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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Pertanian Indonesia ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic Madaniya Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Makara Journal of Science Journal of Data Science and Software Engineering Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Integra: Journal of Integrated Mathematics and Computer Science
Muhammad Reza Faisal, Muhammad Reza
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences

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Multimodal Biometric Recognition Based on Fusion of Electrocardiogram and Fingerprint Using CNN, LSTM, CNN-LSTM, and DNN Models Agustina, Winda; Nugrahadi, Dodon Turianto; Faisal, Mohammad Reza; Saragih, Triando Hamonangan; Farmadi, Andi; Budiman, Irwan; Parenreng, Jumadi Mabe; Alkaff, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5098

Abstract

Biometric authentication offers a promising solution for enhancing the security of digital systems by leveraging individuals' unique physiological characteristics. This study proposes a multimodal authentication system using deep learning approaches to integrate fingerprint images and electrocardiogram (ECG) signals. The datasets employed include FVC2004 for fingerprint data and ECG-ID for ECG signals. Four deep learning architectures—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Deep Neural Network (DNN)—are evaluated to compare their effectiveness in recognizing individual identity based on fused multimodal features. Feature extraction techniques include grayscale conversion, binarization, edge detection, minutiae extraction for fingerprint images, and R-peak–based segmentation for ECG signals. The extracted features are combined using a feature-level fusion strategy to form a unified representation. Experimental results indicate that the CNN model achieves the highest classification accuracy at 96.25%, followed by LSTM and DNN at 93.75%, while CNN-LSTM performs the lowest at 11.25%. Minutiae-based features consistently yield superior results across different models, highlighting the importance of local feature descriptors in fingerprint-based identification tasks. This research advances biometric authentication by demonstrating the effectiveness of feature-level fusion and CNN architecture for accurate and robust identity recognition. The proposed system shows strong potential for secure and adaptive biometric authentication in modern digital applications.
Application of Random Forest Method Classification for Glycosylation in Lysine Protein Sequences Fitriyana, Silfia; Syarif, Admi; Rossyking, Favorisen; Faisal, Mohammad Reza
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241218

Abstract

Grouping glycosylated lysine proteins into groups according to the type of glycosylation seen in the lysine protein sequence is known as glycosylation in the lysine protein sequence. In this work, the sensitivity, specificity, accuracy, and Matthew’s correlation coefficient (MCC) of the random forest approach for classifying the glycosylation of lysine protein sequences were examined. With 214 positive and 406 negative data, the lysine protein dataset derived from benchmark data contains 620 total proteins with a protein length of 15 sequences. 90% of the dataset is used for training, while 10% is used for testing. Using the R package BioSeqClass version 1.44.0, feature extraction employed protein descriptors, specifically AA Index, CTD, and PseAAC, with a total of 60 features. The Random Forest classification algorithm was used to reprocess the results with Mtry values of 4, 8, and 16. The number of trees (ntree) was randomly set to 250, 500, 750, and 1000. The best results were achieved with a dataset split of 90% training data and 10% test data, using Mtry of 42 and 1000 trees, resulting in 89.97% sensitivity, 92.79% specificity, 80.76% MCC, and 90.42% accuracy. These results demonstrate that the combination of feature extraction and the Random Forest algorithm is effective in classifying lysine proteins.
Seleksi Fitur Hybrid Grey Wolf Optimization dan Particle Swarm Optimization pada Distance Biased Naive Bayes untuk Klasifikasi Kanker Payudara Ratna Septia Devi; Triando Hamonangan Saragih; Mohammad Reza Faisal
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.4737

