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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika ELKHA : Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System JADECS (Journal of Art, Design, Art Education and Culture Studies) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi dan Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JE (Journal of Empowerment) Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Ramadhan Rakhmat Sani
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Ghozi, Wildanil
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2051

Abstract

The rapid advancement of malware poses a significant threat to devices, like personal computers and mobile phones. One of the most serious threats commonly faced is malicious software, including viruses, worms, trojan horses, and ransomware. Conventional antivirus software is becoming ineffective against the ever-evolving nature of malware, which can now take on various forms like polymorphic, metamorphic, and oligomorphic variants. These advanced malware types can not only replicate and distribute themselves, but also create unique fingerprints for each offspring. To address this challenge, a new generation of antivirus software based on machine learning is needed. This intelligent approach can detect malware based on its behavior, rather than relying on outdated fingerprint-based methods. This study explored the integration of machine learning models for malware detection using various ensemble algorithms and feature selection techniques. The study compared three ensemble algorithms: Gradient Boosting, Random Forest, and AdaBoost. It used Information Gain for feature selection, analyzing 21 features. Additionally, the study employed a public dataset called ‘Malware Static and Dynamic Features VxHeaven and VirusTotal Data Set’, which encompasses both static and dynamic malware features. The results demonstrate that the Gradient Boosting algorithm combined with Information Gain feature selection achieved the highest performance, reaching an accuracy and F1-Score of 99.2%.
Improved imperceptible engagement-based 2D sigmoid logistic maps, Hill cipher, and Kronecker XOR product Lestiawan, Heru; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam; Rachmawanto, Eko Hari; Purwanto, Purwanto; Sari, Christy Atika; Doheir, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8331

Abstract

Image encryption is a crucial facet of secure data transmission and storage, and this study explores the efficacy of combining sigmoid logistic maps (SLM), Hill cipher, and Kronecker's product method in enhancing image encryption processes. The evaluation, conducted on diverse images such as Lena, Rice, Peppers, Cameraman, and Baboon, unveils noteworthy findings. The Lena image emerges as the most successfully encrypted, as evidenced by the lowest mean squared error (MSE) at 92.81 and the highest peak signal-to-noise ratio (PSNR) at 19.43, reflecting superior fidelity and quality preservation. Additionally, the encryption of 64×64 pixels images consistently demonstrate robustness, with a high number of pixels change rate (NPCR) and unified average change intensity (UACI) values, particularly notable for the Cameraman image. Even for 128×128 pixels images, commendable encryption performance persists across the tested images. The amalgamation of SLM, Hill cipher, and Kronecker's product emerges as an effective strategy for balancing security and perceptual quality in image encryption, with the Lena image consistently outperforming others based on comprehensive metrics. This research provides valuable insights for future studies in the dynamic domain of image encryption, emphasizing the potential of advanced cryptographic techniques in ensuring secure multimedia communication.
Fairer Public Complaint Classification on LaporGub: Integrating XLM-RoBERTa with Focal Loss for Imbalance Data Zahro, Azzula Cerliana; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Fahmi, Amiq; Megantara, Rama Aria; Naufal, Muhammad; Azies, Harun Al; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15260

Abstract

The advancement of digital technology has provided opportunities for governments to improve the quality of public services through citizen complaint channels. One example of this implementation in Indonesia is Lapor Gub, managed by the Dinas Komunikasi dan Informasi Provinsi Jawa Tengah (Communication and Information Agency of Central Java Province). This platform receives thousands of complaints daily, ranging from infrastructure, social issues, to illegal levies. However, the large volume of data and the imbalanced distribution of categories pose significant challenges for both manual and automated processing. This study aims to classify citizen complaint texts using XLM-RoBERTa combined with Focal Loss as an approach to handle data imbalance. The dataset consists of 53,774 complaints after data cleaning and text preprocessing. The training process applied a stratified split (78% training, 18% validation, 10% testing) and fine-tuning for 10 epochs. Model performance was evaluated using accuracy, precision, recall, and macro F1-score. The results show that the model without Focal Loss achieved 78.1% accuracy with a macro F1-score of 0.606, while the model with Focal Loss improved the macro F1-score to 0.625 with 78.5% accuracy. These findings demonstrate that the application of Focal Loss enhances the model’s ability to recognize minority categories without reducing performance on majority classes. Therefore, the combination of RoBERTa and Focal Loss offers an effective solution to support faster, fairer, and more transparent public complaint management.
Enhancing Entity Extraction in E-Government Complaint Data using LDA-Assisted NER Umam, Ahmad Khotibul; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Rohmani, Asih; Prabowo, Dwi Puji; Pergiwati, Dewi; Megantara, Rama Aria; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15292

