pribadi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3613

Abstract

Mental health disorders such as depression, anxiety, and stress are global problems that require accurate early detection. This study proposes a mental health classification model using machine learning algorithms based on data from the Depression Anxiety Stress Scales (DASS-42) questionnaire and respondent demographic features. The main method used is Least Squares Support Vector Machine (LSSVM) combined with Synthetic Minority Oversampling Technique (SMOTE) and Backward Elimination feature selection. From testing on 39,775 respondent data, Backward Elimination successfully reduced more than 40% of the feature dimensions by selecting the most statistically significant attributes (p-value < 0.05). Oversampling with SMOTE proved successful in overcoming class imbalance in minority labels. Performance evaluation showed that LSSVM using the Radial Basis Function (RBF) kernel provided the most optimal results compared to the Linear and Polynomial kernels, with an F1-Score of 83.96% for Depression, 77.30% for Anxiety, and 82.00% for Stress. This proposed model contributes to the development of a more computational, efficient, and accurate mental health screening system.Keywords: Backward Elimination; DASS-42; LSSVM; Mental Health; SMOTE AbstrakGangguan kesehatan mental seperti depresi, kecemasan, dan stres merupakan masalah global yang memerlukan deteksi dini yang akurat. Penelitian ini mengusulkan model klasifikasi tingkat kesehatan mental menggunakan algoritma machine learning berdasarkan data kuesioner Depression Anxiety Stress Scales (DASS-42) dan fitur demografis responden. Metode utama yang digunakan adalah Least Squares Support Vector Machine (LSSVM) yang dikombinasikan dengan Synthetic Minority Oversampling Technique (SMOTE) dan seleksi fitur Backward Elimination. Dari pengujian terhadap 39.775 data responden, Backward Elimination berhasil mereduksi lebih dari 40% dimensi fitur dengan menyeleksi atribut yang paling signifikan secara statistik (p-value < 0.05). Oversampling dengan SMOTE terbukti berhasil mengatasi ketidakseimbangan kelas pada label minoritas. Evaluasi kinerja menunjukkan bahwa LSSVM menggunakan kernel Radial Basis Function (RBF) memberikan hasil paling optimal dibandingkan kernel Linear dan Polynomial, dengan pencapaian F1-Score sebesar 83.96% untuk Depresi, 77.30% untuk Kecemasan, dan 82.00% untuk Stres. Model yang diusulkan ini berkontribusi dalam pengembangan sistem screening kesehatan mental yang lebih komputasional, efisien, dan akurat. 
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.
Co-Authors -, Felicia Adi Saputra Aditya Al Assad Adrian Chen Ahmad Dumyati Ahmad Zaky Nadimsyah Albert Cahayadi Alwin Marcellino Amarullah, Rendy Ampu Syura Andreas Andreas Andreas Danny Agus W Andreas Saputra Andrian Wijaya Angel Kelly Angelica, Steffanie Asyraq, Cerwyn Bakti Ananda Fernando Bautista, Christian Bella Jenni Ourelia Boy Putra Calvin Bertnas Valentino Calvin Saputra Carissa Maharani Chandra Caroline, Fellycia Chandra Saputra Christian Richie Wijaya Clara Meyhazlinda Putri Clement, Michael Joy Daffa Yudha Musyaffa Daniel Daniel Daniel Johan Daniel Wijaya Darwin Saputra David Sebastian Dedy Hermanto Desta Rahman Theja Desy Iba Ricoida Devina Suryanto, Serenity Dicky Ryanto Fernandes 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 Fajar Ariansyah, Muhammad Farisi, Ahmad Farisi, Ahmad Fathimah Azzahra Feliansyah, Fernando Felicia Felicia Felix Gunawan Fellyca Effendi Fellycia Caroline Feriyanto Feriyanto Ferliansyah, Fernando Fernandi Indi Nizar G 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 Hansen Hansen Hendrawan, Malvin Hendry Hindriyanto Dwi Purnomo Hujaya, Alvin 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 Jerin, Nathaniel Jonathan Tanujaya Joseph Eduard Uly Loni Kasanova, Sinyo Kelvin Dwi Wahyudi Kevin agustria zahri Kevin Andreas KGS M Ammar Yazid Klaudius Audie Irsansaputra Kurniawan, Ricky Arie Kusuma, Aditya Ali Laksana, Jovansa Putra 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 Michael michael Wijaya Millenia Mudita Chandra Muhammad Abdul Azizul Hakim Muhammad Alfa Rizi Muhammad Azril Fahrezi Muhammad Dafhi Mayrizkiy Muhammad Dody Muhammad Fadli Muhammad Hamdandi Muhammad Naufal Anugrah Muhammad Radja Juang Jamemiko Muhammad Redho Saputra Muhammad Reyza Nirwana Muhammad Robi, Muhammad Nabila Syiva Altarisa Nabilah Dayanah Nathacia Lais Naufal Akbar Neilsen Nicholas Komah Nicolas Jacky Pratama Hasan Nova Ariansyah Pambudi, Readysna Krisna Paula, Bebin Pebrian, Hafizh Peter Reynard Susanto Pibriana, Desi Prasetyo, Zavier Billy Pratama, Brilliant Chandra Purwasih, Opita Putra Laksana, Jovansa Putri, Agnes Anastasia 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 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 Siti Fatimah Az Zahrah Sonia Sonia Sri Yulianto Joko Prasetyo 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 Vincent Vincent Virgiansyah, Muhammad Rifqi 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