Abstract

Kanker payudara adalah penyebab utama kematian akibat kanker tertinggi kedua di dunia. Pasien Kanker payudara terus mengalami peningkatan dan menjadi masalah kesehatan yang cukup serius di seluruh dunia, termasuk juga di Indonesia. Diagnosis dini adalah salah satu pendekatan terbaik untuk mencegah penyakit ini semakin meningkat dan berkembang. Machine learning dapat melakukan penambangan data menggunakan serangkaian fitur pada sebuah data. Penelitian ini menggunakan dataset public dari UCI machine learning repository yaitu Breast Cancer Wisconsin (Diagnostic). Pada dataset ini memiliki atribut sebanyak 32 fitur, namun banyaknya fitur pada sebuah data juga akan memperlambat waktu komputasi dari metode klasifikasi yang digunakan. Pada penelian ini, akan dilakukan seleksi fitur menggunakan metode Hybrid Grey Wolf Optimization dan Particle Swarm Optimization (HGWOPSO) untuk memilih fitur yang paling informatif dan signifikan untuk digunakan pada klasifikasi. Metode klasifikasi yang digunakan adalah Distance Biased Naive Bayes (DBNB) yang terdiri dari dua modul yaitu Weighted Naïve Bayes Module (WNBM) dan Distance Reinforcement Module (DRM). Dari penelitian ini, didapatkan performa akurasi tertinggi pada model DBNB tanpa seleksi fitur sebesar 94,90%, DBNB dengan GWO sebesar 95,08%, DBNB dengan PSO sebesar 95,25%, dan DBNB dengan HGWOPSO sebesar 96,13%. Dapat disimpulkan bahwa model DBNB dengan seleksi fitur HGWOPSO mengalami peningkatan dibandingkan dengan DBNB tanpa seleksi fitur maupun dengan seleksi fitur individualnya.
Pendampingan Ujian Kompetensi Peminatan Sains Bagi Peserta Didik SMP Muhammadiyah 1 Banjarbaru Susanti, Dewi Sri; Gafur, Abdul; Uthami, Mariza; Noordyanti, Erna; Faisal, Muhammad Reza; Farid, Fuad Muhajirin; Maisarah, Maisarah; Rahkmawati, Yeni; Riza, Yusi
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

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

Abstract

Kemampuan literasi dan komunikasi peserta didik pada jenjang pendidikan dasar perlu ditingkatkan agar lebih siap mengikuti pembelajaran pada kurikulum merdeka. Salah satu cara untuk meningkatkan potensi ini adalah dengan melaksanakan Ujian Kompetensi Peminatan bagi peserta didik menjelang kelulusan. Kegiatan pengabdian pada masyarakat yang dilakukan ini bertujuan untuk meningkatkan kemampuan literasi dan komunikasi peserta didik kelas IX SMP Muhammadiyah 1 Banjarbaru. Melalui kegiatan ini dapat diukur sekaligus mengembangkan kemampuan literasi dan komunikasi sains pada peserta didik agar nantinya lulusan lebih siap untuk memberikan unjuk kerja pada pembelajaran sains di jenjang pendidikan berikutnya. Dalam kegiatan ini, secara bersama-sama dosen dan guru-guru melaksanakan proses pembimbingan dan memberikan penilaian terhadap kegiatan ujian kompetensi peminatan sains. Kegiatan diawali dengan proses penentuan topik yang akan dipilih oleh peserta didik untuk disajikan sebagai presentasi ilmiah dan penyusunan kerangka acuan kerja. Setelah peserta didik menyelesaikan penyusunan presentasi ilmiah, dilakukan proses ujian lisan. Kerangka penilaian disusun dengan memperhatikan aspek kognitif, afektif dan psikomotorik. Peserta didik yang mengikuti ujian sebanyak 24 (duapuluh empat) peserta didik, dimana 3 (tiga) diantaranya adalah peserta didik berkebutuhan khusus. Setelah dilakukan ujian, diperoleh nilai peserta didik dari ketiga aspek tersebut berada dalam rentang 87.25 ± 5.59k. Hal ini menunjukkan bahwa kemampuan peserta didik kelas 9 SMP Muhammadiyah 1 Banjarbaru dalam literasi dan komunikasi sains sangat memuaskan.
Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory Halim, Kevin Yudhaprawira; Nugrahadi, Dodon Turianto; Faisal, Mohammad Reza; Herteno, Rudy; Budiman, Irwan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26354

Abstract

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification. 
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Nafiz, Muhammad Fauzan; Kartini, Dwi; Faisal, Mohammad Reza; Indriani, Fatma; Saragih, Triando Hamonangan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
Design of Application Framework for Vital Monitoring Mobile-Based System Rizky Ananda, Muhammad; Faisal, Mohammad Reza; Herteno, Rudy; Nugroho, Radityo Adi; Abadi, Friska
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28416

