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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal IMPACT: Implementation and Action Journal of Electronics, Electromedical Engineering, and Medical Informatics Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat (Mediteg) Jurnal Abdimas Madani dan Lestari (JAMALI) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Madaniya Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Journal of Embedded Systems, Security and Intelligent Systems Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Dodon Turianto Nugrahadi
Lambung Mangkurat University
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Enhancing Natural Disaster Monitoring: A Deep Learning Approach to Social Media Analysis Using Indonesian BERT Variants Fitriani, Karlina Elreine; Faisal, Mohammad Reza; Mazdadi, Muhammad Itqan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Prastya, Septyan Eka
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/t158qq37

Abstract

Social media has become a primary source of real-time information that can be leveraged by artificial intelligence to identify relevant messages, thereby enhancing disaster management. The rapid dissemination of disaster-related information through social media allows authorities to respond to emergencies more effectively. However, filtering and accurately categorizing these messages remains a challenge due to the vast amount of unstructured data that must be processed efficiently. This study compares the performance of IndoRoBERTa, IndoRoBERTa MLM, IndoDistilBERT, and IndoDistilBERT MLM in classifying social media messages about natural disasters into three categories: eyewitness, non-eyewitness, and don’t know. Additionally, this study analyzes the impact of batch size on model performance to determine the optimal batch size for each type of disaster dataset. The dataset used in this study consists of 1000 messages per category related to natural disasters in the Indonesian language, ensuring sufficient data diversity. The results show that IndoDistilBERT achieved the highest accuracy of 81.22%, followed by IndoDistilBERT MLM at 80.83%, IndoRoBERTa at 79.17%, and IndoRoBERTa MLM at 78.72%. Compared to previous studies, this study demonstrates a significant improvement in classification accuracy and model efficiency, making it more reliable for real-world disaster monitoring. Pre-training with MLM enhances IndoRoBERTa’s sensitivity and IndoDistilBERT’s specificity, allowing both models to better understand context and optimize classification results. Additionally, this study identifies the optimal batch sizes for each disaster dataset: 32 for floods, 128 for earthquakes, and 256 for forest fires, contributing to improved model performance. These findings confirm that this approach significantly improves classification accuracy, supporting the development of machine learning-based early warning systems for disaster management. This study highlights the potential for further model optimization to enhance real-time disaster response and improve public safety measures more effectively and efficiently.
Dimensionality Reduction Using Principal Component Analysis and Feature Selection Using Genetic Algorithm with Support Vector Machine for Microarray Data Classification Kartini, Dwi; Badali, Rahmat Amin; Muliadi, Muliadi; Nugrahadi, Dodon Turianto; Indriani, Fatma; Saputro, Setyo Wahyu
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/mr7x9713

Abstract

DNA microarray is used to analyze gene expression on a large scale simultaneously and plays a critical role in cancer detection. The creation of a DNA microarray starts with RNA isolation from the sample, which is then converted into cDNA and scanned to generate gene expression data. However, the data generated through this process is highly dimensional, which can affect the performance of predictive models for cancer detection. Therefore, dimensionality reduction is required to reduce data complexity. This study aims to analyze the impact of applying Principal Component Analysis (PCA) for dimensionality reduction, Genetic Algorithm (GA) for feature selection, and their combination on microarray data classification using Support Vector Machine (SVM). The datasets used are microarray datasets, including breast cancer, ovarian cancer, and leukemia. The research methodology involves preprocessing, PCA for dimensionality reduction, GA for feature selection, data splitting, SVM classification, and evaluation. Based on the results, the application of PCA dimensionality reduction combined with GA feature selection and SVM classification achieved the best performance compared to other classifications. For the breast cancer dataset, the highest accuracy was 73.33%, recall 0.74, precision 0.75, and F1 score 0.73. For the ovarian cancer dataset, the highest accuracy was 98.68%, recall 0.98, precision 0.99, and F1 score 0.99. For the leukemia dataset, the highest accuracy was 95.45%, recall 0.94, precision 0.97, and F1 score 0.95. It can be concluded that combining PCA for dimensionality reduction with GA for feature selection in microarray classification can simplify the data and improve the accuracy of the SVM classification model. The implications of this study emphasize the effectiveness of applying PCA and GA methods in enhancing the classification performance of microarray data.
Effectiveness of SMOTE in Enhancing Adult Autism Spectrum Disorder Diagnosis Predictive Performance With Missforest Imputation And Random Forest Musyaffa, Muhammad Hafizh; Saragih, Triando Hamonangan; Nugrahadi, Dodon Turianto; Kartini, Dwi; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.66

