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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Teknik dan Ilmu Komputer JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Informatika dan Teknik Elektro Terapan Computatio : Journal of Computer Science and Information Systems Jurnal Sinergitas PkM & CSR MITRA: Jurnal Pemberdayaan Masyarakat MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Muara Ilmu Sosial, Humaniora, dan Seni Jurnal Infortech Jurnal Bakti Masyarakat Indonesia Journal of Computer and Information Systems Ampera Prosiding Konferensi Nasional PKM-CSR Indonesian Journal of Multidisciplinary Science Prosiding Seri Seminar Nasional Madani : Jurnal Pengabdian Kepada Masyarakat Communication and Information Journal International Journal of Application on Economics and Business Inovasi Teknologi Masyarakat International Journal of Advances in Artificial Intelligence and Machine Learning Jurnal Serina Abdimas
Viny Christanti M
Program Studi Teknik Informatika Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Other

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Sosialisasi Aplikasi Chatbot Berbasis Android Untuk Guru SD Immanuel Jakarta Orlando, Fundroo; Mawardi, Viny Christanti; Lie, Nadia Natha
Inovasi Teknologi Masyarakat (INTEKMAS) Vol. 1 No. 1 (2023): June 2023
Publisher : Wadah Inovasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53622/intekmas.v1i1.193

Abstract

Saat ini sudah banyak dilakukan pengembangan metode belajar mengajar dengan mengasosiasikan penggunaan berbagai teknologi demi meningkatkan semangat dan motivasi dalam kegiatan belajar mengajar. Sekolah Dasar Immanuel telah melakukan pengembangan metode belajar mengajar dengan menggunakan aplikasi yang berbasis Android. Aplikasi berbasis Android sendiri adalah aplikasi yang disusun dengan menggunakan bahasa pemrograman yang mana dapat diakses oleh siapapun, dimanapun, dan kapanpun, baik dengan batasan maupun tanpa batasan. Sosialisasi ini bertujuan untuk meningkatkan pengetahuan tentang penggunaan aplikasi berbasis Android. Pengetahuan tentang penggunaan aplikasi berbasis Android perlu ditingkatkan sehingga dapat membantu penggunanya dalam menyelesaikan pekerjaannya, membuat sebagian besar atau seluruh dari pekerjaannya menjadi lebih efisien. Pengetahuan yang ditingkatkan dalam menggunakan aplikasi berbasis Android juga dapat menjadi suatu media atau sarana hiburan tersendiri bagi penggunanya karena menjelajah sesuatu yang baru. Dalam sosialisasi aplikasi Chatbot berbasis Android untuk Guru Sekolah Dasar Immanuel Jakarta Barat, dilakukan dua kegiatan, yaitu pemanduan guru-guru Sekolah Dasar Immanuel dalam proses instalasi aplikasi dan demonstrasi langsung dalam penggunaan aplikasi tersebut. Hasil dari kegiatan ini adalah berhasilnya proses instalasi aplikasi Chatbot berbasis Android. Berdasarkan survei yang diisi oleh 17 responden yang merupakan guru Sekolah Dasar Immanuel, 100% guru setuju bahwa aplikasi Chatbot berbasis Android mudah digunakan, mudah dimengerti, dan dapat memberikan pengalaman baru dalam proses belajar mengajar. Hasil yang diperoleh dari pengolahan data keseluruhan indikator adalah peserta setuju bahwa penggunaan aplikasi android ini lebih mudah dan memiliki tampilan yang sudah sesuai. Selain itu pelaksanaan pelatihan telah memberikan peningkatan pengetahuan guru terhadap pengertian chatbot dan kegunaannya.
Fine-Tuning LLaMA-2-Chat untuk ChatBot Penerjemah Bahasa Gaul menggunakan LoRA dan QLoRA SUSILO, ANDRI; CHRISTANTI, VINY; LAURO, MANATAP DOLOK
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.248-260