Abstract

With the rapid development of information technology, governments are increasingly challenged to provide digital channels that enhance public participation in governance. LaporGub, an official platform managed by the Central Java Provincial Government, accommodates citizens' aspirations and complaints, but faces challenges in processing large amounts of unstructured text. Manual analysis is time-consuming and error-prone, resulting in delayed responses and decreased service quality. Conventional Named Entity Recognition (NER) models struggle to handle informal Indonesian-language text, while transformer-based approaches require substantial computing resources that are not widely available in local government environments. Therefore, this study aims to develop a lightweight NER approach by integrating Latent Dirichlet Allocation (LDA) as a semantic pre-annotation tool to improve the accuracy of entity extraction in Indonesian e-government complaint data. To achieve this goal, a dataset of 53,858 complaint reports from the LaporGub platform (2022–2025) was processed using LDA topic modeling (k=10) to provide semantic context during annotation. Next, the enriched dataset was used to train a spaCy-based NER model targeting three entity types: LOCATION, ORGANIZATION, and PERSON, with a training-validation-test split ratio of 70:15:15 using stratified sampling. The evaluation showed that the proposed NER+LDA model achieved a precision of 90.03%, a recall of 81.86%, and an F1-score of 85.75%, representing improvements of +5.78, +2.55, and +4.04, respectively, compared to the baseline NER model (F1-score: 81.71%). Furthermore, the most significant improvements occurred in the detection of ORGANIZATION and PERSON entities. These findings confirm that the integration of LDA as a pre-annotation strategy effectively improves NER performance on informal complaint texts in Indonesia, thus offering a practical and resource-efficient alternative to transformer-based methods for e-government applications.
Perancangan Aplikasi E-Commerce Top-Up Game Online Berbasis Website dengan Integrasi Payment Gateway Ramadhani, Dwi Arya; Nugraha, Purwa Esti; Syahrizal, Muhammad Iqbal; Rakhmat Sani, Ramadhan
Jurnal Teknologi dan Informasi (JATI) Vol 15 No 2 (2025): Jurnal Teknologi dan Informasi (JATI)
Publisher : Program Studi Sistem Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jati.v15i2.17055

Abstract

Pada era digital yang terus berkembang, transaksi top-up game online menjadi kebutuhan penting di kalangan gamer. Namun maraknya kasus penipuan dan lemahnya sistem keamanan dalam proses transaksi online menimbulkan kekhawatiran terutama ketika pengguna diminta memberikan data sensitif. Untuk mengatasi permasalahan tersebut, penelitian ini bertujuan merancang dan mengimplementasikan aplikasi top-up game online berbasis web bernama DartGameCorner dengan mengintegrasikan payment gateway sebagai solusi transaksi yang aman dan efisien. Metodologi yang digunakan adalah model Waterfall dimulai dari analisis kebutuhan, desain sistem, implementasi, pengujian menggunakan metode Blackbox hingga tahap pemeliharaan. Hasil implementasi menunjukkan bahwa aplikasi mampu memberikan pengalaman transaksi yang lebih terstruktur, aman, dan cepat, dengan fitur utama seperti autentikasi pengguna, validasi data, manajemen transaksi, dan opsi pembayaran digital melalui MidTrans. Pengujian sistem juga menunjukkan bahwa fungsi-fungsi utama berjalan sesuai kebutuhan meskipun tahap pengujian lanjutan masih diperlukan. Aplikasi ini diharapkan dapat meningkatkan kepercayaan pengguna dalam bertransaksi serta menjadi alternatif terpercaya bagi layanan top-up game online.
Pelatihan Desain Poster Promosi untuk UMKM Binaan Dinsospermasdes Kabupaten Jepara Ghozi, Wildanil; Prabowo, Dwi Puji; Rafrastara, Fauzi Adi; Pramunendar, Ricardus Anggi; Sani, Ramadhan Rakhmat
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 8, No 3 (2025): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v8i3.8379

