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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Dinamisia: Jurnal Pengabdian Kepada Masyarakat Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Journal of Applied Engineering and Technological Science (JAETS) Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Indonesia Journal La Multiapp Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Eduvest - Journal of Universal Studies Jurnal INFOTEL Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika IJoICT (International Journal on Information and Communication Technology) Indonesian Journal on Computing (Indo-JC)
Yuliant Sibaroni
Universitas Telkom
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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Memeriksa Mekanisme Perhatian dalam Hybrid Deep Learning untuk Analisis Sentimen di Seluruh Panjang Teks Livia Naura Aqilla; Yuliant Sibaroni
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1396

Abstract

Sentiment analysis is a key task in natural language processing (NLP) with applications in a wide range of domains. This study examines the impact of self-attention and global attention placement in CNN-BiLSTM and CNN-LSTM models, exploring their effectiveness when positioned before, after or both before and after BiLSTM/LSTM, particularly for texts of different lengths. Instead of applying attention mechanisms in a fixed position, this research explores the most suitable type and placement of attention to improve model understanding and adaptability across datasets with different text lengths. Experiments were conducted using the IMDB Movie Reviews Dataset and the Twitter US Airline Sentiment dataset. The results show that for long texts, CNN-BiLSTM with self-attention before and after BiLSTM achieves an F1 score of 93. 77% (+2. 72%), while for short texts, it reaches 82.70% (+2.24%). These findings highlight that optimal attention placement significantly improves sentiment classification accuracy. The study provides insights into designing more effective hybrid deep learning models. It contributes to future research on multilingual and multi-domain sentiment analysis, where attention mechanisms can be adapted to different textual structures.
Penerapan Data Sains untuk Analisis Preferensi Wisatawan dalam Pengembangan Paket Wisata Sibaroni, Yuliant; Prasetiyowati, Sri Suryani; Puspandari, Diyas
Jurnal Pengabdian Masyarakat Bhinneka Vol. 4 No. 3 (2026): Bulan Februari
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v4i3.1035

Abstract

Perkembangan teknologi informasi telah memberikan dampak signifikan pada industri pariwisata, terutama melalui media sosial yang memengaruhi preferensi wisatawan dalam memilih destinasi. Data digital dari aktivitas wisatawan di platform seperti Instagram, TikTok, dan YouTube dapat dimanfaatkan untuk memahami tren, minat, serta perilaku wisatawan. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan keterampilan siswa SMKN 3 Bandung dalam menganalisis data preferensi wisatawan menggunakan pendekatan data sains. Metode yang digunakan meliputi penentuan fitur preferensi, pemodelan berbasis konten, segmentasi wisatawan dengan K-Means, serta pembangunan paket wisata menggunakan pendekatan heuristik. Data yang dianalisis berasal dari konten media sosial wisata Bandung dan sekitarnya, kemudian dibersihkan dan dikategorikan berdasarkan jenis destinasi. Hasil pelatihan menunjukkan peningkatan pengetahuan peserta melalui pre-test dan post-test, serta keberhasilan dalam merancang paket wisata inovatif yang sesuai dengan minat wisatawan, seperti “Bandung Serenity Escape” dan “Garut Hidden Paradise.” Evaluasi kegiatan memperlihatkan lebih dari 90% peserta merasa pelatihan bermanfaat dan relevan dengan kebutuhan mereka. Dengan demikian, penerapan data sains terbukti efektif dalam mendukung pengembangan paket wisata berbasis preferensi wisatawan dan memberikan kontribusi praktis bagi industri pariwisata lokal.
A Comparative Study on Handling Imbalanced Data in Indonesian Hate Speech Detection Using FastText and BiLSTM Akmal Muhamad Faza; Yuliant Sibaroni; Sri Suryani Prasetiyowati
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 2 (2025): Vol. 11 No. 2 Dec 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i2.9513