Abstract

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.
Effect of SMOTE Variants on Software Defect Prediction Classification Based on Boosting Algorithm Aflaha, Rahmina Ulfah; Herteno, Rudy; Faisal, Mohammad Reza; Abadi, Friska; Saputro, Setyo Wahyu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28521

Abstract

Detecting software defects early on is critical for avoiding significant financial losses. However, building accurate software defect prediction models can be challenging due to class imbalance, where the data for defective modules is much less than for standard modules. This research addresses this issue using the imbalanced dataset NASA MDP. To address this issue, researchers have proposed new methods that combine data level balancing approaches with 14 variations of the SMOTE algorithm to increase the amount of defective module data. An algorithm-level approach with three boosting algorithms, Catboost, LightGBM, and Gradient Boosting, is applied to classify modules as defective or non-defective. These methods aim to improve the accuracy of software defect prediction. The results show that this new method can produce a more accurate classification than previous studies. The DSMOTE and Gradient Boosting pair with 0.9161 has the highest average accuracy (0.9161). The DSMOTE and Catboost model achieved the highest average AUC value (0.9637). The ADASYN kernel and Catboost showed the best ability to perform the average G-mean value (0.9154). The research contribution to software defect prediction involves developing new techniques and evaluating their effectiveness in addressing class imbalance.
Improving with Hybrid Feature Selection in Software Defect Prediction Pratama, Muhammad Yoga Adha; Herteno, Rudy; Faisal, Mohammad Reza; Nugroho, Radityo Adi; Abadi, Friska
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1307

Abstract

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.
Cross-Temporal Generalization of IndoBERT for Indonesian Hoax News Classification Riadi, Agus Teguh; Indriani, Fatma; Mazdadi, Muhammad Itqan; Faisal, Mohammad Reza; Herteno, Rudi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4757

Abstract

The spread of hoaxes in digital media poses a major challenge for automated detection systems as language and topics evolve over time. Although Transformer-based models such as IndoBERT have demonstrated high accuracy in previous studies, their performance across different time periods remains underexplored. This study examines the cross-temporal generalization ability of IndoBERT for hoax news classification. The model was trained on labeled articles from 2018–2023 and tested on data from 2025 to evaluate its robustness against temporal distribution shifts. The results indicate high accuracy on similar-period data (99.67–99.89%) but a decrease on 2025 data (95.45–95.87%), with most errors occurring as false negatives in the hoax class. These findings highlight the impact of temporal distribution shifts on model reliability and underscore the importance of adaptive strategies such as periodic retraining and domain-based data augmentation. Practically, this model has the potential to assist social media platforms and government institutions in developing dynamic and time-adaptive hoax detection systems. The cross-temporal approach employed in this study also offers methodological innovation compared to conventional random validation, as it better reflects real-world conditions where misinformation patterns continually evolve.
Co-Authors Abd. Gafur Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Admi Syarif Aflaha, Rahmina Ulfah Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Andi Farmadi Andi Farmadi Angga Maulana Akbar Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Dewi Sri Susanti Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emma Andini Fatma Indriani Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fitra Ahya Mubarok Fitriani, Karlina Elreine Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Fuad Muhajirin Farid Ghinaya, Helma Halim, Kevin Yudhaprawira Hanif Rahardian Hartati Hartati Herteno, Rudi Herteno, Rudy Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Mahmud Mahmud Maisarah Maisarah, Maisarah Mauldy Laya Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Mada Muhammad Mursyidan Amini Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Sholih Afif Muhammad Zaien MUJIZAT KAWAROE Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Mustofa, Fahmi Charish Nafiz, Muhammad Fauzan Ngo, Luu Duc Noordyanti, Erna Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Pratama, Muhammad Yoga Adha Purnajaya, Akhmad Rezki Putri Nabella Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reza Rendian Septiawan Riadi, Agus Teguh Riadi, Putri Agustina Rinaldi Riza Susanto Banner Riza, Yusi Rizal, Muhammad Nur Rizki, M. Alfi Rizky Ananda, Muhammad Rizky, Muhammad Hevny Rossyking, Favorisen Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Sari, Risna Sa’diah, Halimatus Septyan Eka Prastya Setyo Wahyu Saputro Siti Aisyah Solechah Solly Aryza Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utami, Juliyatin Putri Uthami, Mariza Vina Maulida, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Wildan Panji Tresna Winda Agustina Yeni Rahkmawati Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi