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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Journal of ICT Research and Applications Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics JADECS (Journal of Art, Design, Art Education and Culture Studies) Jurnal Teknologi dan Sistem Komputer SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Information System Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Fuzzy Systems and Control (JFSC) Journal of Information System and Application Development Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies INOVTEK Polbeng - Seri Informatika
De Rosal Ignatius Moses Setiadi
Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Deteksi Stres Berbasis Teks pada Dreaddit Menggunakan Fine Tuning DeBERTa-v3 Ramadhan, Pramudia; Setiadi, De Rosal Ignatius Moses
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9318

Abstract

Mental health has become an important issue in the digital era, as psychological expressions are increasingly reflected through social media posts such as Reddit. This study uses the publicly available Dreaddit dataset containing Reddit user texts labeled with stress categories. The objective is to compare two text-based stress detection approaches: fine-tuning the transformer model DeBERTa-v3 and the classical TF-IDF LinearSVC method. Both approaches are implemented as binary classification systems to automatically distinguish stress and non-stress texts. The research workflow includes data preprocessing, tokenization, model training, validation, and evaluation using Accuracy, Precision, Recall, F1-score, and AUROC metrics. DeBERTa-v3 is fine-tuned using contextual representations with a self-attention mechanism, while TF-IDF LinearSVC relies on statistical n-gram weighting. Experimental results show that DeBERTa-v3 achieves superior performance with an Accuracy of 0.830, Precision of 0.802, Recall of 0.889, F1-score of 0.843, and AUROC of 0.918. Meanwhile, TF-IDF LinearSVC obtains an Accuracy of 0.732, Precision of 0.722, Recall of 0.783, F1-score of 0.751, and AUROC of 0.817. The experiments were conducted with consistent training configurations, data splits, and evaluation procedures to ensure a fair comparison. The confusion matrix analysis indicates that DeBERTa-v3 produces fewer false positives and false negatives, demonstrating stronger capability in recognizing implicit stress expressions. These findings highlight the advantages of transformer-based models in capturing emotional and semantic context and indicate the potential for real-time deployment in social-media-based mental health monitoring systems.
Enhancing Aspect-Based Sentiment Analysis via Hugging Face Fine-Tuned IndoBERT Aprilah, Thania; Setiadi, De Rosal Ignatius Moses; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11409

Abstract

Aspect-Based Sentiment Analysis (ABSA) on hotel reviews faces significant challenges regarding semantic complexity and severe class imbalance, particularly in low-resource languages like Indonesian. This study evaluates the effectiveness of fine-tuning IndoBERT, a pre-trained Transformer model, to address these issues by benchmarking it against classical statistical methods (TF-IDF) and static embeddings (Sentence-BERT). Utilizing the HoASA dataset, the experiment implements a Random Oversampling strategy at the text level to mitigate data sparsity in minority classes. Empirical results demonstrate that the fine-tuned IndoBERT significantly outperforms baselines on the majority of aspects, achieving a global accuracy of 97% and macro F1-score of 0.92. Granular per-aspect analysis reveals that the model’s self-attention mechanism captures linguistic context robustly in tangible aspects (e.g., wifi, service), yet faces persistent challenges in highly ambiguous aspects such as smell (bau) and general. Statistical significance tests (Paired t-test and Wilcoxon) confirm that the performance gains over baselines are statistically significant (p < 0.05) and not due to random chance. The study concludes that leveraging contextual representations from IndoBERT, combined with data balancing strategies, offers a superior and statistically robust solution for handling linguistic variations and class bias in the Indonesian hospitality domain.
Bi-LSTM with Explainable AI for Session Duration-Based Customer Lifetime Value Proxy on Multi-Category E-Commerce Platforms Nartriani, Yulian Dwi; Setiadi, De Rosal Ignatius Moses
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11578

Abstract

The rapid growth of multi-category e-commerce platforms has increased the importance of behavioral data for predicting Customer Lifetime Value (CLV). However, monetary-based CLV estimation is often infeasible due to incomplete or unavailable transaction records. This study adopts session duration as a short-term behavioral proxy for CLV and proposes a Bidirectional Long Short-Term Memory (Bi-LSTM) model enhanced with a Temporal Attention mechanism to improve predictive accuracy. The publicly available REES46 dataset, consisting of 1,6 million events and 276.000 unique sessions, is used with preprocessing steps including label encoding, temporal feature construction, and outlier-aware sampling to address the highly right-skewed distribution of session durations. Four baseline models Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and conventional Long Short-Term Memory (LSTM) are implemented for comparative evaluation. The baseline LSTM achieves MAE = 0,0080 and RMSE = 0,0322. The proposed Bi-LSTM v3 model, equipped with Temporal Attention and structured sampling, demonstrates substantial performance improvement, achieving MAE = 0,0043 (≈368 seconds) and RMSE = 0,0172 (≈1466 seconds), representing an accuracy gain of approximately 45–50% over the baseline. Explainability analysis using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) confirms that the time_diff feature is the dominant contributor at both global and local levels, aligning with the behavior of the attention mechanism. Additionally, the integration of Explainable Artificial Intelligence (XAI) provides transparent insights into model decision patterns. These findings show that combining Bi-LSTM, Temporal Attention, and XAI yields an accurate and interpretable framework for session duration prediction, supporting the use of session duration as a feasible CLV proxy in multi-category e-commerce environments.
Evaluasi Dampak Pelatihan Portofolio Digital Berbasis Google Sites pada Siswa SMKN 9 Semarang Al Azies, Harun; Pertiwi, Ayu; Sutojo, T.; Setiadi, De Rosal Ignatius Moses; Pratama, Ananta Surya; Irnanda, Muhammad Diva; Umam, Taufiqul
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.17536

Abstract

Perkembangan teknologi digital menuntut siswa sekolah menengah kejuruan memiliki kemampuan mendokumentasikan pengalaman dan kompetensi secara terstruktur melalui portofolio digital. Namun, pemanfaatan portofolio digital sebagai media representasi diri dan personal branding siswa masih belum optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengevaluasi dampak pelatihan portofolio digital berbasis platform Google Sites terhadap pemahaman siswa Organisasi Siswa Intra Sekolah di SMK Negeri 9 Semarang. Metode yang digunakan adalah pendekatan evaluatif dengan desain one-group pretest–posttest. Data dikumpulkan melalui instrumen tes pemahaman yang mencakup klaster personal branding dan konsep portofolio digital, serta dianalisis menggunakan uji Wilcoxon Signed-Rank Test. Hasil kegiatan menunjukkan adanya peningkatan skor pemahaman siswa setelah pelaksanaan pelatihan, yang didukung oleh perbedaan skor pretest dan posttest yang signifikan secara statistik. Analisis berdasarkan klaster pemahaman juga menunjukkan peningkatan ketepatan jawaban pada kedua klaster yang diukur. Kegiatan ini menunjukkan bahwa pelatihan portofolio digital berbasis Google Sites memberikan dampak positif terhadap penguatan pemahaman siswa. Kegiatan pengabdian ini berpotensi dikembangkan melalui pendampingan berkelanjutan agar portofolio digital dapat dimanfaatkan secara optimal sebagai media dokumentasi dan pengembangan diri siswa.
Plant Diseases Classification based Leaves Image using Convolutional Neural Network Satrio Bagus Imanulloh; Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.8877

Abstract

Plant disease is one of the problems in the world of agriculture. Early identification of plant diseases can reduce the risk of loss, so automation is needed to speed up identification. This study proposes a custom-designed convolutional neural network (CNN) model for plant disease recognition. The proposed CNN model is not complex and lightweight, so it can be implemented in model applications. The proposed CNN model consists of 12 CNN layers, which consist of eight layers for feature extraction and four layers as classifiers. Based on the experimental results of a plant disease dataset consisting of 38 classes with a total of 87,867 image records. The proposed model can get high performance and not overfitting, with 97%, 98%, 97% and 97%, respectively, for accuracy, precision, recall and f1-score. The performance of the proposed model is also better than some popular pre-trained models, such as InceptionV3 and MobileNetV2. The proposed model can also work well when implemented in mobile applications.
High-Performance Convolutional Neural Network Model to Identify COVID-19 in Medical Images Macellino Setyaji Sunarjo; Hong-Seng Gan; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.8936