Abstract

AbstrakBahasa gaul, yang berkembang pesat di kalangan generasi Z dan Alpha, sering kali sulit dipahami oleh generasi lain atau dalam konteks formal. Bahasa ini memiliki variasi yang tidak terstruktur dan terus berubah, memerlukan model bahasa yang adaptif untuk memahaminya. Penelitian ini bertujuan untuk mengukur kualitas hasil terjemahan fine-tuning model LLaMA-2 dalam menerjemahkan bahasa gaul ke bahasa formal, dengan menggunakan metrik evaluasi BLEU Score sebagai alat utama. Selain itu, pendekatan LoRA dan QLoRA digunakan untuk meningkatkan efisiensi fine-tuning dengan mengurangi kebutuhan komputasi dan memori. Dataset yang digunakan terdiri dari data media sosial dan data buatan yang diformat dalam bentuk percakapan untuk menangkap konteks secara lebih baik. Hasil evaluasi menunjukkan skor BLEU terbaik sebesar 0.0369, yang menegaskan bahwa model masih perlu disempurnakan untuk menghasilkan terjemahan bahasa gaul yang optimal.Kata kunci: bahasa gaul, LLaMA-2, LoRA, QLoRAAbstractSlang, which is growing rapidly among generations Z and Alpha, is often difficult for other generations to understand or in formal contexts. This language has unstructured variations and is constantly changing, requiring adaptive language models to understand it. This research aims to measure the quality of the translation results of fine-tuning the LLaMA-2 model in translating slang into formal language, using the BLEU Score evaluation metric as the main tool. Additionally, LoRA and QLoRA approaches are used to improve fine-tuning efficiency by reducing computing and memory requirements. The dataset used consists of social media data and artificial data formatted in conversational form to better capture context. The evaluation results show the best BLEU score of 0.0369, which confirms that the model still needs to be refined to produce optimal slang translations.Kata Kunci: slang language, LLaMA-2, LoRA, QloRA
Abstractive Text Summarization Berita Bahasa Indonesia Menggunakan Retrieval-Augmented Generation Antonius Sakti Wiradinata; Viny Christanti Mawardi
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v13i1.32861

Abstract

This research discusses the application of Abstractive Text Summarization (ATS) to Indonesian language news using the Retrieval-Augmented Generation (RAG) method. Increased access to news through various digital platforms often causes users to have difficulty identifying relevant information among the large amount of news available. RAG integrates retrieval and generation techniques to produce coherent and informative news summaries. In this research, news from the CNN and CNBC sites was collected via web scraping to form a dataset. The data is processed through several stages, including preprocessing, embedding, information retrieval, and summary generation. Summary quality evaluation was carried out using the ROUGE metric, where the test results show that this system has good performance in the precision aspect, with a ROUGE-1 Precision value of 0.7432 and ROUGE-2 Precision of 0.6174. However, a lower ROUGE Recall value indicates that there is important information that is not fully included in the summary. These results indicate that the RAG method in ATS is effective in helping users obtain core information concisely, but there needs to be improvement in capturing the entire news context
Virtual Assisten Dengan Metode Rule Base Untuk UMKM Latitaka Borneo Berbasis Telegram Devi Ayu Permatasari; Viny Christanti Mawardi; Manatap Dolok Lauro
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v13i1.32866

Abstract

Latitaka Borneo MSMEs play a role in preserving local culture through typical Kalimantan herbal products. However, limitations in providing responsive customer service are a challenge amidst market competition. To overcome this problem, this research develops a virtual assistant based on a rule-based method that is integrated with the Telegram platform. This system is able to answer general questions, provide product information, and assist customers in the ordering process automatically. System testing involves evaluation using confusion matrices and cosine similarity to assess response accuracy and semantic relevance. The evaluation results show that the virtual assistant is able to increase operational efficiency and consistency of Latitaka Borneo services, so that it can better meet customer needs. It is hoped that this research can be a solution to increase the competitiveness of MSMEs through customer service automation.
Pembuatan Website Online Store Dilengkapi dengan Chatbot Dinata, Fredickson; Mawardi, Viny Christanti; Hendryli, Janson
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 9 No. 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v9i1.11561

Abstract

The advance of technology and the increased number of internet usage, have caused many companies to build their slot web as a way to market their product. But getting the attention of people isn’t an easy thing to achieve, so livechat and chatbot are implemented into the system to increase the quality of services. This chatbot was developed using the Vector Space Model which will calculate the similarity of each question and the input, before using the vector space model each question will be weighted with term weighting. The chatbot was tested directly and the result is calculated to get the precision of 0.813, recall of 0.751, and f-measurement of 0.766. From the results, we can say the performance of the chatbot is quite decent for it has increased the quality of the services which the online slot web provided.
CLUSTERING BERITA SEPAK BOLA DENGAN METODE K-MEANS Riyanto, Radika Yudha; Mawardi, Viny Christanti; Perdana, Novario Jaya
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24072

Abstract

Until now, many Indonesian people like soccer, both domestically and abroad. With so many football enthusiasts, people are becoming more active in finding news related to football. As time goes by, the amount of news circulating on the internet will also be more and more widespread. The large number of news makes the news need to be clustered or clustered to make it easier to access existing news. The website created is intended to group soccer news from several websites, namely: vivagoal.com, goal.com and bolasport.com. The method used on this sbobet is handicap to group news into clusters, then the method used to evaluate the quality of the clusters formed is the Silhouette coefficient method. The Silhouette coefficient value is 0.54, which means that the quality of the cluster formed is moderate.
Sistem Rekomendasi Perguruan Tinggi Swasta Menggunakan Metode LSTM ARDAN, MOHAMAD; MAWARDI, VINY CHRISTANTI; SUTRISNO, TRI
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.18-33

Abstract

AbstrakPenelitian ini bertujuan mengembangkan sistem rekomendasi berbasis Long Short-Term Memory (LSTM) untuk membantu calon mahasiswa dalam memilih perguruan tinggi swasta yang sesuai. Data dikumpulkan melalui UiPath dari situs resmi perguruan tinggi dan Pangkalan Data Pendidikan Tinggi (PDDIKTI). Sistem ini dilengkapi dengan chatbot berbasis Natural Language Processing (NLP) untuk memahami kebutuhan pengguna dan memberikan informasi sesuai dengan preferensi pengguna. Pemilihan model LSTM didasarkan pada kemampuannya dalam menangani data sekuensial dan memahami konteks, sehingga mampu menghasilkan rekomendasi yang akurat. Hasil evaluasi menunjukkan tingkat akurasi rekomendasi sebesar 90% dalam memberikan rekomendasi dan informasi yang tepat. Selain itu, tingkat kepercayaan chatbot dalam menjawab pertanyaan pengguna mencapai 100%, menunjukkan efektivitas tinggi dalam memberikan  berbasis data.Kata kunci: chatbot, rekomendasi, LSTM, web scraping, PDDIKTI.AbstractThis research aims to develop a recommendation system based on Long Short-Term Memory (LSTM) to assist prospective students in choosing a suitable private university. Data was collected through UiPath from the official websites of universities and the Higher Education Database (PDDIKTI). The system is equipped with a Natural Language Processing (NLP) based chatbot to understand user needs and provide information according to user preferences. The selection of the LSTM model is based on its ability to handle sequential data and understand context, so as to produce accurate recommendations. The evaluation results show a recommendation accuracy rate of 90% in providing the right recommendations and information. In addition, the level of confidence of the chatbot in answering user questions reached 100%, indicating high effectiveness in providing data-based.Keywords: chatbot, recommendation, LSTM, web scraping, PDDIKTI.
Kinerja BART dalam Automatic Summarization Berita Otomotif TAKESHI, CECILIANA; MAWARDI, VINY CHRISTANTI; PERDANA, NOVARIO JAYA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.61-72

Abstract

ABSTRAKIndustri otomotif menghadapi tantangan besar dalam mengelola dan menyajikan informasi yang relevan serta terstruktur di tengah meningkatnya volume data digital. Penelitian ini memperkenalkan sistem pencarian dan peringkasan berbasis teks menggunakan model BART (Bidirectional and Auto-Regressive Transformers) untuk meningkatkan efisiensi pencarian informasi dan peringkasan konten. Sistem ini mengintegrasikan web scraping, preprocessing teks, TF-IDF, dan teknik cosine similarity untuk mengekstrak dan memproses data, menghasilkan hasil yang ringkas dan akurat. Evaluasi menggunakan metrik ROUGE dan umpan balik pengguna menunjukkan kemampuan sistem dalam menghasilkan ringkasan yang informatif dan efisien dengan waktu pemrosesan yang lebih cepat. Sistem ini mencapai performa sebesar rata rata 78.5 berdasarkan evaluasi yang dilakukan. Temuan ini menyoroti efektivitas BART dalam menangani data otomotif yang kompleks dan memenuhi kebutuhan pengguna untuk mendapatkan ringkasan berita yang relevan, mendukung pengambilan keputusan berbasis data di sektor otomotif.Kata kunci: Pencarian Informasi, BART, NLP, Otomotif, Peringkasan Teks.ABSTRACTThe automotive industry faces significant challenges in managing and presenting relevant and structured information amidst the growing volume of digital data. This study introduces a text-based search and summarization system using the BART (Bidirectional and Auto-Regressive Transformers) model to enhance the efficiency of information retrieval and content summarization. The system integrates web scraping, text preprocessing, TF-IDF, and cosine similarity techniques to extract and process data, delivering concise and accurate results. Evaluations using ROUGE metrics and user feedback demonstrate the system’s ability to produce informative and efficient summaries with reduced processing time. The system achieved a performance average score of 78.5 based on the evaluation. The findings highlight the effectiveness of BART in handling complex automotive data and meeting user needs relevant news summaries, thereby supporting data-driven decision-making in the automotive sector.Keywords: Information Retrieval, BART, NLP, Automotive, Text Summarization.
Comparative Study of CNN and Vision Transformers on Indonesian Tradisional Cakes Classification Trisnawarman, Dedi; Supriyanton, Adolf Asih; Mawardi, Viny Christanti; Okengwu, Ugochi A
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 2 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i2.405