Abstract

Internet sebagai salah satu dorongan utama dalam perkembangan teknologi memungkinkan setiap manusia untuk menjangakau informasi tanpa batasan ruang dan waktu. Saat ini di Indonesia, lebih dari 196,7 juta penduduk memanfaatkan internet dalam aktivitasnya sehari-hari. Pada provinsi Jawa Tengah terdapat 25,6 juta pengguna internet aktif. Tingginya pengguna internet menjadi peluang untuk memperluas target pemasaran produk UMKM. Pemerintah Kabupaten Jepara, melalui Dinas Sosial, Pemberdayaan Masyarakat dan Desa (Dinsospermasdes) Kabupaten Jepara memiliki tanggung jawab dalam program rehabilitasi Penyandang Masalah Kesejahteraan Sosial (PMKS) dimana salah satu programnya adalah pembinaan UMKM. Saat ini, UMKM binaan Dinsospermasdes belum mampu membuat desain poster promosi yang baik dan menarik pembeli. Penulis memberikan pelatihan desain poster dengan menggunakan aplikasi canva kepada para pelaku UMKM binaan. Pelatihan tersebut telah berhasil meningkatkan pemahaman konsep desain, kemampuan pengambilan foto produk, dan kemampuan membuat desain poster promosi para pelaku UMKM. Poster-poster baru yang dihasilkan pada kegiatan pelatihan menjadi indikator keberhasilan para peserta mengikuti pelatihan. Dengan demikian, diharapkan kemampuan yang telah dimiliki dapat membantu meningkatkan penjualan produk UMKM binaan.
XGBoost-Powered Ransomware Detection: A Gradient-Based Machine Learning Approach for Robust Performance Ghozi, Wildanil; Lestiawan, Heru; Sani, Ramadhan Rakhmat; Hussein, Jassim Nadheer; Rafrastara, Fauzi Adi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2405

Abstract

Ransomware remains a rapidly evolving cyber threat, causing substantial financial and operational disruptions globally. Traditional signature-based detection systems are ineffective against sophisticated, zero-day attacks due to their static nature. Consequently, machine learning-based approaches offer a more effective and adaptive alternative. This study proposes an approach utilizing XGBoost for highly effective ransomware detection. We conducted a rigorous comparative analysis of prominent ensemble learning algorithms—XGBoost, Random Forest, Gradient Boosting, and AdaBoost—on the RISS Ransomware Dataset, comprising 1,524 instances. Our experimental results unequivocally demonstrate XGBoost as the superior ensemble model, achieving an impressive 97.60% accuracy and F1-Score. This performance surpassed Gradient Boosting (97.20%), Random Forest (96.94%), and AdaBoost (96.50%). Furthermore, this study benchmarked XGBoost against established state-of-the-art (SOTA) methods, including Support Vector Machine (SVM) and the SA-CNN-IS deep learning approach. The comprehensive results underscore the core contribution of this study: by applying XGBoost with a carefully structured machine learning pipeline, our approach consistently outperforms two state-of-the-art methods (SVM and SA-CNN-IS) as well as other ensemble algorithms. This highlights the critical role of methodological precision in maximizing detection performance against evolving ransomware threats.
Deep Learning Factor Investing in the Indonesian Stock Market Atha Rohmatullah, Fawwaz; Alzami, Farrikh; Rakhmat Sani, Ramadhan; Novita Dewi, Ika; Winarno, Sri; Sulistyono, Teguh
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112549

Abstract

Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe > 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -> β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.
Mitigating Class Imbalance in DDoS Detection: The Impact of Random Over Sampling on Machine Learning Performance Ghozi, Wildanil; Hussein, Jasim Nadheer; Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Paramita, Cinantya; Supriyanto, Catur
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.95037

Abstract

Distributed Denial of Service (DDoS) attacks are a major cybersecurity threat, involving malicious traffic generated from numerous compromised sources to overwhelm and disable targeted services. Although machine learning (ML) has shown promise in detecting DDoS attacks through network traffic analysis, a key challenge remains: the class imbalance in datasets such as UNSW-NB15, where normal traffic significantly outweighs attack instances. This imbalance leads to biased predictions and degraded detection performance for minority attack classes. To address this issue, our study investigates the impact of Random Over Sampling (ROS), a simple yet effective balancing technique on improving detection accuracy in multi-class DDoS classification tasks. While prior works have primarily focused on ensemble algorithms or feature selection, our approach is distinct in emphasizing the effect of data balancing on macro evaluation metrics such as macro precision, macro recall, and macro F1-score. ROS was selected over more complex alternatives, such as SMOTE or ADASYN, due to its computational efficiency and ability to establish a performance baseline without introducing synthetic noise. We evaluate four machine learning algorithms: Decision Tree, Naïve Bayes, Random Forest, and XGBoost, using the UNSW-NB15 dataset. The results show that Decision Tree combined with ROS yields the highest improvement in macro F1-score, increasing by 36%. However, this improvement is accompanied by a moderate reduction in accuracy for certain algorithms. These findings highlight the critical role of class balancing in enhancing the reliability of DDoS detection models, especially in imbalanced multi-class scenarios.
Rancang Bangun Sistem Try Out Berbasis Paperless untuk Evaluasi Hasil Belajar Siswa dengan MVC Sani, Ramadhan Rakhmat; Kurniawan, Defri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 3: Juni 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3881.436 KB) | DOI: 10.25126/jtiik.2019631181