Abstract

Convolutional neural network (CNN) is a deep learning (DL) model that has significantly contributed to medical systems because it is very useful in digital image processing. However, CNN has several limitations, such as being prone to overfitting, not being properly trained if there is data duplication, and can cause unwanted results if there is an imbalance in the amount of data in each class. Data augmentation techniques are used to overcome overfitting, eliminate data duplication, and random under sampling methods to balance the amount of data in each class, to overcome these problems. In addition, if the CNN model is not designed properly, the computation is less efficient. Research has proved that data augmentation can prevent or overcome overfitting, eliminating duplicate data can make the model more stable, and balancing the amount of data makes the model unbiased and easy to learn new data as evidenced through model evaluation and testing. The results also show that the custom convolutional neural network model is the best model compared to ResNet50 and VGG19 in terms of accuracy, precision, recall, F1-score, loss performance, and computation time efficiency
Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm Mamet Adil Araaf; Kristiawan Nugroho; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9185

Abstract

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.
Dataset and Feature Analysis for Diabetes Mellitus Classification using Random Forest Fachrul Mustofa; Achmad Nuruddin Safriandono; Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9190

Abstract

Diabetes Mellitus is a hazardous disease, and according to the World Health Organization (WHO), diabetes will be one of the main causes of death by 2030. One of the most popular diabetes datasets is PIMA Indians, and this dataset has been widely tested on various machine learning (ML) methods, even deep learning (DL). But on average, ML methods are not able to produce good accuracy. The quality of the dataset and features is the most influential thing in this case, so deeper investment is needed to examine this dataset. This research will analyze and compare the PIMA Indians and Abelvikas datasets using the Random Forest (RF) method. The two datasets are imbalanced, in fact, the Abelvikas dataset is more imbalanced and has a larger number of classes so it is be more complex. The RF was chosen because it is one of the ML methods that has the best results on various diabetes datasets. Based on the test results, very contrasting results were obtained on the two datasets. Abelvikas had accuracy, precision, and recall, reaching 100%, and PIMA Indians only achieved 75% for accuracy, 87% for precision, and 80% for the best recall. Testing was done with 3, 5, 7, 10, and 15 tree number parameters. Apart from that, it was also tested with k-fold validation to get valid results. This determines that the features in the Abelvikas dataset are much better because more complete glucose features support them.
Image Encryption using Half-Inverted Cascading Chaos Cipheration De Rosal Ignatius Moses Setiadi; Robet Robet; Octara Pribadi; Suyud Widiono; Md Kamruzzaman Sarker
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9388

Abstract

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption
Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation Harish Trio Adityawan; Omar Farroq; Stefanus Santosa; Hussain Md Mehedul Islam; Md Kamruzzaman Sarker; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9443

Abstract

Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.
Co-Authors Abdul Syukur Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abugor Okpako, Ejaita Aceng Sambas Achmad Nuruddin Safriandono Achmad Nuruddin Safriandono Adhitya Nugraha Adigwe, Wilfred Adimabua Ojugo, Arnold Afotanwo, Anderson Afridiansyah, Rahmanda Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Agustina, Feri Ahmad Ilham Ahmad Rofiqul Muslikh Ahmad Salafuddin Ajib Susanto Akbar Aji Nugroho Akbar, Ismail Akhmad Dahlan Ako, Rita Erhovwo Alvin Faiz Kurniawan Amir Musthofa Anak Agung Gede Sugianthara Andik Setyono Antonio Ciputra Antonius Erick Handoyo Aprilah, Thania Arnold Adimabua Ojugo Arnold Adimabua Ojugo Arya Kusuma Ayu Pertiwi Bimo Haryo Setyoko Binitie, Amaka Patience Budi Widjajanto Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Chris Chukwufunaya Odiakaose Christian, Henry Christy Atika Sari Chukwudi Aghaunor, Tabitha Cinantya Paramita Ciputra, Antonio Daniel Nomolas Wicaksono Danu Hartanto Daurat Sinaga Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Devi Purnamasari Dhendra Marutho Dhendra Marutho Dian Kristiawan Nugroho Dumebi Okpor, Margaret Dwi Puji Prabowo Dwi, Bernadetta Sri Endah Eboka, Andrew Okonji Edy Winarno Edy Winarno Eferhire Valentine Ugbotu Egia Rosi Subhiyakto Ejeh, Patrick Ogholuwarami Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Ella Budi Wijayanti Eluemnor Anazia, Kizito Enadona Oweimeito, Amanda Erhovwo Ako, Rita Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Etika Kartikadarma Fachrul Mustofa Farah Zakiyah Rahmanti Ferda Ernawan Fidelis Obukohwo Aghware Firnando, Fadel Muhamad Fita Sheila Gomiasti Fittria Shofrotun Ni&#039;mah Florentina Esti Nilawati Florentina Esti Nilawati Frances Uche Emordi Gan, Hong-Seng Geteloma, Victor Ochuko Ghosal, Sudipta Kr Giovani Ardiansyah Hanny Haryanto Harditya Prayoga Harish Trio Adityawan Harun Al Azies Hendy Kurniawan Henry Christian Herowati, Wise Heru Agus Santoso Hong-Seng Gan Hussain Md Mehedul Islam Hussain Md Mehedul Islam Ibnu Gemaputra Ramadhan Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibor, Ayei Egu Ihya Ulumuddin, Dimas Irawan Imanuel Harkespan Imanuel Harkespan Indra Gamayanto Irnanda, Muhammad Diva Islam, Hussain Md Mehedul Isworo Nugroho Iwan Setiawan Wibisono Jutono Gondohanindijo Kusuma, Edi Jaya L. Budi Handoko Lalang Erawan M. Dalvin Marno Putra Macellino Setyaji Sunarjo Mamet Adil Araaf Margaret Dumebi Okpor Maureen Ifeanyi Akazue Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Minh T. Nguyen Mohamad Afendee Mohamed Mohammad Rizal, Mohammad Muchamad Akbar Nurul Adzan Muh Galuh Surya Putra Kusuma Muhamad Akrom Muhamad Akrom Muhamada, Keny Muhammad Munsarif Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslikh, Ahmad Rofiqul Nantalira Niar Wijaya Nartriani, Yulian Dwi Nizar Rafi Pratama Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Nova Rijati Ochuko Geteloma, Victor Octara Pribadi Odiakaose , Christopher Chukwufunaya Odiakaose, Christopher Chukwufunaya Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Omar Farooq Omar Farooq Omar Farroq Omoruwou, Felix Patrick Ogholuwarami Ejeh Pradana, Akbar Ganang Prajanto Wahyu Adi Pratama, Ananta Surya Purnamasari, Devi Pushan Kumar Dutta Rahadian Kristiyanto Rachman Ramadhan, Pramudia Reuben Akporube Abere Ricardus Anggi Pramunendar Rita Erhovwo Ako Robet Robet Rume Elizabeth Yoro Ruri Suko Basuki Safuan Safuan Sahu, Aditya Kumar Sandy Nugroho Santoso, Siane Sarker, Md Kamruzzaman Sasono Wibowo Satrio Bagus Imanulloh Setiawan, Marcell Adi Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Stefanus Santosa Sudibyo, Usman Sukamto, Titien S Suyud Widiono Suyud Widiono Suyud Widiono Syahputra, Zulfikar Adi Syahroni Wahyu Iriananda Syahroni Wahyu Iriananda, Syahroni Wahyu T Sutojo T. Sutojo T. Sutojo Tabitha Chukwudi Aghaunor Tan Samuel Permana Tan Samuel Permana Titien S sukamto Trisnapradika, Gustina Alfa Ugbotu, Eferhire Valentine Umam, Taufiqul Valentine Ugbotu, Eferhire Victor Ochuko Geteloma Warto Wellia Shinta Sari Wellia Shinta Sari Wendy Sarasjati Wibowo, Mochammad Abdurrochman Ari Wise Herowati Yusianto Rindra Zuama, Leygian Reyhan