Abstract

Background of study: Food image classification is a challenging task in computer vision, particularly when dealing with traditional food items that exhibit subtle visual variations. While Convolutional Neural Networks (CNNs) have long been the standard for image recognition, their limitation in capturing long-range dependencies has led to the emergence of Vision Transformers (ViTs). In this context, the classification of Indonesian traditional cakes offers a culturally rich yet complex problem for automated image recognition systems.Aims and scope of paper: This study aims to conduct a comparative analysis between EfficientNet-B0 (CNN-based) and ViT-B/16 (Transformer-based) architectures in classifying eight categories of Indonesian traditional cakes. The research evaluates not only classification accuracy but also the strengths and limitations of each model in handling fine-grained visual distinctions.Methods: Both models were fine-tuned using the “Kue Indonesia” dataset from Kaggle. The methodology includes image preprocessing, model training with consistent parameters, and evaluation using accuracy, precision, recall, and F1-score. A confusion matrix was also used to visualize misclassifications and analyze per-class performance.Result: ViT-B/16 achieved slightly higher accuracy (96.25%) compared to EfficientNet-B0 (95.62%). ViT performed better in classes with subtle variations, such as kue lapis and kue dadar gulung, while EfficientNet-B0 showed superior efficiency and high accuracy on visually distinct cakes.Conclusion: Both CNN and ViT models demonstrate strong performance in traditional food classification. ViT is more robust in fine-grained visual analysis, whereas EfficientNet-B0 is preferable for resource-constrained environments. This study highlights the role of AI in supporting digital preservation of culinary heritage.
Pemanfaatan Chatbot Retrieval-Based dan Analisis Sentimen untuk Meningkatkan Layanan Informasi Interaktif di Radio Untar Gian Praista; Viny Christanti Mawardi; Irvan Lewenusa
Comit: Communication, Information and Technology Journal Vol. 3 No. 2 (2025): Comit: Communication and Information Journal
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/comit.v3i2.8424

Abstract

This article discusses the implementation of a retrieval-based chatbot integrated with sentiment analysis to improve the efficiency of information services at Radio Untar. The chatbot developed uses the TF-IDF and cosine similarity methods to match user questions with FAQ data, and is able to handle requests for songs, articles, and podcasts. Sentiment analysis was performed on user interaction logs to assess satisfaction and effectiveness of answers. Based on the results of testing 150 interactions, the chatbot showed an increase in MRR scores from 0.468 to 0.91 and a satisfaction level from 50% to 92% after the fine-tuning process. These findings indicate that a lightweight chatbot retrieval-based system can be used effectively in a campus environment to improve digital interactions.
Co-Authors Agus Budi Dharmawan Albert Jeremy Aleksander Nihcolson Andre Ertanto Andre Raymond Andreas Andreas Andreas Andreas Andreas Khosasi Anggreiny, Phoebe Cecilia Angkasa, Adhelia Anindita Septiarini, Anindita Antonius Sakti Wiradinata ARDAN, MOHAMAD Ardianto Ardianto Arwi, Adelia Vannissa Augusfian, Fendy Bagus Mulyawan bagus Mulyawan Benedicta, Cheria Berlin Ong Karo Karo Billy Fernando Bryan Filemon Buana, Salsabila Ayunda Martsa Calvin Calvin Carlene Lim Carlene Lim Caroline Wili Harto Chintia, Tiffany Dali S Naga Dali S. Naga Dali S. Naga Dali S. Naga Dali S. Naga, MMSI Dali S.Naga Dali Santun Naga Daniel Daniel Daniel Daniel Darius A Haris Darius Andana Haris Darryl Kresnadi Nugroho Davin Pratama Dedi Trisnawarman Denis Kusbowo Desi Arisandi Desi Arisandi Desi Arisandi Dessy Yanti Destu Adiyanto Devi Ayu Permatasari Devin Abipraya Dewi Triani Didit Suprihanto, Didit Dinata, Fredickson Dyah Larasati, Annita Edward Darmaja Edy Susanto Endah Purnamasari Endah Setyaningsih Erikson T Erikson T Erwin Erwin Ery Dewayani Fat, Joni Fendy Augusfian Ferry Ruben Yudistira Ferry Ruben Yudistira Yudistira, Ferry Ruben Yudistira Freddy Kurniawan Fredickson Dinata Fundroo Orlando Geraldine, Karmelia Gerry Geraldicky Gian Praista Gunadi, Alvin Nicolas Haikal M, Andrew Hamdani Hamdani Handoko Susanto Handoko Susanto, Handoko Handry Wardoyo Hanven Pradana Hartanto, Jonathan Chris Helen, Helen Hendri Yukianto Hendri Yukianto, Hendri Hendryli, Janson Henry Hartono Herman, Sylvia Hetty Karunia Tunjungsari Husada, Yusianne Kasih Irvan Lewenusa Irvan Lewenusa, Irvan Ivanka, Risa James Eklie Janson Hendryli Janson Hendryli Janson Hendryli Janson Hendryli Janson Hendryli Januar Mansur Jeanny Pragantha Jeanny Pragantha Jeffri Alimin Jesica Jesica Jesica Kurniadi, Jesica Jesslyn Jesslyn Jimmy Jimmy Joko Joko Jonathan Adrian Wibowo Joshua Octavianus Joshua Octavianus, Joshua Julius Evan Harya Chandra Kalyani, Khema Dwi Karo Karo, Berlin Ong Kenneth Hakim Kevin kevin Kevin Kurniawan H. Kevin Prasetio Kevin The Kuncoro Yoko Lavenia Lely Hiryanto Lie, Nadia Natha Livienia Livienia Manatap Dolok Lauro, Manatap Dolok Maria Asinta Marpaung Maria Asinta Marpaung, Maria Asinta Marsel Dwiputra Marsel Dwiputra, Marsel Martsha Buana, Salsabilla Ayundha Marvellino Mei Ie Meiliansyah, Carens Berliyanti Meiriani Tjandra Meiriani Tjandra Meiske Yunitree Suparman Michael, Valentino Muhammad Farras Mutiara Ramadhani Sugiri Mutiara, Maitri Widya Nadia Natha Lie Naga, Dali S. Natasya, Stephanie Niki Valentine Niki Valentine, Niki Novario Jaya Perdana Nurmadewi, Dita Okengwu, Ugochi A Orlando, Fundroo Pangandaheng, Grasella Aldonia Pharadya Ajeng Swari Sukmawati Phung, Mulan Prabu Alif Anggadiputra Prof. Dr. Ir. Dali S. Naga, MMSI Pusaka, Semerdanta Putra Lukita Putri, Aneesa Joenice rani puspitasari Rendi Kristyadi Ricky Cangniago Ricky Martin Rini, Cika Puspita Riwanda, Josephine Kayla Riyanto, Radika Yudha Rizqi Amelia, Aulya Robertus Budihalim Robertus Budihalim, Robertus Rudy Rudy Salsabila, Nur Maya Saskia Lavinsky Septiasari, Abellia Sharlene Solikhah, Nafia Stenly Tirta Wijaya stephanie stephanie Steven Steven Dharmawan Steven Muliadi Steven Muliadi, Steven Steven Steven Supriyanton, Adolf Asih Susilo, Andri Sylvia Wulandari, Sylvia TAKESHI, CECILIANA Tania Rizgitta Tony Tony Tony Tony TRI SUTRISNO TRI SUTRISNO Utama, Didi Widya Vanesa Nellie Vincent Marcellino Wati, Masna Widi Santoso Wijaya, Dion Dwi Willyanto, Vinnie Wilson Gozal, Wilson Yagyu Munenori M.E. Yasser, Achmad Yohan Prasetyo Sugianto Yohanes Calvinus Yolanda, Aubrey Yosua Pandapotan Sianipar Yukianti, Chiara Rizka Yulianto Yulianto Yulianto Yulianto Zyad Rusdi Zyad Rusdi