Abstract

Sistem pelaksanaan Ujian Nasional di Indonesia saat ini mulai beralih menggunakan komputer sebagai media dalam pelaksanaannya menggantikan sistem yang lama. Sebagai bentuk dukungan dalam mengurangi penggunaan kertas dalam pembelajaran dan evaluasi baik lembar soal maupun materi di sekolah. Tujuan dari penelitian ini adalah menghasilkan purwarupa model sistem tryout berbasis paperless untuk evaluasi hasil belajar siswa untuk menggantikan pemakaian kertas dengan konsep yang diberikan oleh media komputer. Sehingga memberikan keuntungan dalam efisiensi waktu dan biaya, mengurangi kecurangan  serta mempercepat dalam proses evaluasi. Metode yang digunakan dalam pengembangan aplikasi ini menggunakan Rapid Application Development (RAD) yang meliputi tahapan analisa kebutuhan perangkat lunak, perancangan perangkat lunak, implementasi perangkat lunak dan pengujian perangkat lunak dengan penerapan konsep Sistem Development Life Cycle (SDLC) sehingga cepat untuk dievaluasi oleh pengguna. Untuk bahasa pemodelan sistem menggunakan UML (Unified Modeling Language) yang terdiri use case diagram, sequential diagram dan pemodelan database. Penggunaan framework Codeigniter memberikan kemudahan dalam konsep Object Oriented Programing (OOP) dengan menggunakan arsitektur web MVC (Model, View, Controller). Pemisahan logika pembuatan kode pada tampilan website menjadikan lebih terstruktur, sederhana dan aman sehingga memberikan kemudahan bagi developer maupun programmer dalam pengembangannya tanpa harus dimulai dari awal. Hasil pengujian sistem menggunakan blackbox testing menunjukan hasil yang baik dan sudah mencapai 90% dari prinsip usability yang telah diimplementasikan.AbstractThe current National Examination System in Indonesia has begun to switch to using computers as a medium in its implementation to replace the old system. As a form of support in reducing paper use in learning and evaluating both question sheets and material at school. The purpose of this study was to produce a prototype paperless based tryout system model for evaluating student learning outcomes to replace paper use with the concepts provided by computer media. So as to provide benefits in time and cost efficiency, reduce fraud and accelerate the evaluation process. The method used in the development of this application uses Rapid Application Development (RAD) which includes the stages of software requirements analysis, software design, software implementation and software testing with the application of the concept of System Development Life Cycle (SDLC) so that it is quickly evaluated by users. For system modeling languages use UML (Unified Modeling Language) which consists of use case diagrams, sequential diagrams and database modeling. The use of CodeIgniter framework provides convenience in Object Oriented Programing (OOP) concepts using the MVC web architecture (Model, View, Controller). Separation of logic in making code on the website display makes it more structured, simpler and safer so that it makes it easy for developers and programmers to develop without having to start from scratch. The results of testing the system using blackbox testing showed good results and has reached 90% of the usability principle that has been implemented.
Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abu Salam Ade Nurul Aisyah Agnes Putri Istiwana Agung Priyo Utomo, Rino Ahmad Khotibul Umam, Ahmad Khotibul Al zami, Farrikh Alzami, Farrikh Annisa Ratna Salsabilla Ardytha Luthfiarta ARIYANTO, MUHAMMAD Arta Moro Sundjaja, Arta Moro Arunia, Aurelya Prameswari Asih Rohmani Asih Rohmani Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Bernadette Chayeenee Norman , Maria Budi Harjo Budi, Setyo Candra Irawan Catur Supriyanto Christy Atika Sari Darnell Ignasius Defri Kurniawan Defri Kurniawan Diana Aqmala Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erika Devi Udayanti Fahmi Amiq Farah Syadza Mufidah Farrikh Al Zami Farrikh Al Zami Fauzi Adi Rafrastara Fauzi Adi Rafrastara Florentina Esti Nilasari Florentina Esti Nilawati Galih Mentari Pangesti Guruh Fajar Shidik Hanny Haryanto Harun Al Azies Hercio Venceslau Silla Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ignasius, Darnell Ika Novita Dewi Ika Novita Dewi Ikhwansyah Kurniawan Indra Gamayanto Iswahyudi ISWAHYUDI ISWAHYUDI Ivan Bayu Fachreza Junta Zeniarja Karin, Tan Regina Kiki Widia Kurniawan, Defri L. Budi Handoko Lekso Budi Handoko Maszuda, Akbar Alvian Megantara, Rama Aria Melati Anggreni Sitorus Muhammad Fais Ramadhani Muhammad Nabhan Rifa’i Muhammad Naufal MY. Teguh Sulistyono Nadya Azizah Najwa Ratu Afi Nida Aulia Karima Novita Dewi , Ika Nugraha, Purwa Esti Paramita, Cinantya Pergiwati, Dewi Priyo Utomo, Rino Agung Pulung Nurtantio Andono Purwanto Purwanto Ramadhani, Dwi Arya Resha Meiranadi Caturkusuma Rhyan David Levandra Ricardus Anggi Pramunendar Richard Emmerig S. Sukamto, Titien Sarker, Md. Kamruzzaman Sasono Wibowo Sendi Novianto Sendi Novianto Sendi Novianto Setyo Budi Setyo Budi Sirait, Tamsir Hasudungan Soares, Gilardinho Javiere Oscoraldo Pedrosa Sri Winarno Sri Winarno Suharnawi Suharnawi Suharnawi Suharnawi Suharnawi Sukamto, Titien S. Sukamto, Titien Suhartini Sulistyono, Teguh Syahrizal, Muhammad Iqbal Titien Suhartini Sukamto Titien Suhartini Sukamto Utomo, Danang Wahyu Wibowo, Isro' Rizky Wildanil Ghozi Winarsih, Nurul Anisa Sri Wulan Puspita Loka Yani Parti Astuti Yanuaresta, Dianna Yunita Ayu Pratiwi Yupie Kusumawati Yuventius Tyas Catur Pramudi Zahro, Azzula Cerliana Zami, Farrikh Al