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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) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics 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 Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control 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 International Journal of Advances in Data and Information Systems Madaniya Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Makara Journal of Science Journal of Data Science and Software Engineering Journal of Computing Theories and Applications 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 Jurnal Komputasi
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 Energy 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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The impact of software metrics in NASA metric data program dataset modules for software defect prediction Adinda Ayu Puspita Ramadhani; Radityo Adi Nugroho; Mohammad Reza Faisal; Friska Abadi; Rudy Herteno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25787

Abstract

This paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics.
3D word embedding vector feature extraction and hybrid CNN-LSTM for natural disaster reports identification Mohammad Reza Faisal; Dodon Turianto Nugrahadi; Irwan Budiman; Muliadi Muliadi; Mera Kartika Delimayanti; Septyan Eka Prastya; Imam Tahyudin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26091

Abstract

Social media contain various information, such as natural disaster reports. Artificial intelligence is used to identify reports from eyewitnesses early for disaster warning systems. The artificial intelligence system includes a text classification model with feature extraction and classification algorithms. Word embedding-based feature extraction is widely used for 1-dimensional (1D) and 2-dimensional (2D) data, suitable for traditional or deep learning algorithms. However, applying feature extraction to 3-dimensional (3D) data for text classification is limited. Previous studies focused on word embedding for 1D, 2D, and 3D outputs with convolutional neural network (CNN). Yet, using 3D data and CNN did not perform well. Despite using CNN and 3D variants, identifying natural disaster reports remains below 80% accuracy. This research aims to improve identifying earthquakes, floods, and forest fires with 3D data and hybrid CNN long short-term memory (LSTM). The study found models with accuracies of 83.38%, 83.72%, and 89.03% for each disaster type. Hybrid CNN LSTM significantly enhanced identification compared to CNN alone, supported by statistical tests with P value less than 0.0001.
Comparative Analysis of Distance Metrics in KNN and SMOTE Algorithms for Software Defect Prediction Khusnul Rahmi Maulidha; Mohammad Reza Faisal; Setyo Wahyu Saputro; Friska Abadi; Dodon Turianto Nugrahadi; Puput Dani Prasetyo Adi; Hariyady Hariyady
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3008

Abstract

As the complexity and scale of projects increase, new challenges arise related to handling software defects. One solution uses machine learning-based software defect prediction techniques, such as the K-Nearest Neighbors (KNN) algorithm. However, KNN’s performance can be hindered by the majority vote mechanism and the distance/similarity metric choice, especially when applied to imbalanced datasets. This research compares the effectiveness of Euclidean, Hamming, Cosine, and Canberra distance metrics on KNN performance, both before and after the application of SMOTE (Synthetic Minority Over-sampling Technique). Results show significant improvements in the AUC and F-1 measure values across various datasets after the SMOTE application. Following the SMOTE application, Euclidean distance produced an AUC of 0.7752 and an F1 of 0.7311 for the EQ dataset. With Canberra distance and SMOTE, the JDT dataset produced an AUC of 0.7707 and an F-1 of 0.6342. The LC dataset improved to 0.6752 and 0.3733 in tandem with the ML dataset, which climbed to 0.6845 and 0.4261 with Canberra distance. Lastly, after using SMOTE, the PDE dataset improved to 0.6580 and 0.3957 with Canberra distance. The findings confirm that SMOTE, combined with suitable distance metrics, significantly boosts KNN’s prediction accuracy, with a P-value of 0.0001.
Automatic Analysis of Natural Disaster Messages on Social Media Using IndoBERT and Multilingual BERT Yasmin Dwi Safitri; Mohammad Reza Faisal; Dwi Kartini; Triando Hamonangan Saragih; Friska Abadi; Adam Mukharil Bachtiar
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3140

Abstract

Information about natural disasters disseminated through social media can serve as an important data source for mitigation processes and early warning systems. Social media platforms, such as X (formerly known as Twitter), have become primary channels for conveying real-time information, especially during disaster emergencies. With the large amount of unstructured disaster-related text that must be processed, the main challenge is accurately filtering and classifying messages into three categories: eyewitness, non-eyewitness, and don’t know. This research aims to compare the performance of four BERT-based natural language processing models, namely IndoBERT, IndoBERT with Masked Language Modeling (MLM), Multilingual BERT, and Multilingual BERT with MLM, in classifying Indonesian-language disaster messages. The dataset used in this study was obtained from previous research and publicly available data on GitHub, consisting of annotated messages related to floods, earthquakes, and forest fires. The method applied is a deep learning approach using the hold-out technique with an 80:20 ratio for training and testing data, and the same ratio applied to split the training data into training and validation subsets, with stratification to maintain balanced class proportions. In addition, variations in batch size were explored to evaluate their effect on model performance stability. The results show that the IndoBERT model achieved the highest performance on the flood and earthquake datasets, with accuracies of 80.67% and 81.50%, respectively. Meanwhile, IndoBERT with MLM pre-training recorded the highest accuracy on the forest fire dataset, 88.33%. Overall, IndoBERT demonstrated the most consistent and superior performance across datasets compared to the other models. These findings indicate that IndoBERT has strong capabilities in understanding Indonesian disaster-related text, and the results can be used as a foundation for developing automatic classification systems to support real-time disaster monitoring and early warning applications
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Elvina Nur Hana; Mohammad Reza Faisal; Dwi Kartini; Muhammad Itqan Mazdadi; Setyo Wahyu Saputro; Fatma Indriani; Kenji Satou
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10585

Abstract

Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT Priyatama, Muhammad Abdhi; Nugrahadi, Dodon Turianto; Budiman, Irwan; Farmadi, Andi; Faisal, Mohammad Reza; Purnama, Bedy; Adi, Puput Dani Prasetyo; Ngo, Luu Duc
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5299

Abstract

Traditional authentication methods such as PINs and passwords remain vulnerable to theft and hacking, demanding more secure alternatives. Biometric approaches address these weaknesses, yet unimodal systems like fingerprints or facial recognition are still prone to spoofing and environmental disturbances. This study aims to enhance biometric reliability through a multimodal framework integrating electrocardiogram (ECG) signals and fingerprint images. Fingerprint features were extracted using three deep convolutional networks—VGG16, ResNet50, and DenseNet121—while ECG signals were segmented around the first R-peak to produce feature vectors of varying dimensions. Both modalities were fused at the feature level using early fusion and classified with four deep learning algorithms: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), and Graph Attention Network (GAT). Experimental results demonstrated that the combination of VGG16 + LSTM and ResNet50 + LSTM achieved the highest identification accuracy of 98.75 %, while DenseNet121 + MLP yielded comparable performance. MLP and LSTM consistently outperformed GCN and GAT, confirming the suitability of sequential and feed-forward models for fused feature embeddings. By employing R-peak-based ECG segmentation and CNN-driven fingerprint features, the proposed system significantly improves classification stability and robustness. This multimodal biometric design strengthens protection against spoofing and impersonation, providing a scalable and secure authentication solution for high-security applications such as digital payments, healthcare, and IoT devices.
Classification of Eyewitness Social Media Messages for Natural Disaster Monitoring using BERT Variants Hanafi, Muhammad Bashir; Faisal, Mohammad Reza; Abadi, Friska; Budiman, Irwan; Saputro, Setyo Wahyu; Mbeledogu, Njideka Nkemdilim
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5317