Abstract

Autism Spectrum Disorder (ASD), originally described by Leo Kanner in 1943, is a complex developmental condition that manifests through social, emotional, and behavioral challenges, often including speech delays and difficulties in interpersonal interactions. Despite significant advancements in diagnostic criteria over the years, accurate diagnosis of ASD in adults remains challenging due to limited access to comprehensive datasets and inherent methodological constraints. The Autism Screening Adult dataset used in this study exemplifies these issues, as it contains missing values and exhibits a marked class imbalance, both of which can adversely affect model performance. To address these challenges, we proposed a framework that integrates Random Forest classification with MissForest imputation and the Synthetic Minority Over-sampling Technique (SMOTE). MissForest effectively imputes missing data by employing an iterative random forest approach that preserves the underlying structure of the data without relying on strict parametric assumptions. Meanwhile, SMOTE generates synthetic samples for the minority class, thereby balancing the dataset and reducing prediction bias. Experimental evaluation through 10-Fold Cross Validation demonstrated that the application of SMOTE significantly enhanced model performance. Notably, the overall accuracy improved from 70.17% to 79.32%, and the AUC-ROC increased from 47.13% to 85.84%, indicating a robust improvement in the model’s ability to distinguish between positive and negative cases. These results underscore the critical importance of addressing data imbalance and missing values in predictive modeling for ASD. The promising outcomes of this study provide a solid foundation for developing more reliable diagnostic tools for adult ASD, and future research may further refine feature selection and incorporate additional data sources to optimize performance even further.
Application of Solar-Based Light Lumen to Papuyu Fish Farmers in Penggalaman Village, West Martapura: Indonesia Dwi Kartini; Nugrahadi, Dodon Turianto; Ramadhani, Rahmat
Jurnal Abdimas Madani dan Lestari (JAMALI) Volume 06, Issue 01, Maret 2024
Publisher : UII

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jamali.vol6.iss1.art7

Abstract

Penggalaman Village, West Martapura, Banjar Regency, South Kalimantan is one of the papuyu fisheries development areas. The location of the village is very strategic, located along the irrigation flow of Penggalaman Village and has very sufficient potential in the fisheries business for fish farmers. Villagers have carried out papuyu fish seed cultivation activities that have ponds for artificial fertilization of papuyu fish, papuyu fish egg hatching ponds and enlargement ponds but have not implemented lighting in the process of fish farming activities. This is because it requires a lot of electrical energy and costs a lot for these activities. The purpose of this service activity is to introduce and apply solar-based electrical energy sources for fish farmers in improving fisheries businesses as an alternative to electrical energy. This tool is equipped with yellow and white lumens, controller, battery, aerator and solar panel and automatic control system. The application of solar-based lumens will provide a technological solution to the lighting of papuyu fingerlings and enlargement ponds for papuyu fish farmers in Penggalaman Village. Yellow and white lumens are able to provide lighting and reduce predator attacks at night. The results of the application of this technology are the fulfillment of electrical energy by applying solar-based lumens to papuyu fish nurseries and increasing the lighting of fish nurseries and papuyu fish enlargement by approximately 80%, and reducing night predator attacks by 70%.
Gender Classification of Twitter Users Using Convolutional Neural Network Fitra Ahya Mubarok; Mohammad Reza Faisal; Dwi Kartini; Dodon Turianto Nugrahadi; Triando Hamonangan Saragih
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3318

Abstract

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and
NATURAL DISASTER REPORT ON SOCIAL MEDIA CLASSIFICATION METHOD BASED ON WORD EMBEDDING AND GRAPH ATTENTION NETWORK Mohammad Reza Faisal; Irwan Budiman; Dodon Turianto Nugrahadi; Muhammad Rafi; Mera Kartika Delimayanti; Luu Duc Ngo; Moses Okechukwu Onyesolu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2613-2630

Abstract

Natural disasters frequently occur unexpectedly and seriously threaten human safety and infrastructure. Traditional detection systems rely heavily on IoT sensors and satellite monitoring, which are often costly and less accessible in resource-limited or remote areas. In contrast, social media provides a rich and real-time source of information, as users frequently post eyewitness reports during disaster events. However, automatically classifying these posts into relevant disaster categories remains challenging due to the short and informal nature of the text. The research aims to develop a high-performing classification model for disaster-related tweets using graph-based neural architectures and structured word embedding representations. The method used is a comparative implementation of Graph Convolutional Network (GCN) and Graph Attention Network (GAT) models, with input constructed by concatenating vectors from three word embedding techniques—Word2Vec, FastText, and GloVe—across seven multilingual datasets. The result of this study is that GAT outperformed GCN in all scenarios, with FastText embeddings yielding the highest individual performance. In contrast, combined embeddings sometimes led to performance degradation due to redundancy. The average F1-score for GCN is 0.749, while GAT achieves 0.915. The research conclusions indicate that GAT with word embedding input provides a novel and effective multilingual disaster tweet classification framework, offering valuable insights for future AI-based natural disaster monitoring systems.
Performance Analysis of the Fuzzing Method in Detecting API Vulnerabilities in Mobile Healthcare Application X Based on OWASP API Security Top 10 Muhammad Ikhwanul Hakim; Radityo Adi Nugroho; Dodon Turianto Nugrahadi; Rudy Herteno; Setyo Wahyu Saputro
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3149

Abstract

Traditional perimeter security measures, such as Web Application Firewalls (WAFs) and static analysis, often fail to detect logic-based vulnerabilities in healthcare Application Programming Interfaces (APIs), creating significant risks for patient data confidentiality. Addressing the scarcity of empirical performance evaluations in this domain, this study employs a grey-box controlled experimental design to assess the effectiveness of automated HTTP fuzzing against a production-grade mobile health application ("Application X"). Using the FFUF tool configured with sequential identifier injection, status-code filtering, and hidden-field probing, the experiment tested 33 endpoints against the OWASP API Security Top 10 2023 benchmarks. To ensure data reliability, a rigorous multi-step validation protocol including replay testing and environmental noise elimination was applied to filter false positives. The results identified 88 distinct vulnerabilities distributed across six categories, with a critical dominance of Security Misconfiguration (API8) and Broken Object Property Level Authorization (API3). Analytically, the high prevalence of API3 reveals a systemic failure in backend serialization, where sensitive fields  including password hashes and internal administrative flags were exposed due to the absence of Data Transfer Objects (DTOs), contradicting the assumption of secure client-side filtering. Limitations of this study include the restriction to a single patient-role perspective and the exclusion of third-party integrations. The study concludes that automated fuzzing is superior to static analysis in detecting runtime data leakage and recommends mandatory Server-Side Output Filtering through explicit DTOs as a critical standard for secure health API development and data privacy compliance.
Dampak dari Parameter Variasi Koneksi, Node dan Kecepatan Node Terhadap Delay pada Routing Protocol AODV dan BATMAN Jaringan MANET Dodon Turianto Nugrahadi; M Reza Faisal; Liling Triyasmono; Muhammad Janawi
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2675

Abstract

Mobile ad-hoc Network (MANET) is a multihop wireless network that a many collection of mobile nodes that are dynamic. MANET each node on the network have the same position, so it needs the appropriate routing protocol, to support the exchange of data to be optimal. In this study, the routing protocol to be tested is AODV and BATMAN based scenario increasing the number of connections, nodes and speed. Simulation parameter scenarios is number connection 1 UDP, 2 UDP, 3 UDP, and number of node 25 node, 50 node, 100 node, and then number node speed 20 m/s, 50 m/s. in this AODV routing protocol will establish a rute from the source node to the destination only if there is a request from the source node. BATMAN routing protocols, all decisions and information disseminated throughout the node and will regularly update on each node. The performance parameters to be measured such as delay by using OMNET ++ 4.6. Output of simulation will analysis with two way anova and multivariate to know correlation between variation scenario impact to delay. The results obtained in this study AODV and BATMAN have their respective advantages, analisys with two-way anova show that both AODV and BATMAN get the impact of the scenario from incrising the number of connections, the number of nodes and the number of nodes speed with a p-value of 0.012212 (<0.05) with two-way anova. From all scenarios, the number of UDP connections has the greatest impact, from UDP 1, UDP 2 and UDP 3. Followed by the number of speed 50 and node 100. So it can be concluded that the connection has an effect on increasing delay. The increasing number of speed and nodes can contribute to an increase in delay if number of nodes above 100 and speed above 50. With multivariate analysis, the BATMAN protocol had the most impact on the delay under the scenario then AODV.
Analisis Komparasi Implementasi Steganografi White-Space dan White-Space Modified pada Artikel Terenkripsi AES dalam HTML5 Rudy Herteno; Dodon Turianto Nugrahadi; Muhammad Sholih Afif; M Reza Faisal; Friska Abadi
Jurnal Komputasi Vol. 8 No. 1 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i1.2525

Abstract

The level of internet usage continues to increase until now.  information exchange requires security that cannot be predicted by others.  one technique for securing information is steganography.  Steganography techniques are the science and art of hiding information.  This technique can hide the content of information in media that cannot be guessed by ordinary people, so as not to arouse suspicion of the people who see it.  One of the media that can implement the white-space modified steganography method is HTML pages.  in addition, AES (Advanced Encryption Standard) is a lighter encryption security algorithm compared to other algorithms. In this study, plain text that has been encrypted into cipher text is then inserted with white-space and white-space modification steganography techniques. Data changes have occurred but only less than 1 percent.  In experiments that have been implemented on Google Chrome and Mozilla Firefox are the same except in Internet Explorer, which changes the data slightly larger.The implementation of AES encryption and stegano white-space original, has 100% success but the 80% decryption process is successful, but the decryption results contain additional binaries. This happen because the use of tabulation (tabs) instead of spaces in HTML5 articles, and this is often found in HTML articles. while the implementation of AES encryption and stegano whitespace modified, has a success of 100% and the decryption process of 90% succeeded without any changes. 1 article failed because the number of articles is too small compared to the amount of space provided. The conclusion that implementation of AES encryption and white-space modified is more appropriate to be implemented in HTML5 articles, and than the use of tabulation and the number of characters also consequences on the implementation.Keywords: Information, Steganography, White-space modified, Security, AES, Web Browser 
Studi Ekstraksi Fitur Berbasis Vektor Word2Vec pada Pembentukan Fitur Berdimensi Rendah irwan budiman; M Reza Faisal; Dodon Turianto Nugrahadi
Jurnal Komputasi Vol. 8 No. 1 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i1.2517

Abstract

Klasifikasi teks adalah salah satu metode untuk mengelola dan mencari informasi penting yang terdapat pada format tekstual yang tidak terstruktur. Ekstraksi fitur merupakan proses penting pada klasifikasi teks untuk mengubah format tekstual yang tidak terstruktur menjadi terstruktur sehingga dapat diproses oleh algoritma machine learning untuk mengklasifikasikan ke class yang telah ditentukan. Salah satu teknik ekstraksi fitur yang umum digunakan adalah vector space representation. Teknik ini mudah digunakan tetapi berpotensi menghasilkan data dengan dimensi banyak yang berakibat kepada peningkatan waktu komputasi bahkan tidak dapat diproses karena limitasi perangkat keras. Pada riset ini kami melakukan studi terhadap teknik ekstraksi fitur yang mampu menghasilkan data berdimensi sedikit. Ekstraksi fitur yang digunakan memanfaatkan vektor word2vec untuk mengontrol jumlah fitur yang dihasilkan. Pada riset ini kami membandingkan beberapa model yang dihasilkan sendiri dengan jumlah fitur yang bervariasi dan model yang telah disedikan oleh Google. Hal ini dilakukan untuk mengetahui jumlah fitur yang dapat menghasilkan kinerja klasifikasi terbaik. Hasilnya didapat nilai kinerja tertinggi akurasi yaitu 0.877 dengan jumlah fitur adalah 300 dari model yang dihasilkan sendiri.
Co-Authors Abdul Gafur Adi Mu'Ammar, Rifqi Adi, Puput Dani Prasetyo Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Aji Triwerdaya Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Ando Hamonangan Saragih Apriana, Susi Ardiansyah Sukma Wijaya Arfan Eko Fahrudin Arifin Hidayat Azwari, Ayu Riana Sari Azwari, Ayu RianaSari Bachtiar, Adam Mukharil Badali, Rahmat Amin Bagaskara Ridho Vandio Bahriddin Abapihi Bedy Purnama Cahyadi, Rinova Firman Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emy Iryanie, Emy Faisal Murtadho Fajrin Azwary Fatma Indriani Fatma Indriani Fatma Indriani Fhadilla Muhammad Fitra Ahya Mubarok Fitria Agustina fitria Fitriani, Karlina Elreine Fitrinadi Friska Abadi Friska Abadi Gunawan Gunawan Gunawan Gunawan Hafiz Ilhami Hariyady Hariyady Herteno, Rudy Heru Kartika Candra, Heru Kartika Huynh, Phuoc-Hai Ichsan Ridwan Imam Tahyudin Indah Ayu Septriyaningrum Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Ismail Didit Samudro Julius Tunggono Jumadi Mabe Parenreng Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kartika, Najla Putri Kenji Satou Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Khusnul Rahmi Maulidha Liling Triyasmono Luu Duc Ngo M Kevin Warendra M. Apriannur Martalisa, Asri Mera Kartika Delimayanti Mera Kartika Delimayanti Midfai Yabani Miftahul Muhaemen Mohammad Reza Faisal Mohammad Reza Faisal Moses Okechukwu Onyesolu Muhamad Ihsanul Qamil Muhammad Alkaff Muhammad Alkaff Muhammad Alvin Alfando Muhammad Anshari Muhammad Haekal Muhammad Hasan Muhammad Ikhwanul Hakim Muhammad Irfan Saputra Muhammad Itqan Masdadi Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Rafi Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Aulia Ramadhan Muhammad Rofiq Muhammad Sholih Afif Muhammad Solih Afif Muliadi Muliadi Muliadi MULIADI -, MULIADI Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Musyaffa, Muhammad Hafizh Nafis Satul Khasanah Nahdhatuzzahra Nahdhatuzzahra Ngo, Luu Duc Nila Yoga Tama Nurwati Noor Hidayah Nursyifa Azizah Ori Minarto Padhilah, Muhammad Pirjatullah Pirjatullah Pirjatullah Prastya, Septyan Eka Priyatama, Muhammad Abdhi Puput Dani Prasetyo Adi Puput Dani Prasetyo Adi Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani, Rahmat Reza Faisal, Mohammad Riadi, Putri Agustina Rifki Izdihar Oktvian Abas Pullah Rifki Riza Susanto Banner Rizal, Muhammad Nur Rizki Amelia Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Saman Abdurrahman Saragih, Triando Hamonangan Selvia Indah Liany Abdie Septyan Eka Prastya Setyo Wahyu Saputro Setyo Wahyu Saputro sholih Afif Siti Napi'ah Soesanto, Oni Sri Cahyo Wahyono Sri Rahayu Sri Redjeki Sri Redjeki Syarif Maulana, Syarif Totok Wianto Totok Wiyanto Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utomo, Edy Setyo Wahyu Dwi Styadi Wardana, Muhammad Difha Winda Agustina Yanche Kurniawan Mangalik YILDIZ, Oktay Yudha Sulistiyo Wibowo Zamzam, Yra Fatria