Abstract

Online hate speech has become a serious threat to social harmony in Indonesia, with cases increasing significantly in recent years. This study develops and evaluates a system for detecting Indonesian hate speech using a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model, complemented by FastText word embeddings. To address the common issue of data imbalance in hate speech datasets, this study implements and compares three oversampling techniques: Random Oversampler, Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The research utilizes the Indonesian Hate Speech Superset, a dataset comprising 14,306 comments. The model's performance is evaluated using Stratified K-fold Cross-Validation, with metrics including Accuracy, Precision, Recall, and F1-score. Results, visualized using a Confusion Matrix to demonstrate that applying oversampling techniques enhances model performance, particularly by improving the Recall and F1-score metrics. These findings contribute to the development of hate speech classification systems that are fairer, more adaptive, and better suited to the unique characteristics of the Indonesian social media landscape.
Geospatial Sentiment Analysis of Negative Comments on the 2024 Election Using the Robustly Optimized BERT Approach (RoBERTa)Geospatial Sentiment Analysis of Negative Comments on the 2024 Election Using the Robustly Optimized BERT Approach (RoBERTa)Geospat Haidar ali; Yuliant Sibaroni
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 2 (2025): Vol. 11 No. 2 Dec 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i2.9575

Abstract

This study develops a geospatial sentiment analysis system to detect and map hate speech related to the 2024 Election using the Robustly Optimized BERT Approach (RoBERTa). The dataset consists of 11,903 social media comments that have undergone comprehensive preprocessing, including text normalization, stopword removal, and stemming. The RoBERTa model was implemented using 10-fold cross-validation for multi-class classification (HS_Weak, HS_Strong, Not_Abusive) and achieved an average accuracy of 91.54% (±1.08%), with a final model accuracy of 94.29%. Geospatial analysis using geocoding and Folium visualization revealed that 75% of the data originated from Indonesia, with the highest concentration in the Jakarta area. The distribution of hate speech showed consistent patterns between Indonesia (45.6% hate speech) and outside Indonesia (44.3% hate speech), with the HS_Strong category dominating at 96.4%. Heatmap analysis identified hate speech hotspots on the island of Java and a global distribution across various continents. The findings confirm the effectiveness of RoBERTa for sentiment analysis in the Indonesian language and provide valuable insights into the geographic patterns of hate speech in the context of digital politics, which can be used to develop mitigation strategies and real-time monitoring systems.
Prediction and Classification of Vehicle Traffic Congestion in Bandung City Using the Random Forest and K-Nearest Neighbour Algorithm Muhammad Alauddin Angka Kurniawan; Sri Suryani Prasetiyowati; Yuliant Sibaroni
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 2 (2025): Vol. 11 No. 2 Dec 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i2.9602

Abstract

Traffic congestion remains one of the problems that continue to arise, especially in urban areas, oneof which is Bandung City, when the causes of the problem are not managed properly. Continuousmanagement of the causes of congestion problems will result in a controlled traffic system for theforeseeable future. This condition can be achieved if there is a congestion classification predictionsystem available. A reliable prediction and classification system can support the government informulating data-based traffic management strategies. The Random Forest and K-NearestNeighbour machine learning classification methods are strengthened with time-based featureexpansion to capture traffic behavior in various time frames, so that the objectives can be achieved.The dataset obtained from Area Traffic Control System Bandung includes traffic flow recorded at15-minute intervals at several intersections. Additional features such as red light duration, roadwidth, and spatial proximity to residential and commercial areas are included to improve modelperformance. The results show that the Random Forest classifier with time-based feature expansionoutperforms K-Nearest Neighbors, achieving the highest performance of 96%. These results showthe potential contribution in short-term traffic prediction and its effectiveness in supporting urbantraffic planning and congestion mitigation efforts in Bandung.
Sentiment Analysis of the Mobile Legends: Bang Bang Application Using a Hybrid CNN-LSTM Model Eric Nur Rahman; Yuliant Sibaroni
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2025): August, 2025
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i1.9687

Abstract

The increasing number of user reviews on the Google Play Store is a challenge in understanding user opinions and experiences with apps. One of the most discussed apps is Mobile Legends: Bang Bang (MLBB), a popular game with millions of downloads and reviews from Indonesian users. The problem faced is the limitation of conventional sentiment analysis models in understanding sentences and context simultaneously, making it less than optimal in analyzing user sentiment. This study proposed a comprehensive sentiment analysis system for MLBB application reviews, utilizing a hybrid CNN-LSTM architecture with a systematic optimization approach. A dataset comprising 30,000 balanced Indonesian user reviews was extracted from the Google Play Store using web scraping techniques and then processed through an extensive pre-processing pipeline, which included data cleaning, case folding, stopword removal, and stemming. Five experimental scenarios were conducted to optimize model performance through feature engineering and algorithmic enhancement. The baseline CNN-LSTM model achieved 71.97% accuracy, which was progressively improved through TF-IDF vectorization with optimal N-gram (1,2) configuration, max features optimization reaching 10,000 features, FastText embedding feature expansion using a 300-dimensional Indonesian pre-trained model, and optimizer selection experiments across five algorithms. The final optimized hybrid CNN-LSTM model, using the RMSprop, demonstrated a breakthrough performance of 88.84% accuracy with remarkable consistency (standard deviation of 0.000754), representing a 23.4% improvement over the baseline. This research contributes to the field of sentiment analysis, especially for game applications, by proving that a combined approach can produce a more accurate and reliable system for understanding user opinions.
Effectiveness of Word2Vec and TF-IDF in Sentiment Classification on Online Investment Platforms Using Support Vector Machine Rifaldy, Fadil; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6055

Abstract

Investing in Indonesia is increasingly popular, especially among the millennial generation. investments such as deposits, gold, stocks, and online investment applications are increasingly in demand. This research focuses on the sentiment classification of user reviews of the Nanovest online investment application on the Google Play Store using the Support Vector Machine (SVM) method. SVM is used because it can classify opinions into positive and negative sentiment classes with good accuracy, by evaluating how effective Word2Vec features extraction that can convert words in a text into numerical vectors and TF-IDF that is capable of high-dimensional word weighting and TF-IDF Weighted Word2Vec combination features to produce richer vector representations. Tests were conducted using four SVM kernels namely Linear, Polynomial, RBF, and Sigmoid. The results show that Word2Vec with RBF kernel and 300 vector size produces the highest accuracy of 95.46%, the combination of TF-IDF Weighted Word2Vec also gives good performance with 95.29% accuracy on RBF kernel. However, TF-IDF alone resulted in the lowest accuracy of 93.31% on the Sigmoid kernel. This research shows that Word2Vec and combined feature extraction methods are effective in improving sentiment classification performance compared to TF-IDF.
Cross-Domain Fake Reviews Identification Based on Deep Learning Neural Network With Rolling Collaborative Training Irham Aryandi Basir; Yuliant Sibaroni
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.7944

Abstract

Identifying fake reviews in the current digital era has become an interesting study, especially in cross-domain context. This process is based on the limitations of the situation, where not all review domains have labels, and the labeling process takes a long time. In the context of machine learning, cross-domain learning involves learning from data and prediction processes, using data from different domains. Identifying fake reviews, several previous studies have conducted cross-domain, and the results of these studies indicate that there are still several issues in detecting fake reviews. The main problem with cross-domain methods is the difficulty of the model in understanding the differences in characteristics between domains, such as differences in language style, word structure in reviews, and the context present in the reviews. The main problem with the cross-domain method is the difficulty of the model in understanding the differences in characteristics between domains, such as differences in language style, word structure in reviews, and the context present in reviews. Based on these issues, this research adopts an approach to identify fake reviews using the Convolutional Neural Network and Bidirectional Long Short Term Memory models, utilizing the Multi Feature Rolling Collaborative Training (MRCT) algorithm with data from Yelp dan Amazon. The experimental results show that by conducting two scenarios,  Scenario-1 provides better performance with an accuracy of 98.59%, while scenario-2 is only capable of providing an accuracy performance of 79.64%. Additionally, by using multi-features, the model experienced a 24.96% improvement in detecting fake reviews across domains. Based on these results, it can be seen that the use of multi-features and rolling collaborative training with the CNN-BiLSTM model works effectively in identifying fake reviews across domains.
Analysis of Provocative Speech During the 2025 DPR Demonstration on X Using the IndoBERTweet Method Nazhrin Nazarudin Achmad; Yuliant Sibaroni; Sri Suryani Prasetyowati
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1127

Abstract

Social media platforms have become important channels for public discussion during political events. During the DPR demonstrations in August 2025, online discussions on X (formerly Twitter) contained various forms of expressions, including provocative speech that may influence public opinion and collective behavior. Detecting such content automatically is challenging due to the informal language, slang, and contextual nuances commonly found in social media texts. This study aims to analyze provocative speech on the social media platform X using text classification techniques and transformer-based models. A total of 8,899 Indonesian tweets related to the demonstration period from August 25 to August 31, 2025 was collected using the Tweet Harvest crawling tool. The dataset was manually labeled into two categories, namely provocative and non-provocative, using a majority voting approach by three annotators. Several preprocessing steps were applied, including cleaning, normalization, stemming, tokenization, and stopword removal. Several models were evaluated, including Multinomial Naïve Bayes, Linear Support Vector Machine, BiLSTM, IndoBERT, and IndoBERTweet. Experimental results show that transformer-based models outperform traditional machine learning approaches. The best performance was achieved by the IndoBERTweet model with a learning rate of 3×10⁻⁵, achieving an accuracy of 93.07% and an F1-score of 91.56%. These findings indicate that domain-specific language models are effective for detecting provocative speech in Indonesian social media discussions related to political events.
Effect of Word Embedding on Indonesian Social Media Hate Speech Classification Using Hybrid CNN-SimpleRNN Mas Muhammad Rizqi Adiguna; Yuliant Sibaroni
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10046

Abstract

The rapid rise in the number of people using social media platforms, specifically platform X, poses great difficulties for spotting any possible harm from hate speech. In particular, due to the high informality of discourse of Indonesian internet users, expressed through slang, abbreviation, and distorted spelling, automatic moderation becomes even more complicated. The current study attempts at classifying hate speech on social media platform X, proposing a hybrid model of CNN combined with a Bidirectional SimpleRNN architecture, alongside a comparative study on word embedding approaches. The combination between CNN and SimpleRNN is chosen because it allows for exploiting both CNN's capability to extract local spatial features by finding toxic n-grams and the strength of Bidirectional SimpleRNN for capturing long contextual dependency in the sequence of text data. Given that the problem of OOV is highly significant, TF-IDF, FastText, and Word2Vec have been rigorously tested not only separately but also combined in different ways. Compared to the baseline configuration using standalone TF-IDF (which achieved 84.56% accuracy), the results show that the use of the hybrid TF-IDF + FastText provided the best performance with average accuracy of 86.49%, average precision of 86.32%, recall of 86.80% and F1 score of 86.55%. Conversely, the combination of multiple dense semantic vectors (Word2Vec and FastText) led to semantic drift and feature ambiguity; this created feature overlap and computational noise that obscured classification decision boundaries, resulting in redundancy and poor performance. It shows that the combination of lexico-statistical significance and semantic subword context greatly contributes to achieving better results in the Indonesian language setting and is very resistant to slang and OOV words found in digital settings. This study contributes to the field of natural language processing by providing a lightweight, highly accurate, and computationally efficient lexico-semantic framework tailored for moderating highly unstructured Indonesian social media text.
Co-Authors Abduh Salam Adhe Akram Azhari Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aditya Iftikar Riaddy Adiwijaya Agi Maulana Akmal Muhamad Faza Alfauzan, Muhammad Fikri Alya, Hasna Rafida Andrew Wilson Angger Saputra, Revelin Aniq, Aniq Atiqi Rohmawati Annisa Aditsania Apriani, Iklima Aqilla, Livia Naura Ardana, Aulia Riefqi Arista, Dufha Arminta, Adisaputra Nur Arya Pratama Anugerah Asramanggala, Muhammad Sulthon Atikah, Balqis Sayyidahtul Attala Rafid Abelard Aufa, Rizki Nabil Aulia Rayhan Syaifullah Aura sabina Aurora Az Zahra, Elita Azmi Aulia Rahman Bunga Sari Chamadani Faisal Amri Chindy Amalia Claudia Mei Serin Sitio Daffa Fadhilah Putra Damar, Muhammad Damarsari Cahyo Wilogo Delvanita Sri Wahyuni Derwin Prabangkara Desianto Abdillah Devi Ayu Peramesti Dhina Nur Fitriana Dhina Nur Fitriana Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Eric Nur Rahman Erwin Budi Setiawan Fadhilah Nadia Puteri Fadli Fauzi Zain Fairuz, Mitha Putrianty Faiza Aulia Rahma Putra Farizi, Azziz Fachry Al Fatha, Rizkialdy Fathin, Muhammad Ammar Fatihah Rahmadayana Fatri Nurul Inayah Fauzaan Rakan Tama Feby Ali Dzuhri Fery Ardiansyah Effendi Ferzi Samal Yerzi Fhira Nhita Fitriansyah, Alam Rizki Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Haidar ali Hanif, Ibrahim Hanurogo, Tetuko Muhammad Hanvito Michael Lee Hawa, Iqlima Putri Haziq, Muhammad Raffif I Gusti Ayu Putu Sintha Deviya Yuliani I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Indra Kusuma Yoga Indwiarti irbah salsabila Irfani Adri Maulana Irham Aryandi Basir Irma Palupi Islamanda, Muhammad Dinan Izzan Faikar Ramadhy Izzatul Ummah Janu Akrama Wardhana Jauzy, Muhammad Abdurrahman Al Kemas Muslim Lhaksmana Kinan Salaatsa, Titan Ku Muhammad Naim Ku Khalif Lanny Septiani Laura Imanuela Mustamu Lesmana, Aditya Lintang Aryasatya Lisbeth Evalina Siahaan Livia Naura Aqilla Made Mita Wikantari Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mahmud Imrona Mas Muhammad Rizqi Adiguna Maulida , Anandita Prakarsa Mauluvy Senjaya, Argya Mitha Putrianty Fairuz Muhamad Agung Nulhakim Muhammad Alauddin Angka Kurniawan Muhammad Arif Kurniawan Muhammad Damar Muhammad Faisal Zaenudin Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Kiko Aulia Reiki Muhammad Novario Ekaputra Muhammad Rajih Abiyyu Musa Muhammad Reza Adi Nugraha Muldani, Muhamad Dika Nabiel Muhammad Al Ghazali Nanda Ihwani Saputri Naufal Alvin Chandrasa Nazhrin Nazarudin Achmad Ni Made Dwipadini Puspitarini Niken Dwi Wahyu Cahyani Novitasari, Ariqoh Nuraena Ramdani Okky Brillian Hibrianto Okky Brillian Hibrianto Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Prasetiyowati, Sri Prasetyo, Sri Suryani Prasetyowati, Sri Sulyani Prawiro Weninggalih Priyan Fadhil Supriyadi Purwanto, Brian Dimas Puspandari, Dyas Putra, Ihsanudin Pradana Putra, Maswan Pratama Putri, Dinda Rahma Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafik Khairul Amin Rafika Salis Rahmanda, Rayhan Fadhil Raisa Benaya Revi Chandra Riana Rian Febrian Umbara Rian Putra Mantovani Ridha Novia Ridho Isral Essa Ridho, Fahrul Raykhan Rifaldy, Fadil Rifki Alfian Abdi Malik Riski Hamonangan Simanjuntak Rizki Annas Sholehat Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Saniyah Nabila Fikriyah Saragih, Pujiaty Rezeki Satyananda, Karuna Dewa Septian Nugraha Kudrat Septian Nugraha Kudrat Serly Setyani Shyahrin, Mega Vebika Sinaga, Astria M P Siti Inayah Putri Siti Uswah Hasanah Sri Suryani Prasetyowati Sri Suryani Prasetyowati Sri Utami Sujadi, Cika Carissa Suryani Prasetyowati, Sri Syarif, Rizky Ahsan Umulhoir, Nida Varissa Azis, Diva Azty Viny Gilang Ramadhan Vitria Anggraeni WAHYUDI, DIKI WibhawaMn, Igd Raditya Widya Pratiwi Ali Winico Fazry Wira Abner Sigalingging Zaidan, Muhammad Naufal Zain, Fadli Fauzi ZK Abdurahman Baizal