Abstract

The rapid growth of disaster-related social media data demands effective monitoring. However, its real-time source presents challenges due to large volumes of unstructured and noisy data. This study aims to improve effective monitoring with BERT variants to classify eyewitness reports on Twitter/X. Earlier studies have applied machine-learning and deep-learning models to automate the monitoring of eyewitness messages on social media, but these models still have shortcomings. Traditional machine-learning models rely on handcrafted and frequency-based features, limiting their ability to capture contextual semantics. Deep-learning models offer improved performance but still face challenges in modeling long-range dependencies and handling high-volume social media streams. This issue is pronounced in social media streams. This study employs transformer-based models using several BERT variants (BERT, RoBERTa, DistilBERT, ELECTRA, and ALBERT). Each model is pre-trained with the Masked Language Modeling (MLM) objective, and batch-size optimization is applied to boost performance. Experimental results indicate that a batch size of 16 consistently yields the best performance, with the standard BERT model achieving the highest macro-F1 score of 0.762. By disaster type, macro-F1 scores reach 0.744 for hurricane, 0.793 for flood, 0.756 for earthquake, and 0.750 for wildfire. BERT (16) outperforms the other BERT variants and twelve baseline models from prior research. Unlike previous approaches, this study leverages pre-trained Masked Language Models to optimize classification on disaster-related datasets. The findings contribute to the development of transformer-based architectures for text classification in real-time disaster informatics, leading to more accurate situational awareness and reduced delays in emergency decision-making.
Comparative Performance Evaluation of Linear, Bagging, and Boosting Models Using BorutaSHAP for Software Defect Prediction on NASA MDP Datasets Kartika, Najla Putri; Herteno, Rudy; Budiman, Irwan; Nugrahadi, Dodon Turianto; Abadi, Friska; Ahmad, Umar Ali; Faisal, Mohammad Reza
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5393

Abstract

Software defect prediction aims to identify potentially defective modules early on in order to improve software reliability and reduce maintenance costs. However, challenges such as high feature dimensions, irrelevant metrics, and class imbalance often reduce the performance of prediction models. This research aims to compare the performance of three classification model groups—linear, bagging, and boosting—combined with the BorutaSHAP feature selection method to improve prediction stability and interpretability. A total of twelve datasets from the NASA Metrics Data Program (MDP) were used as test references. The research stages included data preprocessing, class balancing using the Synthetic Minority Oversampling Technique (SMOTE), feature selection with BorutaSHAP, and model training using five algorithms, namely Logistic Regression, Linear SVC, Random Forest, Extra Trees, and XGBoost. The evaluation was conducted with Stratified 5-Fold Cross-Validation using the F1-score and Area Under the Curve (AUC) metrics. The experimental results showed that tree-based ensemble models provided the most consistent performance, with Extra Trees recording the highest average AUC of 0.794 ± 0.05, followed by Random Forest (0.783 ± 0.06). The XGBoost model provided the best results on the PC4 dataset (AUC = 0.937 ± 0.008), demonstrating its ability to handle complex data patterns. These findings prove that BorutaSHAP is effective in filtering relevant features, improving classification reliability, and strengthening transparency and interpretability in the Explainable Artificial Intelligence (XAI) framework for software quality improvement.
Analysis of Static and Contextual Word Embeddings in Capsule Network for Sentiment Analysis of The Free Nutritious Meal Program on Twitter Raditya, Virgi Atha; Saragih, Triando Hamonangan; Faisal, Mohammad Reza; Abadi, Friska; Muliadi, Muliadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5424

Abstract

Public discourse surrounding Indonesia’s Makan Bergizi Gratis (MBG) program reflects diverse opinions that have not yet been systematically examined using computational methods. This study addresses that gap by evaluating the effectiveness of static and contextual word embeddings within a Capsule Network (CapsNet) framework for sentiment analysis of MBG-related tweets on Twitter. A total of 7,133 Indonesian-language tweets were collected through web crawling, preprocessed, and manually labeled into positive, neutral, and negative categories. Four embedding techniques—Word2Vec, FastText, ELMo, and IndoBERT—were tested under two preprocessing settings, raw and stemming. The experimental results show that Word2Vec on raw text achieved the highest accuracy of 96.17%, while FastText obtained the best performance on stemmed data with 94.10%. These findings indicate that morphological normalization benefits static and subword-based embeddings, whereas contextual models maintain stable performance without extensive fine-tuning. Overall, this study demonstrates the potential of combining CapsNet with appropriate embedding strategies for Indonesian-language sentiment analysis and provides evidence that natural language processing can support data-driven evaluation of public programs such as MBG.
Enhancing Classification of Self-Reported Monkeypox Symptoms on Social Media Using Term Frequency-Inverse Document Frequency Features and Graph Attention Networks Rizian, Rizailo Akfa; Budiman, Irwan; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Ahmad, Umar Ali
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5482

Abstract

Early detection of infectious diseases plays a crucial role in minimizing their spread and enabling timely intervention. In the digital era, social media has emerged as a valuable source of real-time health information, where individuals often share self-reported symptoms that can serve as early warning signals for disease outbreaks. However, textual data from social media is typically unstructured, noisy, and contextually diverse, posing challenges for conventional text classification methods. This study proposes a hybrid model combining Term Frequency–Inverse Document Frequency (TF-IDF) feature representation with a Graph Attention Network (GAT) to enhance the early detection of Monkeypox-related self-reported symptoms on Indonesian social media. A dataset of 3,200 tweets was collected through Tweet-Harvest and subsequently preprocessed and manually labeled, producing a balanced distribution between positive (51%) and negative (49%) samples. TF-IDF vectors were used to construct a document similarity graph via the k-Nearest Neighbors (k-NN) method with cosine similarity, enabling GAT to leverage both textual and relational information across posts. The model’s performance was evaluated using accuracy, precision, recall, and macro-F1, with macro-F1 serving as the primary indicator. The proposed TF-IDF + GAT model achieved 93.07% accuracy and a macro-F1 score of 93.06%, outperforming baseline classifiers such as CNN (92.16% macro-F1), SVM (85.73%), Logistic Regression (84.89%). These findings demonstrate the effectiveness of integrating classical text representations with graph-based neural architectures for improving social media based disease surveillance and supporting early epidemic response strategies.
Co-Authors Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adam Mukharil Bachtiar Adawiyah, Laila Adi, Puput Dani Prasetyo Adinda Ayu Puspita Ramadhani Adini, Muhammad Hifdzi Admi Syarif Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Amalia, Raisa Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Anggi Mardiyono Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat As’ary Ramadhan Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Bedy Purnama budiman, irwan Dewi Sri Susanti Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Elvina Nur Hana Emma Andini Erick Kurniawan Fadhillah, Muhammad Alif Fatma Indriani Fatma Indriani Fatma Indriani Fitra Ahya Mubarok Fitriani, Karlina Elreine Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Fuad Muhajirin Farid Ghinaya, Helma Hafiz Ilhami Hanafi, Muhammad Bashir Hanif Rahardian Hariyady Hariyady Herteno, Rudi Herteno, Rudy Hertono, Rudy Imam Tahyudin Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kamil, Hawariul Kartika, Najla Putri Kenji Satou Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Khusnul Rahmi Maulidha Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Lumbanraja, Favorisen R Mafazy, Muhammad Meftah Mahmud Mahmud Maisarah Maisarah, Maisarah Mauldy Laya Mbeledogu, Njideka Nkemdilim Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Adika Riswanda Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fattah Edric Camilo Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Khalid Maulana Muhammad Mada Muhammad Mursyidan Amini Muhammad Nabil Muyassar Rahman 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 Naufal Said Ngo, Luu Duc Noordyanti, Erna Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Pratama, Muhammad Yoga Adha Priyatama, Muhammad Abdhi Puput Dani Prasetyo Adi Puput Dani Prasetyo Adi Purnajaya, Akhmad Rezki Putri Nabella Raditya, Virgi Atha Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Rahmina Ulfah Aflaha Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reisa Siva Nandika Reza Rendian Septiawan Riadi, Agus Teguh Riadi, Putri Agustina Rinaldi Riza Susanto Banner Riza, Yusi Rizal, Muhammad Nur Rizian, Rizailo Akfa Rizki, M. Alfi Rizky Ananda, Muhammad Rizky, Muhammad Hevny Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rudy Herteno 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 Septyan Eka Prastya Setyo Wahyu Saputro Siti Aisyah Solechah Solly Aryza Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Syamsiar, Syamsiar Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Umiatin, Umiatin Utami, Juliyatin Putri Uthami, Mariza Vina Maulida, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Warsuta, Bambang Wildan Panji Tresna Winda Agustina Yasmin Dwi Safitri Yeni Rahkmawati Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi