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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 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 IJoICT (International Journal on Information and Communication Technology) Indonesian Journal on Computing (Indo-JC)
Yuliant Sibaroni
Telkom University
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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Prediction of Classification of Air Quality Distribution in Java Island using ANN with Time-Based Feature Expansion and Spatial Analysis Gutama, Soni Andika; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1035

Abstract

Air pollution is a major concern that significantly impacts human health and the environment, especially in densely populated and economically active areas like Java, Indonesia. Air pollution is primarily caused by motor vehicles and industrial activities, leading to higher concentrations of harmful pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM10). In this study, an Artificial Neural Network (ANN) model is employed to forecast air quality classifications across Java Island, utilizing time-based features and spatial analysis. The model achieves an impressive accuracy and an F1-score of 92.19%, demonstrating its capability in capturing the intricate dynamics of air quality. These results highlight the potential of the ANN model in supporting effective policy-making, crisis management, and the development of environmentally sustainable infrastructure.
Sentiment Analysis For The 2024 Presidential Election (Pilpres) Using BERT CNN Putra, Daffa Fadhilah; Sibaroni, Yuliant
Eduvest - Journal of Universal Studies Vol. 4 No. 11 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i11.49961

Abstract

The 2024 presidential election in Indonesia has generated tremendous enthusiasm on social media, particularly on the X platform. This research aims to analyze public sentiment regarding the 2024 presidential election by utilizing BERT and CNN methods. Sentiment analysis in the digital era is key to understanding the diverse social perspectives within society. The use of BERT, which has proven effective in understanding natural language context, and CNN, initially used for image analysis, will help in understanding public sentiment on X leading up to the 2024 presidential election. The research results show that the BERT model provides the best performance with an average accuracy of 90.02%, while CNN achieved 88.19%. The sentiment-based predictions using BERT for the three presidential candidates indicate that Prabowo Subianto is predicted to receive the highest support at 43.82%, followed by Ganjar Pranowo with 33.83%, and Anies Baswedan with 22.35%. A comparison of the prediction results with the actual election results shows that Prabowo Subianto was predicted to receive 43.82% of the vote, while the actual election results reached 58.58%, a difference of 14.76%. Ganjar Pranowo was predicted to receive 33.83% of the vote, while the actual results were 16.47%, with a difference of 17.36%. Anies Baswedan was predicted to receive 22.35% of the vote, with the actual result being 24.95%, a difference of 2.60%. This study indicates that the BERT model is effective in providing an accurate depiction of the 2024 Indonesian presidential election results.
Sentiment Analysis of Wondr by BNI App Reviews on Play Store using the CNN-LSTM Method Putra, Ihsanudin Pradana; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7477

Abstract

As the use of digital applications in banking services increases, user opinions about these applications become an important source of data to study Wondr by BNI, which receives many user reviews, is one of the applications studied in this research. This research aims to build an accurate sentiment classification model and compare the effectiveness of two word representation methods, Word2Vec and FastText, to automatically classify sentiment into two classes, positive and negative, from unstructured review text using informal language. The data was processed through pre-processing, labeling, and processing stages using a hybrid CNN-LSTM model with 20,000 reviews available on the Google Play Store. The training process is carried out using 5-fold cross-validation and hyperparameter optimization using the random search method. The results show that the model with FastText has an accuracy of 86.38%, precision of 86.82%, recall of 86.46%, and F1-score of 86.46%. In contrast, the model with Word2Vec has an accuracy of 85.90%, precision of 86.38%, recall of 85.80%, and F1-score of 85.87%. These results show that FastText is better in accuracy and performance consistency compared to Word2Vec. This research provides a better understanding of how word representation methods affect sentiment analysis in app reviews and is expected to be a reference for future development of similar models.
Multi-Aspect Sentiment Analysis of Movie Reviews Using BiLSTM on Platform X Data Sinaga, Astria M P; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7509

Abstract

The film industry generates scores of movie reviews annually, reflecting viewer opinion towards various aspects of movies such as story, music, performances, and so on. They are a good source to publicly analyze opinion automatically. Aspect-based and sentiment analysis of movie reviews based on a multitask classification model rooted in the Bidirectional Long Short-Term Memory (BiLSTM) structure is the theme of this study. The objective of this research is to develop and evaluate a multitask BiLSTM-based model capable of simultaneously classifying sentiment polarity and movie review aspects to enhance fine-grained opinion mining. Data was collected from Platform X through web crawling and subjected to various text preprocessing steps before feeding them into the model. Unlike traditional approaches that treat sentiment and aspect classification as independent operations, the method proposed in this work is performing both simultaneously—sentiment prediction (positive, neutral, negative) and aspect categories (plot, music, actors, others). The model was compared between three different sizes of BiLSTM layers—32, 64, and 128 units—to investigate the influence of model capacity on performance. A 10-fold cross-validation scheme also implemented to confirm the reliability and robustness of results. Experiment findings reveal that the 128-unit BiLSTM model outperformed other models across the board, particularly at picking up subtle contextual relationships, to achieve the highest accuracy score in both tasks. Although this model significantly longer to train, its improved generalization—most notably for difficult sentiment- aspect pairs such as neutral or low-resource categories—validated the trade-off. The findings validate the effectiveness of BiLSTM-based multitask learning for comprehensive movie review analysis, demonstrating the importance of model capacity in tackling complex language patterns and fine-grained opinion identification.
Multi-Aspect Sentiment Analysis on Gojek Application Reviews Using CNN-LSTM Method Saragih, Pujiaty Rezeki; Sibaroni, Yuliant; Prasetyowati, Sri Sulyani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7511

Abstract

Since its initial release in 2010, Gojek has remained the most used online transport service by Indonesians. Multi-aspect sentiment analysis is a method applied to determine user sentiment towards various specific aspects in their comments. By applying this method, there will be deeper understanding of user views regarding various components of the Gojek service. The method employed was data scraping from web crawling of Google Play Store user reviews and data preprocessing, i.e., cleaning, case folding, tokenizing, stopword removal, normalization, and stemming. A hybrid CNN-LSTM model was employed since it is capable of extracting spatial features using CNN and long-term dependencies using LSTM. The seven most crucial aspects of the Gojek service, i.e., access, time, comfort, information, customer service, availability, and safety, were the central themes of this research. The main objective of this research is to analyze user sentiment across these key aspects using a deep learning-based multi-task approach, in order to gain actionable insights for improving service quality. The performance of the models was evaluated on accuracy as the primary metric, and the experiments attempted three model sizes: 32, 64, and 128 hidden units. Among them, the 64-unit model performed best overall consistently, with both aspect and sentiment classification accuracy being satisfactory. While the 128-unit model achieved slightly better accuracy on some sentiment tasks, it exhibited overfitting. The 64-unit model, however, gave the most balanced results and the best trade-off between model complexity and performance. The findings show the potential of multi-task deep learning approaches to extract valuable insights from user reviews. Such findings can be highly valuable to aid business strategy formulation and service quality improvement, and ultimately greater customer satisfaction, as well as consolidate Gojek's market dominance in Indonesia's online transport business.
Aspect-Based Sentiment Classification of iPhone 15 YouTube Reviews Using VADER-Augmented LSTM Alya, Hasna Rafida; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7514

Abstract

This research investigates the effectiveness of the Long Short-Term Memory (LSTM) model in performing aspect-based sentiment classification on English-language reviews of the iPhone 15 sourced from the YouTube platform. The study focuses on five key product aspects frequently mentioned by users: charger port, camera, screen, design, and battery. To evaluate the model’s performance, two distinct labeling strategies were employed. The first involved manual annotation, where human annotators identified both the relevant aspects and the associated sentiment in each review. The second strategy integrated additional sentiment cues derived from a lexicon-based method, Valence Aware Dictionary and sEntiment Reasoner (VADER). In this approach, the polarity output from VADER was prepended to each review to enrich the input with emotional context. The experimental results demonstrate that supplementing review texts with sentiment polarity information from VADER contributes to a modest but measurable improvement in sentiment classification accuracy. Specifically, using the micro-average accuracy metric, defined as the ratio of correct predictions to the total number of test instances, the model's performance improved from 67% under the manual only annotation to 68% with VADER enhanced input. Additionally, aspect classification remained consistently strong, showing a slight improvement from 90% to 91% after incorporating VADER. Furthermore, based on macro-average accuracy an evaluation metric that calculates the mean performance across all classes regardless of class distribution, accuracy improvements were observed in several aspects, particularly the camera, screen, and design. However, a minor decline in performance was noted for the battery and charger port aspects. These results suggest that enriching review data with sentiment polarity information derived from lexicon-based tools like VADER can enhance the model’s ability to comprehend emotional nuance, leading to more accurate identification of user sentiments within aspect-specific reviews.
Analisis Sentimen Terhadap Pembangunan Kereta Cepat Jakarta - Bandung Pada Media Sosial Twitter Menggunakan Metode SVM dan GloVe Word Embedding Fitriansyah, Alam Rizki; Sibaroni, Yuliant
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak-Proyek kereta cepat Jakarta – Bandung merupakan salah satu proyek besar yang saat ini sedang dibuat di Indonesia. Proyek kereta cepat Jakarta – Bandung menjadi ramai dibicarakan di media sosial Twitter, karena pada pembangunannnya terdapat banyak pihak yang merasa dirugikan, namun ada juga pihak yang merasa diuntungkan. Pada penelitian ini dilakukan analisis sentimen terhadap sentiment publik di media sosial Twitter tentang proyek kereta cepat Jakarta – Bandung. Penelitian ini menggunakan data yang berisi tweet dari keyword yang sudah ditentukan dan menggunakan GloVe word embedding dan metode klasifikasi Support Vector machine. Pada penelitian ini kombnasi terbaik pada parameter GloVe dengan nilai 200 untuk no_of_component, 0.001 untuk learning_rate dan fitur TOP 1 menghasilkan kenaikan pada nilai akurasi klasifikasi SVM dari 72.63% menjadi 77.72% dibandingkan dengan SVM tanpa menggunakan fitur ekspansi GloVe.Kata kunci - sentimen, kereta cepat, twitter, GloVe, SVM
Single Page Aplikasi Website Prediksi Kualitas Udara What The Air Jauzy, Muhammad Abdurrahman Al; Prasetyowati, Sri Suryani; Sibaroni, Yuliant
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak-Polusi udara biasa diartikan sebagai pencemaran udara dimana jumlah bahan pencemar berada diluar batas. Kualitas udara saat ini adalah salah satu faktor penting dalam kehidupan sehari - hari. Terlalu banyak menghirup udara dengan kualitas yang rendah dapat berdampak buruk pada kesehatan. Dengan menggunakan alat pengukur kualitas udara kita bisa mengukur tingkat indeks kualitas udara, namun kenapa hanya berhenti disitu jika kita bisa menggunakan Machine Learning untuk melakukan Prediksi dalam beberapa tahun kedepan. Di studi ini digunakan metode Support Vector Machine yang akan melakukan klasifikasi terhadap data yang didapat dari sensor. SVM dipilih karena dinilai baik dalam mengklasifikasikan data yang berupa kelas - kelas. Data yang diolah adalah SO2, NO2, CO, PM10, PM25 dan O3. Kemudian data hasil klasifikasi akan diproses untuk prediksi dengan teknik perluasan model. Penelitian ini akan menghasilkan pemetaan prediksi polusi udara di provinsi jakarta untuk tahun 2022, diharapkan penelitian ini dapat membantu masyarakat untuk mengetahui tentang kondisi udara.Kata Kunci— kualitas udara, support vector machine, klasifikasi, machine learning
Perbandingan Pembobotan Fitur TF-IDF dan TF-ABS Dalam Klasifikasi Berita Online Menggunakan Support Vector Machine (SVM) Apriani, Iklima; Sibaroni, Yuliant; Palupi, Irma
eProceedings of Engineering Vol. 10 No. 3 (2023): Juni 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak -Berita terjadi karena adanya informasi atau kabar yang berhubungan dengan fakta dan sedang terjadi untuk kemudian disampaikan kepada masyarakat. Seiring dengan perkembangan teknologi kini penyebaran informasi dilakukan melalui media sosial yaitu website yang bisa diakses dengan media dekstop ataupun handphone. Pemilihan berita untuk dimasukan pada kategori tertentu jika dilakukan oleh manusia bisa menyebabkan human eror, terlebih berita yang dipakai sangat banyak bisa menyebabkan kurang efisien. Maka dari itu, sistem klasifikasi otomatis akan menjadi solusi pada permasalahan ini. Dalam klasifikasi, fitur extraction merupakan proses dasar dalam kategorisasi yang penting untuk dilakukan dan diketahui. Fitur tersebut kemudian akan direpresentasikan kedalam bentuk vektor, nilai vektor diperoleh dari pembobotan kata. Penelitian ini membandingkan pembobotan Term Frequency - Inverse Document Frequency (TF.IDF) dan Term Frequency Absolute (TF.ABS) yang dikombinasikan dengan fitur extraction unigram dengan metode klasifikasi Support Vector Machine (SVM). Dari hasil penelitian menunjukkan pembobotan TF-IDF mendapat akurasi sebesar 96,63% dengan hasil dengan hasil f1-score mendapat 97,06%. Sedangkan pembobotan TF-ABS mendapat akurasi sebesar 89,66% dengan hasil f1-score 96,63%. Dengan menggunakan pembobotan TF-IDF dapat menaikkan akurasi sebesar 6,97%. daripada mengunakan TF-ABS.Kata kunci-berita, klasifikasi, support vector machine, TF-IDF, TF-ABS
Combatting Misinformation: Leveraging Deep Learning for Hoax Detection in Indonesian Political Social Media Sibaroni, Yuliant; Mahadzir, Shuhaimi; Prasetiyowati, Sri Suryani; Ihsan, Aditya Firman
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1139

Abstract

The rampant spread of hoax news in social media, especially in the political domain, poses a significant challenge that requires immediate attention. To address this issue, automatic hoax news detection using machine learning-based artificial intelligence has emerged as a promising approach. With the approaching presidential election in Indonesia in 2024, the need for effective detection methods becomes even more pressing.This research focuses on proposing an efficient deep learning model for detecting political hoax news on Indonesian social media. Word2vec feature representation and three deep learning models – LSTM, CNN, and Hybrid CNN-LSTM – are evaluated to determine the most effective approach. Experimental results reveal that the CNN-LSTM hybrid model outperforms the others, achieving an accuracy of 96% in detecting hoax news on Indonesian social media in the political domain. By leveraging state-of-the-art deep learning techniques, particularly the CNN-LSTM hybrid model, this study contributes to the advancement of hoax news detection in Indonesia's political landscape. The findings underscore the importance of utilizing sophisticated machine learning methods to combat the spread of misinformation, particularly during crucial political events such as elections.
Co-Authors Abduh Salam Adhe Akram Azhari Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aditya Iftikar Riaddy Adiwijaya Agi Maulana Akmal Muhamad Faza Al Ghazali, Nabiel Muhammad Alfauzan, Muhammad Fikri Alya, Hasna Rafida Andrew Wilson Angger Saputra, Revelin 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 Aurora Az Zahra, Elita Azmi Aulia Rahman Bunga Sari Chamadani Faisal Amri Chindy Amalia Claudia Mei Serin Sitio 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 F. 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 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 Maulida , Anandita Prakarsa Mauluvy Senjaya, Argya Mitha Putrianty Fairuz Muhamad Agung Nulhakim Muhammad Alauddin Angka Kurniawan Muhammad Arif Kurniawan Muhammad Damar 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 Nanda Ihwani Saputri Naufal Alvin Chandrasa 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, Daffa Fadhilah 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 Prasetiyowati Sri Suryani Prasetyowati Sri Suryani Sri Suryani Sri Utami Sujadi, Cika Carissa Suryani Prasetyowati, Sri Syarif, Rizky Ahsan Umulhoir, Nida Varissa Azis, Diva Azty Viny Gilang Ramadhan Vitria Anggraeni WAHYUDI, DIKI Widya Pratiwi Ali Winico Fazry Wira Abner Sigalingging Zaenudin, Muhammad Faisal Zaidan, Muhammad Naufal Zain, Fadli Fauzi ZK Abdurahman Baizal